thesis environmental modelling

Data Mining and Machine Learning for Environmental Systems Modelling and Analysis

--> Ayala Solares, Jose Roberto (2017) Data Mining and Machine Learning for Environmental Systems Modelling and Analysis. PhD thesis, University of Sheffield.

This thesis provides an investigation of environmental systems modelling and analysis based on system identification techniques. In particular, this work focuses on adapting and developing a new Nonlinear AutoRegressive with eXogenous inputs (NARX) framework, and its application to analyse some environmental case studies. Such a framework has proved to be very convenient to model systems with nonlinear dynamics because it builds a model using the Orthogonal Forward Regression (OFR) algorithm by recursively selecting model regressors from a pool of candidate terms. This selection is performed by means of a dependency metric, which measures the contribution of a candidate term to explain a signal of interest. For the first time, this thesis introduces a package in the R programming language for the construction of NARX models. This includes a set of features for effectively performing system identification, including model selection, parameter estimation, model validation, model visualisation and model evaluation. This package is used extensively throughout this thesis. This thesis highlights two new components of the original OFR algorithm. The first one aims to extend the deterministic notion of the NARX methodology by introducing the distance correlation metric, which can provide interpretability of nonlinear dependencies, together with the bagging method, which can provide an uncertainty analysis. This implementation produces a bootstrap distribution not only for the parameter estimates, but also for the forecasts. The biggest advantage is that it does not require the specification of prior distributions, as it is usually done in Bayesian analysis. The NARX methodology has been employed with systems where both inputs and outputs are continuous variables. Nevertheless, in real-life problems, variables can also appear in categorical form. Of special interest are systems where the output signal is binary. The second new component of the OFR algorithm is able to deal with this type of variable by finding relationships with regressors that are continuous lagged input variables. This improvement helps to identify model terms that have a key role in a classification process. Furthermore, this thesis discusses two environmental case studies: the first one on the analysis of the Atlantic Meridional Overturning Circulation (AMOC) anomaly, and the second one on the study of global magnetic disturbances in near-Earth space. Although the AMOC anomaly has been studied in the past, this thesis analyses it using NARX models for the first time. The task is challenging given that the sample size available is small. This requires some preprocessing steps in order to obtain a feasible model that can forecast future AMOC values, and hindcast back to January of 1980. In the second case study, magnetic disturbances in near-Earth space are studied by means of the Kp index. This index goes from 0 (very quiet) to 9 (very disturbed) in 28 levels. There is special interest in the forecast of high magnetic disturbances given their impact on terrestrial technology and astronauts' safety, but these events are rare and therefore, difficult to predict. Two approaches are analysed using the NARX methodology in order to assess the best modelling strategy. Although this phenomenon has been studied with other techniques providing very promising results, the NARX models are able to provide an insightful relationship of the Kp index to solar wind parameters, which can be useful in other geomagnetic analyses.

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• Open access
• Published: 13 February 2018

Toward a consistent modeling framework to assess multi-sectoral climate impacts

• Erwan Monier   ORCID: orcid.org/0000-0001-5533-6570 1 ,
• Sergey Paltsev   ORCID: orcid.org/0000-0003-3287-0732 1 ,
• Andrei Sokolov 1 ,
• Y.-H. Henry Chen 1 ,
• Xiang Gao 1 ,
• Qudsia Ejaz 1 ,
• Evan Couzo 1   nAff4 ,
• C. Adam Schlosser 1 ,
• Stephanie Dutkiewicz 1 ,
• Charles Fant 1 ,
• Jeffery Scott 1 ,
• David Kicklighter 2 ,
• Jennifer Morris 1 ,
• Henry Jacoby 1 ,
• Ronald Prinn 1 &
• Martin Haigh 3  

Nature Communications volume  9 , Article number:  660 ( 2018 ) Cite this article

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• Climate and Earth system modelling
• Climate-change impacts
• Environmental impact

Efforts to estimate the physical and economic impacts of future climate change face substantial challenges. To enrich the currently popular approaches to impact analysis—which involve evaluation of a damage function or multi-model comparisons based on a limited number of standardized scenarios—we propose integrating a geospatially resolved physical representation of impacts into a coupled human-Earth system modeling framework. Large internationally coordinated exercises cannot easily respond to new policy targets and the implementation of standard scenarios across models, institutions and research communities can yield inconsistent estimates. Here, we argue for a shift toward the use of a self-consistent integrated modeling framework to assess climate impacts, and discuss ways the integrated assessment modeling community can move in this direction. We then demonstrate the capabilities of such a modeling framework by conducting a multi-sectoral assessment of climate impacts under a range of consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations.

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Introduction

Estimating the impacts of climate change is challenging because they span a large number of economic sectors and ecosystems services, and can vary strongly by region 1 , 2 , 3 . Many integrated assessment models (IAMs) rely on simple box climate models and use a damage function approach to estimate a social cost of carbon 4 that relates changes in emissions to economic damage 5 . These models are useful. For example, the US EPA and other government agencies use these estimates to evaluate the climate benefits of rulemakings 2 . But this approach has also attracted criticism 6 , 7 as the existing literature offers sparse theoretical support and provides scant empirical evidence for a specification of economic damages, especially at temperatures outside the historical range.

Another widely used approach relies on model intercomparison projects (MIPs) that apply the results of detailed biogeophysical models. They can offer valuable insights into specific climate impacts (e.g., the Agricultural Model Intercomparison and Improvement Project (AgMIP) for agriculture 8 ). However, these exercises suffer from a rigid and complex framework, driven by the need for international coordination, so they must rely on a limited number of socio-economic scenarios, like the four representative concentration pathways (RCP) scenarios 9 . Since the developers and the users of these scenarios come from different research groups and disciplinary communities, major inconsistencies in their implementation, such as socio-economic assumptions and ecosystem characteristics, can easily occur. For example, when a common land-use scenario is implemented in different Earth system models (ESMs), differences appear in cropland and pastureland areas because of the different interpretations of land-use classes by the ESMs 10 . The resulting differences in the carbon cycle and land-use forcing are thus difficult to interpret. Also, each of the four RCP scenarios was developed by a different IAM group, and their projections of future air pollutant emissions are inconsistent with one another 11 , making comparisons of air quality among RCP scenarios of little value. Since many climate impact assessments do rely on MIPs, and are not done within a cohesive IAM framework, these inconsistencies can contaminate analysis of the benefits of climate policies.

Furthermore, the MIPs lack flexibility, and responsiveness to changes in economic and environmental policies (like the recent Paris Agreement), and thus they are of limited usefulness in analysis of policy choice. In addition, because of their single sector focus these exercises do not capture important inter-dependencies, linkages and feedbacks, and this lack of integration among sectors is likely to lead to misrepresentation of climate impacts 12 . Moreover, IAMs that use a single-sector macroeconomic representation of the global economy lack the capability for evaluation of particular sectors of the economy where damages occur. Finally, there is little effort and limited capability to synthesize the many MIPs into an overarching assessment of climate impacts across sectors of the economy, which further limits the information value these exercises bring to the decision process.

In recent years, major efforts have been pursued toward the development of consistent modeling frameworks to assess climate impacts using a new generation of IAM, which place a greater emphasis on representing the coupled human-Earth system (CHES) model—essentially IAM version 2.0. Such modeling frameworks include both a detailed representation of economic activities, to track inter-sectoral and inter-regional links, and a detailed representation of the various physical, chemical, and biological components of the Earth system that are impacted by human activity. The aim is to provide a tight integration among three communities that, though internally collaborative, have remained largely isolated from one another: the IAM, the Earth System Modeling (ESM), and the impacts, adaptation, and vulnerability (IAV) communities. An advantage of such an approach is that research groups can construct new scenarios of climate change and conduct climate impact assessments, while ensuring consistent treatment of interactions among population growth, economic development, energy and land system changes and physical climate impacts. Such new scenarios can provide improved estimates of the impact of current and proposed international agreements, and other aspects of climate policy 13 .

To provide an example of such a CHES modeling framework and demonstrate its capabilities, we examine socio-economic and climate change impacts under a range of consistent and integrated economic and climate scenarios using the MIT Integrated Global System Model (IGSM) 14 , 15 , 16 , 17 . The IGSM couples a human system model to an ESM of intermediate complexity (EMIC), and links to a series of geospatially resolved physical impact models (see Methods section). While we showcase the IGSM, other models could be used as well, as other IAM groups have made similar improvements in the integration of the coupled human and Earth systems. Examples include the Integrated Model to Assess the Greenhouse Effect (IMAGE) 18 , the Global Change Assessment Model (GCAM) 19 , the model for energy supply strategy alternatives and their general environmental impact (MESSAGE) 20 and the Asia Pacific Integrated Model (AIM) 21 . In this paper, we first discuss strategies for coupling between human and ESMs and the improved integration of geospatially resolved physical impact models. We then present a multi-sectoral climate impact assessment focusing on ocean acidification, air quality, water resources and agriculture under consistent and integrated economic and climate scenarios that are responsive to new policies and business expectations. This example then provides a basis for arguing the advantages of such a shift toward a consistent CHES modeling framework to assess climate impacts.

Strategies for coupling human and ESMs

The human system component of a CHES model should represent the world’s economy, disaggregated into multiple regions and with sectoral detail (i.e., agriculture, services, industrial and household transportation, energy-intensive industry). It also should include trade, investments, savings, and consumption decisions, as well as abatement of greenhouse gases (GHGs) through the implementation of policies like carbon taxes, emissions trading, measures to support specific technologies (e.g., wind, solar, carbon capture), and regional fuel and emissions standards. The Earth system component should simulate the coupled atmosphere, ocean, land (including rivers and lakes) and cryosphere (sea ice, land ice, permafrost), including the dynamical and physical processes (i.e., river flow, ocean eddies, cloud processes, erosion), chemical processes (chemical gases and aerosols), biogeochemical processes (life-mediated carbon-nutrient dynamics), and biogeophysical processes (life-mediated water and energy balance).

In practice, because state-of-the-art ESMs are computationally expensive, a CHES model can be built by coupling a human system model to a simplified model of the climate system and to specific impact models for key ecosystems and sectors of the economy (Fig.  1 ). Different coupling strategies exist 22 , from off-line one-way information exchange between research communities to fully coupled modeling approaches that yield more or less instantaneous (depending on the timestep of the coupling) two-way interactions between the human and Earth system components. Other strategies include improving the representation of the Earth system in IAMs or improving the representation of societal elements within ESMs. Beyond the challenge of coupling the human and Earth systems, an important characteristic of CHES models should be a detailed representation of the biophysical impacts of climate change, spanning key economic sectors and ecosystem services. (Additional details on the various coupling strategies and their advantages are provided in the Supplementary Table  1 .)

Conceptual representation of a coupled human-Earth system model with improved integration of physical impact models. Different coupling strategies between the various components of the modeling framework are represented by the different arrows. Potential impacts are listed in the right box

While the coupling strategy remains an important issue, and full coupling between the human and Earth systems is an aspirational goal, the effort will not be very insightful if it involves dubious damage functions, like it does in social cost of carbon models 23 , 24 . Also, full coupling raises many additional challenges, e.g., difficulties of coupling different software systems, complexities of representing the cascading of uncertainty among components of the system, and differences in temporal and spatial scale of the various components. As a result, full coupling is generally limited to a specific pathway, like the land system 25 . In addition, full integration is not warranted unless there is evidence that it would substantially change the estimates of climate impacts. In the process of developing a CHES model, therefore, a one-way coupling where physical impacts of climate change are explicitly modeled, but do feed back onto GHG emissions and the climate system (e.g., land-use change), is a useful first step. A salient response from the one-way testing will then warrant exploration of two-way coupling which, if found to produce significant new insights, can be incorporated in subsequent versions of the model. A similar approach is suggested to interactions among impact models, for example between air quality and agriculture.

Improved integration of physical impact models

To simulate regional changes in temperature and precipitation, the IGSM can be combined with statistical emulation techniques (pattern scaling) to represent the differences in the regional patterns of change exhibited by different climate models 26 , 27 , or it can be coupled to a 3-dimensional atmospheric model when 3-dimensional and highly-resolved temporal climate information is required or to assess the role of natural climate variability at the regional scale 28 . To examine the fate of the oceans under future climate change, the IGSM includes a 3-dimensional dynamical, biological, and chemical ocean general circulation model capable of physically estimating global and regional changes in ocean acidification, the meridional overturning circulation, or the structure of phytoplankton communities 29 , 30 , 31 .

To analyze the co-benefits of GHG mitigation on air quality, the IGSM is linked to a 3-dimensional atmospheric chemistry model 32 , 33 that simulates, among others, changes in ground level fine particulate matter (PM 2.5 ) concentrations, where the human system model is combined with a detailed emissions inventory to provide anthropogenic emissions of precursors. Because the influence of climate change on air quality has been found to be small compared to the impact of reduction in emissions, we do not couple the atmospheric chemistry model to the climate model in the IGSM, instead using fixed meteorological fields for a chosen year (e.g., 2010) or set of years that capture distinct climate conditions (e.g., El Niño/La Niña/neutral year) for all simulations. To assess the changes in water resources driven by climate change and socio-economic drivers (e.g., population increase) the IGSM includes a river basin scale model of water resources management 34 , 35 , representing the competition for water among industry, agriculture and domestic use in the face of changes in water demand and water supply in 282 Assessment Sub-Regions (ASRs) over the globe—but that can also run at a more spatially resolved capacity over specific regions 36 , 37 .

Finally, to investigate the future of agriculture, the IGSM is coupled to a global gridded process-based terrestrial ecosystem/biogeochemistry carbon-nitrogen model, which simulates the impact of climate (temperature, precipitation, and solar radiation), atmospheric chemistry (CO 2 fertilization and ozone damage) and nitrogen limitation on crop yield, and accounts for land-use change adaptation decisions made by the human system model 38 , 39 . Because ozone damage has been identified as a major stressor on land productivity 40 , it is included in this analysis. However, the impact of land-use change on the climate system, through GHG emissions and changes in surface albedo 10 , 41 , is not included because it has not been demonstrated to be a key feedback on agricultural productivity. (More details are provided in Methods section.)

Integrated economic and climate scenarios

The integrated economic and climate scenarios are developed following three typical approaches in business, government and academia to explore the future: the desired, a normative scenario aimed at limiting global warming in 2100 to 2 °C from pre-industrial (named 2C) using a global economy-wide carbon tax; the likely, an outlook based on existing policy, here an assessment of the results from the UN COP-21 meeting 42 (named Paris Forever), assuming no additional climate policy after 2030, resulting in 3.5 °C warming in 2100, emphasizing that the current pledges are not sufficient to meet the goal to stay “well below 2 °C” 43 ; and the plausible, two exploratory scenarios to assess the potential development of low-carbon energy technologies (named Oceans and Mountains ) 44 , with warmings of 2.7 °C and 2.4 °C in 2100, respectively. Compared to the RCP scenarios (Fig.  2 ), these scenarios are responsive to and grounded in the latest existing climate policies (UN COP-21). They do not include a business-as-usual scenario like the RCP8.5, and they avoid scenarios based on unproven negative emissions technologies, like the RCP2.6. We provide a few applications of integrated impact assessments focusing on ocean acidification, air quality, water resources and agriculture (Fig.  3 ). (Additional details on the scenarios are provided in Supplementary Table  2 , and GHG emissions are shown in Supplementary Fig.  1 .)

Radiative forcing and global temperature change. Time series of a total anthropogenic radiative forcing (W m −2 ) and b global surface warming (°C) relative to 1871–1880 for the Paris Forever, Oceans, Mountains and 2C integrated economic and climate scenarios along with the 4 RCP scenarios. The multi-model ensemble mean is shown for global mean temperature in the 4 RCPs. Numbers in parentheses represent the number of climate models in the ensemble under each RCP scenario. Temperature observations are from the Berkeley Earth Surface Temperatures (BEST 92 )

Sample multi-sectoral climate impact assessment using the improved coupled human-Earth system modeling framework. Climate impacts are focusing on a ocean acidification (pH), b air quality over China and India (PM 2.5 , no dust), c water resources (Water Stress Index) and d agriculture (crop productivity). Except for the air quality analysis, present day, 2050 and 2100 correspond to, respectively, 10-year averages over the 2001–2010, 2046–2055 and 2091–2100 periods. For the water resources and agriculture climate impact assessments, results are shown for a dry (model M) and wet (model N) climate model via statistical emulation techniques

Multi-sectoral climate impact assessment

Under the Paris Forever scenario, the global ocean pH would drop to levels under 7.9 by 2100, which would significantly impact all calcareous phytoplankton that are the base of the ocean food chain, and would damage or destroy coral reefs 45 , but the ocean acidification is significantly reduced under the 2C scenario. China and India, two countries that currently experience severely polluted ambient air (with annual mean concentrations of PM 2.5 greater than air quality standards over major areas), would see increased pollution by 2100 under the Paris Forever scenario, with PM 2.5 concentrations doubling in many regions. However, these countries would experience significant co-benefits of imposing a carbon tax under the 2C scenario, with reductions in co-emitted air pollutants including PM 2.5 . By 2100, the population exposed to water stress is generally projected to increase by several hundred million under most scenarios, mainly driven by increases in water demand from a growing population. However, the use of different climate models—through statistical emulation techniques—results in contrasting estimates of the impact of climate mitigation, because of differences in regional patterns of precipitation change. Under a relatively dry climate model pattern (model N), the higher warming scenario is associated with stronger regional decreases in precipitation and thus increased water scarcity over densely-populated areas. Emissions mitigation reduces the degree of water scarcity. On the other hand, this finding is reversed under a relatively wet climate model pattern (model M), thus motivating the implementation of much larger ensemble simulations to properly assess these risks 46 . Finally, large increases in temperature, exceeding the damaging temperature thresholds for crop productivity 47 , 48 , and major ozone damage 40 are projected under the Paris Forever scenario. Even under cropland relocation, extension and intensification, the overall global crop yield (over crop land areas) decreases by 2100. Emissions mitigation results in substantial reductions in warming and surface ozone concentrations, so land-use change adaptation can lead to benefits to the agriculture sector. (Additional analyses for all scenarios are provided in Supplementary Figs.  2 , 3 and 4 , with a summary of the major findings in Supplementary Table  3 .)

Our results show varying levels of agreement with existing impact assessments, especially those within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework 49 . The ocean acidification analysis is consistent with existing ESM intercomparison under the RCP scenarios 50 . The population exposed to water stress is generally in agreement with the analysis from a large ensemble of global hydrological models forced by five global climate models under the RCP scenarios 51 , but it lies on the lower end because we explicitly integrate the biogeophysical modeling of water resources with a water resources management model and thus optimize water resources. Also, our finding that there are conditions under which GHG mitigation could increase water scarcity resonates with an analysis focusing on the US 52 . The major co-benefits of reducing GHG emissions on air quality are consistent with existing estimates 11 , although the actual magnitude of the co-benefits can vary substantially among studies because of differences in the scenarios and differences in the treatment of criteria pollutant emissions by different IAMs. Finally, the climate impacts on agricultural productivity differ from AgMIP analyses 53 because our estimates include ozone damage and land-use change adaptation. Few studies bring together estimates of climate impacts across ecosystems and sectors of the economy under a consistent modeling framework, using consistent socio-economic and climate scenarios.

Assessing climate change impacts is a challenging task, and many researchers are cautious about reducing impacts to a monetary value. Despite being an active area of research, there is little theory to guide the damage functions needed to directly translate change in global mean temperature to impacts on gross domestic product (GDP), and in many cases arbitrary functional forms and corresponding parameter values are chosen 54 . In contrast, we focus on understanding the chain of actual physical changes at the regional and sectoral levels and then estimating the economic impacts, thus bridging the gaps among the IAM, ESM, and IAV communities. Our results show that the projected climate impacts vary dramatically across the globe, with large uncertainties in the physical climate impacts associated with differences in the magnitude and patterns of climate change from different climate models, thus putting in question the adequacy of damage functions based on global mean temperature. These results also support the need to rely on probabilistic ensembles of climate simulations to determine the full range of outcomes and move into quantitative climate risk assessments. Such probabilistic impact assessment has been conducted with the IGSM for specific regions of the world 46 . Furthermore, our analysis shows that more stringent emission reduction scenarios (Oceans, Mountains, 2C) are successful in mitigating a large portion of these climate impacts. These projections demonstrate the relative value of each emissions mitigation policy by relying on consistent economic and climate projections that provide a sound physical basis for the estimates of climate impacts. Finally, they do not require internationally coordinated modeling efforts that can be cumbersome and time consuming, and that sometimes lag the implementation of climate policies in the real world.

This strategy to move toward a more integrated and self-consistent representation of the coupled human and Earth systems, with a geospatially resolved physical representation of climate impacts, has largely emerged from a recent research focus on the food-energy-water (FEW) nexus and outcomes from MIPs, like ISIMIP, which have shown the importance of linking IAMs with physical impact models 53 , 55 , 56 , 57 . The improvement of existing IAMs, implementing the paradigm shift that a CHES approach represents, is ongoing in many integrated assessment modeling research groups 58 , with different levels of integration, number of impacts considered, and speed of model development 59 , 60 , 61 , 62 , 63 , 64 . Thus far, however, these efforts have focused largely on individual sectors of the economy, like energy for heating and cooling 65 , 66 , 67 , water resources 68 , 69 , 70 , 71 or air pollution 72 .

While an uncertainty analysis is beyond the scope of this paper, we recognize that the climate impact results discussed above are subject to substantial uncertainty. The common approach to address uncertainty in climate impact studies is through multiple impact model ensembles driven by multiple climate model ensembles (e.g., ISIMIP/AGMIP). Because of the lack of flexibility and responsiveness of these coordinated multi-model exercises, we argue that a complementary approach is to use model emulators (e.g., crop yield emulators 73 or climate emulators 26 , 27 , 46 ) along with large model ensembles—perturbing the physics, parameters, and initial conditions—within a consistent CHES modeling framework 13 , 27 , 74 , 75 . Such an approach could not only help quantify parametric or scenario uncertainty, but also address structural uncertainty (associated with the use of different models) by using emulators to reproduce and account for the varying behavior of different models.

We further argue that it is possible to develop computationally-efficient models, which represent the various essential components of the Earth system and provide a physical representation of climate impacts, albeit in reduced forms (e.g., EMIC or emulators). These modeling frameworks can be used for risk analysis instead of relying on box models and dubious damage functions. At the same time, computationally demanding process-based impact models are still required to assess the climate impacts on specific sectors, such as air quality and health. The need for state-of-the-art models is well illustrated by recent evidence of the important role of natural climate variability on regional atmospheric chemistry 76 , 77 , further questioning the adequacy of damage functions based on global mean temperature. At the very least, the relevance of these damage functions could be tested against the more geospatially resolved and physically grounded CHES modeling framework.

The modeling framework presented in this study can offer a new and complementary way for multi-sectoral climate impact assessments under a wide range of up-to-date policy scenarios while ensuring the needed consistency among the various components of the human and Earth systems. We propose that the development of more integrated and self-consistent models of the coupled human and Earth systems, with a geospatially resolved physical representation of climate impacts be the next step beyond the traditional RCP and MIP approaches. Such an effort will promote an increasingly tighter collaboration among the IAM, ESM, and IAV communities. While there is still a need to bridge the gap between physical impacts and the resulting monetary values for economic damages, ongoing research shows important progress in this direction, such as efforts on health impacts 78 , 79 and agricultural impacts 80 , and continued focus should be devoted on this aspect of climate impact research.

Coupled human-Earth system model

In this study, we use the MIT Integrated Global System Modeling (IGSM) framework 14 , 15 , 16 , 17 that links a human system model, the economic projection and policy analysis (EPPA) model, to an ESM of intermediate complexity, the MIT Earth System Model (MESM). The schematic of the IGSM is provided in Supplementary Fig.  5 .

Human system model

To evaluate long-term scenarios of energy and economic development we employ the EPPA model 81 , 82 , which provides a multi-region, multi-sector dynamic representation of the global economy. The Global Trade Analysis Project (GTAP) dataset 83 provides the base information on the input-output structure for regional economies, including bilateral trade flows. The base year for the model is 2010, based on the calibration of the GTAP data for 2007, and from 2010 the model solves at 5-year intervals. We also further calibrate the data for 2010–2015 based on the data from the International Monetary Fund (IMF) World Economic Outlook 84 and the International Energy Agency (IEA) World Energy Outlook 85 .

The model includes a representation of CO 2 and non-CO 2 (CH 4 , N 2 O, HFCs, PFCs, and SF 6 ) GHG emissions abatement, and calculates reductions from gas-specific control measures as well as those occurring as a byproduct of actions directed at reducing emissions of CO 2 . The model also tracks major air pollutants: sulfates (SO x ), nitrogen oxides (NO x ), black carbon (BC), organic carbon (OC), carbon monoxide (CO), ammonia (NH 3 ), and non-methane volatile organic compounds (VOCs).

Future scenarios can be calibrated to specified energy or emissions profiles or driven by economic growth (resulting from savings and investments) and by exogenously specified productivity improvement in labor, energy, and land. Demand for goods produced from each sector increases as GDP and income grow; stocks of limited resources (e.g., coal, oil, and natural gas) deplete with use, driving production to higher cost grades; sectors that use renewable resources (e.g., land) compete for the available flow of services from them, generating rents. Combined with policy and other constraints, these drivers change the relative economics of different technologies over time and across scenarios, as advanced technologies only enter the market when they become cost-competitive.

The production structure for electricity is the most detailed of all sectors, and captures technological changes that will be important to track under a GHG emissions mitigation policy. The deployment of advanced technologies is endogenous to the model. Advanced technologies, such as cellulosic biofuel or wind and solar technologies, enter the market when they become cost-competitive with existing technologies. Technologies are ranked according to their levelized cost of electricity, plus additional integration costs for wind and solar. When a carbon price exists, low carbon technologies are introduced. Initially, a fixed factor is required to represent costs of deployment (e.g., institutional costs, learning costs) for new technologies that—while competitive—require some time to penetrate into the market. The fixed-factor supply grows each period as a function of deployment until it becomes non-binding, allowing for large-scale deployment of the new technology. A complete description of the nesting structure of electricity generation and other production sectors in the EPPA model can be found in the model description 81 .

Earth system model

The MESM 86 couples a zonally-averaged model of atmospheric dynamics, physics and chemistry, a land model with a representation of the terrestrial ecosystem biogeochemistry, and a choice of either a mixed layer anomaly diffusive ocean model or a 3-dimensional dynamical ocean component based on the MIT ocean general circulation model 87 , 88 , including a detailed representation of physical, chemical, and biological processes 29 , 30 , 31 , along with carbon cycle and thermodynamic sea-ice submodels.

The atmospheric model is a zonally-averaged statistical dynamical model that explicitly solves the primitive equations for the zonal mean state of the atmosphere and includes parameterizations of heat, moisture, and momentum transports by large-scale eddies based on baroclinic wave theory. The parameterizations of physical processes include clouds, convection, precipitation, radiation, boundary layer processes, and surface fluxes. The radiation code includes all significant GHGs (H 2 O, CO 2 , CH 4 , N 2 O, CFCs, and O 3 ) and eleven types of aerosols. The land model simulates terrestrial water, energy, carbon, and nitrogen budgets including carbon dioxide (CO 2 ) and trace gas emissions of methane (CH 4 ) and nitrous oxide (N 2 O). The MESM also includes an urban air chemistry model and a detailed global scale zonal-mean atmospheric chemistry model that consider the chemical fate of 33 species, 41 gaseous-phase, and 12 aqueous-phase chemical reactions.

The global climate response of the MESM can be varied by modifying its climate sensitivity, strength of aerosol forcing and rate of ocean heat and carbon uptake, thus allowing for uncertainty analysis in global climate change. For regional studies, the MESM can be coupled to the NCAR 3-dimensional Community Atmosphere Model (CAM) 28 or to a climate emulator that relies on a pattern-scaling method that extends the MESM zonal mean variables based on climate change patterns from various climate models 26 .

Geospatially resolved physical representation of impacts

To simulate the physical impacts of global change, the MIT IGSM is linked to a series of impact models. In this study, we focus on the representation of climate impacts on ocean acidification, air quality, water resources and agriculture.

Changes in pH are simulated using the MESM 3-dimensional dynamical ocean, with a detailed representation of physical, chemical, and biological processes. The MESM can simulate changes in ocean carbon uptake and acidification under various scenarios of global change, consistent with the associated changes in the physical ocean (e.g., warming and changes in the meridional overturning circulation). In addition, since the ocean model is fully coupled within the MESM, changes in ocean circulation and carbon impact the global climate system.

To estimate regional atmospheric pollutant concentrations, the IGSM is linked to GEOS-Chem version 9.02, a three-dimensional chemical transport model 89 with a 2° × 2.5° horizontal grid cell resolution. Non-agricultural anthropogenic emissions are projected in ten-year intervals out to 2100 using the projections from EPPA. In previous studies, we have coupled a three-dimensional chemical transport model to the MESM to examine the impact of climate change on air quality 77 , 79 . However, here, meteorological fields from 2010 were used, thus isolating the air quality impact of anthropogenic emissions changes, while biomass burning and biogenic emissions are left constant at 2010 levels.

We assess trends in water stress using the Water Resource System (WRS) 34 , 35 , a river basin scale model of water resources management, which is forced by global simulations of climate change as well as socioeconomic drivers simulated by the IGSM. The WRS framework includes: (1) water supply: the collection, storage, and diversion of natural surface water and groundwater; (2) water requirements: the withdrawal, consumption, and flow management of water for economic and environmental purposes; and (3) the supply/requirement balance at river basin scale and measures of water scarcity. We assess changes in water stress for the globe at 282 Assessment Sub Regions (ASRs), which are geographic regions delineated by large river basin and country boundaries.

Finally, we estimate changes in agricultural productivity using the Terrestrial Ecosystem Model (TEM) component of the MESM, which is a process-based model that describes the carbon, nitrogen, and water dynamics of plants and soils for terrestrial ecosystems over the globe 38 , 90 , 91 . TEM uses spatially referenced information on climate, elevation, soils, and vegetation as well as soil-specific and vegetation-specific parameters to estimate important carbon, nitrogen, and water fluxes and pool sizes of terrestrial ecosystems and land productivity for a large number of vegetation types, including crops. TEM has a 1-month time step and a 0.5° × 0.5° horizontal grid cell resolution. TEM is coupled to the EPPA model to provide an integrated modeling framework to project land-use change and its associated changes in land productivity and net land carbon fluxes 38 , 39 , 91 , driven by changes in atmospheric carbon dioxide (CO 2 ) and ozone (O 3 ) concentrations and climate variables (i.e., temperature, precipitation, radiation) from the MESM model. In most studies, there is no feedback of land-use change GHG emissions and changes in albedo onto the climate system, however, the two-way coupling has been implemented for targeted studies 41 .

Code availability

Various codes that support the findings of this study are publicly available. A public version of the EPPA 6 model can be downloaded upon request by emailing [email protected]. The MESM source code will be publicly available via repository once the user license is completed (email [email protected] for further information). The MITgcm source code is publicly available via repository at http://mitgcm.org . The versions of WRS and TEM models used in this study are maintained by the MIT Joint Program on the Science and Policy of Global Change and service requests should be directed to the corresponding authors. The GOES-Chem model is managed by the GEOS-Chem Support Team, based at Harvard University and Dalhousie University with support from the US NASA Earth Science Division and the Canadian National and Engineering Research Council and a public release of the model can be obtained at http://geos-chem.org/ .

Data availability

The underlying data supporting the findings of the study are available at the DSpace@MIT ( http://hdl.handle.net/1721.1/113296 ).

Ciscar, J. C. et al. Climate Impacts in Europe: The JRC PESETA II Project . Report No. EUR 26586EN (European Commission, 2014).

U.S. Environmental Protection Agency. Climate Change in the United States: Benefits of Global Action . Report No. 430-R-15-001 (EPA, 2015).

Waldhoff, S. T. et al. Overview of the special issue: a multi-model framework to achieve consistent evaluation of climate change impacts in the United States. Clim. Change 131 , 1–20 (2015).

Article   CAS   Google Scholar  

Interagency Working Group on Social Cost of Carbon. Technical Support Document: Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866.  (EPA, 2013).

Weitzman, M. L. What is the ‘damages function’ for global warming — and what difference might it make? Clim. Chang. Econ. 1 , 57–69 (2010).

Article   Google Scholar  

Pindyck, R. S. Climate change policy: what do the models tell us? J. Econ. Lit. 51 , 860–872 (2013).

Burke, M. et al. Opportunities for advances in climate change economics. Science 352 , 292–293 (2016).

Article   ADS   CAS   PubMed   Google Scholar  

Rosenzweig, C. et al. The Agricultural Model Intercomparison and Improvement Project (AgMIP): protocols and pilot studies. Agric. For. Meteorol. 170 , 166–182 (2013).

Article   ADS   Google Scholar  

van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109 , 5–31 (2011).

Brovkin, V. et al. Effect of anthropogenic land-use and land-cover changes on climate and land carbon storage in CMIP5 projections for the twenty-first century. J. Clim. 26 , 6859–6881 (2013).

West, J. J. et al. Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nat. Clim. Chang. 3 , 885–889 (2013).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Harrison, P. A., Dunford, R. W., Holman, I. P. & Rounsevell, M. D. A. Climate change impact modelling needs to include cross-sectoral interactions. Nat. Clim. Chang. 6 , 885–890 (2016).

Paltsev, S., Monier, E., Scott, J., Sokolov, A. & Reilly, J. Integrated economic and climate projections for impact assessment. Clim. Change 131 , 21–33 (2015).

Sokolov, A. P. et al. The MIT Integrated Global System Model (IGSM) Version 2: Model Description and Baseline Evaluation. (MIT Joint Program on the Science and Policy of Global Change, 2005).

Prinn, R. et al. Scenarios with MIT integrated global systems model: significant global warming regardless of different approaches. Clim. Change 104 , 515–537 (2011).

Reilly, J. et al. Valuing climate impacts in integrated assessment models: the MIT IGSM. Clim. Change 117 , 561–573 (2013).

Prinn, R. G. Development and application of earth system models. Proc. Natl Acad. Sci. USA 110 , 3673–3680 (2013).

van Vuuren, D. P. et al. RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C. Clim. Change 109 , 95–116 (2011).

Thomson, A. M. et al. RCP4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109 , 77–94 (2011).

Riahi, K. et al. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Clim. Change 109 , 33–57 (2011).

Fujimori, S., Masui, T. & Matsuoka, Y. Development of a global computable general equilibrium model coupled with detailed energy end-use technology. Appl. Energy 128 , 296–306 (2014).

van Vuuren, D. P. et al. A comprehensive view on climate change: coupling of earth system and integrated assessment models. Environ. Res. Lett. 7 , 24012 (2012).

Rose, S. K. The role of the social cost of carbon in policy. WIREs Clim. Change 3 , 195–212 (2012).

Rose, S. K., Diaz, D. B. & Blanford, G. J. Understanding the social cost of carbon: a model diagnostic and inter-comparison study. Clim. Chang. Econ. 8 , 1750009 (2017).

Collins, W. D. et al. The integrated Earth system model version 1: formulation and functionality. Geosci. Model Dev. 8 , 2203–2219 (2015).

Schlosser, C. A. et al. Quantifying the likelihood of regional climate change: a hybridized approach. J. Clim. 26 , 3394–3414 (2013).

Monier, E., Sokolov, A. P., Schlosser, C. A., Scott, J. R. & Gao, X. Probabilistic projections of 21st century climate change over Northern Eurasia. Environ. Res. Lett. 8 , 45008 (2013).

Monier, E., Scott, J. R., Sokolov, A. P., Forest, C. E. & Schlosser, C. A. An integrated assessment modeling framework for uncertainty studies in global and regional climate change: the MIT IGSM-CAM (version 1.0). Geosci. Model Dev. 6 , 2063–2085 (2013).

Dutkiewicz, S., Sokolov, A. P., Scott, J. & Stone, P. H. A Three-Dimensional Ocean-Seaice-Carbon Cycle Model and its Coupling to a Two-Dimensional Atmospheric Model: Uses in Climate Change Studies. (MIT Joint Program on the Science and Policy of Global Change Report Series, 2005).

Dutkiewicz, S., Follows, M. J. & Bragg, J. G. Modeling the coupling of ocean ecology and biogeochemistry. Glob. Biogeochem. Cycles 23 , GB4017 (2009).

Dutkiewicz, S. et al. Impact of ocean acidification on the structure of future phytoplankton communities. Nat. Clim. Chang. 5 , 1002–1006 (2015).

Article   ADS   CAS   Google Scholar  

Selin, N. E. et al. Global health and economic impacts of future ozone pollution. Environ. Res. Lett. 4 , 44014 (2009).

Matus, K. et al. Health damages from air pollution in China. Glob. Environ. Chang. 22 , 55–66 (2012).

Strzepek, K. et al. Modeling water resource systems within the framework of the MIT Integrated Global System Model: IGSM-WRS. J. Adv. Model. Earth Syst. 5 , 638–653 (2013).

Schlosser, C. A. et al. The future of global water stress: an integrated assessment. Earth’s Future 2 , 341–361 (2014).

Blanc, É. et al. Modeling U.S. water resources under climate change. Earth’s Future   2 , 197–224 (2014).

Blanc, E., Caron, J., Fant, C. & Monier, E. Is current irrigation sustainable in the United States? An integrated assessment of climate change impact on water resources and irrigated crop yields. Earth’s Future   5 , 877–892 (2017).

Melillo, J. M. et al. Indirect emissions from biofuels: how important? Science 326 , 1397–1399 (2009).

Reilly, J. et al. Using land to mitigate climate change: hitting the target, recognizing the trade-offs. Environ. Sci. Technol. 46 , 5672–5679 (2012).

Reilly, J. et al. Global economic effects of changes in crops, pasture, and forests due to changing climate, carbon dioxide, and ozone. Energy Policy 35 , 5370–5383 (2007).

Hallgren, W. et al. Climate impacts of a large-scale biofuels expansion. Geophys. Res. Lett. 40 , 1624–1630 (2013).

Jacoby, H. D., Chen, Y.-H. H. & Flannery, B. P. Informing transparency in the Paris Agreement: the role of economic models. Clim. Policy 17 , 873–890 (2017).

Fawcett, A. A. et al. Can Paris pledges avert severe climate change? Science 350 , 1168–1169 (2015).

Shell. New Lens Scenarios: A Shift in Perspective for a World in Transition.  (Shell, 2013).

Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: the other CO 2 problem. Ann. Rev. Mar. Sci. 1 , 169–192 (2009).

Article   PubMed   Google Scholar  

Fant, C., Schlosser, C. A., Gao, X., Strzepek, K. & Reilly, J. Projections of water stress based on an ensemble of socioeconomic growth and climate change scenarios: a case study in Asia. PLoS ONE 11 , e0150633 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA. 106 , 15594–15598 (2009).

Schauberger, B. et al. Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun. 8 , 13931 (2017).

Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework. Proc. Natl Acad. Sci. USA. 111 , 3228–3232 (2014).

Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences 10 , 6225–6245 (2013).

Schewe, J. et al. Multimodel assessment of water scarcity under climate change. Proc. Natl Acad. Sci. 111 , 3245–3250 (2014).

Hejazi, M. I. et al. 21st century United States emissions mitigation could increase water stress more than the climate change it is mitigating. Proc. Natl Acad. Sci. USA 112 , 10635–10640 (2015).

Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111 , 3268–3273 (2014).

Pindyck, R. S. The use and misuse of models for climate policy. Rev. Environ. Econ. Policy 11 , 100–114 (2017).

Nelson, G. C. et al. Climate change effects on agriculture: economic responses to biophysical shocks. Proc. Natl Acad. Sci. USA 111 , 3274–3279 (2014).

Elliott, J. et al. Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proc. Natl Acad. Sci. USA 111 , 3239–3244 (2014).

Schmitz, C. et al. Land-use change trajectories up to 2050: insights from a global agro-economic model comparison. Agric. Econ. 45 , 69–84 (2014).

Weyant, J. Some contributions of integrated assessment models of global climate change. Rev. Environ. Econ. Policy 11 , 115–137 (2017).

Calvin, K. et al. Implications of simultaneously mitigating and adapting to climate change: initial experiments using GCAM. Clim. Change 117 , 545–560 (2013).

Rao, S. et al. Better air for better health: forging synergies in policies for energy access, climate change and air pollution. Glob. Environ. Chang. 23 , 1122–1130 (2013).

Wise, M., Calvin, K., Kyle, P., Luckow, P. & Edmonds, J. Economic and physical modeling of land use change in GCM 3.0 and an application to agricultural productivity, land, and terrestrial carbon. Clim. Chang. Econ. 5 , 1450003 (2014).

van Vuuren, D. P. et al. Pathways to achieve a set of ambitious global sustainability objectives by 2050: explorations using the IMAGE integrated assessment model. Technol. Forecast. Soc. Change 98 , 303–323 (2015).

Hasegawa, T., Fujimori, S., Masui, T. & Matsuoka, Y. Introducing detailed land-based mitigation measures into a computable general equilibrium model. J. Clean. Prod. 114 , 233–242 (2016).

Klein, D. et al. The value of bioenergy in low stabilization scenarios: an assessment using REMIND-MAgPIE. Clim. Change 123 , 705–718 (2014).

Isaac, M. & van Vuuren, D. P. Modeling global residential sector energy demand for heating and air conditioning in the context of climate change. Energy Policy 37 , 507–521 (2009).

Zhou, Y., Eom, J. & Clarke, L. The effect of global climate change, population distribution, and climate mitigation on building energy use in the U.S. and China. Clim. Change 119 , 979–992 (2013).

Labriet, M. et al. Worldwide impacts of climate change on energy for heating and cooling. Mitig. Adapt. Strateg. Glob. Chang. 20 , 1111–1136 (2015).

Hanasaki, N. et al. A global water scarcity assessment under shared socio-economic pathways - Part 2: water availability and scarcity. Hydrol. Earth Syst. Sci. 17 , 2393–2413 (2013).

Hejazi, M. I. et al. Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies. Hydrol. Earth Syst. Sci. 18 , 2859–2883 (2014).

Strzepek, K. et al. Benefits of greenhouse gas mitigation on the supply, management, and use of water resources in the United States. Clim. Change 131 , 127–141 (2015).

Kim, S. H. et al. Balancing global water availability and use at basin scale in an integrated assessment model. Clim. Change 136 , 217–231 (2016).

Rogelj, J. et al. Air-pollution emission ranges consistent with the representative concentration pathways. Nat. Clim. Chang. 4 , 1–5 (2014).

Blanc, É. Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models. Agric. For. Meteorol. 236 , 145–161 (2017).

Sokolov, A. P. et al. Probabilistic forecast for twenty-first-century climate based on uncertainties in emissions (without policy) and climate parameters. J. Clim. 22 , 5175–5204 (2009).

Webster, M. et al. Analysis of climate policy targets under uncertainty. Clim. Change 112 , 569–583 (2012).

Fiore, A. M., Naik, V. & Leibensperger, E. M. Air quality and climate connections. J. Air Waste Manag. Assoc. 65 , 645–685 (2015).

Article   CAS   PubMed   Google Scholar  

Garcia-Menendez, F., Monier, E. & Selin, N. E. The role of natural variability in projections of climate change impacts on U.S. ozone pollution. Geophys. Res. Lett . 44 , 2911–2921 (2017).

Thompson, T. M., Rausch, S., Saari, R. K. & Selin, N. E. A systems approach to evaluating the air quality co-benefits of US carbon policies. Nat. Clim. Chang 4 , 917–923 (2014).

Garcia-Menendez, F., Saari, R. K., Monier, E. & Selin, N. E. U. S. Air quality and health benefits from avoided climate change under greenhouse gas mitigation. Environ. Sci. Technol. 49 , 7580–7588 (2015).

Sue Wing, I., Monier, E., Stern, A. & Mundra, A. US major crops’ uncertain climate change risks and greenhouse gas mitigation benefits. Environ. Res. Lett . 10 , 115002 (2015).

Paltsev, S. et al. The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4 . (MIT Joint Program on the Science and Policy of Global Change, 2005).

Chen, Y.-H. H., Paltsev, S., Reilly, J. M., Morris, J. F. & Babiker, M. H. Long-term economic modeling for climate change assessment. Econ. Model. 52 , 867–883 (2016).

Narayanan, B., Aguiar, A. & McDougall, R. Global Trade, Assistance, and Production: The GTAP 8 Data Base . (Center for Global Trade Analysis, 2012).

Google Scholar  

International Monetary Fund. World Economic Outlook 2016, Subdued Demand: Symptoms and Remedies.  (IMF, 2016).

International Energy Agency. World Energy Outlook 2016 . (IEA, 2016).

Sokolov, A. et al. Description and Evaluation of the MIT Earth System Model (MESM) . (MIT Joint Program on the Science and Policy of Global Change, 2018).

Marshall, J., Hill, C., Perelman, L. & Adcroft, A. Hydrostatic, quasi-hydrostatic, and nonhydrostatic ocean modeling. J. Geophys. Res. 102 , 5733 (1997).

Marshall, J., Adcroft, A., Hill, C., Perelman, L. & Heisey, C. A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J. Geophys. Res. Ocean. 102 , 5753–5766 (1997).

Bey, I. et al. Global modeling of tropospheric chemistry with assimilated meteorology: model description and evaluation. J. Geophys. Res. Atmos. 106 , 23073–23095 (2001).

Felzer, B. S., Cronin, T., Reilly, J. M., Melillo, J. M. & Wang, X. Impacts of ozone on trees and crops. Comptes Rendus - Geosci. 339 , 784–798 (2007).

Kicklighter, D. W. et al. Potential influence of climate-induced vegetation shifts on future land use and associated land carbon fluxes in Northern Eurasia. Environ. Res. Lett. 9 , 35004 (2014).

Rohde, R. et al. A new estimate of the average earth surface land temperature spanning 1753 to 2011. Geoinformatic Geostat. Overv. 1 , 1–7 (2013).

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Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA

Erwan Monier, Sergey Paltsev, Andrei Sokolov, Y.-H. Henry Chen, Xiang Gao, Qudsia Ejaz, Evan Couzo, C. Adam Schlosser, Stephanie Dutkiewicz, Charles Fant, Jeffery Scott, Jennifer Morris, Henry Jacoby & Ronald Prinn

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E.M., S.P., A.S., C.A.S., R.P. and M.H. designed the research; E.M., S.P., A.S., Y.-H.H.C., X.G., Q.E., E.C., C.A.S., S.D., C.F., J.S., J.M. and D.K. performed the simulations and analyzed the data; and mainly E.M., S.P. and H.J. wrote the paper.

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Monier, E., Paltsev, S., Sokolov, A. et al. Toward a consistent modeling framework to assess multi-sectoral climate impacts. Nat Commun 9 , 660 (2018). https://doi.org/10.1038/s41467-018-02984-9

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Predictive Modeling of Environmental Systems: Applications of parameter estimation, data assimilation, sensitivity analysis, and model emulation

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• Advisor(s): VRUGT, JASPER

In 2013, the World Meteorological Organization (WMO) urged the global community for coordinated international action against accelerating and potentially devastating climate change. Preliminary data indicated that CO2 levels increased more between 2012 and 2013 than during any other year since 1984, and this was possibly related to reduced uptake by the Earth's biosphere in addition to the steadily increasing emissions from the Earth's surface. In the upcoming decades, it will be critical for scientists and policy makers to not only resolve the problem of carbon emissions by assessing human behavior, but also to understand as thoroughly as possible the underlying coupled processes of the Earth's atmosphere and biosphere in order to adequately measure and estimate the fluxes of carbon, water, and energy that are dictating the climatic trends we observe today. Fortunately, our ability to understand Earth's processes and predict climate change is improving.

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Conceptual model for environmental science applications on parallel and distributed infrastructures

• Denisa Rodila 1 , 2 ,
• Nicolas Ray 1 &
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Environmental Systems Research volume  4 , Article number:  23 ( 2015 ) Cite this article

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The global changes that are currently threatening the natural environment demand appropriate answers and solutions by the environmental science community. The increasing amount of heterogeneous data—Big Data—needed for that endeavor typically requires large computational and storage resources. This manuscript presents a general conceptual model for easily porting environmental applications on different parallel and distributed infrastructures.

We developed the conceptual model for a general environmental application and illustrate it through a use case on hydrological modeling. We also positioned this concept in a general methodology that will be used for efficiently porting applications on different computing environments.

The proposed conceptual model of an environmental application facilitates and simplifies not only the understanding of the structure of the application but also the general execution flow and the data flow. It provides a platform-independent, flexible and convenient way to execute the described application in a heterogeneous computing environment.

At the beginning of the 21st century, global changes linked to climate, biodiversity and habitat loss, environmental degradation and pollution, are threatening our natural environment and the human society at large, with already tangible negative outcomes [see Climate Change 2014 Synthesis Report—IPCC ( 2014 )]. Intensified droughts, ocean acidification, global sea level rise, increases in frequency of extreme weather events and glaciers melting are examples of such outcomes that are thought to intensify if appropriate international policies are not endorsed and applied.

Responding effectively to all these complex changes has become an important challenge for policy makers, but also for the scientific community that demands access to continuously increasing quantities of heterogeneous data and resources [see e-IRG Report on Data management—ESFRI ( 2009 )]. Scientists need to understand the inter-linkage between natural phenomena and human-induced activities and an important aspect for achieving this is the accessibility and processing of environmental data from various disciplines and geographic scales (local, regional, national and global).

Turning this data into knowledge is not an easy task, especially when locating and accessing the right resources (e.g. data, information, tools and services which can be information about the state of the Earth, relevant services, project results, applications, etc.) is done in a very scattered way through different state organizations, operators, service companies, data catalogs, scientific institutes, etc.

In the domain of Earth and environmental science there is an unprecedented avalanche of data due to a large extent to the fast evolution and availability of sensor/detector technologies. The advances in IT that enabled the capture, analysis and storage of massive amounts of data contributed also to this avalanche of data. The so-called “digital data deluge” is a phenomena caused not only by the ease with which these large quantities of new data can be created but also by the output of re-analysis of already existing archived data [e-IRG Report on Data management—ESFRI ( 2009 )]. This phenomenon is considerably changing the way science and research is being conducted in many disciplines as they are dealing with unprecedented sizes of data that needs massive computing capacities to handle it. The concept of “Big Data” emerged in this context.

Big Data are usually defined not just as massive data sets but also as data having very complex and varied structures, making further actions (e.g., storage, analysis, visualization, processing) very difficult. New satellite, airborne and ground-based remote sensing systems characterized by high spatial, temporal and radiometric resolution are, or will be soon, available. With the launch of three families of Sentinels satellites, Copernicus will be producing for example, 8 TB of Earth Observation data per day (approximately 3000 TB per year) [Big Data Workshop—Copernicus ( 2014 )], which will lead to an increase of data volume, diversity and also value. Based on this, the main characteristics of Big Data are gathered around the “5V” (Demchenko et al. 2012 ):

Volume: available amount of data;

Velocity: rate of data collection;

Variety: the variety of sources producing Big Data but also the implementation of services dealing with these different types of data;

Veracity: validity and accuracy of the data must be taken into account considering that data sources can be of different qualities, especially when it comes to coverage, accuracy and timeliness;

Value: how meaningful the row data is and how valuable is the obtained information (the main purpose of Big Data is to produce meaningful Small Data).

Big Data is already embedded in environmental science studies and is mainly produced by three important sources (Yang and Huang 2013 ): (1) From the impressive array of sensors that are placed in space (via remote sensing satellites) and in situ, used to measure and monitor weather, precipitations, vegetation, land cover, water quality, as well as other geophysical parameters. These collections of data sets satisfy all the characteristics of Big Data (the 5 Vs). (2) From the various scientific model simulations used for predicting physical phenomena. Climate change for example can be considered one of the largest use cases of scientific modeling and simulations. Nowadays climate simulation can be run on a daily basis with increasingly higher horizontal (hundreds of meters rather than tens of kilometers) and vertical (more model layers in the atmosphere) spatial resolution, as well as higher temporal resolution (minutes or hours rather than days or weeks). The update of these models is done more frequently and with much higher quantities of new data. Therefore the amount of data coming out of these simulations is very large, reaching typically petabytes of data from just one simulation. Based on this we can conclude that this data can as well be considered Big Data. (3) From data assimilation, the process by which models are updated with the latest observational data to be able to correct and validate the assumptions made in the model due to different factors like missing parameters, incorrect data, etc. Analysis of this Big Data can give unprecedented possibilities for better decision making for understanding and mitigating the effects of climate changes.

Nativi et al. ( 2015 ) emphasize the Big Data challenges in Global Earth Observation System of Systems—GEOSS (GEO 2005 )—and particularly its common digital infrastructure (GEOSS Common Infrastructure—GCI). The presented challenges can be identified along all the Big Data dimensionalities: volume, variety, velocity, veracity and visualization.

Environmental data are most of the time spatially referenced (i.e., referring to a geographic location) and as such belongs to geospatial data or geodata. Geospatial data describes geographical locations by giving attributes/information about their spatial and/or temporal extents (Giuliani et al. 2011 ). The amount of geospatial data has grown dramatically in the last 30 years mostly due to the rapid progress of communication means, as well as technologies to capture this type of data (e.g., GPS, sensors, satellites). Geospatial data is typically voluminous, complex, heterogeneous and geographically distributed. All these attributes make it generally difficult to access, share and distribute geospatial data, often with challenges to combine it with other types of data sets. Nowadays geospatial data is used and analyzed most of the times within a Geographical Information System (GIS) that has capabilities such as assembling, storing, manipulating, displaying, and merging data from different sources (Giuliani et al. 2011 ). In environmental sciences, GIS can be used in conjunction with Spatial Data Infrastructures (SDIs) that are widely used to share, discover, retrieve and visualize geospatial data through standardized services [e.g., Open Geospatial Consortium services—OGC ( 1994 )]. SDIs are therefore more than just data repositories, although suffering from limited analytic capabilities. Making use of GIS and SDI, a wealth of geospatial applications, technologies and initiatives have emerged recently in order to handle the increasing amount of environmental data, and to extract useful information out of it.

IBM for example, offers free supercomputing hours in the World Community Grid ( http://www.worldcommunitygrid.org/ ) for researchers who are analyzing and studying climate change. Google has also donated 1000+ terabytes of cloud storage for satellite observations and climate models. After the White House’s Climate Data Initiative ( https://www.whitehouse.gov/the-press-office/2014/03/19/fact-sheet-president-s-climate-data-initiative-empowering-america-s-comm ), in March 2014 a large amount of climate data has been made public from different organizations and agencies (NOAA, NASA, US Geological Survey, US Department of Defense). The goal of this was to encourage data providers, scientists and the public in general to share data and make use of the obtained information. From March to June 2014, ESRI hosted the Climate Resilience App Challenge ( http://www.esri.com/software/landing_pages/climate-app ) for governments, private industries and non-profit organizations to submit climate resilience applications. The number of useful submissions, addressing different aspects of climate change, was outstanding. In May 2014 the United Nations started a new initiative on climate change—the Big Data Climate Challenge ( http://unglobalpulse.org/big-data-climate/ )—that aims to use Big Data for supporting climate change actions, i.e. “to bring forward data-driven evidence of the economic dimensions of climate change”.

Applications that are used to solve different environmental issues, use specific data as input and produce outputs that are useful for the Earth and environmental community at large can be labeled as “environmental science applications” (or simply “environmental applications” hereafter). Since the 1990s, the number and diversity of environmental applications have increase dramatically. Many software systems were developed to integrate data coming from various thematic areas such as agriculture and soil science, ecology, terrain modeling, hydrology, land use/land cover, population distribution, education and health planning, energy resources, etc. The specificity of the majority of these environmental applications is the requirement of large computational and storage resources due to the massive amount of input and/or output data that is typically due to a combination of high spatial and temporal resolutions. Other reasons for high performance requirements include also the utilization of compute-intensive algorithms, the execution of large number of scenarios, the urgent need of responses, etc. Different parallel and distributed infrastructures, such as Grids, Clouds, and High Performance Computing (HPC) systems can satisfy the necessary requirements for running these applications (Nativi et al. 2013 ).

Despite the popularity of Big Data nowadays and the existence of solutions to handle the afferent challenges (with respect to storage, management, interoperability, governance and analysis), putting these solutions into practice is still a time consuming endeavor. Big Data storage management is indeed among the most important challenges for computing environments since many data intensive applications usually involve a high degree of data access locality. Data locality is thus a key aspect in providing performance for Big Data processing as transferring such large amounts of data would considerably slow down the process. Typical high performance computing systems did not take data locality into consideration as they used to focus on performing CPU-intensive computations over a moderate to medium volume of data (Assuncao et al. 2015 ), where the ratio of data transfer between the computing units to processing time is still small. Considering the context of Big Data, this solution is in most of the cases inefficient. The alternative is to move the computation as close as possible to where the data is. Existing parallel and distributed infrastructures already have built in mechanisms for efficient transfer of data among the computing units, although, considering the increasing volume of data we are dealing with, this option is no longer efficient. In (Assuncao et al. 2015 ), the authors argue different existing and on-going solutions for dealing with data locality in Cloud environments; similar initiatives are carried for other computing infrastructures such as Grid (Kumar and Bawa 2012 ).

Considering all the efforts of computing infrastructures to keep up with the increasing demands of Big Data, parallelism and distribution are still good solutions to efficiently execute data intensive applications. Some examples of environmental applications taking advantage of the capabilities offered by parallel and distributed infrastructures are those using parameter estimation, model calibration (Vrugt et al. 2006 ; gSWAT 2011 ), Web Processing Service on the Grid (Giuliani et al. 2012 ), numerical weather prediction (Maity et al. ( 2013 )) and satellite images workflows over the Grid (GreenLand 2011 ).

The choice of the appropriate parallel or distributed infrastructure depends on the application features, data model, and processing requirements of the environmental application. To run on one or several of these distributed or parallel infrastructures (i.e., an heterogeneous computational environment), the application has to be modified to have a particular structure or to use particular programming interfaces for accessing the resources of the infrastructures. This is typically done without knowing too much details about the final infrastructure(s) on which the application will run.

However, to our knowledge there is no convenient tool/framework to allow a user to easily express and control the execution of an environmental application in a heterogeneous computing environment, without having expertise in sophisticated workflow systems or control of the backend functionality. The main goal of this manuscript is to fill that gap and to propose a conceptual model of environmental applications, which will be a key component in a general methodology for porting these applications on different parallel and distributed infrastructures. The conceptual model facilitates and simplifies not only the understanding of the application structure but also the general execution on different computational platforms. It provides a platform-independent, robust, convenient and easy way to use a mechanism that allows a user to execute an application on a heterogeneous computing environment, and as such provides a first step towards the automation of this process.

Figure 1 shows the overall conceptual model architectural context and the goal of our final methodology.

Conceptual model architectural context

The methodology consists in proposing solutions to easily and efficiently port and execute environmental applications on different parallel and distributed infrastructures, using the conceptual model proposed in this manuscript. The main steps in this general methodology are: (1) conceptualize the environmental application (i.e. create the conceptual model), (2) instantiate the conceptual model with specific application data, (3) collect user specifications (data formats, application type, execution preferences, etc.), (4) check for similar executions performed in the past (history), (5) execute the application, and (6) collect the results. The execution of the applications and the selection of the computing environment(s) can be done automatically by a Mediator component, based on a complete conceptual model as well as on application related information provided by the user and other useful information such as availability of computing environments, previous execution history of the application, etc.

The development of this general methodology is still a work in progress. The purpose of the current manuscript is to detail the Conceptual Model which is a key component in this methodology.

The final methodology, based on the proposed conceptual model, will bring important contributions to the environmental science community but also to the parallel and distributed computation field. Despite the fact that the conceptual model is based on environmental applications experiments, it is flexible enough to be reused in other scientific areas. The simultaneous usage of different computational infrastructures is still a research challenge due to the complexity of each individual infrastructure but also due to the complexity of the interoperability between them.

Environmental science applications

Environmental science is a multidisciplinary field that integrates physical, biological and information sciences to study together the systems, the problems and the solutions of the environment. In the beginning of environmental science in the 1960s, the scientific community was more focused on disciplines, trying to develop knowledge in particular fields (such as geology, ecosystems, hydrology, etc.) but in the 1980s it became more and more obvious that these disciplines are strongly connected and the scientific community started to study them as interacting elements in a single big system (Dozier 2009 ). After this shift, it was easier to understand complex, system-oriented phenomena that link concepts from different fields (climate change involves atmospheric science, biology, human behavior, etc.) but also to understand and make a better use of the collected data (such as these coming from satellite observations). The growing understanding of these complex processes lead also to the development of new models. The knowledge gathered mainly for scientific understanding, begins to be used more to support practical decisions and actions, redirecting the environmental science to environmental applications. The role between basic science and applications is emphasized by the societal needs. After collecting and analyzing the gathered information, the community needs also a more fundamental, process-based understanding of the phenomena—a science of environmental applications. This science is guided more by societal needs than by scientific curiosity, focusing more on specific actions as well as on their consequences (Dozier 2009 ).

In environmental science there is data that is considered “independent” and data considered “dependent” . The “independent” variables are the ones being manipulated and selected to determine its relationship to an observed phenomena. These are normally the input variables that are observed in its naturally occurring variation. The “dependent” variables are the observed results of the independent variables and are usually the output variables that cannot be directly controlled. The distinction of dependent and independent data is done by the researcher and by the context in which it is applied. Now considering the form of the response (dependent) environmental data, we can specify several types of data: continuous data (such as temperature, mass, distance), counts (simple—the number of plants infected by a disease, or categorical—the number of infected plants classified into tree species and town), proportions (such as: percent mortality, sex ratio), binary data (ex: alive or dead, present or absent), time to death/failure (ex. the time it takes juveniles to disperse out of the study area), time series (such as temperature data measured at fixed intervals, river discharged measured over time) and circular (ex: day of the year). A detailed description of all these types is done by Piegorsh and Bailer ( 2005 ), and in (Environmental Data Analysis 2005 ). There are also many de-facto standards for delivering environmental data such as: HDF (Hierarchical Data Format), HDF-EOS, NetCDF (network Common Data Form), NetCDF-4, XML with initiatives such as GML (Geography Markup Language), CSML (Climate Science Modeling Language), ESML (Earth Science Markup Language), etc.

Conceptual modeling

Conceptual modeling is the activity of formally describing properties and actions of the physical and social world, with the purpose of better understanding, communicating and visualizing these properties and actions. The descriptions that arise from conceptual modeling are meant to be used both by humans and machines. The approach of conceptual modeling was first associated to semantic data modeling, but it soon found applications in many other fields such as modeling organizational environments, modeling software development processes or even modeling different parts of the world for better human communication and understanding (Mylopoulos 1992 ).

Conceptual models, mostly graphical, are used to represent both static and dynamic phenomena and they usually play an important role in communication between developers and users, for example for understanding of a new domain, providing a good documentation or providing input during the design process. High quality conceptual models also enable early detection and correction of errors (Wand and Weber 2002 ). A conceptual model is always an approximation, with different levels of details, of the real world system being modeled. It is a physical, mathematical or logical representation of a system, phenomenon or process and serves as a representation of an event/thing that is real or deliberately created. A model is thus produced by abstracting from reality a description of the system, with the observation that not all aspects of the system are represented, as this would be typically too time-consuming, complex and expensive.

Considering that environmental science is a complex and interdisciplinary domain, conceptual models are useful methods for meeting the challenges of deep understanding of the studied environmental phenomena. Conceptual models are useful in improving the coherence and analyzing the environmental issues and integrating knowledge. They can help the user not only to understand the complexity of an environmental system, but also to comprehend the variety of existing scientific approaches used to formulate and solve environmental problems (Fortuin et al. 2011 ). Not much research work has been reported, to our knowledge, on the usage of conceptual models for describing environmental applications. We review below what is found in the literature on that subject.

ISO191xxx ( 2003 ) is a series of standards defining and managing geographic information that is based on conceptual modeling. The main goal of ISO 191xx series is to facilitate the interoperability of geographic information systems by providing abilities to discover, access, understand and use the information and tools independently from the platform supporting them. ISO 18101 also defines a fundamental concept of geographic data—the feature—that is an abstraction of the real world phenomena. The research presented in (Fortuin et al. 2011 ) highlights the usage of conceptual modeling in facing the challenges given by the complexity and interdisciplinary character of the environmental science curricula. However, the usage of the conceptual models is oriented there more on solving the problems at the academic level instead of actually providing a deep understanding of environmental science applications and their interactions with different computational environments like we intend to do.

Nativi et al. ( 2013 ) present the concept of Model Web—a Model as a Service approach which will increase environmental model access and sharing, facilitate modeler to modeler and interdisciplinary interaction and reduce reinvention. The final idea is to have a wide network of interconnected models, data, and tools accessible via websites that are available as a resource for decision makers, researchers and the general public. In describing the Model Web conceptual framework, the manuscript introduces an entity (Model), which represents the conceptual and mathematical structure of an environmental model. Together with this entity, it also introduces other concepts, all part of the conceptual framework: Application, ModelRepresentation, ConfigurationParameter, ModelParameter, ModelRun, ModelEngine, Dataset, Service, InputData, ModelOutput. All these entities are elements in a procedural representation of an environmental model.

Wand and Weber ( 2002 ) and Davies et al. ( 2006 ) present a detailed description and a framework on conceptual modeling. Mylopoulos ( 1992 ) presents the process of conceptual modeling through the existence of four different kinds of knowledge: subject world, usage world, development world and system world. With these, conceptual models can reach a very high degree of complexity while trying to integrate as many aspects as possible from the simulated process/studied phenomena. However, the purpose of our study is to keep things as simple as possible and to highlight only the necessary details, allowing a more flexible execution of environmental science applications on different computational environments.

In (Modeling and Simulations Fundamental, Sokolowski and Banks 1970 ), the authors discuss about the degree of uncertainty that each real world system has got and about the way in which this uncertainty and variability can be included in conceptual modeling through random variables and random processes. Parekh ( 2005 ) proposes the use of ontologies and Semantic Web technologies to tackle the complexity and diversity of knowledge and data within the environmental sciences and engineering with the purpose of enabling efficient data sharing. “Ontologies provide shared domain models that are understandable to both humans as well as machines.” Ontologies provide an abstract conceptualization of information by defining basic concepts in specific domains together with their relations. The advantage is that all these definitions are both human and machine interpretable, leading to efficient automated mechanisms for information sharing and integration. The goal of their research work is to use ontologies to provide semantic interoperability among heterogeneous data, semantic descriptions of the datasets, as well as a common conceptual model for those datasets. In environmental science, the modeling activity can be complex and difficult, even for a specialist: acquiring knowledge of individual computational models, searching, gathering and analyzing raw data, ensuring high quality of data, transforming the data into formats compatible with the computational models, and then finally performing the modeling. This process typically takes several days to months. The ultimate vision of Parekh ( 2005 ) is to build intelligent and powerful environmental information systems that will enable efficient data sharing and integration mechanisms.

GC3Pie (Maffioletti and Murri 2012 ; GC3Pie 2012 ) is a Python framework that aims to orchestrate the execution of external commands over different computing resources (such as a Sun/Oracle/Open Grid Engine cluster, the Swiss National Distributed Computing Infrastructure SMSCG, OpenStack Cloud, ARC-based computational grid, etc.). It is a flexible framework that allows the implementation of command line driver scripts (in the form of Python object classes) that can be customized easily by overriding specific object methods. GC3Pie also conceptualizes the executed applications but using plain programming language (i.e., you describe your application using a set of Python classes which can be extended and specialized). The tool was designed to coordinate the execution of independent applications meaning that it is used to steer the computation, not to perform it. The description of application in a programming manner offers many advantages, but with the drawback of a certain complexity as not all users have programming capabilities. Our solution tries to simplify things as much as possible for the user, by allowing non-specialists in programming to create a simple conceptual model of the executed application.

Klischewski and Wetzel ( 2012 ) present an interesting approach in workflow management area by introducing a flexible vision for heterogeneous workflow networks. The idea is to redefine the workflow management to meet today’s challenges. The process execution realized based on predefined process patterns and resource relations (“processing by definition”) is replaced by a process execution driven by recurrent process evaluation and service contracting (“process by contract”). This approach supports decentralized resource management through dynamically interrelating services and contracting resources as services during workflow execution.

Based on our experience in environmental science applications and on our research done in this area, and also taking into account the previous discussed works done around the topics of conceptual modeling of a phenomena and execution of environmental applications on parallel and distributed infrastructure, we are formulating in the following section a proposition for a simple and efficient conceptual model of an environmental application in general. The proposed model can be applied to any type of application but the parser used to extract the information was developed specifically for environmental science applications, that is it takes into account the specific environmental input and output data, as well as the algorithms used in this field.

Conceptual modeling of an environmental application

The development of our conceptual model was mainly based on the experience that we have gathered in executing different environmental applications (see below) on different computing infrastructures (Clouds, Clusters and Grids). After analyzing the characteristics and the behavior of environmental applications, while executing them on several computing infrastructures, we came up with a solution to flexibly describe, in a conceptual way, a general environmental application. This conceptual model will be later integrated in a methodology that will allow scientists to easily map their applications on different computing infrastructures.

To narrow a little the large area of environmental science applications, we have started our research mainly through hydrological modeling but this does not limit the applicability of the proposed methodology to this research area. Flood and drought forecasting, water management, and prediction of the impact of natural and human induced changes in hydrological cycle are just a few examples in which distributed hydrological models can be very useful. As many other environmental applications, these models have to simulate a large variety of physical processes that lead not only to a high complexity but also to a high degree of parameterization (Silvestro et al. 2013 ). Hydrological models have evolved a lot in the past decade, both because of the exponential development of computational capacities and because of the progress of Earth observation techniques that allow one to access large amounts of data readily available.

In the future we will consider also global and regional climate models (Dai et al. 2001 ; Wang et al. 2015 ) in our experiments, as they also pose diverse challenges regarding the storage and the computational resources.

In what follows we briefly present the hydrological models used in our study.

Continuum (Silvestro et al. 2013 ) is a distributed and continuous hydrological model that aims at balancing the necessity for a complete description of physical processes with the goal of avoiding over-parameterization. This means that special attention is given to reducing as much as possible the parameterization of the physical processes (so that land information can be extensively used as a constraint to parameter calibration) but at the same time, the model indents to maintain the necessary details of all the terms of the hydrological cycle. The model was designed to be implemented in different contexts but especially on data-scarce environments (with no stream flow data). It has been used notably in the context of the Global Flood model for the UNISDR/UNEP Global Assessment Report (see Global Assessment Report on Disaster Risk Reduction—GAR 2013 ).

SWAT (Soil Water and Assessment Tool, SWAT 2009 ) is a physically-based hydrological model used for simulating different physical processes and predicting the impact of land management in large, complex watersheds, with varying soils, land uses, and management conditions. Like most of the other hydrological models, SWAT has to be calibrated first for obtaining meaningful results. The execution of SWAT hydrological models usually involves a large set of input and output data and a large number of simulations for performing model calibration on many parameters. This implies the necessity of large storage and computational resources.

Experiments

We performed several experiments with the presented applications:

Execution of a test SWAT hydrological model calibration on different types of parallel and distributed infrastructures: Grid (gLite middleware), Cluster and Cloud (different instances of OpenStack and Windows Azure)

Swat execution on cloud: openstack and windows azure.

The testing steps on these infrastructures are as follows:

Prepare the necessary SWAT input files and pack them in an archive

Upload the input archive in the Cloud storage

Launch the necessary number of virtual machines (VMs) (depending on the performed SWAT use case), with a predefined image and Linux flavour

On each instantiated VM, execute a script that:

copies the input archive locally, from the Cloud storage

executes the SWAT model on this input

retrieves and copies the results back into the storage

For Windows Azure execution, we have developed a program that starts automatically a given number of Linux VMs on Azure. Upon starting, each VM runs a script that starts the execution as described above. On OpenStack, we have executed the tests on two instances. The particularity of one instance, in executing the above mentioned SWAT calibration steps, was that the input data was stored on a proxy machine and we have copied the input data, to each launched VM, using multicast (UDPCast software). This approach reduced significantly the download time. On the second instance, the execution was performed using the boto library [A Python interface to Amazon Web Service—boto ( 2015 )] to access the data from and to S3 ( 2006 )—Amazon Simple Storage Service.

SWAT execution on the “Baobab Cluster” of University of Geneva

The execution on this infrastructure was done using SLURM ( 2003 ) workload manager. The steps are quite similar with the ones performed in the Cloud except that the input files were placed in the common file system and instead of launching VMs, we have launched jobs on individual nodes in the cluster.

SWAT execution on Grid running gLite middleware

Tests for executing the calibration of different instances of SWAT model have been performed in the framework of the enviroGRIDS ( 2009 ) project. In our case, the execution steps differ from those performed in the Cloud in that the input data is uploaded on a Storage Element instead of a Cloud Storage and the jobs are launched in the Grid using utilities such as Ganga [Gaudi/Athena and Grid Alliance—Ganga ( 2009 )—CERN] and DIANE [Distributed Analysis Environment—DIANE ( 2007 )] instead of starting VMs. Apart form this, the execution flow remains the same. A detailed description of these experiments is found in (Rodila et al. ( 2012 )).

Execution of the global flood model for the UNISDR/UNEP Global Assessment Report

The Global Flood model contains two procedures: downscaling the data and execution of the Continuum hydrological model, procedures which were executed on 13 out of 30 geographic areas (domains) covering the entire surface of the Earth. The execution was done on the Baobab cluster, provided by University of Geneva, using the SLURM workload manager, and the tests were performed using different distribution techniques.

Gridification of OGC web services

The OGC [Open Geospatial Consortium OGC ( 1994 )] Web services (OWS) are Geospatial services used to exchange information in an interoperable and efficient way over a distributed environment. We have made several test on these services executed over the Grid infrastructure [Grid Middleware—gLite ( 2002 )], with a varying number of features in the database (amount of data) and a varying request complexity (number of performed service requests). These tests were done during the enviroGRIDS project and they proved the existence of a complexity boundary for the execution on each computing background. Depending on the level of complexity of the model, the efficiency of the execution varies on different computational platforms. For a detailed description of the experiments and obtained results, see (Rodila and Gorgan 2012 ).

The performed practical experiments and the knowledge gathered after reviewing the scientific literature in this area formed together the starting point and the foundation on which our conceptual model, for describing a general environmental application, was built. The conceptual description contains specific details of the mapped application, such as: name, description, initial and intermediary inputs, outputs, executable processes, cost associated with each execution, etc. All these details are stored in a file and are used not only in determining the structure of the application but also the execution flow (i.e. control flow, specifying the order of the activities to be executed) and the data flow (specifying the input and the output data for each activity/task to be executed). Having this information in a common standard way is a step forward to automatize the mapping of applications on different computing infrastructures.

The execution flow of the application actually describes a workflow that is composed by connecting multiple tasks according to their dependencies. In general, a workflow can be represented as a Directed Acyclic Graph (DAG) or a non-DAG in which the nodes are execution tasks and the edges represent the communications lines between these tasks. In DAG workflows, the tasks can be structured as sequential tasks, parallel tasks or choice tasks (Costan 2010 ). The sequential tasks (or sequence) can be seen as an ordered series of tasks in which a task starts only after the previous one has completed. Parallel tasks are performed concurrently, while choice tasks are executed at runtime only when certain conditions are fulfilled. Using our experience in environmental applications, there are a lot of cases in which a set of tasks have to be executed several times, such as the calibration of a hydrological model for example, in which the process is executed in a large number of iterations but with different input parameters. In non-DAG workflows, besides the above-mentioned structures, we can also have iterations structures in which a section of tasks in the workflow can be repeated in an iteration block (i.e. loop). Using these entire structure types we can compose/decompose very complex workflows for an application. The proposed conceptual model has to be general enough to cover all the execution use cases of an application. As this goal is quite hard to achieve as one cannot foresee all the possibilities that might appear, the conceptual model has to be flexible enough to allow the extensions of new flows if this is the case for certain particular applications. It must allow the execution of arbitrary control flows through dependency graphs, taking into account conditional executions, looping, error handling and recovering, etc.

Execution flows

Possible executions flows based on the identified workflow structure types:

Simple execution

The execution flow in this case (Fig.  2 ) is a simple one in which the user defines the input(s) that are entries for a single execution point producing some output(s). The inputs and the outputs can be of different types and can be specified in different formats.

Conceptual model—simple execution

Sequential execution

In this case (Fig.  3 ) the execution flow is modeled as a sequence of several executions. The execution of a step normally depends on the results of a previous execution. That is why synchronization has to be taken into consideration. Simple Execution is a particular case of Sequential Execution in which we only have one execution node.

Conceptual model—sequential execution

Parallel execution

The execution flow in this case (Fig.  4 ) is composed of several executions that are run in parallel. Each step is independent and can be executed concurrently with the others. A “Simple Execution” is also a particular case in which we only have one execution node.

Conceptual model—parallel execution

Composed execution

The composed execution (Fig.  5 ) has a complex structure in which one can include different types of executions: simple, sequential or parallel. This is useful if a user wants to save/use previously computed executions without having to define them again. All other mentioned cases: Simple Execution, Sequential Execution and Parallel Execution can be considered particular use cases of this type.

Conceptual model—composed execution

Loop execution

The execution flow in this case (Fig.  6 ) consists in executing the same module several times. The module can be composed of several types of executions or it can be one of the already presented types. This is useful when the same set of executions has to be repeated several times. An example of this case can be the calibration of a climatic model. The inputs can be the same set of parameters or a slightly different one, but the outputs are usually different.

Conceptual model—loop execution

The proposed conceptual model covers all the above use cases. Using this model, a user can easily describe the structure and the execution flow (workflow) of his/her application. The actual execution of this model can be done through instantiation, i.e. binding the workflow tasks to specific resources (different for each application and for each execution use case). The conceptual model provides a flexible way of specifying an environmental application without being concerned with the low-level implementation details. The tasks in the conceptual model can be mapped on any executable platform at run time using mapping mechanism. In a concrete model, the specific resources of the applications are bind to the tasks. At this level, new tasks may also appear, related to data movement between tasks and/or repositories. The concrete model can be generated (either full or partial) either before or during the execution.

The steps to complete the conceptual model for a specific application are the following ones:

Define all the inputs of the application. Here the user has to specify for each input what is its type, how it can be accessed and to which execution task it belongs. The inputs can either be initial inputs or they can also be outputs from other executions.

Define all the outputs of the application by specifying as well their type, where should they be stored and from what execution they come from.

Define the executions tasks within the applications. Depending on what inputs are associated with a specific task, we can decide if the task will be executed in parallel or sequentially with other tasks. The loop executions are specifically described within a loop tag in the file in which the user has to specify what executions are part of the loop and how many times the loop is repeated.

Define the Composed Executions if any. At this point the user can specify the path to an already defined conceptual model of a previous application.

The parser intended to process the defined conceptual model will explore all these information. The conceptual description can be used in general for any type of application so far but the specificity of the environmental science field will be modeled in the parser component, as this is the level where the differences appear concerning especially the input and output data, as well as the algorithms used to handle environmental data.

SWAT use case: conceptual instantiation

The SWAT hydrological model allows a number of different physical processes to be simulated in a watershed. The inputs of the SWAT model are specific information about weather, soil properties, vegetation, topography, and land management practices of the watershed. To apply and successfully use hydrological models, both good calibration and good prediction analysis are required. Calibration is the process of estimating the model parameters to obtain a better system that closely resembles the system that the model intends to represent. SWAT calibration involves:

large set of input and output data

high number of simulations

time constrain (in certain cases): decision makers may need to obtain near real time output from the SWAT model to be able to make reliable and meaningful predictions and to deal with emergency environmental disasters.

The parallelization of SWAT calibration, using SUFI2 (Sequential Uncertainty Fitting) algorithm (Abbaspour et al. 2007 ), is accomplished at the simulation level by simply executing several SWAT runs with different parameters. Each simulation runs the same SWAT model but with different input parameter values. The execution of an iteration consists in performing three important phases:

Pre-processing phase is executed only once for each iteration. In this phase, the input parameter values are generated randomly but within a specific range for each simulation, based on the parameters intervals and using Latin hypercube sampling. A combination of parameters is generated for each simulation. Each simulation in an iteration represents one task which is executed on a node in a certain computing platform. Depending on the available resources, a node can execute one or more tasks (i.e. simulations) from an iteration.

Execution phase each simulation is run on different nodes.

Post-processing phase The output of each simulation is retrieved and processed after all the simulations have finished.

The SWAT model instance used in our experiments was developed in the EU/FP7 enviroGRIDS ( 2009 ) (Black Sea Catchment Observation and Assessment System supporting Sustainable Development) project and uses high-resolution data to model the Black Sea catchment. This large hydrological model was built using the SWAT2009 program (SWAT 2009 ) and covers the Danube River Basin. The Danube River flows for a distance of 2826 km and the model covers an area of 801,093 km \(^2\) . The region was divided into 1224 smaller sub-basins and the simulation period was set to 5 years. A detail description of this model and a comparative execution on Multicore and Grid infrastructure can be found in (Rodila et al. 2012 ).

The application flow of this hydrological model can be described using the proposed conceptual model in the following way:

Conclusions and future work

The challenges that the environmental science community is facing are immense in light of the current global environmental changes occurring at various scales. Part of the solution is to be able to efficiently analyze the growing set of available environmental “Big Data”, and this has become possible recently both due to the increasing capabilities of computational resources (and hardware advances) and to the availability of tools, algorithms and techniques used to take advantages of these resources. But it often remains difficult to easily integrate environmental applications with high performance computing resources. To ease that step, we have introduced there a solution to easily model environmental applications and to facilitate their integration with different parallel and distributed infrastructures.

Taking into account the growing need for computational speed, storage and scalability that environmental applications demand, the users usually tend to use—or to switch between—more than one execution platform for obtaining the necessary resources. To be able to easily switch between these platforms we have proposed an application conceptual model that hides the complexity of different types of environmental applications and that provides an easy and flexible way to map an environmental application to an execution platform. Using this model, a user can describe the structure, the data flow, as well as the execution flow (workflow) of the application. The model is built to cover different execution flows, such as: simple, sequential and parallel executions, as well as composed and loop executions. It also allows the definition of a new type of execution if necessary and the re-usage of an existing one. The actual execution of the model is done through instantiation, i.e. binding the described concepts to specific application resources, which are different for each application and for each execution use case. Using this approach, we managed to conceptualize an application and to disconnect it from the low-level implementation details of an execution environment.

As specified before, the proposed conceptual model is a key component in a general methodology for easily and efficiently porting environmental applications on different parallel and distributed infrastructures.

Following the availability of this conceptual model, the next step in our future work would be to develop a scheduling and execution component that would allow the user to easily submit an instantiated conceptual model (a specific environmental application bind to the conceptual model) to one or more available computing infrastructures. This component will also estimate which of the available computing infrastructure is more appropriate for execution based on several criteria such as: number of parallel jobs, user preferences, history, etc.

To be able to evaluate and verify the correctness and the interoperability of the proposed conceptual model as well as the efficiency of an application execution based on the conceptual model, we also have to develop a set of metrics and a validation component. The development of this methodology is still a work in progress but we have the confidence that it will bring important contributions to the urgent need of environmental community in using parallel and distributed infrastructures for better processing and analyzing the large amounts of data that is collected daily. The proposed conceptual model and its mapping to different computational infrastructures will allow many environmental applications to be more efficiently used. The hope is therefore that better-informed decision-making will follow, responding more effectively to the changes that are threatening our environment and the society at large.

Abbaspour KC, Vejdani M, Haghighat S (2007) SWAT-CUP calibration and uncertainty programs for SWAT. In: Oxley L, Kulasiri D (eds) PMODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand: December 2007, pp 1596–1602

Assuncao MD, Calheiros RN, Bianchi S, Netto MAS, Buyya R (2015) Big Data computing and Clouds: trends and future directions. Special Issue on Scalable Systems for Big Data Management and Analytics. J Parallel Distrib Comp 79–80:3–15

Article   Google Scholar  

Boto (2015) A python interface to amazon web service. https://boto.readthedocs.org

Copernicus. Big Data workshop, Bruxelles, 2014. http://www.copernicus.eu/library/detail/212

Costan A (2010) Autonomic Behavior of Large Scale Distributed Systems based on Monitoring Information. PhD thesis, Polytechnic University of Bucharest, Computer Science Department

DIANE. Distributed analysis environment, 2007. http://it-proj-diane.web.cern.ch/it-proj-diane/

Dai X, Meehl GA, Washington WM, Wigley TM, Arblaster JM (2001) Ensemble simulation of twenty-first century climate changes: business-as-usual versus co2 stabilization. Bull Am Meteorol Soc 82(11):2377–2388

Davies I, Green P, Rosemann M, Indulska M, Gallo S (2006) How do practitioners use conceptual modeling in practice? Data Knowl Eng 58:358–380

Demchenko Y, Zhao Z, Grosso P, Wibisono A, De Laat C (2012) Addressing Big Data challenges for scientific data infrastructure. In: IEEE Computing Society, editor, IEEE 4th International Conference on Cloud Computing Technology and Science (CloudCom 2012), pp 614–617

Dozier J (2009) The fourth paradigm: data intensive scientific discovery. In: Tolle K, Tansley S, Hey T (eds) The emerging science of environmental applications. Microsoft Research, Redmond, pp 13–19

ESFRI. e-irg report on data management, 2009. http://ec.europa.eu/research/infrastructures/pdf/esfri/publications/esfri_e_irg_report_data_management_december_2009_en.pdf

Environmental Data Analysis. Course, 2005. http://www.umass.edu/landeco/teaching/ecodata/schedule/environmental.data.pdf

enviroGRIDS. European project, 2009. http://www.envirogrids.net/

Fortuin KPJ, Van Koppen CSA, Leemand R (2011) The value of conceptual models in cooping with complexity and interdisciplinarity. Environ Sci Educ Biosci 61(10):802–814

Google Scholar  

GAR. Global assessment report on disaster risk reduction, 2013. http://www.preventionweb.net/english/hyogo/gar/2013/en/home/GAR_2013/GAR_2013_2.html

GC3Pie. Software, 2012. https://code.google.com/p/gc3pie/

GEO (2005) Geoss: 10-year implementation plan reference document. ESA publications division, 2005. https://www.earthobservations.org/documents/10-Year%20Plan%20Reference%20Document.pdf

Ganga. Gaudi/Athena and Grid alliance-CERN, 2009. http://ganga.web.cern.ch/ganga/

Giuliani G, Nativi S, Lehmann A, Ray N (2012) WPS mediation: an approach to process geospatial data on different computing backends. Comp Geosci 47:20–33

Giuliani G, Ray N, Schwarzer S, De Bono A, Dao H, Peduzzi P, Beniston M, Van Woerden J, Witt R, Lehmann A (2011) Sharing environmental data through GEOSS. Int J Appl Geospatial Res 2(1):1–17

gLite. Grid middleware, 2002. http://grid-deployment.web.cern.ch/grid-deployment/glite-web/

GreenLand. Software, 2011. http://cgis.utcluj.ro/applications/greenland

gSWAT. Software, 2011. http://cgis.utcluj.ro/applications/gswat

IPCC. Climate change 2014 synthesis report, 2014. http://www.ipcc.ch/pdf/assessment-report/ar5/syr/SYR_AR5_LONGERREPORT_Corr2.pdf

ISO191xxx. Series of geographic information standards, 2003. www.wmo.int/pages/prog/www/TEM/ET-WISC-I/ISO_191xx.doc

Klischewski R, Wetzel I (2012) Processing by contract; turning the wheel within heterogeneous workflow networks. Business Process Manag J 11(3):237–354

Kumar A, Bawa S (2012) Distributed and Big Data storage management in Grid computing. Int J Grid Comp Appl 3(2):19

Maffioletti S, Murri R (2012) GC3PIE: a python framework for high-throughput computing. In: Proceedings of EGI Community Forum 2012, EMI Second Technical Conference, Munich

Maity S, Bonthu SR, Sasmal K, Warrior H (2013) Role of parallel computing in numerical weather forecasting models. In: IJCA Special Issue on International Conference on Computing, Communication and Sensor Network CCSN2012, Vol 4, pp 22–27

Mylopoulos J (1992) Conceptual modeling, databases, and case: an integrated view on information systems development. In: Loucopoulos P, Zicari R (eds) Conceptual modeling and Telos. McGraw Hill, New York. pp 49–68

Nativi S, Mazzetti P, Geller G (2013) Environmental model access and interoperability: the GEO model web initiative. Environ Model Softw 39:214–228

Nativi S, Mazzetti P, Santoro M, Papeschi F, Craglia M, Ochiai O (2015) Big Data challenges in building the global earth observation system of systems. Environ Model Softw 68:1–26

OGC. Open geospatial consortium, 1994. http://www.opengeospatial.org/

Parekh V (2005) Applying Ontologies and Semantic Web technologies to Environmental Sciences and Engineering, Master of Science. PhD thesis, University of Maryland, Baltimore County, 2005

Piegorsh WW, Bailer AJ (2005) Analyzing environmental data. John Wiley and Sons, England

Book   Google Scholar  

Rodila D, Bacu V, Gorgan D (2012) Comparative parallel execution of SWAT hydrological model on multicore and Grid architectures. Int J Web Grid Serv 8(3):304–320

Rodila D, Gorgan D (2012) Geospatial and Grid interoperability through OGC services gridification. Int J Select Topics Appl Earth Observ Remote Sens 5(6):1650–1659

S3. Amazon simple storage service, 2006. http://aws.amazon.com/s3/

SLURM. Tool, 2003. http://slurm.schedmd.com/

SWAT. Soil and water assessment tool, 2009. http://swat.tamu.edu/

Silvestro F, Gabellani S, Delogu F, Rudari R, Boni G (2013) Exploiting remote sensing land surface temperature in distributed hydrological modelling: the example of the continuum model. Hydrol Earth Syst Sci 17(1):39–62

Sokolowski JA, Banks CM (1970) Modeling and simulations fundamental. John Wiley and Sons INC. Publication, Suffolk

Vrugt JA, Nuallián BÓ, Robinson BA, Bouten W, Dekker SC, Sloot PMA (2006) Application of parallel computing to stochastic parameter estimation in environmental models. Comp Geosci 32(8):1139–1155

Wand Y, Weber R (2002) Research commentary: Information systems and conceptual modeling—a research agenda. Inform Syst Res 13(4):363–376

Wang X, Huang G, Liu J, Li Z, Zhao S (2015) Rensemble projections of regional climatic changes over ontario, Canada. J Climat 28:7327–7346

Yang C, Huang Q (2013) Spatial Cloud computing: a practical approach. CRC Press, Boca Raton

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Authors’ contributions

The work presented here was carried out in collaboration between all authors. DR designed and conducted the research work including data preparation, concept development, results analysis and drafted the manuscript. Both NR and DG contributed during the conception and design of the proposal and revised it critically for important intellectual content. They have been supervising the progress of the research and providing guidance: NR on the environmental issues and DG on the technical side. All authors read and approved the final manuscript.

Acknowledgements

This research was partially founded by the SCIEX (Scientific Exchange Programme NMS.CH) scholarship ( http://www.sciex.ch ) through the project 12.206.

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The authors declare that they have no competing interests.

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Rodila, D., Ray, N. & Gorgan, D. Conceptual model for environmental science applications on parallel and distributed infrastructures. Environ Syst Res 4 , 23 (2015). https://doi.org/10.1186/s40068-015-0050-1

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Theses from 2022 2022

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Theses from 2021 2021

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Theses from 2019 2019

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Theses from 2018 2018

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Theses from 2017 2017

Accounting For Biotic Variability In Streams With Low Levels of Impervious Cover: The Role of Reach- and Watershed-Scale Factors , Catherine Bentsen, Environmental Conservation

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DIRECT AND INDIRECT EFFECTS OF CLIMATE ON BIRD ABUNDANCE ALONG ELEVATION GRADIENTS IN THE NORTHERN APPALACHIANS , Timothy Duclos, Environmental Conservation

EVALUATION OF THE RECREATIONAL CATCH-AND-RELEASE FISHERY FOR GOLDEN DORADO SALMINUS BRASILIENSIS IN SALTA, ARGENTINA: IMPLICATIONS FOR CONSERVATION AND MANAGEMENT , Tyler Gagne, Environmental Conservation

Botswana’s Elephant-Back Safari Industry – Stress-Response in Working African Elephants and Analysis of their Post-Release Movements , Tanya Lama, Environmental Conservation

Factors Influencing Shrubland Bird and Native Bee Communities in Forest Openings , H. Patrick Roberts, Environmental Conservation

A Mixed-methods Study on Female Landowner Estate Planning Objectives , rebekah zimmerer, Environmental Conservation

Theses from 2016 2016

Factors Influencing Household Outdoor Residential Water Use Decisions in Suburban Boston (USA) , Emily E. Argo, Environmental Conservation

Understory Plant Community Structure in Forests Invaded by Garlic Mustard (Alliaria petiolata) , Jason Aylward, Environmental Conservation

Factors Affecting Habitat Quality for Wintering Wood Thrushes in a Coffee Growing Region in Honduras , Brett A. Bailey, Environmental Conservation

Invasive Species Occurrence Frequency is not a Suitable Proxy for Abundance in the Northeast , Tyler J. Cross, Environmental Conservation

Population Genetic Analysis of Atlantic Horseshoe Crabs (Limulus polyphemus) in Coastal Massachusetts. , Katherine T. Johnson, Environmental Conservation

Modeling Historical and Future Range of Variability Scenarios in the Yuba River Watershed, Tahoe National Forest, California , Maritza Mallek, Environmental Conservation

The Life History Characteristics, Growth, and Mortality of Juvenile Alewife, Alosa pseudoharengus, in Coastal Massachusetts , Julianne Rosset, Environmental Conservation

Specific Phosphate Sorption Mechanisms of Unaltered and Altered Biochar , Kathryn D. Szerlag, Environmental Conservation

Trophic Relationships Among Caribou Calf Predators in Newfoundland , Chris Zieminski, Environmental Conservation

Theses from 2015 2015

Ant (Hymenoptera: Formicidae) Assemblages in Three New York Pine Barrens and the Impacts of Hiking Trails , Grace W. Barber, Environmental Conservation

Niche-Based Modeling of Japanese Stiltgrass (Microstegium vimineum) Using Presence-Only Information , Nathan Bush, Environmental Conservation

Assessing Mammal and Bird Biodiversity and Habitat Occupancy of Tiger Prey in the Hukaung Valley of Northern Myanmar , Hla Naing, Environmental Conservation

Generating Best Management Practices for Avian Conservation in a Land-Sparing Agriculture System, and the Habitat-Specific Survival of a Priority Migrant , Jeffrey D. Ritterson, Environmental Conservation

Experimental Test of Genetic Rescue in Isolated Populations of Brook Trout , Zachary L. Robinson, Environmental Conservation

UNDERSTANDING STAKEHOLDERS PERCEPTION TOWARDS HUMAN-WILDLIFE INTERACTION AND CONFLICT IN A TIGER LANDSCAPE-COMPLEX OF INDIA , Ronak T. Sripal, Environmental Conservation

Impacts of Land Cover and Climate Change on Water Resources in Suasco River Watershed , Ammara Talib, Environmental Conservation

Theses from 2014 2014

A Comparison of American, Canadian, and European Home Energy Performance in Heating Dominated – Moist Climates Based on Building Codes , Stephanie M. Berkland, Environmental Conservation

Spatio-Temporal Factors Affecting Human-Black Bear Interactions in Great Smoky Mountains National Park , Nathan Buckhout, Environmental Conservation

Estimating the Effective Number of Breeders of Brook Trout, Salvelinus fontinalis, Over Multiple Generations in Two Stream Systems , Matthew R. Cembrola, Environmental Conservation

An Assessment of Environmental Dna as a Tool to Detect Fish Species in Headwater Streams , Stephen F. Jane, Environmental Conservation

Assessing Wild Canid Distribution Using Camera Traps in the Pioneer Valley of Western Massachusetts , Eric G. LeFlore, Environmental Conservation

Quantifying the Effect of Passive Solar Design in Traditional New England Architecture , Peter Levy, Environmental Conservation

Ecology and Conservation of Endangered Species in Sumatra: Smaller Cats and the Sumatran Rhinoceros (Dicerorhinus Sumatrensis) As Case Studies , Wulan Pusparini, Environmental Conservation

The Cumulative Impacts of Climate Change and Land Use Change on Water Quantity and Quality in the Narragansett Bay Watershed , Evan R. Ross, Environmental Conservation

Patterns in Trash: Factors that Drive Municipal Solid Waste Recycling , Jared Starr, Environmental Conservation

Theses from 2013 2013

Greening the Building Code: an Analysis of Large Project Review Under Boston Zoning Code Articles 37 and 80 , Sandy J. Beauregard, Environmental Conservation

Vernal Pool Vegetation and Soil Patterns Along Hydrologic Gradients in Western Massachusetts , Kasie Collins, Environmental Conservation

Implementation of Aquaponics in Education: An Assessment of Challenges, Solutions and Success , Emily Rose Hart, Environmental Conservation

Aquatic Barrier Prioritization in New England Under Climate Change Scenarios Using Fish Habitat Quantity, Thermal Habitat Quality, Aquatic Organism Passage, and Infrastructure Sustainability , Alexandra C. Jospe, Environmental Conservation

The Energy Benefits of Trees: Investigating Shading, Microclimate and Wind Shielding Effects in Worcester and Springfield, Massachusetts , Emma L. Morzuch, Environmental Conservation

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Environmental Systems Analysis (ESA)

Students who major in Environmental Systems Analysis will learn to further develop the methodology and scientific tools of systems analysis and to apply these in strategic research topics, which are mainly society-driven. The applications aim at describing and analysing the causes, mechanisms, and effects of specific environmental problems in order to find potential solutions.

More about this thesis track

Integrated approach.

The main characteristic of Environmental Systems Analysis is its integrated approach. Knowledge from natural and social sciences as well as technology are combined. This integrated approach of ESA research, which usually includes the experiences and views of different stakeholders as well, is necessary to address complex environmental problems and to make an important contribution to sustainable planning and management.

Research focus

Students of the msc programme environmental sciences can choose to do a thesis in a topic that is closely related to ongoing research projects within the esa group or focus on applying systems analysis concepts and methods to an environmental issue of their own choice., ongoing research projects within the esa group are in the domains of:.

• Ecosystem Services and Biodiversity (e.g. quantification and valuation of ecosystem functions and services);
• Pollution & nutrients management (e.g. causes and impacts of pollution, nitrogen fluxes, uncertainty analysis and scale issues in modelling);
• Environmental modelling;
• (Participatory) integrated assessment;
• Integrated cost-benefit analysis of multifunctional land use;
• Decision support systems and ecological-economic modelling;
• Adaptation to climate change induced ecological impacts and its related socio-economic impacts.

Students can learn more about the education and research related to this discipline by visiting the website of the Environmental Systems Analysis group .

The following courses are part of the MSc programme Environmental Sciences when selecting the thesis track Environmental Systems Analysis. Next to these thesis specific courses, you will follow courses from the common part and electives as summarized in the programme outline . For more information visit the study handbook or contact the study adviser.

In order to prepare well for your thesis track, you can additionally choose supporting courses. These courses focus, for example, on quantitative and data sciences or laboratory skills. You can find the exact list in the study handbook at the section “Restricted Optionals (2) in Common part ”.

This course overview is based on the Wageningen University  study handbook , where you can find a more detailed course and programme description. The study handbook is guiding in case of any discrepancy.

Below you can see how your schedule might look like for your whole study programme:

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Environmental Modelling (M.Sc.)

Subject advisory service, prof. dr. thilo gross.

Phone: +49 471 4831-2526

Email: thilo.gross@hifmb.de

Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg (HIFMB) Ammerländer Heerstraße 231 26129 Oldenburg

Office hours: by arrangement

Course flyer

Module handbook status winter semester 2023/2024 (partly in german), introductory event 12 oct 2023 (german only), master environmental modelling, important note on the practical accounting of modules.

Modules that are not listed in the module handbook can be taken and included in the degree programme in accordance with the examination regulations. One example is modules for which special permission has been obtained from the examining board. Another, more frequent case occurs when modules from the other degree programmes of the "Environment and Sustainability" cluster are chosen for the supplementary area - the examination regulations offer this possibility.

In order to enable the digital booking of the module grade, the taking of the module must be applied for in good time so that - after approval to take the module - the registration can be carried out manually by the Examinations Office. The following procedure is necessary for this:

• You register with the Examinations Office during the registration period with all relevant data This step is necessary because self-registration is not possible.

After entering the grades, you can inform the Examinations Office by email that there is a " not yet assigned exam" in your grade account and that you wish to rebook it. The exam will then be manually assigned to your subject account .

We will simplify this procedure in the course of a planned system update, but at the moment this is the only way to avoid technical difficulties.

The assignment of modules from the various areas in the degree programme Environmental Modelling to the supplementary area is done informally by email to the Examinations Office. You can inform the Examinations Office of this yourself.

This can be done when the grade has been entered in the system or when you submit the "Declaration for the issuing of the master's degree programme certificate" (you will receive this from the Examinations Office with the notification of admission to the thesis) to the Examinations Office.

Facts and figures

The environmental modelling degree programme.

• is research-oriented
• is distinctly interdisciplinary
• offers extensive methodological and practical training
• includes current projects for independent research

Detailed description

In the Master's Programme Environmental Modelling programme, students acquire knowledge of methods and strategies for modelling and analysing environmental systems. To this end, students learn a wide range of mathematical-scientific, computer science-related and statistical methods for modelling and data analysis, management and evaluation. These are used in environmental research, environmental monitoring and environmental precaution. Due to the participation of various institutes, the research-oriented degree programme is strongly interdisciplinary, so that students learn to link environmental scientific thinking with economic approaches as well as methods of mathematics and computer science. In addition to theoretical study content, a broad practical training is offered, with a focus on computer-based work. Graduates of this degree programme are qualified to analyse concrete problems in environmental modelling and analysis. The Master's programme in Environmental Modelling is supported by the university's scientists organised in the Centre for Environmental Modelling (CEM) and is organisationally located at the ICBM. The participating lecturers come from the Institutes of Biology and Environmental Sciences (IBU) , Chemistry and Biology of the Sea (ICBM) , Mathematics (IfM) , Physics (IfP ), Economics and from the Department of Computer Science. This interdisciplinary structure enables cross-disciplinary training as well as close links to ongoing research projects in different areas of environmental modelling.

Requirements

• Basic knowledge of mathematics and natural sciences
• Interest in environmental data, quantitative models and computer-aided methods
• Interest in interdisciplinary research projects
• Interest in complex contexts
• Communication skills
• Ability to work in a team
• English language skills

Qualification goals

In accordance with the objective of the research-oriented Master's degree programme Environmental Modelling, graduates possess knowledge of the development of models, data analysis methods and decision support systems in the environmental sciences. They have the competence to apply different methods of modern environmental modelling, environmental data analysis and environmental informatics across disciplines according to their specialisation for the analysis of problems in the fields of environmental systems and biodiversity, energy systems and environmental and resource economics. Graduates have a general understanding of environmental systems and their linkage with economic and social issues.

Graduates are able to gain scientific knowledge independently and in cooperation with others and to recognise its significance for society and professional practice. Their qualification is based on a balanced mixture of theory and practice. After completing their studies, they possess extensive knowledge and skills for successful professional activity in the diverse fields of activity of environmental modelling, depending on their individual specialisation.

Occupation and fields of activity

The Master's degree in Environmental Modelling qualifies students to work on complex issues in the fields of

• Environmental monitoring
• Environmental statistics
• Environmental database management
• Development of environmental information systems
• Environmental precaution, planning, monitoring
• Environmental education
• Policy advice

Possible fields of employment arise, for example, in

• Companies that model environmental data for forecasting purposes
• planning offices
• statistical offices, ministries and authorities
• Management of small and medium-sized enterprises
• research institutions

Further qualification, in the form of a doctorate, is possible with appropriate suitability.

What our graduates and students say

Milena Ossenbeck (currently writing the master thesis)

Tanja Sophia Klopper (graduation March 2021)

I decided to do the Master's programme in Environmental Modelling after I enjoyed mathematics and programming the most in my Bachelor's programme, Bioengineering. I did a semester abroad in Trondheim (Norway) after my Bachelor's and took a course on network theory there - I enjoyed it so much that I wanted to specialise in modelling. Since environmental protection has been close to my heart for years, I became aware of the Environmental Modelling degree programme. As a perspective after graduation, I could imagine working in environmental systems research, such as investigating how targeted interventions in the environment work in terms of environmental protection. That also sounded attractive to me in the context of a doctorate. With my background as an engineer, the Computational Science and Engineering programme was also a possibility for me, and I also applied to "Environmental Systems and Resource Management" in Osnabrück. In the end, environmental modelling convinced me because the degree programme offers a lot of flexibility in course selection and you are very free in your specialisation. I also really liked the city of Oldenburg during a short visit while passing through.

The aforementioned flexibility of the degree programme was a little overwhelming for me and many of my fellow students in the first semester. Many courses and specialisations are interesting, but it was sometimes difficult to avoid overlaps. However, it was possible to register for several courses in each semester and make a final decision later. I really liked the small courses and the resulting close contact with the teachers. Practical work and exercises were also an important part of most courses - including research work, developing your own models or presentations. I was able to expand my programming skills as I had hoped and learned a lot about scientific work. The degree programme includes three internships (two smaller projects and one large one), which can be completed both at the university and externally at research institutions or in companies. I really appreciated this freedom and took advantage of all three options. For example, I completed my large internship at Rifcon GmbH in the area of effect modelling and presented the results I obtained there in the form of a poster at the international SETAC conference (this was held digitally in 2020). My participation was funded by the university as part of "forschen@studium". I did another internship at the Helmholtz Centre Dresden-Rossendorf, after which I was able to complete my studies there with my Master's thesis. It was about battery simulations, which fitted in very well with my "energy systems" profile. Throughout my studies, I enjoyed very good supervision and could also count on the support of the lecturers in case of organisational difficulties.

After graduating, I have now decided to do a doctorate at the University of Karlsruhe. The topic has little to do with what I had in mind before my studies - I am now working on the design of a process for battery production and the development of a digital twin for process automation. This may not sound like "environmental modelling", but I can score points with many of the skills I have acquired - especially with my programming experience and routine in writing scientific papers.

Ievgen Vdovychenko (graduation November 2020)

My interest in environmental processes developed while I was still in Ukraine, writing a Master's thesis in another subject, information systems. My Master's thesis at the time was on the topic of "Economics - Ecological Modelling of Operational Processes", although I only touched on ecological issues in passing. Afterwards, I gained experience in an ecological direction in my hometown Dnipropetrovsk, where I organised waste disposal and processing in a company. After all, my plan was to continue my studies in a PhD programme at some point. Since my heart had been beating for the German language for a long time, Germany is generally known for good research, and there is also intensive research on renewable energies here, the preference for the country was an easy decision in the search for PhD programmes. In the course of the search, I realised that, as a career changer, I unfortunately lacked important knowledge in the field of modelling, as well as some background knowledge on environmental systems. That's why I first decided to do another Master's programme. The corresponding search yielded two options: the University of Oldenburg or the TU Cottbus. The decisive factor was that the University of Oldenburg had a good International Student Office and a preparatory language course directly at the university. That's how I ended up in Oldenburg.

After a German course and subsequent DSH exam, which prepared me for the scientific language, I was finally able to start studying for the Master's programme in "Environmental Modelling". However, there was some confusion with the study programme for me at the beginning, as university studies in Germany and Ukraine can differ greatly in many organisational nuances. At this point, the lecturers were a great help to me. They were always open, nice and willing to help with minor questions. Above all, our subject advisor (Prof. Dr. Ulrike Feudel) helped me a lot at that time to choose the profile area. I decided on "Energy Systems" because I think energy issues are essential for our future, the corresponding market is growing rapidly and the subject area already fitted in well with my university background at the time. The courses on offer were quite exciting and so I combined both compulsory internships with it.

For those who are not from here, it is good to know that Oldenburg is a "student city". There are various research groups and institutes in the city or within easy reach (with the semester ticket for the whole of Lower Saxony, as well as to Bremen and Hamburg). Research is also being conducted in the field of wind energy directly in Oldenburg. During my studies, I was able to gain some experience at the research institutes " ForWind " and " OFFIS ". I wrote my Master's thesis in industry at the company " energy & meteo systems GmbH ", with Dr. Jan Freund as my university supervisor. He had already supervised several students who had written such external papers and was therefore able to help me excellently. After the Master's thesis, I was offered a full-time position in the company and now work at " energy & meteo systems " in a research department where I model power flows in electricity networks. Looking back, I find the Master's degree in environmental modelling in Oldenburg quite suitable for this, especially because of its relation to stochastic modelling and time series analysis. I still have PhD plans for my future, but I can also imagine realising them in connection with a job in industry and even know a few colleagues who have already successfully pursued such a path.

Debbora Leip (graduation October 2020)

I have always been interested in abstract mathematics on the one hand, and in sustainability and climate protection on the other. When I chose my Bachelor's degree programme, I decided on mathematics with the aim of combining my two interests in the Master's programme. Between my Bachelor's and Master's degrees, I did two internships in a gap year to get a feel for possible fields of work and to help me choose a Master's degree programme. In the process, I worked with agricultural-economic models, which was a lot of fun. I then looked for a Master's programme that would provide me with useful basic knowledge for a career in this field on the one hand, and on the other hand would correspond to my interest in mathematical solutions to abstract problems. I chose Environmental Modelling in Oldenburg.

What particularly appealed to me about the degree programme was the wide range of options and the three planned internships. This allowed me to learn theoretical aspects of modelling (e.g. "Theory of Dynamic Systems"), as well as helpful means of data analysis (e.g. "Time Series Analysis"), and applied aspects (e.g. in the seminar "Earth System science for sustainability studies" during my semester abroad in Bergen). The internships can be completed internally at the university, but also externally at companies or research institutes. I used this opportunity to work for three months at the Universidad Politécnica de Madrid in the Department of Agricultural Economics, and three months at the Stockholm Resilience Centre. I had a lot of support from the university, especially in organising my internship in Madrid, as I was funded through the Erasmus+ scholarship and there were some administrative hurdles to overcome. I was also able to complete my Master's thesis externally, at the International Institute for Applied Systems Analysis (IIASA). Through the internships and the Master's thesis, I not only learned a lot in terms of content, but was also able to develop further in general and prepare myself for professional life in research.

Overall, the degree programme met my expectations quite well and I am also very satisfied with my choice in retrospect. After graduating, I successfully applied for a PhD position at the Potsdam Institute for Climate Impact Research. My work there is very interesting, and through my studies in Oldenburg I feel well prepared for my current tasks.

Julian Merder (graduation December 2015)

In my Bachelor's programme BioGeo-Analysis at the University of Trier, I was mainly interested in ecological questions. In a lecture on the basis of recording methods of animals and plants in the field, formulas were already listed in the first semester of the Bachelor on how to best assess the biodiversity of a place. Graphs were shown of how lynx and hare in Canada engage in a constant battle of population sizes, a pattern-building up and down, and that this could certainly be calculated. Exactly how the striking pattern came about, which is probably shown at least once in every ecology lecture, was unfortunately not the subject of the course or the focus of the Bachelor's degree, but my interest in modelling was aroused here. I have also always had a strong interest in marine biology, not least due to a longer stay in Southeast Asia, and so I wanted to pursue this direction more closely for the Master's degree.

One of my friends in Oldenburg suggested the University of Oldenburg and the ICBM to me, so that I could combine the two and of course have a few familiar faces for a beer or two in the evening. During my research, I found the Master's programmes Marine Environmental Sciences and Environmental Modelling . After reading up on the module descriptions, I decided - admittedly more on instinct - on the Environmental Modelling programme and applied. After three weeks, I already received an acceptance letter.

Here I got both the in-depth knowledge of the background of theoretical ecology that I had missed in the Bachelor's programme, as well as more comprehensive training in data analysis with the large amounts of data that had accumulated during field exercises for courses in species knowledge in the Bachelor's programme. Special highlights for me were thus, after my specialisation in the focus on process and system-oriented modelling with the courses on population dynamics and critical states in the Earth system , also the profile module Environmental Systems and Biodiversity . I found the often manageable group sizes, which allow direct contact with the lecturers, to be pleasant. I gained practical insight into the ecology of benthic species communities through a joint course with the University of Bremen and the Alfred Wegener Institute on Helgoland.

Inspired by a modelling internship in Ireland, I then researched the differences of benthic species communities on the coasts of Ireland in my Master's thesis. My work was jointly supervised by one lecturer from the ICBM and one from the University of Galway. The close cooperation with my two supervisors also enabled me to take a first step towards a scientific career by writing a scientific article as first author. After submitting the manuscript and revising it after receiving peer reviews, I am currently preparing for resubmission and - hopefully - publication in an international journal.

I am currently working at the wind power company ENERCON on the implementation of shutdown times for bat protection. Even though I can't actually apply much from my degree programme here, the modelling focus and the Matlab skills I acquired helped me a lot during the job interview. Because I definitely want to return to research, I will stay there until I find a suitable doctoral position in the marine department.

Marcel Kuhmann (graduation March 2015)

After completing my Bachelor's degree in mathematics, I decided to give more space to my interest in ecological issues. I had my sights set on future research work at a scientific institute (e.g. the Helmholtz or Fraunhofer Society) or a possible career (e.g. nature park, environmental authority) - but I did not have a clear goal in mind.

Accordingly, I looked around for non-consecutive Master's programmes that fulfilled two requirements: they should have a firm foothold in the field of mathematical and scientific methodology with a stronger application focus, and they should also offer me the chance to gain insights into a wide variety of topics. In Germany, I came across two Master's programmes that fulfilled my desired profile: the Master's in Environmental Modelling at the University of Oldenburg and the Master's in Environmental Systems and Resource Management in Osnabrück. Both programmes convinced me:

• the participation of a wide range of disciplines in the programme and the possibilities for specialisation resulting from this and from the flexible module system
• the thematic and methodical integration in the interface area of mathematics / specialisation area of computer science
• the numerous opportunities to gain valuable insights into research and development through internships, projects and student research projects
• the good contacts of the institute and the lecturers to other research institutions in Germany and abroad as a foot in the door for a semester abroad or a research internship.

The deciding factor for my enrolment in Oldenburg was a day on site when I met with students of the programme who had offered to tell me about their experiences beforehand. After the conversations and the impressions of the campus and the city in general, I had the feeling that this Master's programme was the right fit for me and that it was also a well-rounded programme; I then quickly dropped my plans to visit Osnabrück as well.

The Master's in Environmental Modelling delivered on the promised breadth of education: I was able to deal with the application of game-theoretical methods in evolutionary genetics, was present at a discussion with a head dike judge on land use in East Frisia, took part in a tour of the wind turbine manufacturer ENERCON and a wind farm, and wrote my Master's thesis on soil physics at the Helmholtz Institute for Environmental Research in Leipzig.

As a student in the Master's programme in Environmental Modelling, you enjoy excellent teaching: the lecturers are very committed to their work and the supervision ratio is very comfortable. On the other hand, you regularly have the opportunity to experience university research up close. In my opinion, the degree programme is therefore ideal as preparation for and entry into a career in research. But graduates can also enjoy very good career opportunities in business and industry; my career entry as an actuary can certainly serve as a positive example of this. I benefit daily from both the programming skills trained in the Master's programme and the statistical methods learned. Above all, however, the co-development and application of mechanistic models for the assessment and simulation of elementary risks (earthquakes, storms/hail, flooding, heavy rain) are a home game for me as a graduate in environmental modelling.

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Modeling Water Quality in Watersheds: From Here to the Next Generation

1 Fenner School of Environment and Society and Institute for Water Futures, Australian National University, Canberra, ACT, Australia

J. S. Horsburgh

2 Department of Civil and Environmental Engineering and Utah Water Research Laboratory, Utah State University, Logan, UT, USA

A. J. Jakeman

C. gualtieri.

3 Department of Civil, Architectural and Environmental Engineering, University of Napoli Federico II, Naples, Italy

4 Grey Bruce Centre for Agroecology, Allenford, Ontario, Canada

L. Marshall

5 Water Research Centre, School of Civil and Environmental Engineering, UNSW, Sydney, New South Wales, Australia

T. R. Green

6 Agricultural Research Service, U.S. Department of Agriculture, Fort Collins, CO, USA

N. W. T. Quinn

7 Lawrence Berkeley National Laboratory, Berkeley, CA, USA

8 Helmholtz Centre for Environmental Research—UFZ, Department of Computational Landscape Ecology, Leipzig, Germany

9 Upper Midwest Water Science Center, United States Geological Survey, Middleton, WI, USA

10 Department of Environmental Engineering (DTU Environment), Technical University of Denmark, Kongens Lyngby, Denmark

B. F. W. Croke

11 Mathematical Sciences Institute, Australian National University, Canberra, ACT, Australia

J. D. Jakeman

12 Optimization and Uncertainty Quantification, Sandia National Laboratories, Albuquerque, NM, USA

13 AgResearch—Lincoln Research Centre, Christchurch, New Zealand

B. Rashleigh

14 Office of Research and Development, United States Environmental Protection Agency, Narragansett, RI, USA

Associated Data

Data were not used nor created for this research.

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

1. Introduction

Water quality modeling has increasingly been used globally for water quality reporting, risk assessment, identifying and quantifying the sources of water quality constituents, and exploring potential outcomes of climate, hydrology, and management scenarios ( Abbaspour et al., 2015 ; Whitehead et al., 2009 ). There has been substantial growth of water quality modeling related publications since the 1960s ( O’Connor et al., 1973 ; Owens et al., 1964 ), with continuous publication of books ( Chapra, 1997 ; Ji, 2017 ; Martin & McCutcheon, 2018 ; Thomann & Mueller, 1987 ) and many reviews on topics in the field. A synthesis of these can be found in Fu et al. (2019) who covered topics of watershed-scale water quality model use, development, and performance and discussed a range of challenges including “large-scale applications, model integration, model usability and communication, preliminary data analysis, modelling management practices, technology advancement, incorporating soft data, model identifiability, uncertainty analysis, good modelling practices, capacity building, and differentiating the effects of climate impacts from those associated with land use and management practices.”

However, it seems that progress in the science and practice of water quality modeling has somewhat stalled ( Jakeman et al., 2018 ). This is evident in the increasing numbers of publications focusing on case studies and improvements of existing models and techniques, rather than addressing further fundamental research or challenging water quality issues that remain difficult to solve. A similar observation has been made regarding modeling of cropping systems ( Keating & Thorburn, 2018 ). Some watershed-scale models are criticized because of their overparameterization with respect to the problem context, data and prior knowledge available, and their tendency to be applied with insufficient rigor that objectively assesses the various sources of uncertainty ( Jakeman et al., 2018 ; Pechlivanidis et al., 2011 ).

There may be several reasons why the progress has not advanced faster. First, water quality investigations that require modeling are often funded by government agencies who use models for policy making and planning. Such agencies generally have a stronger preference for investing in improvement of existing and established tools over commissions of new and potentially high-risk tools. The latter are addressed mainly by agencies whose charter is primarily research, but these have more limited resources, which are often leveraged to promote end user applications. The community of professionals who are applying water quality models in practical applications has built a level of acceptance and trust around some existing models that has provided continuity in their development, improvement, and maintenance. This is, in part, motivated by reducing costs, as there is generally a high overhead for an agency to move from one model to another. This can impede progress and the development of improved modeling capability. In addition, practitioners need reliable modeling tools that are accepted by their clients and that are credible in legal settings. In the context of models for policy development or implementation, the provenance, or track record, of the model is important, as previously completed modeling studies may lend credibility regarding the modeling to model users and potential critics. Thus, less investment typically is given to creating cutting-edge, experimental modeling and analyses, compared to improving existing, well-known, more accepted models that may be currently used or maintained by both research and action agencies, hindering new innovation in model development. In addition, many existing watershed water quality models are monolithic in structure. Thus, while an existing model can be enhanced by replacing parts of the model with new science module(s), modelers may be hesitant to make major changes because it is difficult to comprehend cascading effects of the changes.

Second, unlike the weather and climate community where the number of models is smaller because the community has reached some level of consensus on how modeling should be done, watershed water quality model development seems to be more fragmented with greater numbers of independent subdisciplines and special interest groups working on more customized, disparate models. This is, in part, due to the fact that water quality bridges across, and is influenced by, many aspects of disparate disciplines and knowledge domains across the community of water quality modeling experts. This diversity in the water quality domain can hamper the use of common language and development of scientific consensus in water quality modeling.

Third, water quality modeling development is still hindered by limited data availability and model parameter uncertainty ( Arnold et al., 2015 ). This can make it difficult (or impossible) to develop or test a new model innovation. Water quality monitoring programs (including monitoring locations, observed variables, and sampling frequencies) are often not optimized for model population, testing, or for reducing model uncertainty. For example, in the United States, most water quality data are collected by states to assess whether water bodies are meeting designated water quality standards. Data collected for this purpose are rarely adequate to support development of a detailed water quality model. In addition, because each state generally uses their own laboratories for analyses, rarely are robust interlab comparisons performed, and thus, extensions of water quality model domains across political boundaries are often fraught with difficulties. Therefore, while monitoring information is required to support model development, calibration, and uncertainty estimation, available data rarely provide detailed information about the behavior of a system being modeled.

Fourth, modeling of complex, contested environmental issues where uncertainty is rife, such as with water quality management, requires a systematic, comprehensive, and engaging approach by modelers with clients and stakeholders. It requires that the modeling team be diverse so as to have a firm understanding of the phases and inherent steps of modeling and relevant practices to pursue. However, with every problem or project being different in many respects, appropriate practices to pursue depend on the contextual features of the problem at hand. Badham et al. (2019) attempted to demystify the modeling process in water resource management by indicating how one selects practices to follow by identifying a list of key questions to be addressed at each step. The lengthy list is indicative of the experience that must eventually be accrued by learning from a wide range of modeling projects. One essential challenge to accelerate improvements in the practice of water quality modeling would seem to be for modeling teams in the water resources management sector to contribute to the wider community their learnings and patterns of contextual practices from projects that they undertake. Another would be to evaluate the success and limitations of our model-based outcomes more holistically by adopting the paradigm that views modeling more as a social process and assesses our success by a wide range of criteria ( Hamilton et al., 2019 ).

In June 2018 at the Ninth International Congress on Environmental Modeling and Software, about 30 water quality modeling experts and practitioners participated in a workshop on “Water quality modeling: A stock-take of needs and ways forward for supporting decision making.” The workshop aimed to identify potential for improvements in process and empirical representations that largely apply in key problem contexts, especially considering land and water management alternatives, and gaps in the process of developing and applying models. The workshop also aimed at identifying opportunities to further advance watershed-scale water quality modeling to support management and policy. The discussions at the workshop and afterward inspired the writing of this paper, which synthesizes the collective experience of workshop participants and authors on key challenges in and ways forward for water quality modeling in watersheds to support decision making.

The remainder of this article is organized as follows. Centered around watershed water quality model development are six key elements: system representation, parameterization and calibration, data quality control, uncertainty, scale mismatches, and provisioning of modeling tools ( Figure 1 ). We devote section 2 to the discussion of system representation as this underpins the core of watershed water quality model development. More specifically, four key potential areas for improvement in system representation are discussed: environmental interfaces, in-stream water quality and process interactions, soil health and land management, and urban areas. Then, the potential for improvements in other elements of model development are presented in section 3 . Finally, we identify three pillars that can be strengthened as ways forward to advance the science of watershed water quality modeling: bridging gaps between modelers and experimentalists, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations ( section 4 ). The intention of this paper is not to cover every aspect of water quality modeling but rather to provide a discussion piece and highlight what the authors have identified as gaps in the current science and practice of watershed water quality modeling along with areas for improvement.

Topics discussed in this paper and their relationships.

2. Improvements in System Representation

In the following sections we describe the potential for improvements in the way the currently available suite of water quality models represents the aquatic system and those connected systems that affect it. These improvements, as summarized in Box 1 , may manifest themselves in our limited understanding of particular environments or processes and/or limitations in our state-of-the-practice representations of them within the codes of existing water quality models.

Major areas of potential improvement in system representation discussed in this section.

Potential improvements in system representation.

Environmental interfaces – biological, chemical and physical processes of air-water, water-sediment and water-vegetation interfaces; linking importance of interfaces to model uses.

In-stream water quality and process interactions – examining appropriate dimensionality and model complexity; relationship between hydrodynamics and water quality; in-stream water quality data monitoring; combining empirical and physics-based models.

Soil health and land management – role and dynamics of soil biology and biochemistry on the water cycle; dynamics of soil hydraulic properties; impacts of agricultural practices on soil structure and biological properties in watershed water quality models.

Urban areas – mixed land-use interactions; monitoring and modeling highly variable intermittent contaminant discharges; integrated urban water quality monitoring and modeling practices.

2.1. Environmental Interfaces

Water quality can be significantly affected by the processes occurring at the environmental interfaces. These borders have been defined as a surface between two either abiotic or biotic systems that are in relative motion and exchange mass, heat, and momentum through biophysical and/or chemical processes ( Cushman-Roisin et al., 2012 ). For example, the quality of water moving kilometers through an aquifer may change very slowly, but the quality of that same water may change rapidly when it discharges through chemically and biologically active bed sediments to a stream ( Schindler & Krabbenhoft, 1998 ). The presence of environmental interfaces has been addressed in selected water quality studies (e.g., engineered remedial actions involving reactive barriers) but typically is not addressed in water quality assessments because the water quality effects of naturally occurring interfaces are difficult to characterize with typical sampling approaches ( Hunt et al., 1997 ). As a result, our understanding and ability to simulate water quality changes at interfaces are not as advanced as our capabilities for noninterface portions of a flow path (e.g., the larger distance traveled in an aquifer). However, their lack of characterization can belie their importance ( Björneholm et al., 2016 ). Understanding water quality at the point of use, which affects its suitability for ecological needs and human-intended purposes, requires an understanding of the sum of all salient upgradient processes, including those that may occur at interfaces representing a small increment of the total travel time and flow path.

Three main environmental interfaces may be considered in water quality modeling: air-water, water-sediment, and water-vegetation interfaces. The air-water interface of streams, rivers, lakes, and estuaries is subjected to momentum, heat, and mass transfer. Gas-transfer modeling ( Gualtieri & Pulci Doria, 2012 ) or, if the transferred gas is oxygen, reaeration is most relevant in water quality modeling. However, to date, a universal equation to predict reaeration rate is still missing. Recent efforts have suggested that turbulence microscale could be assumed to be the most efficient transporting eddy near the air-water interface controlling gas transfer ( Katul & Liu, 2017 ). Future research is needed to define a predictive equation valid in water quality modeling studies.

At the water-sediment interface , several exchange processes involve solids and solutes. Progress toward better understanding small-scale interactions between particles and turbulence could potentially lead to better identification of the drivers of sediment transport in surface waters and improvement of sediment transport models at the larger scale of water quality studies. Understanding the roles of surface and hyporheic hydraulics and biogeochemical reactions are key elements for upscaling results from the microchannel- and channel-unit scales to the channel-reach and watershed scales ( Tonina, 2012 ). Future research should define common metadata to support interdisciplinary research, facilitate cross-site comparison, and quantify spatial and temporal heterogeneity in hyporheic functions to enable multiscale assessment and prediction of hyporheic processes in the framework of water quality models ( Ward, 2016 ).

Finally, the interaction between the flowing waters and submerged and/or emerged vegetation occurs at the water-vegetation interface ( Nepf, 2012 ). Despite great progress made in recent decades ( Tinoco et al., 2020 ; Wang et al., 2019 ), additional research is needed to accurately reproduce the effects of natural vegetation on water flow, sediment transport, contaminant transformation, and contaminant mixing and to realistically implement these effects in water quality models. Proper parameterization of these effects at larger scales is a necessary step to reproduce and study the morphodynamic behavior of rivers, lakes, and estuaries at the different spatial and temporal scales considered in water quality modeling studies ( Vargas-Luna et al., 2015 ).

The importance of an interface for water quality simulation will depend on the constituent (conservative or nonconservative), type of interface, kinetics of reaction, and flow rates through the interface. The effects of interfaces may be most pronounced when the constituents and interface are associated with microbially mediated reactions, because microbial communities affect the type, and commonly increase the rate, of reaction. Such microbially dominated interfaces can be expected at geochemical contrasts, such as oxic-anoxic interfaces, as these contrasts facilitate communities specialized for each setting and often are well suited to process the resultant products from the other. Close spatial proximity enhances the transfer between the two communities, which can, in turn, enhance biogeochemical processing. Our ability to characterize the type of microbiological community in the field has improved, but quantitative representations needed for water quality simulation still lag behind our ability to simulate abiotic reactions.

Knowing whether the effects of interfaces need to be simulated for a particular study will depend on the intended use of the results and societal concern. Assessing chemical reactions and geochemical gradients within an interface often requires highly refined resolution characterization spatially, and in some cases, temporally. To a large degree, existing water quality models lack detailed representations of the physical, chemical, and biological processes that may be accelerated within interfaces. Furthermore, although our ability to sample and analyze small volumes of water from interfaces is much improved, the level of specialization required for sampling programs to quantify processes within interfaces is rare and costly, making it difficult to test new model formulations. Thus, for the present, our ability to characterize water quality changes within interfaces remains limited. Fortunately, many water quality issues can be identified and addressed at the point of withdrawal where the net effect of interfaces on solute fate and transport are most important. A focus on net effects, in turn, facilitates use of relatively straightforward water quality modeling approaches such as retardation and decay. Thus, detailed knowledge of interface reactions may be more important for small-scale simulations of how water develops its quality than larger system-scale questions such as if a water source in question can be made suitable for a given purpose.

2.2. In-Stream Water Quality and Process Interactions

In-stream water quality entails complex process interactions in space and time, which can be difficult to model. Complex stream geometry is typically simplified to one-dimensional transport to reduce development, simulation, and analysis costs, but criteria for examining the appropriate dimensionality and model complexity are generally lacking or poorly defined.

Anywhere water flow is concentrated into a channel may be considered a stream. Temporally, at a given point in a stream network, flow may be perennial or ephemeral, some with more regular seasonal patterns and some with very infrequent flows. The temporal variability alone makes sampling and estimation of water quality challenging. Spatially, in most water quality modeling, we conceptualize a stream as a single channel with well-defined banks. However, stream topology varies with geology and terrain, where channels vary in sinuosity and channel/bank morphology, which affects the dimensionality needed to capture transport. Anabranches (parallel flow paths) and braided streams present further complexity. In low-gradient landscape positions, where flow rates and Froude numbers ( Detenbeck et al., 2005 ) are very low, transport processes may require higher-dimensional analyses. However, most models applied over seasonal/regional scales within large watersheds require an assumption of one-dimensional transport along a stream reach for simplicity. In addition to natural complexity, most streams are highly managed and regulated within a basin and even with transfer of water between basins. Flow control structures may be unmanaged or passive (e.g., weir or unregulated dam) or highly managed within a channel and via flow diversions. These unnatural flow paths and the human decisions that control them are poorly represented in most watershed-scale water quality models.

In-stream transport and transformation processes are important aspects of water quality modeling. Transport phenomena, such as advection, turbulent diffusion, dispersion, and boundary exchanges, are hydrodynamically based, while transformation processes, such as photolysis and chemically and biologically driven degradation, are kinetics based. Over the past decades, representations of these phenomena and their interactions have evolved from simplified 1-D, steady-state models, often based on the kinematic wave approach or on the Saint-Venant equations, to complex 3-D, time-dependent models of hydrodynamics, sediment, dissolved oxygen, eutrophication, microbes, and toxics. Meanwhile, the computational resources required to run these models have changed from high performance computers to desktop personal computers as personal computers have become more capable. Remote cloud computing using a model-as-a-service ( David et al., 2014 ) is emerging in environmental modeling as an alternative to local computing.

Despite such progress, there is still an urgent need to improve our knowledge about the relationship between hydrodynamics and water quality, where the main difficulty lies in the treatment of turbulence. Turbulent flows are complex, three-dimensional, intrinsically irregular and chaotic and are characterized by intense mixing and dissipation, at a large range of spatiotemporal scales, and by the coexistence of coherent structures and random fluctuations ( Basu, 2013 ; Pope, 2000 ). Turbulence strongly controls the transport of water and water quality constituents within the water column and across environmental interfaces. Hence, the equations governing hydrodynamic and constituent transport processes also reflect the effects of turbulence. However, in turbulent flow, these equations are extremely difficult to solve due to difficulties in resolving the temporal and spatial fluctuations of velocity, pressure, temperature, and concentration associated with turbulence ( Kundu et al., 2015 ; Pope, 2000 ). Various approaches have been attempted to solve these fluctuations ( Rodi et al., 2013 ; Sotiropoulos, 2005 ). Advances in turbulence modeling, especially using Large Eddy Simulation (LES) and hybrid Reynolds-Averaged Navier–Stokes (RANS)-LES methods ( Stoesser, 2014 ), will help improve the modeling of constituent transport processes.

Other open questions and challenges in the hydrodynamics of water quality modeling relate to the parameterization of turbulent diffusion and dispersion processes and the effects from channel eco-morphological features. Despite its importance in water quality modeling, transverse mixing rate estimation in natural channels is mostly based upon empirical equations as for the longitudinal dispersion coefficient, which is used in 1-D water quality models ( Ji, 2017 ; Rutherford, 1994 ). Further research efforts are also needed to fully identify and accurately quantify the effects that channel vegetation, bends, islands, confluences, bed-forms and macroroughnesses, and, more generally, bed friction have on river hydrodynamics and, in turn, on contaminants and sediment transport ( Arfaie et al., 2018 ; Gualtieri et al., 2018 ; Kasvi et al., 2015 ; Liu et al., 2020 ; Vargas-Luna et al., 2015 ). Finally, other challenges in the integration between hydrodynamics and water quality modeling can be identified in the application of 3-D models to large rivers due to the need for large amounts of input data and huge computational power, as well in the definition of standards for the integration with hydrological models ( Hodges, 2013 ).

Furthermore, in-stream processes and their related physical properties, such as flow velocity, depth, shear stress, turbulent features, and temperature, affect ecosystems. In turn, organism community affects the physical environment through hydrologic, geomorphologic, and hydraulic methods ( Tonina & Jorde, 2013 ). Ecohydraulics studies connect flow properties with biological requirements to define habitat availability or to quantify flow-related ecological functions. These studies could be carried out at the microscale and at reach (10–50 m channel width) and segment (50–1,000 m channel width) scale as well as the sub-basin and basin scale ( Tonina & Jorde, 2013 ). The relationship between ecohydraulics and water quality modeling of in-stream processes is complex. First, this relationship requires knowledge of the hydrodynamic processes in a water body and is subject to the same governing equations and solution methods/issues, including the treatment of turbulence ( Rodi et al., 2013 ; Tonina & Jorde, 2013 ). Second, ecohydraulics and water quality modeling are mutually interacting. Hydrodynamic effects on the channel/riparian vegetation affect photosynthesis and respiration which, in turn, affects aquatic ecological communities ( Ji, 2017 ). On the other hand, hydrodynamics and water quality jointly affect fish species abundance and richness ( Gualtieri et al., 2020 ; Trinci et al., 2017 ). Hence, a closer integration between ecohydraulics studies and in-stream water quality modeling should be encouraged.

Conceptual understanding and quantification of dominant constituent transport and transformation processes are essential, regardless of the simulation methods used to estimate water quality variables. Important factors will vary depending upon the type of transport and biochemical reactions involved. For example, phosphorus (P) transport can be strongly related to sediment onto which P is sorbed. Important processes related to the flux of suspended sediment (e.g., sediment settling and resuspension and bedload interaction with suspended sediment, bank erosion, or deposition) may also control P transport and enrichment. Although such process interactions are complex, relatively simple conceptual mathematical models (e.g., Dietrich et al., 1999 ) can capture geochemical dynamics. In-stream geochemistry may also be important for liberating sediment bound chemicals (e.g., P 2 O 5 ) into dissolved bioavailable forms (e.g., phosphate [PO 4 ]) in the water column. Under low-flow conditions and high water temperatures, anoxic conditions may allow biochemical transformations to occur that make P available to algae, often leading to toxic algal blooms. In addition, hyporheic exchange between shallow groundwater and the stream bed can supply nutrients such as nitrate to promote biological activity and eutrophication of water bodies. Thus, any model must be applied with adequate scientific understanding and expertise to avoid common pitfalls.

Estimation of water quality is highly dependent upon the in-stream water quality data collected by various government agencies. This has been an impediment to estimation, but data availability and access have improved with the advent of digital data storage and dissemination, as well as more standardized data protocols. Indeed, much data gathering and processing is now automated. Even so, a large portion of the effort in building a water quality model is data provisioning. There is also a growing disconnect between those who measure and understand data limitations and errors and those who model often without even visiting the field sites of concern.

Both statistical and machine learning methods provide ways of estimating temporal dynamics and patterns of variability in constituent concentrations and loads, assisting the identification of system stressors ( Glendell et al., 2019 ), setting of regulatory targets ( Jung et al., 2020 ), and model simplification ( Jackson-Blake et al., 2017 ). However, extrapolation of fitted behavior beyond the ranges and environmental conditions of measured data requires extreme caution. Water quality is commonly measured in streams/rivers in efforts to quantify concentration ( C , mg/L) and load ( L = C * Q , where Q is average flow rate of water, L/s, over a time interval) as a mass per time (e.g., kg/day) of the constituent of interest. In “well-behaved” streams where variations in Q and C are gradual, cross-correlations of C ( t ) ~ Q ( t ), where t denotes time, may be great enough to estimate C ( Q ) as a unique, monotonic function with high explanatory power; in such cases, L is estimated simply as L = C ( Q ) * Q . However, measured Q ( t ) at a stream gauge more typically explains less than half of the temporal variability in C ( t ) at the same location, even at regional scales ( Guo et al., 2019 ). Empirical/statistical models may only be reliable over a range of measured conditions. For example, estimation of extreme values such as peak concentration may be estimated poorly, particularly when extrapolation of a correlation with surrogate data such as Q ( C ) is erroneous. Errors may vary dramatically with the stream environment (spatially and temporally) and with the chemical or other constituent concentration being estimated. Even so, statistical models, such as SEAWAVE-QEX ( Vecchia, 2018 ), that rely in part upon empirical C ( Q ) relationships have become standard practice in estimating loads for regulatory purposes.

Machine learning is a growing field of exploration in many areas of earth science ( Reichstein et al., 2019 ), including stream hydrology ( Beck et al., 2016 ). Artificial neural networks, for example, often show high skill in fitting measured patterns, but overfitting is common and may lead to greater extrapolation errors than conventional multivariate statistical models. Similar caution is strictly true of more physically based models also, but some degree of extrapolation or “extension” of simulations beyond measured conditions may be less erroneous if the dominant transport processes have been captured. Biochemical and physical process models are often highly parameterized and also require extensive data to provide confidence in model responses to various environmental conditions. Hurdles to their application have been reduced with improved data accessibility and greater options for model calibration and evaluation. Even so, general application of process models remains beyond the capacity of most action agencies and practitioners. Opportunities are rapidly emerging to explore hybrid models by combining statistical methods, machine learning, and process models that may improve estimation of extremes and extension of models to unmeasured conditions in space and time. More work is needed to identify conditions where hydro-bio-chemical process models of in-stream transport can be used to extend statistical models beyond the range of measured concentrations or other conditions. Opportunities for hybrids of machine learning and process-based modeling deserve further exploration, particularly to identify deviations in behavior of machine learning and process model simulated results for extreme values and unmeasured conditions.

2.3. Soil Health and Land Management

Globally, agricultural soils make up about 40% of the entire terrestrial system ( FAO, 2013 ) and in many watersheds more than 90% of land use, highlighting the necessity to adequately model the soil interface layer to understand and manage water quality at watershed scale. Yet, the structural role of soil microbiology on the water cycle—what practitioners call soil health —is poorly understood and modeled ( Allen et al., 2011 ; Schoonover & Crim, 2015 ). Soil health fundamentally affects surface water’s suspended load and its chemical properties. Models that treat soil characteristics as constant over time, invariant in the face of dynamic agricultural practices, and homogenous over large spatial areas neglect the central role of soil and soil management for water and mineral cycles.

Soil is the biologically active interface in the water cycle that determines the pathway that precipitation or irrigation water follows. The soil interface determines whether water infiltrates into the vadose zone or runs off at the surface, as well as the fraction of infiltrate that is retained in what some practitioners refer to as the “soil sponge.” Furthermore, it affects the amount of water that evaporates at the surface, transpires after plant uptake, or percolates into the groundwater. Researchers acknowledge that there is a deficit in our understanding of, and ability to model, the role that soil biology plays in the broader water infiltration process and resulting soil chemistry ( Vereecken et al., 2019 ). In healthy soils, nutrients can be actively transported over tens of meters by mycorrhizal networks and “traded” with plants in exchange for carbon-rich sugars ( Wipf et al., 2019 ). While soil carbon and infiltration rates are first indicators for microbial activity, multiple aspects (especially the history of plant cover, fertilizer application, and mechanical disturbance) determine how soil biology influences the physical and chemical characteristics of water flow ( Baar, 2010 ; Gianinazzi et al., 2010 ; Lovelock et al., 2004 ). By changing its microbiology, soil management can determine the physical behavior of soil. For example, infiltration rates can easily vary by 1 order of magnitude depending on microbial richness ( Franzluebbers, 2002 ), and the (biological) soil structure can either totally eliminate surface runoff or foster severe erosion ( Zhang et al., 2007 ).

For water quality modeling, this means that soil is a dynamic ecosystem that may change its behavior in the water cycle depending on management, affecting both water quantity and water quality ( Bonfante et al., 2019 ). Soil’s physical and chemical characteristics vary with seasonal moisture (e.g., cracking in clay soil leads to strong infiltration until the clay swells and seals itself), affecting the depth of oxygen penetration (e.g., after spring ponding), micropore and macropore structure, and surface sealing after precipitation onto bare soil. Agricultural chemicals and amendments further affect the properties of soil, by either enhancing organic matter and biological activity (e.g., compost, grazing, and biochar) or reducing it via suppressing microbial life (e.g., by inhibiting plant root exudates via phosphorus fertilizer application or by oxidizing organic matter via nitrogen fertilizer application and tillage). As agricultural practices affect soil microbiology and soil structure, water cycles and mineral cycles change. Despite our conceptual models of how these processes work, quantitative understanding of microbial soil dynamics is not well represented in the current suite of water quality models and remains a limiting factor to effectively assess watershed intervention options for nonpoint pollutants.

Likewise in wetland soils, especially those managed as seasonal wetlands subject to a period of inundation during a rainy season and desiccation during drier months, water quality models do not typically account for annual variation in soil physical, chemical, and biological properties. During dry months, dense clay soils form cracks often extending approximately half a meter into the subsoil, creating a porous medium with extremely high infiltration rates during initial seasonal wetland flood-up as water fills up the open cracks and the soils regain moisture. Finally, at saturation, the soils swell and the cracks close, effectively sealing the soils and exhibiting very slow infiltration rates. In addition, rewetting of wetland soils often promotes strong pulses of microbial respiration and switching from aerobic to anaerobic condition. Very few water quality models allow changes to be made in soil hydraulic properties over the course of a simulation, impacting the performance of models that simulate these types of landscapes.

Scale matters. The spatial scale affects the factors that are important in setting the soil organic matter ( Vos et al., 2019 ). Climate, vegetation, and soil texture are important at the global scale; but at the scale of watershed and smaller, agricultural practices also become important ( Guo & Gifford, 2002 ). Changes in soil characteristics depend on the temporal assessment scale ( Vos et al., 2019 ), with different directions of short-term impacts (e.g., tillage increases immediate infiltration) and long-term impacts (e.g., regular tillage reduces soil organic matter, infiltration, and water retention). A review of the literature ( Blanco-Canqui, 2011 ) suggests that practices such as no-till cropping may have complex interacting effects such as increased soil water repellency, which will increase runoff, but also increased aggregate stability, which will reduce the erosive loss of soil via runoff. While some models include the effect of agricultural management on soil organic matter ( Brilli et al., 2017 ; Holzworth et al., 2015 ), the flow-on effects to changes in other soil properties important to water quality modeling, such as soil water storage and surface roughness, are poorly captured ( Palmer et al., 2017 ).

Soil biology creates macropores that are inherently difficult to model ( Beven & Germann, 2013 ; Jarvis, 2007 ). Yet, the lack of representation of soil biology in many watershed water quality models may in part be because many watershed modelers are more likely to come from an engineering or water science background than from a soil or agricultural science background. Thus, they are more likely to have a better understanding of end-of-pipe solutions (e.g., erosion berms, drainage, sedimentation basins, and gully remediation) than solutions in managing soil biology (amendments, changes in tillage, fertilization, and chemical applications). In addition, soil biological solutions are highly uncertain. For example, farmers may or may not adopt recommended practices; there can be significant and variable time delays between implementation of a practice and beneficial effects ( Volk et al., 2009 ); and even well-implemented practices are not guaranteed to produce the desired effects. However, soil biological solutions address the source of the water quality issues and, although they present challenges, contain the most likely broad-scale enduring solutions. Omission of soil biological solutions also excludes farmers and land managers from being part of the solution in improving watershed water quality.

Therefore, the authors advocate for a better representation of soil biology in water quality models. Geospatial digital soil mapping, paired with on-the-ground monitoring, promises to greatly improve our understanding of water quality dynamics in watersheds. Satellite data have been routinely used for vegetation assessment ( Huete et al., 1999 ), yield projections ( Doraiswamy et al., 2003 ), and measurement ( Deines et al., 2019 ). Recently, broad access to satellite data and cloud tools (e.g., the Google Earth Engine) has improved digital soil mapping ( Minasny & McBratney, 2016 ; Zhang, Feng, et al., 2017 ), now enabling remote mapping of mycorrhizal activity in soils ( Soudzilovskaia et al., 2019 ). Such tools may soon dramatically improve our spatial understanding of nonpoint contamination sources, visualizing how soil biology and soil management practices affect water cycles, erosion and water quality, and downstream conditions. Regenerative agricultural systems are emerging from this new paradigm of soil microbiology and especially mycorrhiza ( Gosnell et al., 2019 ). Studies of how this revolution in agricultural production may affect water quality at watershed level are only emerging (e.g., Baffaut et al., 2019 ).

2.4. Urban Areas

Urban areas discharge a wide variety of pollutants, ranging from “traditional” parameters (e.g., sediment, organic matter, nutrients, and bacteria) to micropollutants (e.g., heavy metals and organic compounds) and other emerging contaminants (e.g., pharmaceuticals and endocrine disruptors). These discharges can be continuous (e.g., via the effluent from wastewater treatment plants) or intermittent (e.g., wet-weather induced via combined sewer overflows, separate storm sewer overflows [SSSOs], or wastewater treatment plant overloading). While the traditional parameters are relatively easier to monitor, the latter are characterized by a high inherent variability and pose logistical challenges in performing extensive monitoring, leading to a greater level of uncertainty in the pollution levels attributable to these sources.

Although urban discharges can be characterized as point sources from a watershed perspective, they can also be considered as diffuse sources when looking at single stretches of urban and peri-urban water bodies. For example, there might be hundreds of SSSOs delivering important pollutant loads ( Brombach et al., 2005 ; Masoner et al., 2019 ) to a single river reach. The effects of wet-weather discharges on receiving water bodies are often site specific ( Karlsen et al., 2019 ), with both chronic (often caused by the loads discharged from SSSOs) and acute impacts (often caused by combined sewer overflows resulting in oxygen depletion and/or ammonia toxicity). The latter can also depend on seasonal variations in the river conditions (e.g., oxygen solubility and baseflow), and they can be limited in space ( House et al., 1993 ). Furthermore, existing monitoring protocols are often performed at a frequency that is not able to detect acute impacts from wet-weather discharges ( Boënne et al., 2014 ; Halliday et al., 2015 ; Skeffington et al., 2015 ).

Effectively reducing water quality impacts associated with urban discharges thus requires holistic monitoring and modeling approaches that consider the dynamic interactions between the receiving water body and the variety of constant and intermittent urban discharges ( Chapra, 2018 ). Since the 1980s, such awareness has led to the development of various integrated modeling tools, simulating the different elements of the integrated urban water systems (e.g., sewers, wastewater treatment plants, and rivers) ( Achleitner et al., 2007 ; Erbe & Schütze, 2005 ; Rauch et al., 2002 ; Saagi et al., 2017 ). The International Water Association Task Group on River Water Quality Modeling (RWQM) specifically developed the RWQM1 model for such integration ( Reichert et al., 2001 ; Shanahan et al., 2001 ; Vanrolleghem et al., 2001 ). Being contemporary to the European Union Water Framework Directive, which explicitly promotes holistic approaches in watershed management, RWQM1 still represents the state of the art in integrated urban water quality modeling. However, its applications are quite scarce both in practice and in the published literature, providing an archetype of the limitations in existing modeling tools. Model structure complexity and data availability are among the main reasons for such limited success: available models are often over-parameterized ( Reichert & Vanrolleghem, 2001 ) with respect to the spatial and temporal resolution needed to successfully simulate effects from wet-weather discharges. Indeed, in one of the most successful full-scale implementations of integrated urban water modeling ( Benedetti et al., 2013 ), a simple model (Duflow) was preferred.

Another factor seldom considered by existing models is the high inherent variability in the quality of wet weather discharges, which are difficult to measure and thereby to model ( Bertrand-Krajewski, 2007 ). This implies a strong need for resulting uncertainty assessment and its consequent increase in computational demands. When looking at micropollutant discharges, being one of the major impacts from urban areas ( Dittmer et al., 2020 ), the need for resulting uncertainty assessment is further magnified. High variability in discharged concentrations, lack of data on the behavior of these substances, and oversimplified representation of fate processes (particle settling, partitioning, etc.) are strongly limiting the application of existing models.

New monitoring approaches are becoming available, providing water quality data using advanced technologies for in situ, high-resolution (i.e., subhourly), point-scale monitoring ( Blaen et al., 2016 ; Melcher & Horsburgh, 2017 ; Rode et al., 2016 ) or using unmanned aerial vehicles for rapid, on-demand, spatially distributed water quality measurements ( Koparan et al., 2018 ; McDonald, 2019 ; Yigit Avdan et al., 2019 ). These create new opportunities for (automatic) parameter estimation, data assimilation, and reformulation of model structures that need to be incorporated into existing models.

Finally, urban water systems are increasingly recognized as complex socio-technical systems ( Bach et al., 2014 ), as should more rural watershed systems. This requires more comprehensive understanding of urban water systems and their interactions with society. Economic valuation, energy considerations, and social dynamics are gradually being considered in urban water system models for the design of infrastructure management and adaptation strategies (e.g., Rauch et al., 2017 ). These types of integrated models often contain a high level of uncertainty and thus are generally recommended for exploratory modeling (i.e., testing different assumptions and possible strategies) rather than providing detailed descriptions of system dynamics and absolute predictions.

3. Improvements in the Modeling Process

Where the previous sections illustrated gaps in our knowledge of how to represent the aquatic environment to be simulated within a water quality model, the following sections illustrate areas of potential improvements in conducting modeling studies. These gaps, summarized in Box 2 , represent our limited understanding of how to best apply models to achieve desired results, including arriving at actionable information derived from model output that can be used for decision making.

Major areas of potential improvement in the process of modeling watershed water quality.

Potential improvements in modeling process.

Monitoring data – sensor data acquisition protocols require standardization; greater adoption of quality assurance or quality control protocols needed for continuous data.

Parameterization and calibration – selection of data types for good representation of system functioning; selection of objective functions pertinent to modeling aims.

Uncertainty management – attribution of total uncertainty to its sources and their prioritization; exploratory modeling of risks; tractable and resources–efficient sensitivity and Bayesian methods.

Scale mismatches – scale–aware, temporal and spatial, data fusion techniques; coupling between 1D and multidimensional models.

Provisioning modeling tools – modern open source and flexible modeling systems should be more widely adopted.

3.1. QC of Monitoring Data

Quality control (QC) of data refers to the application of methods or processes that determine whether data meet overall quality goals and defined quality criteria for individual values ( McCarthy & Harmel, 2014 ; Mortimer & Mueller, 2014 ). Scientific and statistical evaluation of data is typically applied to determine if the data obtained are of the appropriate type, quality, and quantity to support their intended use. The data life cycle comprises planning, implementation, and assessment steps that are used to define quantitative and qualitative criteria for determining when, where, and how many samples (measurements) to collect and a desired level of confidence ( EPA, 2006b ). For the U.S. Environmental Protection Agency ( EPA, 2006a , 2006b ), the sampling methods, analytical procedures, and appropriate quality assurance (QA) and QC procedures are documented in a QA Project Plan (QAPP). Data are collected following QAPP specifications. The QAPP summarizes the sampling design and the manner by which samples are collected or measurements taken. This will place conditions and constraints on how the data should be used and interpreted. During the assessment phase, the data are validated and verified to ensure that the sampling and analysis protocols specified in the QAPP were followed and that the measurement systems performed in accordance with the criteria specified in the QAPP ( EPA, 2006b ).

Protocols for discrete data acquisition and assessment are well established and are described in many manuals and procedure documents (e.g., EPA, 2006a , 2006b ). Protocols for real-time or continuous data QA/QC are not as well evolved, although vendors of hydrologic data management systems designed for continuous data acquisition and processing, such as Kisters Inc. WISKI and HYDSTRA and Aquatic Informatics AQUARIUS platforms, provide software used by enterprise-scale water management agencies. Flow and water quality data being reported at 15 min or hourly intervals from numerous monitoring platforms can easily overwhelm analysts and their ability to process the data and perform corrective actions. Automated features utilizing a high degree of customizable visualization allow importation of discrete QC data that can be used to tag records or sets of records that meet or fail to meet a particular criterion, build and update rating curves, derive statistics, and report in real time to meet stakeholder expectations for timely, accurate water information. Other features allow the annotation of actions to appear directly on the data time series plots while retaining the original unfiltered data record. QC is a partner to QA and, when errors are found, can reveal ways to prevent these errors via QA.

A pertinent example in the San Joaquin River Basin of California, USA ( Quinn, Hughes, et al., 2017 ; Quinn, Osti, et al., 2017 ), deals with the development of a water quality forecasting system for dissemination of real-time estimates of river salt load assimilative capacity. During periods when salt load assimilative capacity in the river is exceeded, the system would allow basin west-side agricultural and wetland stakeholders to manage and control salt loading to the river in order to match schedules of basin east-side reservoir releases of dilution flows. The lack of real-time data QA has limited the type of data sharing between stakeholders due to privacy and potential litigation concerns, even though data sharing is considered essential for basin-scale, real-time salinity management. The software solution sought by stakeholders needed to be a commercially available, off-the-shelf solution with the ability to apply business rules specific to the various water districts’ needs, that is, for automatic validation, correction and presentation of data in graphical form, and visualization of information with web services. In terms of adaptation, a software QA solution that had previously been adopted and deployed by two of these water districts was preferred because districts that want to upgrade operations tend to more readily adopt technologies being used by neighboring districts. In addition, information exchange between district stakeholders was more easily facilitated when districts share the same software.

QA/QC of discrete monitoring data is a mature enterprise. Progress has been made over the past decade in standardizing procedures, providing easy web access to data and providing geographic information system-based search algorithms that simplify uploading and retrieval of data in secure, relational databases. The provision of analytical capability in geographic information system platforms and the ubiquity of high-level programming languages such as Python and R, which are now accessible to integrate with these platforms, have increased use of these discrete data resources. At the same time, it has increased the demand for more continuous data, especially for flow and water quality data that can show high variability. Competition between vendors and the continual improvements both in quality and capability of both sensors and telemetered sensor networks have lowered costs and increased demand from customers. Standardization of data acquisition protocols around sensor output formats such as SDI-12, MODBUS, and 4-20 mA, which allowed the rapid expansion of environmental monitoring and accommodation of large numbers of vendors, has not been as successful in the domain of data QA/QC. This remaining technology transfer gap has stymied progress in data access and public agency sharing of data, especially in litigious states like California where data reporting errors can be used against agencies and water districts in the courts.

Public agencies in California have, in the past two years, mandated public access to certain data such as agricultural irrigation diversions from rivers and groundwater pumping records. The public demand for web accessible information and increased accountability was a driver for this legislative action. Although few members of the public will take the time and effort to scrutinize water district and other public agency records, the mere threat of access has been effective, both in diverting greater resources to Information Technology within these entities, and in having Information Technology employees devote more energy to visualization and other tools to make the information presented more understandable and easier to assimilate. Although state and federal government legislation and regulation are sometimes needed to “prime the pump” in these endeavors, much of the progress made to date, and that anticipated in the years ahead, has been driven by an increasingly interested public armed with easily accessible tools for processing data and demanding accountability.

3.2. Parameterization and Calibration

Model parameter estimation is critical to ensuring the reliability of models. While parameter estimation and model calibration remain formidable tasks given the dimensionality and nonidentifiability of many models ( Guillaume et al., 2019 ), a variety of calibration approaches have been suggested and are routinely used. Several recent review papers highlight the state of practice ( Daggupati et al., 2015 ; Moriasi et al., 2012 , 2015 ), indicating the need for all models to be well calibrated and validated as far as possible, so that they can be used to extrapolate beyond previously observed scenarios by representing the system dynamics adequately. Despite well-established calibration schemes (e.g., Abbaspour et al., 2007 ), difficulties remain in properly reconciling models with field or laboratory observations. The central objective in model calibration is to ensure that the model parameters represent the system of interest adequately, usually for a predictive purpose and its water quality attribute(s) of interest; however, this task becomes complicated with limited data, high-dimensional models, and an overreliance on summary statistics that may not properly capture the functional properties (e.g., averages and extremes over appropriate time and spatial scales) of the water quality variables of interest. There remains significant difficulty in calibrating models so that they represent the variables of interest sufficiently in a way that the model is reliable (i.e., representative of the actual relevant processes in the system), as well as fit for purpose (so that the main variable or criteria of interest is modeled with confidence).

Freshwater quality parameters are often routinely measured by environmental regulators in many watersheds, but their typically low resolution will affect model calibration ( Krueger, 2017 ). Often models must be reconciled with sparse data (in both space and time), or variables may be measured at a scale that is different to that represented by the model. The issue of scale for model parameterization can be exacerbated for parameters that are measured in the laboratory rather than the field and then translated to the model scale. Potential artifacts of such scaling are widely recognized in physical flow parameters such as hydraulic conductivity and dispersivity but are also expected in a model’s geochemical representations, such as extrapolating lab-derived rates of reactions to the rates occurring in the field. In all, the frequent lack of data for model calibration at appropriate spatiotemporal scales highlights the need for new types of field and lab experiments (including on land and in water) and high-resolution monitoring that will help parameterize models and ultimately improve process understanding and system representation ( Bol et al., 2018 ). Recent advances in sensor technologies ( Meyer et al., 2019 ; Ross et al., 2019 ) and sediment tracing techniques using coupled isotopes and biomarkers ( Glendell et al., 2018 ) provide one path to potentially improved parameter estimates and ultimately more reliable models.

Modern multiobjective calibration routines are well placed to estimate model parameters using multiple types of data. Less understood is how to develop multiobjective model calibration schemes that lead to model parameters that better represent the system and will be more effectively extrapolated beyond the period of calibration. New field and laboratory experiments, soft data, and new observing technologies would all lead to increased information on system functioning, but calibration routines must ensure parameter estimates lead to greater confidence in model output, not just a reproduction of model residuals. While all automatic calibration routines require specification of a statistical objective function that summarizes the model fit to data, little focus has been placed on developing objective functions that ensure good representation of the system function or that properly capture the variables of interest. Recent research in hydrologic signatures may provide one source of inspiration for developing mathematical functions that properly capture the system function depending on the modeling scenario ( McMillan, 2020 ).

More fundamentally, Bennett et al. (2013) argue the need to justify the selection of objective function for calibration in line with the model purpose. In examining model performance characterization adopted in various relevant fields, they review numerical, graphical, and qualitative methods. They also propose a five-step procedure for performance evaluation of models that includes “(i) (re)assessment of the model’s aim, scale and scope; (ii) characterization of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behavior and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.”

3.3. Uncertainty Management

The importance of addressing uncertainty in the modeling process, especially when involving complex systems as occurs with watershed water quality management, has become more widely recognized. It is clear that an understanding of uncertainty and its relative source strengths can support decision processes such as by estimating the risk associated with certain scenarios or model predictions. Uncertainty analysis can also help diagnose model weaknesses or suggest new experiments that will reduce critical uncertainties. Thus, discussion of simulations of water quality must also consider sources of uncertainty for the outputs of concern ( Loucks & van Beek, 2017 ). There are two related challenges in regard to the uncertainty associated with water quality models: how best to characterize or quantify uncertainties and how to better constrain uncertainties.

The sources of model uncertainty can be largely grouped into two categories: that associated with the model (including its conceptualization, mathematical equations, and parameters) and that associated with the system observations used to drive the model and constrain parameters. Within these two broad categories, a multitude of model choices and types of data will contribute to uncertainty in the model itself. Typically, many water quality models can reproduce the observations (e.g., when they are nonidentifiable or mathematically ill posed), even more so than simulations of water quantity, because there are multiple reaction paths available and the water quality observations available for calibration are typically fewer.

It is also very rare in water quality modeling studies to report where uncertainty stems from and ranks among all the modeling sources. In the latter situation, only a subset of uncertainties tends to be considered with no justification as to why others were ignored. The attribution of the total model uncertainty to its sources remains a significant difficulty but is critical to improving the model and constraining uncertainty. On the other hand, sensitivity analysis can often be a valuable tool to apportion the relative influences of uncertainty in a model ( Koo, Chen, et al., 2020 ; Koo, Iwanaga, et al., 2020 ). In particular, it can aid in establishing which model factors (largely parameters but also inputs) can be safely ignored and fixed at specific values so that calibration and uncertainty can proceed on a reduced order model with shorter runtimes.

The way forward in uncertainty quantification for water quality models therefore must first be an improved understanding of the potential sources of uncertainty and an assessment of their relative magnitudes or impacts. The uncertainty associated with the model itself is a function of the choices made by the modeler, including the implicit and explicit assumptions of the model hypotheses, the type of model calibration, and the model boundary conditions. Recent advances in computational tools aid this task, and the future of uncertainty analysis for water quality models requires further development of efficient and robust methods for uncertainty quantification. Bayesian and pseudo-Bayesian approaches have been well established to tackle uncertainty quantification for water quality models ( Freni & Mannina, 2010 ; Liu et al., 2008 ; Malve et al., 2007 ; Zhao et al., 2014 ), but they may be difficult to apply for high-dimensional or expensive models. It is similarly difficult to properly develop appropriate likelihoods or objective functions that represent model errors ( Wu et al., 2019 ). Model emulation and model surrogates such as polynomial chaos expansions ( Ghanem & Spanos, 1991 ; Xiu & Karniadakis, 2002 ), Gaussian processes ( Williams & Rasmussen, 2006 ; Yang et al., 2018 ), and sparse grids ( Bungartz & Griebel, 2004 ) are examples of powerful tools that can improve understanding of the model response surface and sensitivities, as well as for achieving faster running models for improving uncertainty analysis that tends to require large number of parameter samplings and hence model simulations.

Unfortunately, due to the computational expense of many water quality models (e.g., Buahin & Horsburgh, 2018 ), building an accurate surrogate can still be intractable. Recently, in other fields such as aerospace engineering, multifidelity methods ( Gorodetsky et al., 2020 ; Jakeman et al., 2020 ; Peherstorfer et al., 2018 ) have been used to reduce the computational burden of uncertainty analysis. Multifidelity methods use an ensemble of models of varying complexity, speed, and accuracy (fidelity). A larger number of lower-fidelity simulations, which are faster but less accurate (e.g., models with reduced physics or coarse numerical discretizations), are used to explore the variability of a system and combined with simulations of a higher-fidelity (slower but more accurate) model to maintain predictive accuracy. These approaches enable more rapid convergence to high-fidelity statistics when such lower fidelity models provide predictive utility. In particular, multifidelity uncertainty quantification can converge more rapidly than single-fidelity uncertainty quantification in cases where there is a high-correlation between predictions of the models of varying fidelity ( Jakeman & Jakeman, 2018 ). All these methods have potential to significantly shape uncertainty quantification studies of water quality models.

Effective uncertainty analyses also require closer connection between managers and modelers. The data, quantities, and performance measures submitted to the managers or policy makers may be prodigious, let alone failing to target the risk preferences of the stakeholders. For example, an uncertainty quantification study may be used to estimate the uncertainty in annual average constituent load predictions of a system, but the decision makers may only care about uncertainty in load predictions during large events or uncertainty in the change in constituent loads due to management intervention.

In addition, water quality modeling often comprises multiple component models. In addition to forbidding runtimes of any one or more components that constrain model uncertainty assessments, individual model components may also be managed by different groups, with varying computational software and hardware, which can hinder cohesive and automated modeling ( Buahin et al., 2019 ). Water quality modeling efforts can benefit from leveraging recently developed methods in computational mathematics and engineering that decompose system uncertainty analysis into uncertainty analysis of individual model components that can be performed in parallel, thereby allowing those analyses to be combined resourcefully to assess system level uncertainties ( Amaral et al., 2014 ; Guzzetti et al., 2020 ; Sankararaman & Mahadevan, 2012 ).

With the advances in efficient and robust methods for quantifying model uncertainty, uncertainty quantification and attribution itself then become a powerful tool for constraining uncertainty. By identifying the sources of uncertainty, modelers can prioritize improved model representations, consider the need for improving a priori parameter estimates from data, or develop an optimal monitoring network design that reduces parameter uncertainty. In the latter case, optimal experimental design uses models to select experiments that maximize information gain (e.g., change in estimated uncertainty), According to Jakeman and Jakeman et al. (2018) , optimal experimental design has been shown in the computational mathematics literature to drastically improve the cost effectiveness of experimental designs for a variety of models based on ordinary differential equations ( Bock et al., 2013 ), partial differential equations ( Horesh et al., 2010 ), and differential algebraic equations ( Bauer et al., 2000 ) and has been developed in both Bayesian and non-Bayesian settings ( Atkinson et al., 2007 ; Chaloner & Verdinelli, 1995 ; Walsh et al., 2017 ).

Uncertainty in data requires special consideration when understanding the sources of total model uncertainty. As noted in section 3.2 , water quality parameters are typically very difficult (if not impossible) to observe at the scale they are represented in the model. Observational data may be sparse and observed at a single point in space or time, then hard to reconcile with model simulations. Three major types of observational uncertainty are evident: measurement errors, representational errors (due to the unknown spatial and temporal variation of the variable), and proxy measurement errors when variables are inferred via a surrogate (e.g., turbidity as a proxy for total suspended solids, Jones et al., 2011 ; or total phosphorous concentration, Lannergård et al., 2019 ). Proxy measurement is particularly important for water quality simulations because many water quality variables may be costly to measure or cannot be directly observed ( Horsburgh et al., 2010 ). But proxy measurement introduces uncertainties on top of measurement and representational uncertainties that will affect any model parameterization. The uncertainty in observations can (at least in theory) be estimated independently of the model and then incorporated into uncertainty quantification frameworks. This requires understanding of how the data were collected, what factors might affect how accurately they represent reality, and the scale of measurement.

3.4. Scale Mismatches

Mostly because of concerns for cost and practicality, experimentalists often conduct studies and collect data at different scales and levels of detail than are represented in most available water quality models. Thus, there is a scale mismatch between the process formulations encoded within existing models and the data that are available to characterize and/or quantify them. Additionally, the data that experimentalists collect can be challenging to use for modeling because they require a high degree of domain knowledge to translate from what was observed (e.g., a decline in dissolved oxygen concentration in a chamber experiment) to information that is useful for modeling (e.g., an estimate of sediment oxygen demand that can be applied in a model).

Scale disparity between models and available data may be both spatial and temporal. Although remote sensing data sets are advancing rapidly, most water quality sampling programs rely on grab sampling at a limited number of carefully chosen locations and may be coupled with in situ sensors where budgets permit. Data fusion techniques that combine data from multiple sources and scales offer useful examples of how to resolve spatial mismatches ( Zhang, 2010 ). But resolving temporal mismatches between data and models can still be difficult, given measurements for many important water quality constituents and variables must still be made in a laboratory setting on physical samples retrieved from the field. Grab samples rarely capture the temporal dynamics of natural systems. On the other hand, we can now collect data using in situ sensors at rates much higher than they are typically used in models. Thus, there are challenges in both using existing data and in designing new data collection efforts to satisfy modeling needs.

Scaling issues also arise when interfacing or coupling models. As an example, 1-D-2-D hydraulic model coupling is often implemented for simulating stormwater runoff, flooding, green infrastructure design, and assessment of the best management practices because these two types of models are complementary ( Buahin & Horsburgh, 2018 ). 1-D models accurately and efficiently simulate flows in channels, pipes, and other conduits, while 2-D models are more suitable for landscape processes and overland flows. However, combining these two types of models requires decisions regarding how they should be coupled—that is, determining how water and constituent mass are transferred from a computational cell of the 2-D model to an element of the 1-D model in a way that satisfies the principles of conservation of mass and momentum. Time stepping may also be a significant issue because coupled models may not use the same time stepping routine. The HydroCouple framework ( Buahin & Horsburgh, 2018 ) is an adaptation of the Open Modeling Interface model coupling framework and provides useful examples of how these challenges can be overcome. However, there is still much work to be done in testing these types of methods for model coupling, in terms of both effectiveness and computational efficiency ( Buahin & Horsburgh, 2015 ).

3.5. Provisioning of Modeling Tools

Provisioning of modeling tools refers to the setting up of infrastructure within which models are executed. The assortment of model codes, the languages and environments in which they were developed, and the variety in computational environments in which they are executed have contributed to a diverse ecosystem of water quality modeling activities. Coding languages and environments used by modelers are still many and varied, with no consensus or standards across model development efforts. While the codes for many water quality models are open source and have grown a community of developers and users, others are proprietary. Proprietary codes can be a barrier to reproducibility of work completed using these models not just because one must have a license for the model software to run it. They also limit contributions to model advancement because there is no mechanism for potential contributors outside of the model development team to add to or modify the code. This is not necessarily an indictment of the performance or quality of proprietary models, some of which are very highly regarded and have been successfully used in many applications. Rather, it is simply an observation that proprietary codes do not lend themselves to broad use, study, and advancement within the community of water quality modelers because they are not open.

Even where the model codes and data associated with model instances (i.e., the application of a particular model to a given area) are openly available, sharing and publication of modeling and analysis workflows for reproducibility of results are currently difficult. This type of reproducibility is important in establishing the credibility of models. Documenting which model was used for an analysis, which version of the model was used, and ensuring that the computational environment in which the model was executed is available or can be reproduced by others can be difficult ( Morsy et al., 2017 )—especially as time passes and popular/well-known/well-used computational environments change. Analysis workflows are often considered as a semistructured activity and are not well published in scientific literature. Furthermore, there is still a large gap between the skill set of most modelers and the skill set required to effectively use high-performance computing systems for robust experimental-type simulations (e.g., model intercomparison studies, calibrations, sensitivity analyses, and uncertainty analyses). Better tools are needed to support the development, sharing, and reuse of modeling and analysis workflows, supported by better publishing of the workflows ( Fu et al., 2020 ). For example, the recently developed open source Mobius model building system provides a virtual environmental laboratory for practitioners with little programming experience to quickly develop and evaluate watershed water quality models, making uncertainty analysis more accessible to model users ( Norling et al., 2020 ).

Finally, as we contemplate a next generation of water quality models, one paradigm that has emerged relatively recently to address the complexity of integrated assessment studies is that of loosely coupled or component-based modeling. As in other domains, the need for model integration in simulating water quality arises because there is rarely a single model that can simulate the needed processes at the different scales and complexities required ( Argent et al., 1999 ; Beven et al., 1980 ; Buahin & Horsburgh, 2018 ). Several framework technologies are available that enable component-based modeling, including the Open Modeling Interface ( Moore & Tindall, 2005 ), the Community Surface Dynamics Modeling System ( Peckham et al., 2013 ), the Object Modeling System ( David et al., 2002 ), and others. However, these coupled modeling frameworks are still not widely enough accepted or used within the water quality modeling community for there to be robust software implementations and a robust and extensive library of model components available for coupling in new model compositions.

4. Ways Forward

The authors’ combined experience and perspective in developing and applying water quality models suggest that several recommendations related to the potential improvements discussed above can be proposed. It is hoped that these (see below) provide a path forward for improving water quality modeling science, its infrastructure, and practices, with the ultimate beneficiary being progress in water quality management and planning. The ways forward in the sections below and summarized in Box 3 are based on our experience over the past decades but also coalesced from the discussions and outcomes during and after the workshop that was held at the 2018 iEMSs conference.

Ways forward to advance the next generation of water quality models and modelers.

Ways forward - building on the three pillars.

With Experimentalists – build stronger collaborations between experimentalists and water quality modelers to improve:

• transfer of new system knowledge to modeling;
• understanding of uncertainty/limitations of both data and models.

With Stakeholders – bridge gaps between modelers and stakeholders to enhance our ability to improve modeling:

• to make it more fit-for-purpose;
• facilitate co-learning between scientists, policy makers and communities.

With Organizations – cultivate and apply procedural knowledge for improved governance of modeling within organizations, including building:

• human capacity and knowledge;
• operational frameworks around modeling activities and cyberinfrastructure.

4.1. Bridging Gaps Between Experimentalists and Modelers

Early model development was primarily for researchers’ own use to better understand the systems in which they worked ( Box, 1979 ). The model was likely only used by one person who had a deep understanding of the model’s assumptions and limitations and who was primarily concerned with the model reflecting their understanding of how the system components interacted to result in system-level outcomes—so they were implicitly or explicitly testing hypotheses regarding the inner workings of the systems. This usage/purpose fell into the category of nomothetic research ( Oquist, 1978 ), and the modeler was likely also the experimentalist.

With increasing specialization, a division has formed between the people collecting the data (experimentalists or observationists) and modelers. This has led to modelers being able to develop more comprehensive models and frameworks (e.g., the Soil and Water Assessment Tool [SWAT], Gassman et al., 2007 ; Source, eWater, 2019 ) that are well written and documented. But it has also led to a lack of understanding among many modelers of the issues associated with collecting data or characterizing systems. This can have two results. One is a tendency for modelers to treat data as truth, disregarding or not understanding possible biases and uncertainties. The other is for modelers to regard the data, or even whole subsystems, as too unreliable to incorporate in models. The advent of such models has led to a third group appearing: model users. Model users apply existing models/platforms like SWAT and Source to particular problems without modifying the code. This third group focuses on applications of models to address particular problems and, consequently, are less engaged with the model algorithms as well as the data. They may have less understanding of the simplifications, omissions, and uncertainties in the model. They may also select models they are most familiar with, which may not be optimal for the problem.

While specialization is necessary to improve efficiency, in order to move forward, researchers in water quality modeling need to strive to overcome the divisions described above. This unification is essential to provide understanding of data uncertainty and data limitations that are important for development, calibration, and evaluation of models. This requires scientists who collect data to adequately report information about data collection procedures (i.e., metadata) so that data can be interpreted by others outside of the group who collected the data. It requires modelers to document the assumptions their models are built upon, along with limitations that may affect their suitability for use. It also requires that model users and data consumers use the metadata provided to understand the limitations of the models and data being used. Building capacity for water quality modeling includes increasing access to training, providing standards and documentation, and building thriving partnerships and networks, such as through user groups and communities of practice. These are all instrumental to propel the advancement of science in water quality modeling.

Furthermore, addressing known gaps in science with a next generation of water quality models and toolkits will also require bridging across experimentalists and modelers. Here we refer to well-known gaps in our current understanding of watershed and in-stream processes along with important scientific questions that have not yet been solved. These gaps manifest themselves as assumptions and simplifications in the formulations of our current suite of models. As an example from the hydrology community, Blöschl et al. (2019) condensed a list of 260 questions submitted by more than 200 scientists down to a list of 23 unsolved problems in hydrology grouped around time variability and change, space variability and scaling, variability of extremes, interfaces in hydrology, measurements and data, modeling methods, and interfaces with society. Given the intimate relationships between hydrology and water quality, many of these questions and their groupings are well aligned with the gaps and opportunities we have identified in this paper. Solving them will require integrated studies that pair new observations with new model formulations to test alternative hypotheses. Like Blöschl et al. (2019) , we believe that the diversity among experimentalists and modelers in the community is an asset that can be capitalized upon in addressing these unsolved problems in a holistic observation/modeling context.

4.2. Bridging Gaps Between Modelers and Stakeholders

Scale-appropriate simulation of contaminant fluxes and balances is necessary to avoid disparities for models versus management versus policy, because structures, functions, and processes change with scale ( Heathwaite, 2003 ; Quinn, 2004 ; Volk et al., 2008 ). Volk et al. (2008) argue that three spatial scales (microscale, mesoscale, and macroscale) are required for adequately describing water balance and quality, conducting economic assessments, and determining the scale-specific applicability of different models and assessment systems. The need to simulate the multiple attributes of system function across a range of scales demands us to foster the integration of diverse perspectives ( Hipsey et al., 2015 ). Enhancing our ability to combine different sources of data, knowledge, and modeling capabilities from different groups such as scientists, policy makers, and the general public has the potential to provide novel insights into the biophysical and social-economic dimensions that the models need to represent ( Mackay et al., 2015 ).

To this end, there is a need to encourage participation in science by engaging stakeholders in the contribution of knowledge and modeling. Bridging gaps between modelers and stakeholders enables us to improve modeling (including making it more fit for purpose) and facilitate colearning among scientists, policy makers, and communities. For instance, Schönhart et al. (2018) reported that animated discussions among modelers and stakeholders on the topics of uncertainty and future scenarios motivated adaptions of model parameterizations and the interface to the nitrogen cycle between the models Positive Agricultural Sector Model Austria (PASMA) and Modelling Nutrient Emissions in River Systems (MONERIS).

For modelers interacting with stakeholders, many authors have emphasized the importance of integrity and openness, including maintaining communication, building trust, being transparent, making assumptions clear, and maintaining neutrality ( Barnhart et al., 2018 ; Voinov & Gaddis, 2008 ). Stakeholder engagement should occur over the entire process of water quality modeling, from problem framing to model development, evaluation, and scenario analysis ( Badham et al., 2019 ; Hamilton et al., 2015 ). It is critical that stakeholders understand and accept the model(s) selected for a particular water management study, including a recognition of the costs associated with maintaining the model(s) over time ( Loucks & van Beek, 2017 ). Krueger et al. (2012) argued the importance of embracing a plurality of expertise and eventually models, and enhancing the legitimacy and transparency in the processes of engagement and information elicitation.

Another important element when working with stakeholders is communicating uncertainty. Barnhart et al. (2018) present best practices for communicating uncertainty throughout a stakeholder process, including selecting the methods for uncertainty analysis based on stakeholder knowledge and information gaps, providing clearly comprehensible graphs and depictions of uncertainty for all model outputs, and recognizing that certain stakeholder groups may be underrepresented or absent from discussions—which is a form of uncertainty—and providing a mechanism for these groups to become more active in the project. These best practices should be tested in water quality modeling. In addition, new technology developments related to web, social media, and visualization to support how models are built, packaged, and disseminated should be actively adopted by the modelers to communicate uncertainty ( Voinov et al., 2016 ).

4.3. Cultivating and Applying Procedural Knowledge for Modeling Processes

Procedural knowledge encompasses operational guidelines that manage modeling workflows, as well as knowledge management strategies and supporting tools and cyberinfrastructure. It is an emerging issue related to how modeling processes can better be governed and supported within organizations ( Arnold, 2013 ; Arnold et al., 2020 ). Organizations and the water quality modeling community as a whole can focus on improving human capacity and the knowledge base, the operational framework surrounding modeling activities (e.g., guidelines, standards, model management, and technical support services), and the cyberinfrastructure and tools available for modeling (e.g., model codes, data management and analysis tools, workflow tools, and computational resources). The recently released literature review on nutrient-related rates, constants, and kinetics formulations in surface water quality modeling ( Cope et al., 2020 ) is a good example of a knowledge base.

The preference for relying on well-established, legacy models has become a barrier to the development and adaptation of modern modeling frameworks, even though these models do not reflect the ongoing revolution in our understanding of the water quality processes. Therefore, development of new models and modeling frameworks should be encouraged as our knowledge and technology evolve. New model development will also serve as alternative conceptualizations which, when combined with the existing models, could become part of an ensemble approach to modeling water quality (much like we use multiple climate models for climate modeling). As a technology, “loosely” coupling models in a component-based modeling paradigm is one potential path forward, but the software tools to enable this still need to improve.

Rigorous model development and application require considerable model testing, preferably in a range of contrasting environments. In recent times, public repositories of data (e.g., www.hydroshare.org ), along with flexible tools to customize data into the varying formats required by models, are becoming more available. However, work is still needed to curate data collections that could be used for model development and intercomparison studies as has been progressed in other domains (e.g., the Model Parameter Estimation Experiment data set in hydrology; Schaake et al., 2006 ). Data solutions for modeling, including consolidation of access to environmental data and facilitation of data transformation and processing, are being investigated ( Laniak et al., 2013 ). Such repositories and services may provide a way for new or custom models to fast-track their testing. Further, if repositories and tools were expanded to show model outputs against measured data using interfaces suitable for nonmodelers, this may assist with the acceptance of custom models and ease the pathway for new developments.

The choice of software tools needs to be improved to meet operational goals of decision making and to bridge the gap between science and management ( Argent et al., 2016 ). If existing models or tools are to be used, Van Voorn et al. (2016) provide a checklist for assessing model credibility, salience, and legitimacy, and Mateus et al. (2018) guide model managers in selecting tools and models that meet expectations in scope and experience. Real-time monitoring and analysis frameworks ( Wong & Kerkez, 2016 ) and spatial workflow environments ( Nielsen et al., 2017 ; Zhang, Bu, et al., 2017 ) demonstrate technical innovations that still need to be operationalized by agencies. Yet, these innovations in software tools should not distract from the need to build rigorous operational protocols that simplify technical procedures and ensure access to relevant knowledge in a timely manner ( Argent et al., 2016 ; Arnold et al., 2020 ).

To ensure access to knowledge, ensure transparency, and enhance reproducibility, intellectual property rights to software tools must be managed appropriately ( Arnold et al., 2020 ). Several open source codes are now available for water quality and aquatic ecosystem prediction ( Fu et al., 2019 ; Hipsey et al., 2015 ). For instance, there is a substantial worldwide SWAT community, and there has been much investment in educational resources (e.g., videos, manuals, and handbooks), in updated SWAT literature databases and the development of supporting tools to aid the setup, evaluation, and assessment of SWAT. Future development should follow the development of flexible model libraries or even more granular model components ( Buahin & Horsburgh, 2018 ), rather than the adoption of a single model of choice. Soundly constructed model libraries or interchangeable components may allow us to identify levels of process complexity and scale that are adequate to capture trends in observations ( Hipsey et al., 2015 ) while enabling more flexibility in composing models that better meet project needs.

The authors advocate for a research structure that includes open sharing of all elements of the scientific process (ideas, models, tools, and data) as being essential to link theoretical developments and model infrastructure ( Hipsey et al., 2015 ). This should also include assessment protocols for model performance for a rigorous validation of models (both quantitative and qualitative methods), to create standards, a common vocabulary supporting comparisons and synthesis between model applications ( Harmel et al., 2014 ), as well as digital watershed observatories as platforms for engagement and knowledge exchange between watershed scientists, policy makers, and local communities ( Mackay et al., 2015 ).

5. Conclusion

Progress in the science of water quality modeling has somewhat stalled. While there have been increasing numbers of publications on water quality modeling case studies and improvements to existing models and techniques, challenging water quality issues remain difficult to solve. This may in part be due to lower investments globally on the cutting edge of experimental modeling and analyses, fragmentation of subdisciplines and special interest groups on water quality model development, and limited data availability and characterization and quantification of uncertainty.

This review provides a synthesis of some major gaps in the current science and practice of watershed quality modeling along with positing areas in how we can do better. More specifically, we have discussed four key topics in system representation: environmental interfaces, in-stream water quality and process interactions, soil health and land management, and urban areas. While each topic has its own specific characteristics, common themes have also emerged across topics. The first theme is system complexity. This complexity is evident in every part of the land to water systems, from smaller scales at environmental interfaces and local system variations to watershed-scale water quality monitoring and modeling, to the complex interactions between the water systems and associated social-economic systems. Achieving a balance between representation of system complexity and model parsimony thus requires clarity in societal concerns and the intended use of the models, as well as the contextual knowledge and information content in the data available for the problem at hand.

The second theme involves a lack of understanding of certain parts of system behaviors that can be vital in representing water quality processes. Examples include: the processes occurring at environmental interfaces, which may represent a small increment of the total travel time and flow path, but can scientifically alter constituent concentrations; the relationships between hydrodynamics and water quality; and the role of soil biology dynamics and related soil biological solutions in changing surface water’s suspended sediment and chemical properties.

The third theme is the potential for new sources of data to advance system understanding. Emerging data sources, such as satellite data for geospatial digital soil mapping, environmental tracers such as isotopes and biomarkers, and new sensor technologies and unmanned aerial vehicle for in situ high-resolution water quality monitoring, can dramatically improve our understanding of system behaviors.

In addition to system representation, we also discussed five topics related to the modeling process: QC of monitoring data, parameterization and calibration, uncertainty assessment and management, scale mismatches, and provisioning modeling tools. QC of discrete monitoring data is a mature enterprise, with standardized procedures in data acquisitions and assessment protocols. Future development should focus on development of tools to facilitate better visualization and presentation of the data so that the data (and its quality) are more understandable and accessible by the public.

In terms of model parameterization and calibration, while calibration schemes are generally well established, there remains significant difficulty in calibrating models so that they are representative of the relevant processes in the system, and so that the main variable or criteria of interest is modeled with sufficient confidence. Efforts to improve model parameterization and calibration should be directed to not only increase data and information on system functioning but also to development of more comprehensive suites of objective functions that properly capture the quantity of interest.

Uncertainty analysis is important in estimating risk associated with using certain model predictions, diagnosing model weaknesses, and suggesting new experiments to improve the accuracy and precision in model predictions. Significant improvement is still needed to enhance our ability to better identify the sources of uncertainty, advance uncertainty quantification and attribution techniques, combine these with qualitative techniques, and develop approaches to constrain uncertainty.

Issues in scale mismatch between most available water quality models and data collected by experimentalists, and at interfaces between coupled models, remain difficult to resolve. Better understanding of the uncertainty arising from the scale mismatch is warranted. Investigation of data fusion techniques can be useful in approaching the scaling issues. Additional barriers in advancing the water quality modeling process include proprietary codes for some water quality models, difficulties in sharing and publication of model workflows for reproducibility of results, a lack of skills in using high-performance computing systems for robust experimental simulations, and the adaptation of loosely coupled or component-based modeling for more robust software implementations of integrated modeling frameworks.

To conclude our synthesis, we have provided three recommendations to move forward for water quality modeling science, infrastructure, and practices. First, we need to build stronger collaborations between experimentalists and water quality modelers, so that knowledge in system understanding can be adequately transferred to modeling, and uncertainty and limitations of the data and models are appropriately documented and interpreted. Bridging gaps between modelers and stakeholders is also vital to enhance our ability to improve modeling (including making it more fit for purpose) and facilitate colearning among scientists, policy makers, and communities. Finally, we advocate for cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations, including human capacity and the knowledge base, the operational framework around modeling activities, and cyberinfrastructure.

Key Points:

• We assess four potential improvements in water quality modeling: environmental interfaces, in-stream processes, soil health, and urban areas
• Challenges include data quality control, model calibration, uncertainty management, scale mismatches, and model tool provision
• Modelers need to strengthen connections with experimentalists and stakeholders and cultivate procedural knowledge for modeling processes

Acknowledgments

Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. John Jakeman’s work was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program. Baihua Fu’s work was supported by the Australian and Queensland Governments (Queensland Department of Natural Resources, Mines and Energy and Department of Environment and Science) through the Reef Plan and the Queensland Water Modeling Network. Tim Green’s work was supported by the U.S. Department of Agriculture, Agricultural Research Service. Val Snow was supported by the New Zealand Ministry for Business, Innovation and Employment’s Our Land and Water National Science Challenge (contract C10X1507). Barry Croke was supported by the Hilda John Endowment. The views expressed in the article are those of the author(s) and do not necessarily represent the views of the U.S. Department of Agriculture, U.S. Department of Energy, and the U. S. Environmental Protection Agency (EPA) but do represent the views of the U.S. Geological Survey. Any mention of trade names, manufacturers, or products does not imply an endorsement by the U.S. Government or the EPA. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. EPA and its employees do not endorse any commercial products, services, or enterprises.

Data Availability Statement

• Abbaspour KC, Rouholahnejad E, Vaghefi S, Srinivasan R, Yang H, & Kløve B (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model . Journal of Hydrology , 524 , 733–752. [ Google Scholar ]
• Abbaspour KC, Vejdani M, Haghighat S, & Yang J (2007). SWAT-CUP calibration and uncertainty programs for SWAT In Oxley L & Kulasiri D (Eds.), MODSIM 2007 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (pp. 1596–1602). Wellington, NZ: Modelling and Simulation Society of Australia and New Zealand. [ Google Scholar ]
• Achleitner S, Möderl M, & Rauch W (2007). CITY DRAIN©—An open source approach for simulation of integrated urban drainage systems . Environmental Modelling & Software , 22 ( 8 ), 1184–1195. 10.1016/j.envsoft.2006.06.013 [ CrossRef ] [ Google Scholar ]
• Allen DE, Singh BP, & Dalal RC (2011). Soil health indicators under climate change: A review of current knowledge In Singh B, Cowie A, Chan K (Eds.), Soil health and climate change (pp. 25–45). New York: Springer. [ Google Scholar ]
• Amaral S, Allaire D, & Willcox K (2014). A decomposition-based approach to uncertainty analysis of feed-forward multicomponent systems . International Journal for Numerical Methods in Engineering , 100 ( 13 ), 982–1005. 10.1002/nme.4779 [ CrossRef ] [ Google Scholar ]
• Arfaie A, Burns A, Dorrell RM, Ingham DB, Eggenhuisen J, & McCaffrey W (2018). Optimisation of flow resistance and turbulent mixing over bed forms . Environmental Modelling & Software , 107 , 141–147. 10.1016/j.envsoft.2018.06.002 [ CrossRef ] [ Google Scholar ]
• Argent RM, Grayson RB, & Ewing SA (1999). Integrated models for environmental management: Issues of process and design . Environment International , 25 ( 6–7 ), 693–699. 10.1016/S0160-4120(99)00052-5 [ CrossRef ] [ Google Scholar ]
• Argent RM, Sojda RS, Giupponi C, McIntosh B, Voinov AA, & Maier HR (2016). Best practices for conceptual modelling in environmental planning and management . Environmental Modelling & Software , 80 , 113–121. 10.1016/j.envsoft.2016.02.023 [ CrossRef ] [ Google Scholar ]
• Arnold JG, Youssef MA, Yen H, White MJ, Sheshukov AY, Sadeghi AM, et al. (2015). Hydrological processes and model representation: Impact of soft data on calibration . Transactions of the ASABE , 58 ( 6 ), 1637–1660. [ Google Scholar ]
• Arnold T (2013). Procedural knowledge for integrated modelling: Towards the modelling playground . Environmental Modelling & Software , 39 , 135–148. 10.1016/j.envsoft.2012.04.015 [ CrossRef ] [ Google Scholar ]
• Arnold T, Guillaume JH, Lahtinen TJ, & Vervoort RW (2020). From ad-hoc modelling to strategic infrastructure: A manifesto for model management . Environmental Modelling & Software , 123 , 104563 10.1016/j.envsoft.2019.104563 [ CrossRef ] [ Google Scholar ]
• Atkinson A, Donev A, & Tobias R (2007). Optimum experimental designs, with SAS (Vol. 34 ). Oxford: Oxford University Press. [ Google Scholar ]
• Baar J (2010). Development of soil quality metrics using mycorrhizal fungi . Spanish Journal of Agricultural Research , 8 ( S1 ), 137–143. 10.5424/sjar/201008S1-1233 [ CrossRef ] [ Google Scholar ]
• Bach PM, Rauch W, Mikkelsen PS, Mccarthy DT, & Deletic A (2014). A critical review of integrated urban water modelling— Urban drainage and beyond . Environmental Modelling & Software , 54 , 88–107. 10.1016/j.envsoft.2013.12.018 [ CrossRef ] [ Google Scholar ]
• Badham J, Elsawah S, Guillaume JHA, Hamilton SH, Hunt RJ, Jakeman AJ, et al. (2019). Effective modeling for integrated water resource management: A guide to contextual practices by phases and steps and future opportunities . Environmental Modelling & Software , 116 , 40–56. 10.1016/j.envsoft.2019.02.013 [ CrossRef ] [ Google Scholar ]
• Baffaut C, Ghidey F, Lerch R, Kitchen N, Sudduth K, & Sadler E (2019). Long-term simulated runoff and water quality from grain cropping systems on restrictive layer soils . Agricultural Water Management , 213 , 36–48. 10.1016/j.agwat.2018.09.032 [ CrossRef ] [ Google Scholar ]
• Barnhart BL, Golden HE, Kasprzyk JR, Pauer JJ, Jones CE, Sawicz KA, et al. (2018). Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: Successes and challenges . Environmental Modelling & Software , 109 , 368–379. 10.1016/j.envsoft.2018.08.025 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Basu S (2013). Turbulent flow modeling In Fernando H (Ed.), Handbok of Environmental Fluid Dynamics, Volume II: Systems, pollution, modeling and measurements (pp. 273–282). Boca Raton: CRC Press. [ Google Scholar ]
• Bauer I, Bock HG, Körkel S, & Schlöder JP (2000). Numerical methods for optimum experimental design in DAE systems . Journal of Computational and Applied Mathematics , 120 ( 1–2 ), 1–25. [ Google Scholar ]
• Beck HE, van Dijk AI, de Roo A, Miralles DG, McVicar TR, Schellekens J, & Bruijnzeel LA (2016). Global-scale regionalization of hydrologic model parameters . Water Resources Research , 52 , 3599–3622. 10.1002/2015WR018247 [ CrossRef ] [ Google Scholar ]
• Benedetti L, Langeveld J, van Nieuwenhuijzen AF, de Jonge J, de Klein J, Flameling T, et al. (2013). Cost-effective solutions for water quality improvement in the Dommel River supported by sewer–WWTP–river integrated modelling . Water Science and Technology , 68 ( 5 ), 965–973. 10.2166/wst.2013.312 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bennett ND, Croke BFW, Guariso G, Guillaume JHA, Hamilton SH, Jakeman AJ, et al. (2013). Characterising performance of environmental models . Environmental Modelling & Software , 40 , 1–20. 10.1016/j.envsoft.2012.09.011 [ CrossRef ] [ Google Scholar ]
• Bertrand-Krajewski J-L (2007). Stormwater pollutant loads modelling: Epistemological aspects and case studies on the influence of field data sets on calibration and verification . Water Science and Technology , 55 ( 4 ), 1–17. 10.2166/wst.2007.090 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beven K, & Germann P (2013). Macropores and water flow in soils revisited . Water Resources Research , 49 , 3071–3092. 10.1002/wrcr.20156 [ CrossRef ] [ Google Scholar ]
• Beven K, Warren R, & Zaoui J (1980). SHE: Towards a Methodology for Physically-Based Distributed Forecasting in Hydrology (Vol. 129 , pp. 133–137). Wallingford: IAHS Publication. [ Google Scholar ]
• Björneholm O, Hansen MH, Hodgson A, Liu LM, Limmer DT, Michaelides A, et al. (2016). Water at interfaces . Chemical Reviews , 116 ( 13 ), 7698–7726. 10.1021/acs.chemrev.6b00045 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blaen PJ, Khamis K, Lloyd CE, Bradley C, Hannah D, & Krause S (2016). Real-time monitoring of nutrients and dissolved organic matter in rivers: Capturing event dynamics, technological opportunities and future directions . Science of the Total Environment , 569 , 647–660. [ PubMed ] [ Google Scholar ]
• Blanco-Canqui H (2011). Does no-till farming induce water repellency to soils? Soil Use and Management , 27 ( 1 ), 2–9. 10.1111/j.1475-2743.2010.00318.x [ CrossRef ] [ Google Scholar ]
• Blöschl G, Bierkens MFP, Chambel A, Cudennec C, Destouni G, Fiori A, et al. (2019). Twenty-three unsolved problems in hydrology (UPH)—A community perspective . Hydrological Sciences Journal , 64 ( 10 ), 1141–1158. 10.1080/02626667.2019.1620507 [ CrossRef ] [ Google Scholar ]
• Bock HG, Körkel S, & Schlöder JP (2013). Parameter estimation and optimum experimental design for differential equation models In Bock H, Carraro T, Jäger W, Körkel S, Rannacher R, Schlöder J (Eds.), Model based parameter estimation (pp. 1–30). New York: Springer. [ Google Scholar ]
• Boënne W, Desmet N, van Looy S, & Seuntjens P (2014). Use of online water quality monitoring for assessing the effects of WWTP overflows in rivers . Environmental Science: Processes & Impacts , 16 ( 6 ), 1510–1518. [ PubMed ] [ Google Scholar ]
• Bol R, Gruau G, Mellander PE, Dupas R, Bechmann M, Skarbøvik E, et al. (2018). Challenges of reducing phosphorus based water eutrophication in the agricultural landscapes of Northwest Europe . Frontiers in Marine Science , 5 ( 276 ), 1–16. 10.3389/fmars.2018.00276 [ CrossRef ] [ Google Scholar ]
• Bonfante A, Terribile F, & Bouma J (2019). Refining physical aspects of soil quality and soil health when exploring the effects of soil degradation and climate change on biomass production: An Italian case study . The Soil , 5 ( 1 ), 1–14. 10.5194/soil-5-1-2019 [ CrossRef ] [ Google Scholar ]
• Box GE (1979). Robustness in the strategy of scientific model building In Launer RL, Wilkinson GN (Eds.), Robustness in statistics (pp. 201–236). New York: Academic Press. [ Google Scholar ]
• Brilli L, Bechini L, Bindi M, Carozzi M, Cavalli D, Conant R, et al. (2017). Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes . Science of the Total Environment , 598 , 445–470. 10.1016/j.scitotenv.2017.03.208 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brombach H, Weiss G, & Fuchs S (2005). A new database on urban runoff pollution: Comparison of separate and combined sewer systems . Water Science and Technology , 51 ( 2 ), 119–128. 10.2166/wst.2005.0039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Buahin CA, & Horsburgh JS (2015). Evaluating the simulation times and mass balance errors of component-based models: An application of OpenMI 2.0 to an urban stormwater system . Environmental Modelling & Software , 72 , 92–109. 10.1016/j.envsoft.2015.07.003 [ CrossRef ] [ Google Scholar ]
• Buahin CA, & Horsburgh JS (2018). Advancing the Open Modeling Interface (OpenMI) for integrated water resources modeling . Environmental Modelling & Software , 108 , 133–153. 10.1016/j.envsoft.2018.07.015 [ CrossRef ] [ Google Scholar ]
• Buahin CA, Horsburgh JS, & Neilson BT (2019). Parallel multi-objective calibration of a component-based river temperature model . Environmental Modelling & Software , 116 , 57–71. 10.1016/j.envsoft.2019.02.012 [ CrossRef ] [ Google Scholar ]
• Bungartz H-J, & Griebel M (2004). Sparse grids . Acta Numerica , 13 , 147–269. 10.1017/S0962492904000182 [ CrossRef ] [ Google Scholar ]
• Chaloner K, & Verdinelli I (1995). Bayesian experimental design: A review . Statistical Science , 10 ( 3 ), 273–304. 10.1214/ss/1177009939 [ CrossRef ] [ Google Scholar ]
• Chapra SC (1997). Surface water-quality modeling . New York (N.Y.): McGraw-Hill. [ Google Scholar ]
• Chapra SC (2018). Advances in river water quality modelling and management: Where we come from, where we are, and where we’re going? Paper presented at the International Conference on Urban Drainage Modelling, International Water Association, Palermo, Italy. [ Google Scholar ]
• Cope B, Shaikh T, Parmar R, Chapra S, & Martin J (2020). Literature review on nutrient-related rates, constants, and kinetics formulations in surface water quality modeling . Washington, DC: U.S. Environmental Protection Agency. [ Google Scholar ]
• Cushman-Roisin B, Gualtieri C, & Mihailovic DT (2012). Environmental fluid mechanics: Current issues and future outlook In Gualtieri C, & Mihailović DT (Eds.), Fluid mechanics of environmental interfaces (2nd ed., pp. 3–17). Boca Raton: CRC Press. [ Google Scholar ]
• Daggupati P, Pai N, Ale S, Douglas-Mankin KR, Zeckoski RW, Jeong J, et al. (2015). A recommended calibration and validation strategy for hydrologic and water quality models . Transactions of the ASABE , 58 ( 6 ), 1705–1719. [ Google Scholar ]
• David O, Lloyd W, Rojas K, Arabi M, Geter F, Ascough I, et al. (2014). Model-as-a-service (MaaS) using the cloud services innovation platform (CSIP) . School of Engineering and Technology Publications 13; Retrieved from https://digitalcommons.tacoma.uw.edu/tech_pub/13 [ Google Scholar ]
• David O, Markstrom S, Rojas K, Ahuja L, & Schneider I (2002). Chapter 15. The object modeling system In Ahuja L, Ma L, Owell T (Eds.), Agricultural system models in field research and technology transfer . Boca Raton: CRC Press. [ Google Scholar ]
• Deines JM, Wang S, & Lobell DB (2019). Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt . Environmental Research Letters , 14 , 124038 10.1088/1748-9326/ab503b [ CrossRef ] [ Google Scholar ]
• Detenbeck NE, Brady VJ, Taylor DL, Snarski VM, & Batterman SL (2005). Relationship of stream flow regime in the western Lake Superior basin to watershed type characteristics . Journal of Hydrology , 309 ( 1 ), 258–276. 10.1016/j.jhydrol.2004.11.024 [ CrossRef ] [ Google Scholar ]
• Dietrich CR, Green TR, & Jakeman AJ (1999). An analytical model for stream sediment transport: Application to Murray and Murrumbidgee river reaches, Australia . Hydrological Processes , 13 ( 5 ), 763–776. 10.1002/(SICI)1099-1085(19990415)13:5<763::AID-HYP779>3.0.CO;2-C [ CrossRef ] [ Google Scholar ]
• Dittmer U, Bachmann-Machnik A, & Launay MA (2020). Impact of combined sewer systems on the quality of urban streams: Frequency and duration of elevated micropollutant concentrations . Water , 12 , 850 10.3390/W12030850 [ CrossRef ] [ Google Scholar ]
• Doraiswamy PC, Moulin S, Cook PW, & Stern A (2003). Crop yield assessment from remote sensing . Photogrammetric Engineering & Remote Sensing , 69 ( 6 ), 665–674. 10.14358/PERS.69.6.665 [ CrossRef ] [ Google Scholar ]
• EPA (2006a). Guidance on systematic planning using the data quality objectives process (EPA QA/G-4). EPA/240/B-06/001 . Washington, DC: Retrieved from https://www.epa.gov/sites/production/files/2015-06/documents/g4-final.pdf [ Google Scholar ]
• EPA (2006b). Data quality assessment: Statistical methods for practitioners (EPA QA/G-9S). EPA/240/B-06/003 . Washington, DC: Retrieved from https://www.epa.gov/sites/production/files/2015-08/documents/g9s-final.pdf [ Google Scholar ]
• Erbe V, & Schütze M (2005). An integrated modelling concept for immission-based management of sewer system, wastewater treatment plant and river . Water Science and Technology , 52 ( 5 ), 95–103. [ PubMed ] [ Google Scholar ]
• eWater (2019). The national hydrological modelling platform: Adoption and implementation 2012–2018, summary report Retrieved from https://ewater.org.au/publications/national-hydrology-modelling-platform-2012-18/
• FAO (2013). FAO statistical yearbook: World food and agriculture . FAO. [ Google Scholar ]
• Franzluebbers AJ (2002). Water infiltration and soil structure related to organic matter and its stratification with depth . Soil and Tillage Research , 66 ( 2 ), 197–205. [ Google Scholar ]
• Freni G, & Mannina G (2010). Bayesian approach for uncertainty quantification in water quality modelling: The influence of prior distribution . Journal of Hydrology , 392 ( 1–2 ), 31–39. 10.1016/j.jhydrol.2010.07.043 [ CrossRef ] [ Google Scholar ]
• Fu B, Guillaume JHA, Jakeman AJ, & Asher M (2020). A bricolage-style exploratory scenario analysis to manage uncertainty in socio-environmental systems modeling: Investigating integrated water management options . Socio-Environmental Systems Modelling , 2 , 16227 10.18174/sesmo.2020a16227 [ CrossRef ] [ Google Scholar ]
• Fu B, Merritt WS, Croke BFW, Weber TR, & Jakeman AJ (2019). A review of catchment-scale water quality and erosion models and a synthesis of future prospects . Environmental Modelling & Software , 114 , 75–97. 10.1016/j.envsoft.2018.12.008 [ CrossRef ] [ Google Scholar ]
• Gassman PW, Reyes MR, Green CH, & Arnold JG (2007). The soil and water assessment tool: Historical development, applications, and future research directions . Transactions of the ASABE , 50 ( 4 ), 1211–1250. 10.13031/2013.23637 [ CrossRef ] [ Google Scholar ]
• Ghanem RG, & Spanos PD (1991). Stochastic finite elements: A spectral approach . New York: Springer-Verlag New York; 10.1007/978-1-4612-3094-6 [ CrossRef ] [ Google Scholar ]
• Gianinazzi S, Gollotte A, Binet M-N, van Tuinen D, Redecker D, & Wipf D (2010). Agroecology: The key role of arbuscular mycorrhizas in ecosystem services . Mycorrhiza , 20 ( 8 ), 519–530. 10.1007/s00572-010-0333-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glendell M, Jones R, Dungait JAJ, Meusburger K, Schwendel AC, Barclay R, et al. (2018). Tracing of particulate organic C sources across the terrestrial-aquatic continuum, a case study at the catchment scale (Carminowe Creek, southwest England) . Science of the Total Environment , 616–617 , 1077–1088. 10.1016/j.scitotenv.2017.10.211 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Glendell M, Palarea-Albaladejo J, Pohle I, Marrero S, McCreadie B, Cameron G, & Stutter M (2019). Modeling the ecological impact of phosphorus in catchments with multiple environmental stressors . Journal of Environmental Quality , 48 ( 5 ), 1336–1346. 10.2134/jeq2019.05.0195 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gorodetsky A, Geraci G, Eldred M, & Jakeman JD (2020). A generalized approximate control variate framework for multifidelity uncertainty quantification . Journal of Computational Physics , 408 , 109257 10.1016/j.jcp.2020.109257 [ CrossRef ] [ Google Scholar ]
• Gosnell H, Gill N, & Voyer M (2019). Transformational adaptation on the farm: Processes of change and persistence in transitions to ‘climate-smart’ regenerative agriculture . Global Environmental Change , 59 , 101965 10.1016/j.gloenvcha.2019.101965 [ CrossRef ] [ Google Scholar ]
• Gualtieri C, Abdi R, Ianniruberto M, Filizola N, & Endreny TA (2020). A 3D analysis of spatial habitat metrics about the confluence of Negro and Solimões rivers, Brazil . Ecohydrology , 13 , e2166 10.1002/eco.2166 [ CrossRef ] [ Google Scholar ]
• Gualtieri C, Filizola N, de Oliveira M, Santos AM, & Ianniruberto M (2018). A field study of the confluence between Negro and Solimões Rivers. Part 1: Hydrodynamics and sediment transport . Comptes Rendus Geoscience , 350 ( 1–2 ), 31–42. 10.1016/j.crte.2017.09.015 [ CrossRef ] [ Google Scholar ]
• Gualtieri C, & Pulci Doria G (2012). Gas-transfer at unsheared free-surfaces In Gualtieri C, Mihailović DT (Eds.), Fluid mechanics of environmental interfaces (pp. 143–177). Boca Raton: CRC Press. [ Google Scholar ]
• Guillaume JHA, Jakeman JD, Marsili-Libelli S, Asher M, Brunner P, Croke B, et al. (2019). Introductory overview of identifiability analysis: A guide to evaluating whether you have the right type of data for your modeling purpose . Environmental Modelling and Software , 119 , 418–432. 10.1016/j.envsoft.2019.07.007 [ CrossRef ] [ Google Scholar ]
• Guo D, Lintern A, Webb JA, Ryu D, Liu S, Bende-Michl U, et al. (2019). Key factors affecting temporal variability in stream water quality . Water Resources Research , 55 , 112–129. 10.1029/2018WR023370 [ CrossRef ] [ Google Scholar ]
• Guo LB, & Gifford RM (2002). Soil carbon stocks and land use change: A meta analysis . Global Change Biology , 8 ( 4 ), 345–360. 10.1046/j.1354-1013.2002.00486.x [ CrossRef ] [ Google Scholar ]
• Guzzetti S, Alvarez LM, Blanco P, Carlberg KT, & Veneziani A (2020). Propagating uncertainties in large-scale hemodynamics models via network uncertainty quantification and reduced-order modeling . Computer Methods in Applied Mechanics and Engineering , 358 , 112626 10.1016/j.cma.2019.112626 [ CrossRef ] [ Google Scholar ]
• Halliday SJ, Skeffington RA, Wade AJ, Bowes MJ, Gozzard E, Newman JR, et al. (2015). High-frequency water quality monitoring in an urban catchment: Hydrochemical dynamics, primary production and implications for the Water Framework Directive . Hydrological Processes , 29 ( 15 ), 3388–3407. 10.1002/hyp.10453 [ CrossRef ] [ Google Scholar ]
• Hamilton SH, ElSawah S, Guillaume JHA, Jakeman AJ, & Pierce SA (2015). Integrated assessment and modelling: Overview and synthesis of salient dimensions . Environmental Modelling & Software , 64 , 215–229. 10.1016/j.envsoft.2014.12.005 [ CrossRef ] [ Google Scholar ]
• Hamilton SH, Fu B, Guillaume JHA, Badham J, Elsawah S, Gober P, et al. (2019). A framework for characterising and evaluating the effectiveness of environmental modelling . Environmental Modelling & Software , 118 , 83–98. 10.1016/j.envsoft.2019.04.008 [ CrossRef ] [ Google Scholar ]
• Harmel RD, Smith PK, Migliaccio KW, Chaubey I, Douglas-Mankin KR, Benham B, et al. (2014). Evaluating, interpreting, and communicating performance of hydrologic/water quality models considering intended use: A review and recommendations . Environmental Modelling & Software , 57 , 40–51. 10.1016/j.envsoft.2014.02.013 [ CrossRef ] [ Google Scholar ]
• Heathwaite AL (2003). Making process-based knowledge useable at the operational level: A framework for modelling diffuse pollution from agricultural land . Environmental Modelling & Software , 18 ( 8–9 ), 753–760. 10.1016/S1364-8152(03)00077-X [ CrossRef ] [ Google Scholar ]
• Hipsey MR, Hamilton DP, Hanson PC, Carey CC, Coletti JZ, Read JS, et al. (2015). Predicting the resilience and recovery of aquatic systems: A framework for model evolution within environmental observatories . Water Resources Research , 51 , 7023–7043. 10.1002/2015WR017175 [ CrossRef ] [ Google Scholar ]
• Hodges BR (2013). Challenges in continental river dynamics . Environmental Modelling & Software , 50 , 16–20. 10.1016/j.envsoft.2013.08.010 [ CrossRef ] [ Google Scholar ]
• Holzworth DP, Snow V, Janssen S, Athanasiadis IN, Donatelli M, Hoogenboom G, et al. (2015). Agricultural production systems modelling and software: Current status and future prospects . Environmental Modelling & Software , 72 , 276–286. 10.1016/j.envsoft.2014.12.013 [ CrossRef ] [ Google Scholar ]
• Horesh L, Haber E, & Tenorio L (2010). Optimal experimental design for the large-scale nonlinear ill-posed problem of impedance imaging In Biegler L et al. (Eds.), Large-Scale Inverse Problems and Quantification of Uncertainty (pp. 273–290). Chichester, UK: John Wiley & Sons; 10.1002/9780470685853.ch13 [ CrossRef ] [ Google Scholar ]
• Horsburgh JS, Spackman Jones A, Stevens DK, Tarboton DG, & Mesner NO (2010). A sensor network for high frequency estimation of water quality constituent fluxes using surrogates . Environmental Modelling & Software , 25 ( 9 ), 1031–1044. 10.1016/j.envsoft.2009.10.012 [ CrossRef ] [ Google Scholar ]
• House MA, Ellis JB, Herricks EE, Hvitved-Jacobsen T, Seager J, Lijklema L, et al. (1993). Urban drainage-impacts on receiving water quality . Water Science and Technology , 27 ( 12 ), 117–158. 10.2166/wst.1993.0293 [ CrossRef ] [ Google Scholar ]
• Huete AR, Justice CO, & van Leeuwen WJD (1999). MODIS vegetation index (MOD 13). Algorithm theoretical basis document . Retrieved from https://pdfs.semanticscholar.org/2204/b55a9ad69e8b69d19e88ad1f0e1f81a5d72b.pdf
• Hunt RJ, Krabbenhoft DP, & Anderson MP (1997). Assessing hydrogeochemical heterogeneity in natural and constructed wetlands . Biogeochemistry , 39 ( 3 ), 271–293. 10.1023/A:1005889319205 [ CrossRef ] [ Google Scholar ]
• Jackson-Blake LA, Sample JE, Wade AJ, Helliwell RC, & Skeffington RA (2017). Are our dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P . Water Resources Research , 53 , 5382–5399. 10.1002/2016WR020132 [ CrossRef ] [ Google Scholar ]
• Jakeman A, & Jakeman J (2018). An overview of methods to identify and manage uncertainty for modelling problems in the water– environment–agriculture cross-sector In Anderssen R, Broadbridge P, Fukumoto Y, Kajiwara K, Simpson M, Turner I (Eds.), Agriculture as a metaphor for creativity in all human endeavors (pp. 147–171). Singapore: Springer. [ Google Scholar ]
• Jakeman AJ, Croke BFW, & Fu B (2018). Uncertainty in environmental water quality modelling: Where do we stand? In Mannina G (Ed.), New Trends in Urban Drainage Modelling. UDM 2018. Green Energy and Technology (pp. 557–565). Cham: Springer. [ Google Scholar ]
• Jakeman JD, Eldred MS, Geraci G, & Gorodetsky A (2020). Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis . International Journal for Numerical Methods in Engineering , 121 ( 6 ), 1314–1343. 10.1002/nme.6268 [ CrossRef ] [ Google Scholar ]
• Jarvis N (2007). A review of non-equilibrium water flow and solute transport in soil macropores: Principles, controlling factors and consequences for water quality . European Journal of Soil Science , 58 ( 3 ), 523–546. 10.1111/j.1365-2389.2007.00915.x [ CrossRef ] [ Google Scholar ]
• Ji Z-G (2017). Hydrodynamics and water quality: Modeling rivers, lakes, and estuaries . New York: John Wiley & Sons. [ Google Scholar ]
• Jones AS, Stevens DK, Horsburgh JS, & Mesner NO (2011). Surrogate measures for providing high frequency estimates of total suspended solids and total phosphorus concentrations1 . JAWRA Journal of the American Water Resources Association , 47 ( 2 ), 239–253. 10.1111/j.1752-1688.2010.00505.x [ CrossRef ] [ Google Scholar ]
• Jung H, Senf C, Jordan P, & Krueger T (2020). Benchmarking inference methods for water quality monitoring and status classification . Environmental Monitoring and Assessment , 192 , 261 10.1007/s10661-020-8223-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karlsen CS, Flindt MR, Sønderup MJ, Madsen MH, & Egemose S (2019). Impact of land use and runoff on stream quality . Sustainability , 11 , 5479 10.3390/su11195479 [ CrossRef ] [ Google Scholar ]
• Kasvi E, Alho P, Lotsari E, Wang Y, Kukko A, Hyyppä H, & Hyyppä J (2015). Two-dimensional and three-dimensional computational models in hydrodynamic and morphodynamic reconstructions of a river bend: Sensitivity and functionality . Hydrological Processes , 29 ( 6 ), 1604–1629. 10.1002/hyp.10277 [ CrossRef ] [ Google Scholar ]
• Katul G, & Liu H (2017). Multiple mechanisms generate a universal scaling with dissipation for the air-water gas transfer velocity . Geophysical Research Letters , 44 , 1892–1898. 10.1002/2016GL072256 [ CrossRef ] [ Google Scholar ]
• Keating BA, & Thorburn PJ (2018). Modelling crops and cropping systems—Evolving purpose, practice and prospects . European Journal of Agronomy , 100 , 163–176. 10.1016/j.eja.2018.04.007 [ CrossRef ] [ Google Scholar ]
• Koo H, Chen M, Jakeman AJ, & Zhang F (2020). A global sensitivity analysis approach for identifying critical sources of uncertainty in non-identifiable spatially distributed environmental models: A holistic analysis applied to SWAT for input datasets and model parameters . Environmental Modelling & Software , 127 , 104676 10.1016/j.envsoft.2020.104676 [ CrossRef ] [ Google Scholar ]
• Koo H, Iwanaga T, Croke BFW, Jakeman AJ, Yang J, Wang HH, et al. (2020). Position paper: Sensitivity analysis of spatially distributed environmental models—A pragmatic framework for the exploration of uncertainty sources . Environmental Modelling & Software , 134 , 104857 10.1016/j.envsoft.2020.104857 [ CrossRef ] [ Google Scholar ]
• Koparan C, Koc AB, Privette CV, & Sawyer CB (2018). In situ water quality measurements using an unmanned aerial vehicle (UAV) system . Watermark , 10 , 264 10.3390/w10030264 [ CrossRef ] [ Google Scholar ]
• Krueger T (2017). Bayesian inference of uncertainty in freshwater quality caused by low-resolution monitoring . Water Research , 115 , 138–148. 10.1016/j.watres.2017.02.061 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Krueger T, Page T, Hubacek K, Smith L, & Hiscock K (2012). The role of expert opinion in environmental modelling . Environmental Modelling & Software , 36 , 4–18. 10.1016/j.envsoft.2012.01.011 [ CrossRef ] [ Google Scholar ]
• Kundu P, Cohen I, Hu G, & Dowling D (Eds) (2015). Fluid Mechanics (6th ed.). San Diego: Elsevier Academic Press. [ Google Scholar ]
• Laniak GF, Olchin G, Goodall J, Voinov A, Hill M, Glynn P, et al. (2013). Integrated environmental modeling: A vision and roadmap for the future . Environmental Modelling & Software , 39 , 3–23. 10.1016/j.envsoft.2012.09.006 [ CrossRef ] [ Google Scholar ]
• Lannergård EE, Ledesma JL, Fölster J, & Futter MN (2019). An evaluation of high frequency turbidity as a proxy for riverine total phosphorus concentrations . Science of the Total Environment , 651 , 103–113. 10.1016/j.scitotenv.2018.09.127 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu T-H, Wang Y-K, Wang X-K, Duan H-F, & Yan X-F (2020). Morphological environment survey and hydrodynamic modeling of a large bifurcation-confluence complex in Yangtze River, China . Science of the Total Environment , 737 , 139705 10.1016/j.scitotenv.2020.139705 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu Y, Yang P, Hu C, & Guo H (2008). Water quality modeling for load reduction under uncertainty: A Bayesian approach . Water Research , 42 ( 13 ), 3305–3314. 10.1016/j.watres.2008.04.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Loucks DP, & van Beek E (2017). Water Resource Systems Planning and Management: An Introduction to Methods, Models, and Applications . New York: Springer. [ Google Scholar ]
• Lovelock CE, Wright SF, & Nichols KA (2004). Using glomalin as an indicator for arbuscular mycorrhizal hyphal growth: An example from a tropical rain forest soil . Soil Biology and Biochemistry , 36 ( 6 ), 1009–1012. 10.1016/j.soilbio.2004.02.010 [ CrossRef ] [ Google Scholar ]
• Mackay EB, Wilkinson ME, Macleod CJA, Beven K, Percy BJ, Macklin MG, et al. (2015). Digital catchment observatories: A platform for engagement and knowledge exchange between catchment scientists, policy makers, and local communities . Water Resources Research , 51 , 4815–4822. 10.1002/2014WR016824 [ CrossRef ] [ Google Scholar ]
• Malve O, Laine M, Haario H, Kirkkala T, & Sarvala J (2007). Bayesian modelling of algal mass occurrences—Using adaptive MCMC methods with a lake water quality model . Environmental Modelling & Software , 22 ( 7 ), 966–977. 10.1016/j.envsoft.2006.06.016 [ CrossRef ] [ Google Scholar ]
• Martin JL, & McCutcheon SC (2018). Hydrodynamics and transport for water quality modeling . Boca Raton: CRC press. [ Google Scholar ]
• Masoner JR, Kolpin DW, Cozzarelli IM, Barber LB, Burden DS, Foreman WT, et al. (2019). Urban stormwater: An overlooked pathway of extensive mixed contaminants to surface and groundwaters in the United States . Environmental Science & Technology , 53 ( 17 ), 10,070–10,081. 10.1021/acs.est.9b02867 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mateus M, Vieira R, Almeida C, Silva M, & Reis F (2018). ScoRE—A simple approach to select a water quality model . Watermark , 10 ( 12 ), 1811. [ Google Scholar ]
• McCarthy D, & Harmel D (2014). Quality assurance/quality control in stormwater sampling In Zhang C, Mueller JF, Mortimer MR (Eds.), Quality assurance & quality control of environmental field sampling (pp. 98–127). London: Future Medicine Ltd. [ Google Scholar ]
• McDonald W (2019). Drones in urban stormwater management: A review and future perspectives . Urban Water Journal , 16 ( 7 ), 505–518. [ Google Scholar ]
• McMillan H (2020). Linking hydrologic signatures to hydrologic processes: A review . Hydrological Processes , 1–17. 10.1002/hyp.13632 [ CrossRef ] [ Google Scholar ]
• Melcher AA, & Horsburgh JS (2017). An urban observatory for quantifying phosphorus and suspended solid loads in combined natural and stormwater conveyances . Environmental Monitoring and Assessment , 189 ( 6 ), 285. [ PubMed ] [ Google Scholar ]
• Meyer AM, Klein C, Fünfrocken E, Kautenburger R, & Beck HP (2019). Real-time monitoring of water quality to identify pollution pathways in small and middle scale rivers . Science of the Total Environment , 651 , 2323–2333. [ PubMed ] [ Google Scholar ]
• Minasny B, & McBratney AB (2016). Digital soil mapping: A brief history and some lessons . Geoderma , 264 , 301–311. [ Google Scholar ]
• Moore RV, & Tindall CI (2005). An overview of the open modelling interface and environment (the OpenMI) . Environmental Science & Policy , 8 ( 3 ), 279–286. 10.1016/j.envsci.2005.03.009 [ CrossRef ] [ Google Scholar ]
• Moriasi D, Wilson B, Douglas-Mankin K, Arnold J, & Gowda P (2012). Hydrologic and water quality models: Use, calibration, and validation . Transactions of the ASABE , 55 ( 4 ), 1241–1247. [ Google Scholar ]
• Moriasi DN, Zeckoski RW, Arnold JG, Baffaut C, Malone RW, Daggupati P, et al. (2015). Hydrologic and water quality models: Key calibration and validation topics . Transactions of the ASABE , 58 ( 6 ), 1609–1618. [ Google Scholar ]
• Morsy MM, Goodall JL, Castronova AM, Dash P, Merwade V, Sadler JM, et al. (2017). Design of a metadata framework for environmental models with an example hydrologic application in HydroShare . Environmental Modelling & Software , 93 , 13–28. 10.1016/j.envsoft.2017.02.028 [ CrossRef ] [ Google Scholar ]
• Mortimer MR, & Mueller JF (2014). An introduction to quality assurance/quality control in environmental field sampling In Zhang C, Mueller JF, Mortimer MR (Eds.), Quality assurance & quality control of environmental field sampling (pp. 8–24). London: Future Medicine Ltd. [ Google Scholar ]
• Nepf HM (2012). Flow and transport in regions with aquatic vegetation . Annual Review of Fluid Mechanics , 44 , 123–142. [ Google Scholar ]
• Nielsen A, Bolding K, Hu F, & Trolle D (2017). An open source QGIS-based workflow for model application and experimentation with aquatic ecosystems . Environmental Modelling & Software , 95 , 358–364. [ Google Scholar ]
• Norling MD, Jackson-Blake LA, Calidonio JLG, & Sample JE (2020). Rapid development of fast and flexible environmental models: The Mobius framework v1.0 . Geoscientific Model Development Discussions , 2020 1–19. 10.5194/gmd-2020-26 [ CrossRef ] [ Google Scholar ]
• O’Connor DJ, Thomann RV, & Di Toro DM (1973). Dynamic water quality forecasting and management: For sale by the Supt. of Docs., US Govt. Print. Off
• Oquist P (1978). The epistemology of action research . Acta Sociologica , 21 ( 2 ), 143–163. [ Google Scholar ]
• Owens M, Edwards R, & Gibbs J (1964). Some reaeration studies in streams . Air and Water Pollution , 8 , 469. [ PubMed ] [ Google Scholar ]
• Palmer J, Thorburn PJ, Biggs JS, Dominati EJ, Probert ME, Meier EA, et al. (2017). Nitrogen cycling from increased soil organic carbon contributes both positively and negatively to ecosystem services in wheat agro-ecosystems . Frontiers in Plant Science , 8 , 731 10.3389/fpls.2017.00731 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pechlivanidis I, Jackson B, McIntyre N, & Wheater H (2011). Catchment scale hydrological modelling: A review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications . Global NEST Journal , 13 ( 3 ), 193–214. [ Google Scholar ]
• Peckham SD, Hutton EWH, & Norris B (2013). A component-based approach to integrated modeling in the geosciences: The design of CSDMS . Computers & Geosciences , 53 , 3–12. 10.1016/j.cageo.2012.04.002 [ CrossRef ] [ Google Scholar ]
• Peherstorfer B, Willcox K, & Gunzburger M (2018). Survey of multifidelity methods in uncertainty propagation, inference, and optimization . SIAM Review , 60 ( 3 ), 550–591. [ Google Scholar ]
• Pope SB (2000). Turbulent flows . Cambridge: Cambridge University Press. [ Google Scholar ]
• Quinn P (2004). Scale appropriate modelling: Representing cause-and-effect relationships in nitrate pollution at the catchment scale for the purpose of catchment scale planning . Journal of Hydrology , 291 ( 3–4 ), 197–217. [ Google Scholar ]
• Quinn NW, Hughes B, Osti A, Herr J, Raley E, & Wang J (2017). Real-time web-based decision support for stakeholder implementation of basin-scale salinity management . Paper presented at the Environmental Software Systems, Computer Science for Environmental Protection, ISESS 2017, Croatia. [ Google Scholar ]
• Quinn NWT, Osti A, Herr J, Wang J, & Raley E (2017). WARMF-online—A web-based portal supporting real-time salinity management in the San Joaquin River Basin . Open Water Journal , 4 ( 1 ), 4. [ Google Scholar ]
• Rauch W, Krejci V, & Gujer W (2002). REBEKA—A software tool for planning urban drainage on the basis of predicted impacts on receiving waters . Urban Water , 4 ( 4 ), 355–361. [ Google Scholar ]
• Rauch W, Urich C, Bach PM, Rogers BC, de Haan FJ, Brown RR, et al. (2017). Modelling transitions in urban water systems . Water Research , 126 , 501–514. 10.1016/j.watres.2017.09.039 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Reichert P, Borchardt D, Henze M, Rauch W, Shanahan P, Somlyódy L, & Vanrolleghem P (2001). River water quality model no. 1 (RWQM1): II. Biochemical process equations . Water Science and Technology , 43 ( 5 ), 11–30. [ PubMed ] [ Google Scholar ]
• Reichert P, & Vanrolleghem P (2001). Identifiability and uncertainty analysis of the river water quality model no. 1 (RWQM1) . Water Science and Technology , 43 ( 7 ), 329–338. [ PubMed ] [ Google Scholar ]
• Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N, & Prabhat A (2019). Deep learning and process understanding for data-driven Earth system science . Nature , 566 ( 7743 ), 195–204. [ PubMed ] [ Google Scholar ]
• Rode M, Wade AJ, Cohen MJ, Hensley RT, Bowes MJ, Kirchner JW, et al. (2016). Sensors in the stream: The high-frequency wave of the present . Environmental Science & Technology , 50 ( 19 ), 10,297–10,307. 10.1021/acs.est.6b02155 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rodi W, Constantinescu G, & Stoesser T (2013). Large-eddy simulation in hydraulics . New York: CRC Press. [ Google Scholar ]
• Ross MRV, Topp SN, Appling AP, Yang X, Kuhn C, Butman D, et al. (2019). AquaSat: A data set to enable remote sensing of water quality for inland waters . Water Resources Research , 55 , 10,012–10,025. 10.1029/2019WR024883 [ CrossRef ] [ Google Scholar ]
• Rutherford JC (1994). River mixing . New York: John Wiley & Son Limited. [ Google Scholar ]
• Saagi R, Flores-Alsina X, Kroll S, Gernaey KV, & Jeppsson U (2017). A model library for simulation and benchmarking of integrated urban wastewater systems . Environmental Modelling & Software , 93 , 282–295. [ Google Scholar ]
• Sankararaman S, & Mahadevan S (2012). Likelihood-based approach to multidisciplinary analysis under uncertainty . Journal of Mechanical Design , 134 ( 3 ). [ Google Scholar ]
• Schaake J, Cong S, & Duan Q (2006). The US MOPEX data set In Andreassian V, Hall A, Chahinian N, Schaake J (Eds.), Large sample basin experiments for hydrological model parameterization: Results of the Model Parameter Experiment (MOPEX) . IAHS Publication 307. [ Google Scholar ]
• Schindler JE, & Krabbenhoft DP (1998). The hyporheic zone as a source of dissolved organic carbon and carbon gases to a temperate forested stream . Biogeochemistry , 43 ( 2 ), 157–174. [ Google Scholar ]
• Schönhart M, Trautvetter H, Parajka J, Blaschke AP, Hepp G, Kirchner M, et al. (2018). Modelled impacts of policies and climate change on land use and water quality in Austria . Land Use Policy , 76 , 500–514. 10.1016/j.landusepol.2018.02.031 [ CrossRef ] [ Google Scholar ]
• Schoonover JE, & Crim JF (2015). An introduction to soil concepts and the role of soils in watershed management . Journal of Contemporary Water Research & Education , 154 ( 1 ), 21–47. [ Google Scholar ]
• Shanahan P, Borchardt D, Henze M, Rauch W, Reichert P, Somlyódy L, & Vanrolleghem P (2001). River water quality model no. 1 (RWQM1): I. Modelling approach . Water Science and Technology , 43 ( 5 ), 1–9. [ PubMed ] [ Google Scholar ]
• Skeffington R, Halliday S, Wade A, Bowes M, & Loewenthal M (2015). Using high-frequency water quality data to assess sampling strategies for the EU Water Framework Directive . Hydrology and Earth System Sciences , 19 ( 5 ), 2491–2504. [ Google Scholar ]
• Sotiropoulos F (2005). Introduction to statistical turbulence modelling for hydraulic engineering flows In Bates P, Lane SN, Ferguson RI (Eds.), Computational fluid dynamics: Applications in environmental hydraulics (pp. 91–120). Chichester: John Wiley & Sons. [ Google Scholar ]
• Soudzilovskaia NA, van Bodegom PM, Terrer C, van Zelfde M, McCallum I, McCormack ML, et al. (2019). Global mycorrhizal plant distribution linked to terrestrial carbon stocks . Nature Communications , 10 ( 1 ), 1–10. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Stoesser T (2014). Large-eddy simulation in hydraulics: Quo Vadis? Journal of Hydraulic Research , 52 ( 4 ), 441–452. 10.1080/00221686.2014.944227 [ CrossRef ] [ Google Scholar ]
• Thomann RV, & Mueller JA (1987). Principles of surface water quality modeling and control . London: Harper & Row Publishers. [ Google Scholar ]
• Tinoco RO, San Juan JE, & Mullarney JC (2020). Simplification bias: Lessons from laboratory and field experiments on flow through aquatic vegetation . Earth Surface Processes and Landforms , 45 ( 1 ), 121–143. [ Google Scholar ]
• Tonina D (2012). Surface water and streambed sediment interaction: The hyporheic exchange In Gualtieri C, Mihailović DT (Eds.), Fluid mechanics of environmental interfaces (pp. 255–294). New York: CRC Press. [ Google Scholar ]
• Tonina D, & Jorde K (2013). Hydraulic modeling approaches for ecohydraulic studies: 3D, 2D, 1D and non-numerical models In Maddock I, Harby A, Kemp P (Eds.), Ecohydraulics: An integrated approach (pp. 31–66). Chichester: JohnWiley & Sons. [ Google Scholar ]
• Trinci G, Harvey GL, Henshaw AJ, Bertoldi W, & Hölker F (2017). Life in turbulent flows: Interactions between hydrodynamics and aquatic organisms in rivers . Wiley Interdisciplinary Reviews Water , 4 ( 3 ), e1213. [ Google Scholar ]
• Van Voorn G, Verburg R, Kunseler E-M, Vader J, & Janssen PH (2016). A checklist for model credibility, salience, and legitimacy to improve information transfer in environmental policy assessments . Environmental Modelling & Software , 83 , 224–236. [ Google Scholar ]
• Vanrolleghem P, Borchardt D, Henze M, Rauch W, Reichert P, Shanahan P, & Somlyódy L (2001). River water quality model no. 1 (RWQM1): III. Biochemical submodel selection . Water Science and Technology , 43 ( 5 ), 31–40. [ PubMed ] [ Google Scholar ]
• Vargas-Luna A, Crosato A, & Uijttewaal WS (2015). Effects of vegetation on flow and sediment transport: Comparative analyses and validation of predicting models . Earth Surface Processes and Landforms , 40 ( 2 ), 157–176. [ Google Scholar ]
• Vecchia AV (2018). Model methodology for estimating pesticide concentration extremes based on sparse monitoring data (2328-0328) . USGS Scientific Investigations Report 2017–5159 . Retrieved from 10.3133/sir20175159 [ CrossRef ]
• Vereecken H, Weihermüller L, Assouline S, Šimůnek J, Verhoef A, Herbst M, et al. (2019). Infiltration from the pedon to global grid scales: An overview and outlook for land surface modeling . Vadose Zone Journal , 18 ( 1 ), 1–53. 10.2136/vzj2018.10.0191 [ CrossRef ] [ Google Scholar ]
• Voinov A, & Gaddis EJB (2008). Lessons for successful participatory watershed modeling: A perspective from modeling practitioners . Ecological Modelling , 216 ( 2 ), 197–207. [ Google Scholar ]
• Voinov A, Kolagani N, McCall MK, Glynn PD, Kragt ME, Ostermann FO, et al. (2016). Modelling with stakeholders—Next generation . Environmental Modelling & Software , 77 , 196–220. 10.1016/j.envsoft.2015.11.016 [ CrossRef ] [ Google Scholar ]
• Volk M, Hirschfeld J, Dehnhardt A, Schmidt G, Bohn C, Liersch S, & Gassman PW (2008). Integrated ecological-economic modelling of water pollution abatement management options in the Upper Ems River Basin . Ecological Economics , 66 ( 1 ), 66–76. [ Google Scholar ]
• Volk M, Liersch S, & Schmidt G (2009). Towards the implementation of the European Water Framework Directive?: Lessons learned from water quality simulations in an agricultural watershed . Land Use Policy , 26 ( 3 ), 580–588. [ Google Scholar ]
• Vos C, Don A, Hobley EU, Prietz R, Heidkamp A, & Freibauer A (2019). Factors controlling the variation in organic carbon stocks in agricultural soils of Germany . European Journal of Soil Science , 70 ( 3 ), 550–564. 10.1111/ejss.12787 [ CrossRef ] [ Google Scholar ]
• Walsh SN, Wildey TM, & Jakeman JD (2017). Optimal experimental design using a consistent Bayesian approach . ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering , 4 ( 1 ). 10.1115/1.4037457 [ CrossRef ] [ Google Scholar ]
• Wang W-J, Peng W-Q, Huai W-X, Katul GG, Liu X-B, Qu X-D, & Dong F (2019). Friction factor for turbulent open channel flow covered by vegetation . Scientific Reports , 9 ( 1 ), 1–16. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Ward AS (2016). The evolution and state of interdisciplinary hyporheic research . Wiley Interdisciplinary Reviews Water , 3 ( 1 ), 83–103. [ Google Scholar ]
• Whitehead P, Wilby R, Battarbee R, Kernan M, & Wade AJ (2009). A review of the potential impacts of climate change on surface water quality . Hydrological Sciences Journal , 54 ( 1 ), 101–123. [ Google Scholar ]
• Williams CK, & Rasmussen CE (2006). Gaussian processes for machine learning (Vol. 2 ). Cambridge, MA: MIT press. [ Google Scholar ]
• Wipf D, Krajinski F, van Tuinen D, Recorbet G, & Courty PE (2019). Trading on the arbuscular mycorrhiza market: From arbuscules to common mycorrhizal networks . New Phytologist , 223 ( 3 ), 1127–1142. [ PubMed ] [ Google Scholar ]
• Wong BP, & Kerkez B (2016). Real-time environmental sensor data: An application to water quality using web services . Environmental Modelling & Software , 84 , 505–517. [ Google Scholar ]
• Wu X, Marshall L, & Sharma A (2019). The influence of data transformations in simulating Total suspended solids using Bayesian inference . Environmental Modelling & Software , 121 , 104493 10.1016/j.envsoft.2019.104493 [ CrossRef ] [ Google Scholar ]
• Xiu D, & Karniadakis GE (2002). The Wiener–Askey polynomial chaos for stochastic differential equations . SIAM Journal on Scientific Computing , 24 ( 2 ), 619–644. [ Google Scholar ]
• Yang J, Jakeman A, Fang G, & Chen X (2018). Uncertainty analysis of a semi-distributed hydrologic model based on a Gaussian process emulator . Environmental Modelling & Software , 101 , 289–300. [ Google Scholar ]
• Yigit Avdan Z, Kaplan G, Goncu S, & Avdan U (2019). Monitoring the water quality of small water bodies using high-resolution remote sensing data . ISPRS International Journal of Geo-Information , 8 ( 12 ), 553. [ Google Scholar ]
• Zhang G, Chan K, Oates A, Heenan D, & Huang G (2007). Relationship between soil structure and runoff/soil loss after 24 years of conservation tillage . Soil and Tillage Research , 92 ( 1–2 ), 122–128. [ Google Scholar ]
• Zhang G. l., Feng L, & Song XD (2017). Recent progress and future prospect of digital soil mapping: A review . Journal of Integrative Agriculture , 16 ( 12 ), 2871–2885. [ Google Scholar ]
• Zhang J (2010). Multi-source remote sensing data fusion: Status and trends . International Journal of Image and Data Fusion , 1 ( 1 ), 5–24. 10.1080/19479830903561035 [ CrossRef ] [ Google Scholar ]
• Zhang M, Bu X, & Yue P (2017). GeoJModelBuilder: An open source geoprocessing workflow tool . Open Geospatial Data. Software and Standards , 2 ( 1 ), 8. [ Google Scholar ]
• Zhao Y, Sharma A, Sivakumar B, Marshall L, Wang P, & Jiang J (2014). A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments . Environmental Modelling & Software , 57 , 216–226. 10.1016/j.envsoft.2014.03.005 [ CrossRef ] [ Google Scholar ]

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Home > Environmental Studies > ENVSTUDTHESES

Environmental Studies Program

Department of environmental studies: undergraduate student theses.

Amazon Deforestation and Its Effects on Local Climate , Andrew Baker

Observing Wildlife in Different Urban Environments , Colleen Ballinger

AN ANALYSIS OF FACTORS AFFECTING MUNICIPAL BIOCHAR IMPLEMENTATION IN VOLUNTARY CARBON MARKETS , Jadon Basilevac

Evaluating Biophilic Design Characteristics in Lincoln Public Schools , Sarah Burr

Spatiotemporal Activity Patterns of Red Foxes and Coyotes in Wilderness Park, Lincoln, Nebraska. , Adam Carlson

Recycling attitudes and behaviors toward single-use plastics at the University of Nebraska-Lincoln , Jadyn Chasek

Survey of Energy Literacy in Lincoln, NE Households , Bella Devney

Correlation Between Fire and Preservation in the Pacific Northwest & Most Cost-Efficient MitigationTactics , Liam Doherty-Herwitz

Analyzing the Effect of Guided Nature Walks on Human Behavior , Jake Duffy

STUDENT PERSPECTIVES OF SUSTAINABLE TRANSPORTATION USE ON A COLLEGE CAMPUS , Brynn Fuelberth

SOLAR ENERGY IMPLEMENTATION IN RURAL COMMUNITIES , Corie Gleason

State of Utah et al. v Walsh et al. , Ethan Halman Gonzalez

Regenerative Agriculture –A Pathway for Addressing Nebraska’s Water Quality and Soil Degradation Challenges , Kjersten Hyberger

Proposing Urban Agroforestry Designs for Lincoln, Nebraska: A Model From Berlin, Germany , Noah Johnson

Analyzing The Effects Of Cold Frontal Passage On The Feeding Habits Of Micropterus Salmoides (Largemouth Bass) , Ethan Lang

Turfgrass Alternatives for the Modern Yard , John Lonowski

Rails to Trails Program: Neighborhood Dynamics in Lincoln, Nebraska , Emma McCormick

ANALYZING SEA LEVEL RISE ADAPTATION & MITIGATION STRATEGIES IN LOUISIANA AND THE NETHERLANDS , Jonah McDowell

Impact of the Covid-19 Pandemic on Nebraska State Park Visitation , Bailey Mullins

Examining the Psychology and Human Behavior of Sustainable Living: The Impact of Socioeconomic Status on Climate Change Education , Samantha Nielsen

GENDER AND ENVIRONMENTAL SUSTAINABILITY: A CROSS-NATIONAL ANALYSIS ON POLITICAL REPRESENTATION OF WOMEN AND SUBSEQUENT STATE SUSTAINABILITY , Erin O'Sullivan

Perception of Nature Based on Childhood Experiences , Kaitlyn Richards

IDENTIFYING A CONSUMER-PRODUCER AGRICULTURAL KNOWLEDGE GAP , Aspen Rittgarn

EVALUATING ECOSYSTEM HEALTH OF THE SALT CREEK BASIN THROUGH TWO-EYED SEEING , Shelby Serritella

Place-Based Pedagogies in Post-Secondary Science Education: A Scoping Literature Review , Megan Swain

The Decline of Upland Birds in Nebraska: Maximizing Limited Habitat , Hunter Tesarek

Off the Rails: Cinematic Trains as Technological Controls of the Natural World , Trinity Thompson

Roots of Passion in Environmental UNL Students , Shane Vrbicky

Physiological Distancing Affects Climate Change Through Spatial Differences , Janette Williams

Nitrates in Nebraska , Michelle Zenk

NOISE POLLUTION AND ITS EFFECTS ON HUMAN MENTAL AND PHYSICAL HEALTH , Seth Anderson

Outdoor Recreation and its Effect on our Relationship with The Environment , Martin Brannaman

HELD AT BAY: A CASE STUDY OF A LAKE COMMUNITY’S EFFORT TO PREVENT A ZEBRA MUSSEL INVASION , Benjamin Breske

ANALYZING RECYCLING OPTIONS FOR WIND TURBINE BLADE WASTE , Awinita Bunner

The Effects of Human Activity on Reintroduced Bighorn Sheep (Ovis canadensis) Populations , Justine Cherovsky

Eco-dystopian Novels Written By Women: Second, Third, and Fourth Wave Feminism , Trystyn Cox

HOW CAN STAKEHOLDERS IN FOOD SOVEREIGNTY ACHIEVE MORE POLITICAL POWER? , Micah Dierks

Supercritical Water Gasification and Pyrolysis – Cleaning up the Great Pacific Garbage Patch , Kelly L. Emery

Public Health Impacts of the Clothing Industry , Schafer Flowerday

THE ACCESSIBILITY AND SUSTAINABILITY OF LOCAL FOODS: A SNAPSHOT FROM THE FOOD HUBS OF LINCOLN, NEBRASKA , Tess Foxall

Agricultural Carbon Markets: How Could They Work? , Andrew Havens

APPARENT TEMPERATURE & RELATIVE HUMIDITY IN NEBRASKA: A COMPARATIVE ANALYSIS ON WET BULB GLOBE TEMPERATURE (WBGT) TOOLS , Rachel T. Hines

The Evolution of Wildland Fire Risk Management , Matthew Holte

E-WASTE IMPACT ON THE HEALTH OF GUIYU, CHINA CITIZENS: A COMPARISON PRE AND POST CHINA’S PROHIBITION OF FOREIGN GARBAGE IMPORTS , Oliva Hultman

Climate Change Adaptation, Migration, and Promising Developments for Pacific Island States , Ashley Jonas

IDENTIFYING HOW SUMMER CAMP EXPERIENCES AFFECT CHILDREN’S ENVIRONMENTAL LITERACY , Quinn Kimbell

Literature Review on Water Desalination Plant Production and Brine Disposal Methods , Grace Kollars

The Impact of Interactions Among Native Grassland Species: A Study of Interactions Between Two Invasive Species (Bromus tectorum and Setaria faberi) and Two Native Species (Helianthus annuus and Rudbeckia hirta) , Nash Leef

Wildfires & Prescribed Fires: Do They Impact Soil Quality? , Kate Nelson

Eastern Redcedar Reduces Regeneration and Diversity in the Forests of the Niobrara River Valley , Abigail Ridder

Greenhouse Gas Emissions During the Usage Phase of Electric Vehicles in the United States, Now and in the Future , Zach Roza

Reintroduction of the Grey Wolf , Cody Willers

University of Nebraska Sustainability Recommendations , Kat Woerner

Land-Use and Potential Effects on the Western Tiger Salamander (Ambystoma mavortium) , Emily Zappia

Audit of Waste Collected Over One Week From Superior Dental Health of Lincoln , Bryclin Alstrom

Analysis of Drinking Water Disinfection Options , Bryce Carlen

Diversification of Angiosperms During the Cretaceous Period , Sakia Fields

Distribution of Green Spaces in Omaha, Nebraska , Sofia Gavia

The Effect of Agkistrodon contortrix and Crotalus horridus Venom Toxicity on Strike Locations With Live Prey , Chase Giese

Long-Term Impacts of 2019 Flood Experiences on Nebraskans’ Climate Change Perceptions , Caitlin Kingsley

How is Remote Sensing Being Used to Prevent Wildfires Today? , Luke Lauby

Regenerative Agriculture’s Potential Carbon Storage in Nebraska Soils , Jenna McCoy

Relationship of Land Use Categories and Water Quality for Low Order Streams , Jake McEnaney

Impact of Ethnic Markets on Food Accessibility in Lincoln, NE , Connor McFayden

Mitigation Strategies for Municipal Solid Waste Generation in Lincoln , Justine Mileski

Temporal and Spatial Interactions between Coyotes and Red Foxes along the Urban-Rural Interface , Adam Mortensen

Mental Health Incorporation in Nebraskan Recovery Plans Following the 2019 Midwestern Floods , Isabelle Murray

The Effect of Drought on the Bird Species Spiza americana , Emily Nelson

The Formatting of Science Communication and How it Affects Attraction to and Understanding of Scientific Information , Connor Nichols

Effects of Land Use in Nebraska on Insect Biodiversity and Eastern Monarch Populations , Carina Olivetti

The Relationship Between Time and Plant Diversity in Prairie Restorations Within the Prairie Corridor on Haines Branch , Elizabeth Park

Precipitation Impact on Crop Yield , Ian Ritchie

Investigating Predation Risk Experienced by Wintering Birds at a Supplied-Food Garden , Madison Smart

Using the Theory of Planned Behavior to Understand the Behavioral Use of Single-Use Plastic Bags by Students at the University of Nebraska-Lincoln , Josephine Stoessel

Sparking Awareness in Lincoln Electronic Waste Trends and Habits: A Student Behavioral Analysis , Zowie Vincent

Designing a Mobile App and Online Directory to Increase the Visibility of Environmental Organizations in a Community , Kayla Vondracek

The Effect of Urban Forests on Air Quality and Human Health , Chance Wilken

Climate Change & Grief: An Overview Of The Mental Health Effects Of Climate Change & How Biodiversity Loss In The Great Plains Affects Our Emotional Wellbeing , Luke Andersen

Lincoln, NE Composting in Restaurants , Brodie Baum

The Effect Of Wildlife Wellbeing On Environmental Concern , Laura Casne

Observing Spectral Response Differences In Freshwater Lakes Using Remote Sensing Technology , Brady Cooper

Nutritional Value of Crops affected by Elevated Carbon Dioxide Concentrations in Atmospheric Conditions , Alex Joseph Cusimano

A Comparative Analysis Of The Reception Of Laudato Si’ By Progressive And Traditional Catholics , Mikayla Dorff

PLANNING URBAN FORESTS IN A CHANGING CLIMATE , Ethan Dudden

The Role Of University Of Nebraska-Lincoln’s Biodigester On Sustainable Food Waste Reduction Within Selleck Dining Center , Jennifer Gilbert

Adventuring in the Winds: An Exploration of Water Accessibility, Keystone Species, Environmental Justice, and Forest Fires in the Wind River Range , Rhianna Giron

Environmental Factors On The Arctic Food Chain , Sydney Hansen

Incorporating Tallgrass Prairie Into Urban Environments , Daniel Hauschild

The Changing Habitat And Decline Of Ring-Necked Pheasant Populations In Otoe County, Nebraska , Jacob T. Herman

Ecological Perspectives of the Eastern Saline Wetlands Differ Between Visitors and Non-Visitors , Peter Janda

Analysis Of Trees Damaged From Flooding And Ice In Columbus, Nebraska , Alaina Kapla

The Effects Of Street Tree Site Planting Width On Canopy Width And Ability To Provide Ecosystem Services , Ryan Kendall

FUTURE IMPLICATIONS OF EXTREME HEAT ON PUBLIC HEALTH FOR THOSE LIVING IN LINCOLN, NEBRASKA , Mandy Koehler

Fungal Mycelium; The Key to a Sustainable Future , Sawyer Krivanek

Growth and Feeding Response in Python regius in Ambient Temperature vs. Hot-Spot , McKenzie Martinez

The Research and Analysis of Potential Gray Wolf (Canis Lupus) Habitats and Gray Wolf Management in the U.S. , YouHan Mei

Effect of Urban Green Space on Urban Populations. , Jack Mensinger

TO WHAT EXTENT HAS THE RELATIONSHIP BETWEEN HUMANS AND RED FOXES (VULPES VULPES) EVOLVED THROUGHOUT HISTORY? , Abigail Misfeldt

Diet Composition And Analysis Of Fish Species Consumed By The Eurasian Otter In A Marine/Costal Environment , Alexandrea Otto

The Effects of Phosphate on the Metamorphosis of Larval Western Barred Tiger Salamanders (Ambystoma mavortium) , Alexis Jean Polivanov

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Home > Engineering & Technology > Civil & Environmental Engineering > ETDs

Civil & Environmental Engineering Theses & Dissertations

Theses and dissertations published by graduate students in the Department of Civil and Environmental Engineering, College of Engineering, Old Dominion University since Fall 2016 are available in this collection. Backfiles of all dissertations (and some theses) have also been added.

In late Fall 2023 or Spring 2024, all theses will be digitized and available here. In the meantime, consult the Library Catalog to find older items in print.

Theses/Dissertations from 2023 2023

Thesis: Evaluating Direct Filtration as an Alternative to Conventional Carbon-Based Advanced Treatment for Indirect Potable Reuse , Savannah M. Flemmer

Thesis: Biocrude Production From Lignin in Hydrothermal Medium: Effect of Rapid Heating and Short Residence Time , Kyoko Hirayama

Thesis: Adaptation of Nirtrifiers and Heterotrophs to Low Dissolved Oxygen in an Activated Sludge Biological Nutrient Removal Pilot Plant , Shashank Khatiwada

Thesis: Lithium Extraction From Aqueous Solution Using Magnesium Doped Lithium Ion-Sieve Composite , Ujjwal Pokharel

Dissertation: An Effective Model for Dynamic Properties of Local Soils and Their Influence on Seismic Response of a Typical Reinforced Concrete Building , Kaveh Zehtab

Theses/Dissertations from 2022 2022

Dissertation: Quantification of Wave Attentuation of a Marsh Sill Living Shoreline and Application of Numerical Modeling for Design Optimization and Adaptation , Maura K. Boswell

Dissertation: Effectiveness of Suspended Lead Dampers in Steel Buildings Under Localized Lateral Impact and Vertical Pulsating Load , Herish Abdullah Hussein

Dissertation: Data-Driven Framework for Understanding & Modeling Ride-Sourcing Transportation Systems , Bishoy Kelleny

Theses/Dissertations from 2021 2021

Thesis: Stability of Low Crested and Submerged Breakwaters: A Reanalysis and Model Development , Christopher P. Burgess

Dissertation: Integrated Processing of Municipal Solid Waste for Maximizing Waste Reduction, Carbon Recovery and Fuel Production , Kameron J. King

Thesis: Assessment of the Hydrograv® Adapt Variable Height Secondary Clarifier Inlet at HRSD Nansemond Treatment Plant , Matthew Poe

Dissertation: Rainfall-Runoff Mechanisms and Flood Mitigation in a Coastal Watershed with Numerous Wetlands and Ponds , Homa Jalaeian Taghadomi

Dissertation: Hydrothermal Processes for Extraction and Conversion of Biomass to Produce Biofuels and Value-Added Products , Anuj Hemant Thakkar

Dissertation: Spatiotemporal Variations of Precipitation and Climate-Resilient Structure Design in Virginia , Xiaomin Yang

Theses/Dissertations from 2020 2020

Dissertation: A Rule Based Control Algorithm for on-Ramp Merge With Connected and Automated Vehicles , Ehsan Beheshtitabar

Thesis: Lateral-Torsional Instability and Biaxial Flexure of Continuous GFRP Beams Including Warping and Shear Deformations , Waverly G. Hampton

Thesis: The Impacts of Surface Gravity Waves on Buoyant Plume Dilution , Bruce William Husselbee

Dissertation: Truck Trailer Classification Using Side-Fire Light Detection And Ranging (LiDAR) Data , Olcay Sahin

Theses/Dissertations from 2019 2019

Thesis: Developing Algorithms to Detect Incidents on Freeways From Loop Detector and Vehicle Re-Identification Data , Biraj Adhikari

Dissertation: Catalytic Transfer Hydrogenation Reactions of Lipids , Alexander Asiedu

Dissertation: Latent Choice Models to Account for Misclassification Errors in Discrete Transportation Data , Lacramioara Elena Balan

Thesis: Spatiotemporal Downscaling Rainfall Predictions of North American Regional Climate Change Assessment Program for Entire Virginia , Zhaoyi Cai

Thesis: Application of a Biodegradable and Recyclable Chelating Agent for Ash Removal from Algae , Temitope George Daramola

Dissertation: Parallel Jacobi Transformation Algorithm for Generalized Eigen-Solution With Improved Damage Detection of Truss/Bridge-Type Structures , Maryam Ehsaei

Thesis: Sidestream RAS Fermentation for Stable Bio-P Combined with Short Cut Nitrogen Removal in an A/B Process , Lindsey Elise Ferguson

Thesis: Challenges of Designing and Operating a Pilot Scale Short Residence Time Continuous Hydrothermal Flash Hydrolysis Reactor for High Slurry Load Biomass Processing , Mason James Martin

Dissertation: Deep Reinforcement Learning Approach for Lagrangian Control: Improving Freeway Bottleneck Throughput Via Variable Speed Limit , Reza Vatani Nezafat

Thesis: Variable Speed Limit Control at SAG Curves Through Connected Vehicles: Implications of Alternative Communications and Sensing Technologies , Reza Vatani Nezafat

Thesis: Measuring and Modeling Bare Desert Wind Erosion From Steppe Grassland of Northern China as Affected By Soil Moisture and Climate , Nicholas Morgan Potter

Dissertation: Adaptive Control of Base Isolation Systems Using the Transmissibility-Based Semi-Active Controller , Ramin Rabiee

Thesis: Numerical Modeling of Shoreline Response to Storm Tides and Sea Level Rise , Akash Sahu

Dissertation: Numerical Modeling and Field Investigation of Nearshore Nonlinear Wave Propagation , Elham Sharifineyestani

Theses/Dissertations from 2018 2018

Dissertation: Behavior of Piled Raft Foundation in Partially Saturated Soils , Salman Alrubaye

Dissertation: Techno-Economic and Life Cycle Assessment of Hydrothermal Processing of Microalgae for Biofuels and Co-Product Generation , Andrew P. Bessette

Thesis: The Evaluation of Enhancing Biological Phosphorus Removal and Improving Settleability Using Mainstream Hydrocyclones for External Selection , Amanda Carrie Ford

Dissertation: Value Added Products From Lignin and Biomass Derivatives , Chen Li

Dissertation: Predicting Effects of Climate Change and Sea Level Rise on Hydrologic Processes in a Mid-Atlantic Coastal Watershed , Rui Li

Thesis: Flexural Behavior and Strength of Doubly-Reinforced Concrete Beams with Hollow Plastic Spheres , Rutvik R. Patel

Thesis: Modeling Effects of Rainwater Harvesting Systems on Water Yield Increase and Non-Beneficial Evaporation Reduction to Sustain Agriculture in a Water-Scarce Region of China , Tennille Wade

Theses/Dissertations from 2017 2017

Thesis: A Comparative Study of the Effects of External Selection on Settleability and Formation of Aerobic Granular Sludge , Tyler A. Brickles

Thesis: Vulnerability Assessment of Critical Bridges in the Hampton Roads Region of Virginia to Storm Surge Flooding under Sea Level Rise , Luca Castrucci

Dissertation: Behavior and Strength of RC Spandrel Members Under Unsymmetrical Bending and Torsion Including CFRP Retrofitting , Muhammad Fahim

Dissertation: Behavior and Strength of Non-Prestressed and Prestressed Hillman Composite Beam Including CFRP Retrofitting , Wajid Khan

Thesis: Investigation and Analysis of the Fluctuating Brominated to Total Trihalomethane Ratio in the Virginia Beach Distribution System , Christopher Steven Mihalkovic

Dissertation: Investigating Physical Processes Associated With Chesapeake Bay and Changjiang Estuary , Arash Niroomandi

Dissertation: Simulated Dynamics of Soil Water and Pore Vapor in a Semi-Arid Sandy Ecosystem , Shohreh Pedram

Thesis: Quantifying Cyanide Inhibition of Nitrification and Developing Cost-Effective Treatment Processes , Germano M. Salazar-Benites

Dissertation: Efficient Algorithms for Solving Size-Shape-Topology Truss Optimization and Shortest Path Problems , Gelareh B. Sanjabi

Dissertation: Holistic Approach in Microalgae Conversion to Bioproducts and Biofuels Through Flash Hydrolysis , Ali Teymouri

Thesis: A Household Daily Non-Mandatory Activity Participation and Duration Modeling Accounting for Person Level Budget Constraints , Ivana Vukovic

Thesis: Quantifying Pollutant Removal Rates of Bioretention Basins as a Stormwater Best Management Practice , Evan Nathanial Waagen

Theses/Dissertations from 2016 2016

Dissertation: Dynamic Elasto-Plastic Behavior of Steel Building Sub-Assemblage Including CFRP Retrofitting Under Impact Load , Ali Mohammed Salih Aloosi

Dissertation: Methodologies for Estimating Traffic Flow on Freeways Using Probe Vehicle Trajectory Data , Khairul Azfi Anuar

Thesis: Impacts of Operating Parameters on Extracellular Polymeric Substances Production in a High Rate Activated Sludge System with Low Solids Retention Times , Matthew S. Elliot

Dissertation: Efficient Domain Decomposition Algorithms and Applications in Transportation and Structural Engineering , Paul W. Johnson III

Thesis: Changing Trends in Wave Heights in the U.S. Mid-Atlantic Region , Hillary Lane

Thesis: Longitudinal Tidal Dispersion Coefficient Estimation and Total Suspended Solids Transport Characterization in the James River , Beatriz Eugenia Patino

Thesis: Effects of Surrounding Water Table on a Forested Wetland Habitat in East Coast of Virginia , Lane Stokes

Thesis: Investigating the Relationship Between Latent Driving Patterns and Traffic Safety Using Smartphone-Based Mobile Sensor Data , Kenneth Wynne

Thesis: Global Sensitivity Analysis of Mat Foundation Behavior by Using Finite Element Modeling , Yang Zhao

Theses/Dissertations from 2015 2015

Thesis: Experimental and Predicted Behavior of FRP Beam-Columns Including Retrofitting , Ali Al-Huazy

Thesis: Analyzing Driver Behavior and Traffic Flow Breakdowns at the Hampton Roads Bridge Tunnel , Michelle L. Allen

Thesis: Behavior and Strength of Pultruded FRP I-Section Columns Including Uniaxial and Biaxial Bending , Emad M. Amin

Thesis: Techno-Economic Analysis of Protein Concentrate Produced by Flash Hydrolysis of Microalgae , Alexander Nana Yaw Asiedu

Thesis: Life Cycle Assessment Using Argonne GREET Model of Algae Based Biofuels Produced Using Flash Hydrolysis Process , Andrew P. Bessette

Thesis: A Modified Rank Ordered Logit Model to Analyze Injury Severity of Occupants in Multi-Vehicle Crashes , Shelley Bogue

Dissertation: Polychlorinated Biphenyl Source Identification in Fish Tissue Using a Multivariate Statistical Evaluation of Congeners and Stable Isotope Ratio Mass Spectrometry , William Edward Corl III

Thesis: Adsorption-Style Activated-Sludge Is It a Practical Treatment Process in North America? , Jon DeArmond

Thesis: Impact of Limited Organic Carbon Addition on Nitrogen Removal in a Mainstream Anammox Moving Bed Biofilm Reactor , Johnnie Wayne Godwin

Dissertation: Inelastic Behavior and Strength of Steel Beam-Columns with Applied Torsion , Mamadou Konate

Dissertation: A Risk Assessment of the Impacts of Coastal Flooding and Sea Level Rise on the Existing and New Pump Stations 113, Norfolk, VA , David A. Pezza

Dissertation: Hydrothermal Catalytic Liquefaction and Deoxygenation of Biomass for Renewable Fuel Production , Sergiy Popov

Thesis: Accuracy Comparison of Numerical Integration Algorithms for Real-Time Hybrid Simulations , Ganesh Anant Reddy

Dissertation: Characterizing Queue Dynamics at Signalized Intersections From Probe Vehicle Data , Semuel Yacob Recky Rompis

Thesis: Comparing Nutrient Recovery via Rapid (Flash Hydrolysis) and Conventional Hydrothermal Liquefaction Processes for Microalgae Cultivation , Caleb Richard Talbot

Theses/Dissertations from 2014 2014

Thesis: Evaluation of the EPA SWMM Model to Simulate Low Impact Development Features in an Urban Stormwater Environment , Holly Ann Carpenter

Dissertation: Flash Hydrolysis of Microalgae Biomass for Biofuels Intermediates Production, Protein Extraction, and Nutrients Recycle , Jose Luis Garcia Moscoso

Thesis: Anammox Polishing in Mainstream Wastewater Treatment to Meet Stringent Ammonia and Total Nitrogen Limits , Rebecca Mary Holgate

Thesis: Effective CFRP Retrofitting Schemes for Prestressed Concrete Beams , Herish Abdullah Hussein

Dissertation: Feasibility of Mainstream Nitrite Oxidizing Bacteria Out-Selection and Anammox Polishing for Enhanced Nitrogen Removal , Pusker Raj Regmi

Dissertation: Stormwater Infrastructure Optimization on Conjunctive Improvements , Mohammad Hussin Shar

Theses/Dissertations from 2013 2013

Thesis: Linear Programming Algorithm with Mixed Real-Integer Variables in MATLAB Environments , Gelareh Bakhtyar

Thesis: Operation and Modification of a B-Stage for Efficient Nitrogen Removal in an A/B Process Pilot Study , Ryder Bunce

Dissertation: The Role of Proximity in Reducing Auto Travel: Using VMT to Identify Key Locations for Development, from Downtown to the Exurbs , Robert B. Case

Dissertation: Watershed-Scale Hybrid Stochastic-Deterministic Modeling Framework and Diffused Sources Superpositioning , Ruby Juvah Damalie

Thesis: Denitrification and Biological Phosphorus Removal Using Focused, Pulse-Treated, Thickened Waste Activated Sludge as an Internal Carbon Source , Holly Anne Hillard

Thesis: Evaluating Alternatives for Augmented Water Quality Improvement Utilizing Oyster Restoration as Best Management Practice (BMP) , Stephanie Roberts Long

Dissertation: An Investigation of Pavement Distress Variables on Crash Outcomes Using Hierarchical Generalized Linear Regression Modeling , Robert Alan Morgan

Thesis: Optimization of DEMON® Sidestream Treatment and Potential for Anammox Mainstream Bioaugmentation , Andrea Lauren Nifong

Dissertation: Dimensionless Criteria for Selecting Tidally-Influenced Advective-Dispersive Desalination Brine Mixing Plume Characterization Models , Alireza Shahvari

Dissertation: Exploring Travel and Activity Behavior in Transit-Oriented Developments: Insights Into Transportation Benefits and Travel Demand Modelling , Sanghoon Son

Dissertation: Thermo-Elasto-Plastic Behavior of Biaxially Loaded Steel Beam-Columns Inducing Those From World Trade Center Towers , Yanhong Zhao

Theses/Dissertations from 2012 2012

Dissertation: Evaluation of Hydraulic Conductivity of Non Aqueous Phase Liquids in Partially Saturated Soils , Chijioke Ekeleme Akamiro

Thesis: Integrating Probe Vehicles and Stationary Detector Data to Construct Accurate Cumulative Curves to Study Bottlenecks , Khairul Azfi Anuar

Dissertation: Efficient Stand-Alone Generalized Inverse Algorithms and Software for Engineering/Sciences Applications: Research and Education , Subhash Chandra Bose S V Kadiam

Thesis: Biaxial Bending and Lateral-Torsional Instability of Imperfect FRP I-Beams Including Effects of Retrofitting , Jodi Marie Knorowski

Dissertation: The Modified Coastal Storm Impulse Parameter , Sayed Gholamreza Mahmoudpour

Thesis: Demand Responsive Signal Control Strategy (DRSC) Incorporating Queue Length Information in Real-Time Signal Control , Rahul Rajbhara

Dissertation: Static and Impact Load Response of Reinforced Concrete Beams and Slabs with NSM-CFRP Retrofitting , Nakul Ramanna-Sanjeevaiah

Dissertation: Spatial Analysis of Travel Behavior and Response to Traveler Information , Xin Wang

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Environmental Modelling and Data Sciences

We are living in an age of data : sensors, satellites and stations record environmental states around the world, in many systems.

Environmental Modelling and Data Science aims at equipping the students with a wide and relevant range of computer-based skills to address research and application challenges in environmental science. Ever-larger data sets from automatised data collections (remote sensing, omics) and large research and public data collections (weather stations, iNaturalist, ebird) require appropriate data science and modelling competences . As these methods are in constant flux, the profile Environmental Modelling and Data Science (EMDS) develops fundamental skills in statistics and programming and combines them with concrete studies and analyses .

Important facts about this major

Teaching form: on-campus

Pace of study: fulltime

Study location: Freiburg

Start: only in winter term

Duration: 4 Semester

Application periode:  march 1st - may 15th

ECTS: 120 ECTS (80ECTS modules, 10 ECTS Internship, 30ECTS Thesis)

Costs: 161 semester fees, 1.500€ study fees (only non-EU students!)

• Uses process models of environmental systems and statistical, data-driven approaches.
• Teaching in R and Python.
• Profile track is embedded in a Master programme of environmental topics and interests.

Program overview  pdf

Module details

1)    environmental statistics.

introductory module beyond multiple regression builds on and extends statistical knowledge and its application standard machine-learning approaches in R or Python joint module with students from other profile tracks

2)    Environmental monitoring, data analysis and visualisation

• automatised data handling
• data based setup and management
• data-wrangling and related visualisation

3)    Earth System Modelling

• formulating processes as ODEs
• simulating (coupled) (partial) DEs using R/Python
• programming (modules of) a simple ecosystem model

4)    Ecosystem Functioning

• Background knowledge on important processes in ecosystems
• overview of ecological approaches and systems
• link to other profile tracks, and to their students

5)    Remote Sensing & Geoinformatics  

• obtaining and processing remote sensing data
• using open satellite data to quantify environmental processes and states
• automatised big-data processing

6)    Applied Land Surface Modelling

• linking ecosystem models to remote sensing
• parameterisation of land-surface models
• scenario analysis with land-surface models

7)    Bioinformatics

• acquiring omics data from data bases and services
• analytical pipelines for omics data
• combining omics data to address environmental questions

8)    Modelling Environmental Systems

• handling large models for environmental systems
• sensitivity analysis and model application
• examples from agriculture and forestry

9)    Capstone Project

• practicing collaboration with applied environmental scientists
• joint project with forestry, hydrology or environmental science students
• reproducing and re-evaluating published (process or statistical) modelling studies

10)   Advanced Statistics

• generalised mixed-effect models, analysis of temporally/spatially correlated data
• opt. advanced machine learning: validation techniques, error propagation
• opt. neural network architectures and their application

Target Group

Students with a BSc in environmental or natural science or engineering, who want to develop the skills required as a modeller and data scientist in the environmental sciences. An affinity and knowledge for statistics and data should be present. Basic knowledge of R or Python is assumed. (GLM: multiple regression for non-normal data, reading in data, plotting, simple data wrangling, simple statistical analyses, key concepts and practice with raster and vector data)

Career Opportunities

Environmental data, and their link to environmental process models, are at the heart of both academic and administrative work. Geological, hydrological, biological agencies have huge difficulties in keeping up with the development of new methods and require data scientists with understanding of typical environmental data and their context.

In academia, the number of automatised data collections is continuously increasing, making data handling and analysis a central skill. While the actual scientific question comes with knowledge of the field, methods required for addressing these questions build on the content of this profile track.

Application and Requierments

Requirements 

• Degree with grade point average of at least 2.5
• 50 ECTS in natural sciences and ecology
• 20 ECTS in statistics and geomatics
• Knowledge of a higher programming language

      -> Basic knowledge of "R" is assumed!

Application periode: March 1st - May 15th

Application portal:  HISinOne

further information:  "Application"

Coordinator & Contact

Prof. Dr. Carsten Dormann

Department of Biometry and Environmental System Analysis

Studiengangkoordination

Bewerbungsmanagement

Sunniva Dalmühle//Kristin Goldbach

info-msc-umwelt[at]unr.uni-freiburg.de

Studienfachberatung

Dr. Heiko Winter

beratung-msc-umwelt[at]unr.uni-freiburg.de

Universität Freiburg Fakultät für Umwelt & Natürliche Ressourcen Tennenbacher Str. 4 D-79106 Freiburg

Environmental Ethics: Modelling for Values and Choices

• Open Access
• First Online: 30 September 2022

Cite this chapter

You have full access to this open access chapter

• Wei-Ta Fang   ORCID: orcid.org/0000-0002-4460-0652 4 ,
• Arba’at Hassan 5 &
• Ben A. LePage   ORCID: orcid.org/0000-0003-3155-7373 4 , 6  

Part of the book series: Sustainable Development Goals Series ((SDGS))

9359 Accesses

The framework of environmental ethics is built, challenging the way we view or interpret environmental education through the eyes of different stakeholders. In this chapter we consider aspects of land and ecological ethics as well as pedagogy as they relate to environmental ethics to form modelling. We classify that environmental ethics are “anthropocentrism,” or the human-centered approach; “biocentrism,” or the life-centered approach; and “ecocentrism,” or the ecosystem-centered approach. Environmental paradigms are explored, which include the theories and practices regarding to environmental ethics, new environmental, ecological and behavioral paradigms, and paradigm shifts. Regarding to our choices from environmental values and concerns, we may use a model to detect our problem-solving approach to identify environmental problems we face and, find our practical needs and implement solutions toward sustainability.

Ecocentrism goes beyond biocentrism with its fixation on organisms, for in the ecocentric view people are inseparable from the inorganic/organic nature that encapsulates them. They are particles and waves, body and spirit, in the context of Earth’s ambient energy. J. Stan Rowe, Ecocentrism, 1994.

You have full access to this open access chapter,  Download chapter PDF

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Environmental Ethics

1 what is environmental ethics.

Environmental ethics is an epistemological doctrine that is philosophically grounded that explores the relationship between humans and the environment. Many philosophical hypotheses relative to environmental ethics has established such as: All things have intrinsic value (Mazzucato 2020 ; Carney 2021 ). The social and natural sciences clearly have an influence on ethics (Bellah 1983 ; Schwartz 1987 ). Where is the ethics coming from, and when? Following birth of the life myth, we become detached from the warmth of motherhood, resulting in alienation and anxiety. We are born, grow, age, and before we die, we all look for ways to find connections between humans. Finding connections gives us status, identity, and value in the world that is closely related to Mother earth.

1.1 Beliefs of Land Ethics

Land ethics is a theory of environmental philosophy and considers how humans view and/or use the land in a moral sense (Callicott 1989 , 2010 ). The term was coined by Aldo Leopold in A Sand County Almanac (1949) and in the middle twentieth century, it was considered a classic text of the environmental movement (Callicott 2005 ; Callicott et al. 2011 ). Leopold believed humans urgently needed a new ethic that dealt with the relationship between humans and land. He wrote ( 1949 ):

The first ethics dealt with the relation between individuals; the Mosaic Decalogue is an example. Later accretions dealt with the relation between the individual and society. The Golden Rule tries to integrate the individual to society; democracy to integrate social organization to the individual….There is as yet no ethic dealing with man’s relation to land and to the animals and plants which grow upon it. The land-relation is still strictly economic, entailing privileges but not obligations….

This narrative provided an ecology-based land ethic that protected nature, developed the idea of a self-renewing ecosystem. and rejected the human-centered view of the environment. A Sand County Almanac is the first systematic introduction of an ecologically-centric method of environmental protection. While Leopold is credited with coining the term land ethics, numerous philosophical theories that explain how humans should treat the land followed (Callicott 1989 ). Economically-based utilitarianism, libertarianism, egalitarianism, and ecological land ethics were all considered (Callicott 1989 ; Noll 2017 ). Despite the plethora of definitions for the same concept, the UNEP in 1972 adopted Leopold’s definition for designing the curriculum content for environmental education in different countries (Gruenewald 2004 ; Tete and Ariche 2021 ).

1.2 Beliefs of Anthropocentric Value System

The shift in emphasis from humans to nature is important. Theists believe that human beings exist on the earth and that they are superior to other forms of life and occupy a superior position. Therefore, all other forms of life are present to serve humanity’s needs because human beings are superior and created in the image of God (Burdett 2015 ; Kilner 2015 ). But this doctrine is challenged by biblical commentators who believe that God wants human beings to be stewards or protectors of life on earth, thereby highlighting the plasticity of biblical interpretations.

Aristotle and Kant believed that only humans are moral creatures, because only humans have the ability to think rationally (Hurka 1996 ; Taylor 2010 ). This point of view is now referred to as anthropocentrism.

However, compared with the arbitrariness of western culture, oriental culture has its modesty. As Mencius (孟子)(Meng Tzu, 372– 289 BC), one of the Chinese Confucian philosophers, believed that humans and animals are very different from each other and only humans have a moral nature. Most people do not know the value of kindness. So, Mencius in the book of Mencius said (Lau 2004 ; Huang 2010 ): “Slight is the difference between man and beasts.” Therefore, “They often abandon benevolence and morality, and only gentlemen know morality valuable.” He tried to defend his claim of the innate goodness of human beings and claimed the human heart contains the sprouts of the four central Confucian virtues: benevolence ( ren ), righteousness ( yi ), propriety ( li ), and wisdom ( zhi ). Mencius believed that these sprouts needed to be nourished. The Taoist Zhuangzi (莊子)(Chuang Tzu, 369–286 BC) also often used animals to metaphorically view the world in his dreams. For example, he used the image of butterfly in his dream and when he woke (Möller 1999 ; Lee 2007 ), he thought about his lucid self and said: “Is myself coexisted in a butterfly’s dream?” Zhuangzi argued that he’d rather to be a turtle playing in the mud. He talked to the vice-chancellor:

I am told there is a sacred tortoise offered and canonized three thousand years ago, venerated by the prince, wrapped in silk, in a precious shrine on an altar in the temple. What do you think? Is it better to give up one’s life, and leave a sacred shell, as an object of cult in a cloud of incense for three thousand years, or to live as a plain turtle dragging its tail in the mud? “For the turtle,” said the vice-chancellor, “better to live and drag its tail in the mud!” “Go home!” said Zhuangzi. “Leave me here to drag my tail in the mud.”

Willing to be an official is equated with being a companion with a tiger, because being an official is a loss of human freedom. Literally it means staying close to the emperor and serving him is as risky as living with a tiger, for he may be killed by the emperor anytime. It carries a sense of worry and concern. Therefore, neither traditional Chinese Confucianism nor Taoism is the anthropocentric theorist in their human-centered theory. Even Zhuangzi’s ideal is to learn swamp pheasants. “Pheasants in swamps have stepped ten steps, with one peck; one hundred steps, with one drink, and the muddy pheasants do not care about living in a fed cage associated with animal husbandry.” This is a thought that pursues the freedom of humans and species (Wenzel 2003 ), rather than the only thought that respects humans. However, Zhuangzi is also similar in his anthropocentric theory on a dualism from this “radical critique of power and ultimate spiritual life” defined through human criteria from his theory (Kim 2009 ) since some other scholars regarding Zhuangzi as anti-anthropocentric thinker (Parkes 2013 ; D’Ambrosio 2022 ). This is quite controversial for an ancient Chinese scholar from his paradoxes of comments on radical critique of power and ultimate spiritual life in Western philosophy (Barrett 2011 ; Moeller 2015 ).

1.3 Beliefs of Biocentric Ethic Value System

In the past, this discourse on anthropocentrism had not been challenged until modern times. However, because of Darwin’s theory of the evolution, the position of humans as a “superior” species has changed. Biocentrism is a moral point of view that extends the intrinsic value of life to all living things. The center of life is to explain how the earth works, especially what is related to biodiversity. Biocentrism encompasses all living things, extending the status of moral objects from humans to all living things in nature. As such biocentric ethics requires rethinking the relationships between humans and nature because nature no longer exists exclusively for the use or consumption by humans. Biocentrists believe that all species have intrinsic value and that humans are not morally or better than other species. The four pillars of biocentrism are:

Humans and all other species are members of the earth;

All species are part of an interdependent system;

All creatures pursue their own advantages (good) in their own way; and

Human beings are not better than other creatures.

Biocentrism does not imply an idea of equality between animals, as this phenomenon has not been observed in nature due to differences in their capacities (Singer 1997 ). Biocentrism is based on natural observations, not biased in favor of the human (Sterba 1998 ). Biocentrism should not treat humans as superior species (Sterba 1995 ). Proponents of biocentrism often promote biodiversity conservation, animal rights, and environmental protection. Biocentrism combines deep ecology with opposition to industrialism and capitalism (Johns 1992 ; Orton 1996 ; Barnhill and Gottlieb 2010 ; Farida et al. 2019 ) (Fig.  6.1 ).

Proponents of biocentrism often promote biodiversity conservation, animal rights, and environmental protection (Photo by Max Horng)

1.4 Beliefs of Ecological Ethic Value System

Biocentrism contrasts strongly with anthropocentrism (Flores and Clark 2001 ). Anthropocentrism is centered on human values; however, biocentrism extends intrinsic value to the entire natural world (Bennett 2004 ). Because humans are one of many species in the world’s ecosystems, any behaviors that negatively affect these ecosystems then negatively impact humans. Therefore, do we maintain a biocentric worldview or expand the moral category in the world? It depends how to extend all things to have intrinsic value to strengthen the concept of ecological ethics (Sandler 2012 ).

The debate on environmental ethics with respect to an Interconnected World has become increasingly acute because of its interconnectedness and vulnerability to the ecosystem (Droz 2021 ). We previously intimated humans are part of nature and now we are intimating there is a human ecosystem . The human ecosystem should be regarded as an organizing concept in ecosystem management (Machlis et al. 1997 ).

What is Ecocentrism? Do we need to concern humans? Ecocentrism is the broadest term for worldviews that also recognize intrinsic value (Bennett 2004 ) in all lifeforms and ecosystems themselves, including their abiotic components (Washington et al. 2017 ). Proposed by 1990s (de Figueiredo et al. 2022 ), Rowe ( 1994b ) declared ecocentrism puts a new interpretation on community from traditional ecological knowledge. The ideas of ecocentrism are focused on the entire biological community and committed to maintaining the composition and ecological processes of the ecosystem (Shrivastava 2008 ; Fios 2019 ). Therefore, ecocentric approach to environmental ethics uses an eco-holistic perspective with the widest visions (Steverson 1991 ).

However, how is it different from biocentrism? Ecocentrism goes beyond biocentrism since ecocentrism having the widest vision. Biocentrism is implicitly establishing an equality among life-forms that favors or values all animals. Ecocentrism has been concerned about taking a broader view of our common home—planet Earth. Why eco-centrism is the key pathway to sustainability (Washington et al. 2017 )? In a sense, Washington et al. ( 2017 ) declared eco-centrism has been with humanity since it underpins what can be called the ‘ old ’ sustainability . Why we need to examine Leopold’s principle of eco-centrism? Is this an ‘old’ sustainability to be detected from conservation biology? To answer this question, we may read one of the papers: Making the law more ecocentric : Responding to Leopold and conservation biology (Kuhlmann 1996 ).

Leopold ( 1949 ), recognized that all species, including humans, were the products of long-term evolutionary processes that interrelated in their life processes. His views on land ethics and environmental management are the key elements of ecological ethics. Rolston ( 1975 ) considered the responsibilities of the biota in their ecosystems, illustrating the philosophy of nature, and suggested nature needed to be protected according to ethical decisions and processes.

Ecocentrism is not an argument that all living things are of equal value (Washington et al. 2017 ). It does not deny the existence of countless important core issues, such as Nature Needs Half movement (Kopnina et al. 2018 ). Unlike many species, human beings are a resilient species in a rigid situation of under a climate-mediated mechanical change (Madin et al. 2008 ). However, human beings need to learn how to survive from their social networks and their living environment. Therefore, an ecocentric epistemology for ecosocialism can be reproduced social relations, sustaining habitat for sustainability (Salleh 2022 ). We may consider that Disinger ( 1990 ) described environmental world views as placed on an “ecocentric-anthropocentric continuum.” While the dominant social paradigm follows the anthropocentric view. In addition, ecocentric practices also offer an alternative episteme for building a life-affirming civilization from resilience ethics (Bravo-Osorio 2022 ). This is one of the sound-science roots to support of a growing number of conservationists for ecocentric-based approaches addressing human concerns and directing human action regarding to the environment by the concepts of social-ecological resilience (Piccolo et al. 2018 ).

1.5 From Deep Ecology to Animal Rights

Deep ecology is opposed to the worldviews that emerged in the eighteenth century and proponents believe that the world is not a freely exploitable resource for humans (Gladwin et al. 1995 ). Therefore, the ethics of deep ecology holds that the survival of any ecosystem depends on this struggle for their lives for its overall well-being (Næss 1973 ; Bradford 1989 ). Deep ecology states (Næss 1973 , 1985a , b , 1986 , 1987 , 1989 ):

The life of human beings or other living things on the earth itself have “value.” This life value is not determined by the contribution of the non-human world to the human world;

Life forms have value in themselves; moreover, the richness and diversity of life forms contribute to the “realization” of these life values in themselves (Næss 1986 , 2011 );

Human beings have no right to reduce richness and diversity, except for the essential basic needs for sustaining life;

The prosperity of human life and culture is compatible with the small human p opulation. To maintain the abundance of other organisms, a small population needs to be maintained;

At present, human beings’ excessive interference with other living things is rapidly deteriorating;

Humans must change policies that affect basic economic, technological, and ideological structures. As a result, the situation will be very different from what it is now;

Based on the natural value of life, the change in ideology is mainly due to the appreciation of “life quality” (Næss 1986 , 2011 ), not the pursuit of a higher standard of living. We will be profoundly aware that there is a difference between “big ness” and “greatness.” (McElroy 2002 ; Næss 2011 ); and

Anyone who agrees with the above viewpoints has the obligation to participate directly or indirectly in the necessary reforms (Næss 1986 , 2011 ).

Deep ecologists have written an ambitious statement to change the current political and economic system (Devall 1980 ; Næss 1986 ; McLaughlin 1993 ; Pepper 2002 ; Zimmerman 2020 ). Næss ( 1984 , 1986 ) emphasized the intrinsic value based on the relations to individual living being with its sense in a holistic system (Katz 1987 ). He believed that the connection of ecological phenomena affects the whole body in a Gaia sense (Næss 1995 ). Therefore, he believed that human beings should adjust their attitudes towards nature and use ecological worldviews for macro-control, otherwise the global environment will suffer.

Bill Devall (1938–2009) and George Sessions (1938–2016) cite New Physics in their book entitled Deep Ecology , said the ultimate norms of deep ecology suggest a view of the nature of reality in 1985 (Devall and Sessions 1985 ), described the new physics as the view of reality (Sessions 1987 ) as smashing Cartesian (René Descartes, 1596–1650) and Newton’s (Sir Isaac Newton, 1643–1727) cosmic vision.

Devall and Sessions ( 1984 ) agreed to deny the empty image of nature as created by the human, and the New physics created by them denies that nature is a simple linear causal machine. Devall and Sessions ( 1985 ) argued that nature was in a state of constant change and rejected the notion that the observer was independent of the environment. They referred to the new physics presented in The Tao of Physics and the impact of new physics on the interconnectedness of metaphysics and ecology (Capra 1975 ). According to Capra, this should make deep ecology the framework of future human society. Devall and Sessions ( 1985 ) talked about ecological science itself and emphasized the links between ecosystems where is thus closely related to a rigorous determinism (Capra and Luisi 2014 ). They point out that in addition to scientific viewpoints, ecologists and natural historians have developed a profound ecological consciousness, including political and spiritual consciousness (Devall and Sessions 1985 ). They criticize anthropocentrism because ecocentrism is a discourse beyond human perspective.

Capra believed that this kind of complex network organization model will lead to a novel and systemic way of thinking. The ecosystem will be a form of autopoiesis and the structure and function of all ecosystems are complementary so they are indispensable (Fig.  6.2 ). Ecosystems are unbalanced dynamic structures, but at the same time they can maintain dynamic stable structures. At a time when the ecosystem is constantly seeking to improve itself, continuously absorbing energy and matter from the environment, and releasing “entropy” to the environment. The ecosystem can even adopt a model of environmental destruction that exchanges “entropy” with the external environment to maintain its own stable. This presumes that the biotic elements of an ecosystem have the ability to adapt (Mersereau 2016 ; Feliciotti et al. 2018 ). Finally, the ecosystem uses social networks for system information exchange and repairs between systems (Capra and Luisi 2014 ).

The ecosystem will be a form of autopoiesis, and the structure and function of all ecosystems are complementary, so they are indispensable. The ecosystem uses social networks for information exchange with species like humans and animals. ( Bambusicola sonorivox , as the common name Taiwan bamboo partridge, is a subspecies bird endemic to Taiwan (Hong et al. 2014 ) occurs at Qixingshan Trail, Yangmingshan National Park, Taipei, Taiwan) (Photo by Max Horng)

Deep ecology affects the Animal Liberation movement (Flükiger 2009 ). Experts on animal liberation, such as Tom Regan (1938–2017) and Peter Singer (1946– ), put forward the theory of animal protection. Regan inferred from the theory of benefit that human beings are not morally unique and equal judgments based on theory (Singer 1975 ). Regan wrote The Case for Animal Rights , which argues that humans cannot use rationalism as the principle of supremacy and only grant rights to those who have reason. In fact, these rights should be given to infants, vegetative, and non-human. These rights are intrinsic values, and humans should put the case of animals in moral considerations (Regan 1983 ).

Peter Singer’s 1975 book, Animal Liberation , severely criticized anthropocentrism, and Singer disagreed with deep ecology’s belief in the “inner value” of nature (Vilkka 2021 ). Singer took a more practical stand, called “effective altruism,” meaning that protecting animals can bring greater benefits from utilitarian basis (Regan 1980 ).

Deep ecology and animal rights are in relation to environmental education (Kopnina and Gjerris 2015 ). However, animal welfare (AW), animal rights (AR), and deep ecology (DE) have often been absent within environmental education and education for sustainable development (Kopnina and Cherniak 2015 ). Therefore, we may try to realize the concept from “biocentric equality,” according to Devall and Sessions, entails that all organisms and entities in the ecosphere, as parts of the interrelated whole, are equal in intrinsic worth (Devall and Sessions 1985 ) and the nature of reality , ultimately, “intimately connected” with the environment (Borgmann 1995 ). Environmental education, therefore, is intimately related and their connection with nature should be intimately involved in the learning process from animal welfare (AW), animal rights (AR), and deep ecology (DE).

2 Environmental Paradigm

We talked about the philosophical basis of environmental ethics in the aforementioned section, mainly to provide philosophical concepts for environmental education. In addition, this section discloses a new environmental paradigm in modelling, utilizing as a measuring scale of the measurement for an evaluation approach on the environmental education research.

Do we need environmental paradigm? How do we need, and why? What is the relationship from environmental ethics to environmental paradigm? It should be noted, however, that a few studies have examined the relationship between environmental ethics and environmental paradigm (Dunlap and Liere 1984 ), but it does not seem obvious that we, humans, may not clearly understand our relationships with the biosphere where we depend on, and we also do not find out the fate and all well-beings on earth in the future.

Are we smart? May be not. Or we are only just a bug living on earth?

From the previous discussion, environmental ethics is a basic model of human morality; it belongs to a kind of “self-respect” and “external respect” for things that are deep inside and exposed.

In this section we will talk about “paradigm.” What is paradigm? The term of paradigm in the American Heritage Dictionary of the English Language is defined as A set of assumptions, concepts , values , and practices that constitutes a way of viewing reality for the community that shares them, especially in an intellectual discipline . A paradigm for model could be fitting with applications to a real world. Therefore, in social science we do not replace necessarily an old one; various paradigms could be existing side-by-side (Kornai 2002 ). We try to extend the models for our multidimensional paradigm in many refining works on our studies. We tried to study conceptual model for environmental ethics to be constructed in several years.

However, what is a “model”?

Examples of a model include all concepts, assumptions, values, approaches, and benchmarks used to test truth in human activities. The word paradigm is derived from the Greek paradeigma and has the meaning of pattern, model, or plan, and refers to all applicable experimental situations or procedures. Therefore, a paradigm could be a significant scientific view of how to look at the world, which should be recognized by a community that provides a model. Plato (429–347 BC) coined the word paradigm, hoping to use it in the idea of its ideas or forms to resolve the way in which disputes over truth are discussed. The German philosopher Georg Lichtenberg (1742–1799) believes that the “paradigm” is an exemplary achievement. We can use this achievement as a model and use an analogous process to answer questions. Later, Ludwig Wittgenstein (1889–1951) talked about “paradigms” in the concept of language games, hoping to follow the process of analogy, let the questions be answered, and seek the truth in this world. This truth-based paradigm has allowed Riley E. Dunlap, a professor of sociology at Oklahoma State University, to study the nature and origin of environmental problems for 40 years.

2.1 Traditional Beliefs and Values

Dunlap and Liere (1984) emphasized the links between environmental issues, public opinion, and environmental decision-making. When he developed the New Environmental Paradigm Scale, he called the opposing paradigm the Dominant Social Paradigm.

What is Dominant Social Paradigm?

Dominant Social Paradigm (Pirages and Ehrlich 1974 ) was coined as one of the world views in human society, representing that humans are superior to other all other species , the Earth provides unlimited resources for humans, and that progress is an inherent part of human history . This term was developed by Pirages and Ehrlich ( 1974 ) and has been elaborated further by Dunlap and Liere ( 1984 ). In their studies, Dominant Social Paradigm of western industrial society could be containing political, economic, and technological institutions from a capital domain. It is these institutions that determine both the quality of life and environmental constructs within any society (Kilbourne 2006 ).

Dunlap examined the associations between traditional American beliefs and values (e.g., individualism, laissez-faire, and progressivism), environmental attitudes and behaviors (Dunlap 2022 ). Concerned about the beliefs and values of the Dominant Social Paradigm and his concern for environmental quality, Dunlap and his colleagues developed core elements for measuring environmental models and worldviews, and has applied his work in many countries (Dunlap et al. 1983 ; Dunlap and Liere 1984 ; Dunlap 2022 ).

2.2 New Environmental Paradigm

Dunlap’s idea of a New Ecological Paradigm was developed in the 1980s, after he developed a New Environmental Paradigm Scale during 1976 and later published in 1978 (Dunlap and Van Liere 1978); and later published the New Ecological Paradigm Scale in 2000 (Dunlap et al. 2000 ; Dunlap 2008 ). His work is currently focused on an analysis of public opinion on climate change, the polarization of climate science and policy, and the analysis of negative sources and nature of climate change (Dunlap and McCright 2015 ).

We tried to introduce the New Environmental Paradigm (NEP) scale. The earliest model of environmental norms was proposed by environmental sociologist Riley Dunlap and his colleague (Dunlap and Van Liere 1978 ), and it’s currently the most widely used environmental attitude assessment tool (Lalonde and Jackson 2002 ). Lalonde and Jackson ( 2002 ) argued that the NEP scale is limited with respect both to the anachronistic wording of items and its inability to capture people’s increasingly thorough understanding of the nature , severity, and scope of environmental problems over the last four decade from now. The new environmental paradigm model is centered on human development and emphasizes the interaction between humans and nature. After presenting the first version of the scale, Dunlap merged it into a streamlined version of a set of six items by modifying the vocabulary, and a simplified version was used by John Pierce, who shared the information.

New Environmental Paradigm is related to the responses to individual environmental attitude questions (Pienaar et al. 2013 ). Environmental attitudes in our studies are measured by one-order constituents such as caring or not caring in a moral root. Later, environmental attitudes adopted a multi-component concept and were adopted in many studies (Cherdymova et al. 2018 ; Sorokoumova et al. 2021 ). Therefore, we may propose this New Environmental Paradigm model to be refined through history lesson that has some relevance on their multi-component concept to the topic from an idea of the paradigm shift in the theories of behaviors.

3 Paradigm of the Theory of Behaviors

We learned from Riley Dunlap’s concepts of “The creation of paradigm” and “searching to the truth” that we need to rely on environmental psychologists to adopt human attitudes and methods that are different from normal science to conduct experiments on human behavior. British scholar Edmund Burke (1729–1797) said (Burke 1790 ):

The world would then have the means of knowing how many they are; who they are; and of what value their opinions may be, from their personal abilities, from their knowledge, their experience, or their lead and authority in this state.

Because, traditionally, we have thought that as long as humans have knowledge, their attitudes and values, will change their behaviors (Fig.  6.3 ). However, this argument is not absolute (Dunlap 1975 ). To explore the relationship between knowledge, attitude, and behavior is to find out whether the relationship has been wrong. That is, verifying that having environmental knowledge does not necessarily affect environmental attitudes, and that having attitudes does not necessarily affect pro-environmental behaviors. The relationship among them is very complicated.

Environmental efficacy by measured the function of behavioral change could be detected a lower level until the effect has reached the bottom from the educational and/or learning market in a civic society and/or at schools. We argued that in this complex area by presenting the conventional models of the linkages between knowledge and behaviors (Kerkhoff and Lebel 2006 ). The filtering down effect warned that the transfer function of the output, i.e., pro-environmental behaviors, less the scaling gain from the input of environmental knowledge investment enforced in a civic society and/or at schools. Marcinkowski and Reid ( 2019 ) argued that many attitude-behavior (A-B) relationships with substantial evidence were determined to be regarding as a relatively moderate strength (Illustrated by Wei-Ta Fang)

The impact of environmental knowledge and environmental attitudes on people’s indirect actions may be greater than that of people’s direct pro-environmental behaviors (Kollmuss and Agyeman 2002 ). Economic factors, social norms, emotions, and internal logic have a great impact on people’s decision on pro-environmental behavior. We conduct a review of human environmental behavior, including good behavior and bad behavior. We answer the question: “Why do we do what we should do?”.

First, the moment a behavior occurs is a neurobiological explanation. That is, what kind of vision, sound, or scent, when a behavior occurs, causes the nervous system to produce this behavior? Then, what hormones respond to the stimulation of the nervous system in human individuals? In these sensory worlds of neurobiology and environmental endocrinology, we can try to explain what thoughts, attitudes, and behaviors will take place in the next moment (Sapolsky 2017 ) (see Fig.  6.4 ).

In these sensory worlds of neurobiology and environmental endocrinology, we can try to explain what gestures and postures will take place in moments beyond planned behaviors (Merlion, as an official mascot of Singapore, named and designed by Fraser Brunner) (Elegant look by model by Chiao-Yen Chang; Photo by Max Horng)

Of course, all behaviors can be traced back to the effects of structural changes in the nervous system, including adolescence, childhood, fetal life, and genetic makeup. Finally, we should extend the perspective of environmental protection to social and cultural factors. Because, how does environmental protection culture shape personal environmental perceptions, and what ecological factors have formed this kind of environmental protection culture? From the perspective of environmental protection, pro-environmental behavior is one of the dazzling human behavior sciences. These issues involve the biophilia hypothesis, social norms, moral obligations, altruism, free will, and human values (Dunlap et al. 1983 ). All the achievements of environmental protection are human performances. We emphasize that practice itself is a symbol of an unknown hero because environmental protection is a nameless and lonely job. The following is explanation of the paradigm of behavioral theories, including theoretical models such as the Theory of Planned Behavior (TPB).

3.1 Theory of Planned Behavior (TPB)

The Theory of Planned Behavior (TPB) is a behavioral decision model that was used to predict and understand human behavior (Ajzen 1985 , 1991 ). The model is mainly composed of environmental attitudes, subjective norms, perceived behavior control, behavioral intentions, and behaviors theory (Fig.  6.5 ). It specifies the nature of the relationship between belief and attitude. According to the model, human evaluation or attitude to behavior depends on their belief in behavior, where belief is defined as the subjective probability that the behavior produces some result. Specifically, the evaluation of each outcome helps shape the behavior. In other words, a positive environmental attitude strengthens the pro-environmental intention.

The theory of planned behavior (Modified after Ajzen, 1991 ; Illustrated by Wei-Ta Fang)

Subjective norm: An individual’s perception of a particular behavior is influenced by the judgment of important others (such as parents, spouses, friends, teachers).

Perceived behavioral control: The degree to which an individual perceives the difficulty of performing a particular behavior. Here we assume that perceived behavioral control is determined by the total set of accessible control beliefs.

In assessing important factors such as normative beliefs, social norms, attitudes, and perceived behavioral control, we may complete the development of the scale under social and cultural causes. While we clarify the causal relationship between important factors, we will understand the importance of social influence.

The concept of social impact is assessed through the social norms and beliefs. Humans’ detailed thinking on subjective norms is based on whether their friends, family members, and society expect them to perform specific behaviors. Social influence is measured by assessing various social groups. For example, we provide in the case of smoking (Ajzen and Manstead 2007 ). Subjective norms from peer groups, including ideas such as: “Most of my friends smoke,” or “I feel ashamed to smoke in front of a group of non-smoking friends,” and subjective norms of the family, such as: “The idea that family members smoke and it seems natural to start smoking; or “My parents were really mad at me when I started smoking”; and subjective norms from society or culture, including things like: “Everyone is against smoking,” and ideas like “we just assume everyone is a non-smoker.”

Although most models are conceptualized in the individual’s cognitive space, planned behavior theory is based on collectivistic culture-related variables to consider social influences, such as social norms and normative beliefs. Whereas, individual behaviors (including health-related decisions such as diet, condom use, smoking cessation, and alcohol consumption), may be built on social networks and organizational knowledge (for example, peer groups, family members, school faculties, and the workplace colleagues). Social influence has a great influence on the Theory of Planned Behavior. Therefore, in the social norms that affect environmental behavior, in addition to subjective norms, describing norms may also be one of the important variables.

At present, the Theory of Planned Behavior has been applied in research fields related to environmental protection and public health (Fang et al. 2017 ; Liu et al. 2018 ; Woo et al. 2022 ) as well as the similar research modelling studies, such as Fang et al. ( 2021a , b ). The research found that the most important psychological variables that affect behavioral intentions are different in various groups and regions. Respondents have different conditions, such as those with a high degree of environmental care. Perceived behavioral control is an important variable, while those with a low degree of attitude are important variables that affect environmental behavioral intentions. In addition, different regions and interviewees have different conditions and the important intermediary variables that directly affect behavior are also different (Bamberg 2003 ). For example, when buying environmentally friendly products. At the national level, attitudes are the most important variable in Spain (Nyrud et al. 2008 ). Take the example of switching to public transportation without a car. In Frankfurt, Germany, perceived behavioral control is the most obvious, and in Bochum, Germany, attitude is the most important variable (Bamberg et al. 2007 ).

The Theory of Planned Behavior holds that subjective norms can directly influence behavioral intentions (Ajzen 1991 ), but does not discuss whether descriptive norms affect behavioral intentions. In terms of environmentally friendly behavior, in recent years, researchers have tended to include description norms and subjective norms (such as the expectations and support of important people around them) as social norms. Social norms affect individual psychological variables, such as social norms affecting attitudes, and attitudes in turn affect environmentally friendly behavioral intentions. A little different from the Theory of Planned Behavior, social norms influence environmentally friendly behaviors in an indirect way (Thøgersen 2006 ; Bamberg and Möser 2007 ; Hernández et al. 2010 ; McKenzie-Mohr 2011 ).

3.2 The Motivation-Opportunity-Abilities Model

The Theory of Planned Behavior emphasizes environmental attitudes, subjective norms, and perceived behavioral control. Another type of integration model is the “Motivation-Opportunity-Abilities” (MOA) model proposed by Ölander and Thøgersen ( 1995 ). The important structural feature of the MOA model is to integrate motivation, habits, and background factors into a single model of pro-environmental behavior. Because environmental protection behaviors are mainly habitual behaviors, they are not necessarily conscious behaviors based on conscious decisions.

Ölander and Thøgersen ( 1995 ) point out that the improvement of behavioral ability can be predicted by combining the concept of capability to strengthen conditions and transforming behavior into a model through opportunity (Fig.  6.6 ). In the model, in addition to the behavioral environmental attitude, subjective norms, and perceived behavioral control are the contents of the original model of planned behavior theory, the MOA model adds the following:

Motivation-opportunity-ability theory (Modified after Ölander and Thøgersen 1995 ; Thøgersen 2009 ; Illustrated by Wei-Ta Fang)

Motivation : As each person’s value system is different, personal needs and desires may affect their behavior in some way. The so-called motivation is the motivation of behavior. Motivation is a prerequisite for generating incentives and rewards through behavior types and behavior outcomes that are beneficial to the environment. Because of praise or other encouragement, human beings can encourage pro-environmental behaviors based on rewards. For example, motivational rewards can be as simple as volunteers’ efforts to promote environmental education and gain recognition from the general public.

Opportunity : Opportunity is a limitation of the availability of time and resources. The opportunity composition of the MOA model belongs to the “objective prerequisites for environmental behavior.” This model also has some similarities with the concept of perception in planned behavior theory. Often, we look for opportunities to accomplish a task that will benefit us or others.

Ability : Ability is a strength of a person’s cognitive, emotional, technical, or social resources that can be used to perform specific actions. The concept of competence regarding to ability should include knowledge, habits, and tasks. Among them, habit is an independent behavior, and it is also one of the main items that determine the intention of the environment.

3.3 The Value-Belief-Norm Theory

The Value-Belief-Norm theory (VBN) is the development of decision theory by Stern et al. ( 1999 , 2000 ) improved communication between stakeholder groups by establishing consensus on important behaviors affecting the environment (Stern et al. 1999 ; Stern 2000 ). The main structure is through the individual variables linked by the causal chain, he developed the VBN theory (Fig. 6.7 ), which is connected by the causal chain of five variables: values, ecological world view, awareness of consequences, ascription of responsibility, pro-environmental personal norms, and pro-environmental behaviors. Each chain directly affects the next variable, and each variable may also indirectly affect the next variable. Values affect beliefs, beliefs affect personal norms, and personal norms affect pro-environmental behaviors. Values are divided into ecological values, altruistic values, and biosphere values; beliefs are derived from the ecological world view, human’s awareness of the consequences of the adverse environment, and the ascription of responsibilities, so that people believe that their actions can slow the negative factors of the environment; the previous factors affect personal norms. Personal norms are the only variables that affect environmental behavior. Environmental behaviors include activism, nonactivist public-sphere behaviors in the public domain, behavior in the private sphere, and behavior within the organization, as described below (Fig. 6.7 ):

Value-Belief-normative theory (Modified after Stern 2000 : 412; Illustrated by Wei-Ta Fang)

Ecological Worldview : This is a world view of sustainable development. Its purpose is not to maintain the status quo, but to strengthen the health, adaptability, and evolution potential of a fully integrated global social ecosystem. The ecological worldview is a kind of self-regeneration, thus creating conditions for the prosperity and rich future of the ecological environment, including the integrity of the ecological environment, social relations, and the transformative nature of the economy. These models can strengthen the ecological environment of regeneration and sustainability.

Awareness of Consequences (AC) : awareness of the impact of environmental issues (Hansla et al. 2008 ; Fang et al. 2019 ).

Ascription of Responsibility (AR) : The attribution of responsibility is the reason for the occurrence of environmental problems, summarize their causes, and bear the negative facts that need to be assumed, attributed, dealt with, or controlled by the environment. This is the environmental importance influence factors in behavior (Hines et al. 1986 /1987; Kaiser et al. 1999 ; Fang et al. 2019 ; Chao et al. 2021 ).

Pro-environmental Personal Norms : Personal norms are often discussed with morality (De Groot and Steg 2009 ; Fang et al. 2019 ; 2021a ), and are also regarded as a concept of self-value extension. Personal norms are simply the recognition of obligations and morals, and are considered to be a self-disciplined consciousness that may be related to the generation of environmental behavior.

Activism : committed environmental activities and actively participate in environmental organizations.

Non-activist Public -sphere Behaviors : Support or accept public policies is like the willingness to pay higher environmental protection tax. Non-aggressive behavior in the public domain affects public policy and may have a significant impact on the environment, as it can immediately change the behavior of many people or organizations,

Private-sphere Behaviors : The purchase, use, and disposal of personal and household products that have an impact on the environment will have direct environmental consequences, but the effects will be small.

Behaviors in Organizational : Individuals may significantly influence the goodness of the environment by, for example, affecting the behavior of their affiliated organizations. For example, developers may use or ignore environmental standards in their development decision-making process, and may do so because of right or wrong things. Make decisions to reduce or increase pollution from commercial buildings. Organizational behavior is the largest direct source of many environmental problems.

The Value-Belief-Normative theory uses intent-oriented definitions that focus on human beliefs and motivations in order to understand and change target behaviors. Value-Belief-Normative theory provides a description of the reasons for the general tendency to environmental behavior. Environmental behavior depends on a wide range of contingencies; therefore, Stern ( 2000 ) argued that the general theory of environmentalism may not be very useful for changing specific behaviors. Because different kinds of environmental behaviors have different reasons, and their causal factors for causality may be very different between behaviors and individuals, each target behavior should be theoretically separated. If the above causality affects each other, attitude reasons have the greatest predictive value for individual behaviors from different backgrounds. However, for more difficult environmental protection behaviors, environmental factors and personal capabilities may cause more variation. Although VBN theory is concerned with explaining the reasons for environmental behaviors, VBN theory cannot explain all behaviors. He also suggests that future research can identify important behaviors and discuss the factors that affect them (Stern 2000 ).

3.4 Two-Phase Decision-Making Model

Hirose ( 1994 ) considered the process of forming behaviors and proposed that pro-environmental behavior can be explained by a two-phase decision-making model (Fig.  6.8 ). The first phase involves the formation of environmentally friendly attitudes and the second involves various behavioral assessments to determine environmental behavioral intention that will directly or indirectly influence the pro-environmental behaviors. An environmentally friendly attitude refers to “the intent to solve an environmental problem or make a contribution” that supports ecofriendly behavior that is accompanied by a degree of respect for the environment and express concern for ecological issues. It involves three factors:

The two-phase decision-making model (Modified after Hirose 1994 ; Illustrated by Wei-Ta Fang)

Perceived Seriousness: This represents the perception of the consequences of environmental problems (Chao et al. 2021 ). The perceived seriousness emphasizes the perception of environmental risk, the severity of environmental pollution, the likelihood of occurrence, and the perceptions and expectations of the likelihood of occurrence of the environmental problem and the severity of the problem. However, individuals may feel that their power is insignificant despite the impact they have on larger-scale problems.

Ascription of Responsibility : Ascription of responsibility refers to the recognition of the cause of responsibility (Fang et al. 2019 , 2021b ; Chao et al. 2021 ), that is, the perception of responsibility. Specifically, who or what causes environmental pollution and damage. Although it is easy to attribute the cause of complex environmental issues to natural phenomena, residents often place the blame on themselves and therefore different actions can be taken to solve environmental problems depending on the responsibility involved.

Belief in the Effectiveness : This is the recognition of validity of a counter measurement to solve the environmental problem. For instance, a sense of effectiveness can arise if one considers the environmental problem to be solvable by an individual and/or collective efforts of other people. In contrast, if one feels that there will be limited or no effect on addressing the environmental problem regardless of the commitments and efforts put in, then a sense of effectiveness will not substantiate.

Feasibility Evaluation: The non-economic factors that are considered when determining if it is practicable to adopt a pro-environmental action. It also helps to assess whether individuals can engage in pro-environmental behaviors when opportunities arise externally and internally.

Cost–Benefit Evaluation: This type of evaluation assesses the benefits of adopting pro-environmental actions and the costs involved. The main evaluation criteria for comparing the two are the personal benefit and cost evaluation, such as convenience and comfort. If the reduction in personal benefits and the increase in costs of taking pro-environmental actions are significant, then no action is taken and vice versa.

Social Norm Evaluation: The assessment of whether an individual’s behavior conforms to the norms and expectations of an organization or society. In the theory of planned behavior, social norm evaluation corresponds to the subjective norm, so the two-phase decision-making model derived from the theory of planned behavior also uses the subjective norm as an assessment item.

Environmental Behavioral Intention: This refers to the extent to which individuals are willing to consider taking appropriate actions to protect the environment, and this is directly linked to the formation of the target “pro-environmental behavior.”

Chao et al. ( 2021 ) revised the application of the two-phase decision-making model to include the variables of social needs to explain citizen science engagement behaviors. The three influencing variables of social needs were social networks, learning and growth, and belonging and contribution. The results indicated that both the development of an environmentally friendly attitude in the first phase and the series of behavioral assessments generated in the second phase were influenced by the social needs. Therefore, a two-phase decision-making model was developed to incorporate the variables of social needs was proposed (Fig.  6.9 ). There was evidence from Chao et al. ( 2021 ) to indicate the occurrence and effects of social networks and needs in the two-phase decision-making model. Thus, the two-phase decision-making model that incorporated the key variables (i.e., social networks, learning and growth, and belonging and contribution) of social needs had provided a more comprehensive understanding about the citizen science participation behaviors.

Extended two-phase decision-making model with social needs (Modified after Chao et al. 2021 ; Illustrated by Wei-Ta Fang)

4 Paradigm Shift

Environmental protection has been underway for over 60 years and although we’ve made great strides on environmental issues, environmental pollution, reduced biodiversity, and global warming are based on symbols of this era. Some people insist on an early worldview and refuse to deal with the reality that our environment is changing. However, whether this an issue of young people that are environmentally conscious of a younger generation versus that of an older generation needs to be further assessed.

4.1 Dominant Social Paradigm

Dominant Social Paradigms advocate economic growth, but its popularity in the policy world is relatively short lived (Fang 2020 : 12). In 1940, Western governments used gross domestic production to measure economic growth and to support employment goals. In 1950, economic growth became the focus of government policy. This “growth” is currently a goal supported by the Organization for Economic Cooperation and Development. The Dominant Social Paradigm (DSP), however, is too optimistic about social development regarding to concern for environmental quality (Dunlap and Liere 1984 ). In order to solve environmental pollution, a mainstream person proposes to improve the efficiency of resource utilization through technological improvement and sustainable communication (Kilbourne 2004 ), but the consumption of unit resources will produce more products. Therefore, most people believe that the mass production of goods will reduce energy consumption and achieve energy saving and reduction of unit goods. DSP defines the basic belief structures and practices of marketplace actors and is manifested in existing exchange structures (Gollnhofer and Schouten 2017 ). Now, of course, it is also possible to reduce the waste discharge per unit resource and increase the recycling rate, which can also have a slowing effect. The paradigm of mainstream society emphasizes the following characteristics:

Human beings are different from the creatures they control.

Human beings are the masters of their own destiny; they can choose their goals and learn to achieve them.

The world is vast and offers unlimited opportunities for humankind.

Human history is progressive, and every problem can be solved, so progresses endless.

Due to the instruction of DSP, technologies developed in many fields have harmed the environment. For example, the large-scale promotion of fuel-efficient cars has actually increased the total mileage of human beings, but has caused the total amount of fuel consumption to rise, resulting in more carbon emissions. However, more and more people are beginning to realize that economic growth cannot solve all problems in society. The idea of the DSP formed the Jevons paradox (Ruzzenenti et al. 2019 ). This is because the mainstream person’s dependence on science and technology has led to the misconception that science can solve all problems. However, the new ecological paradigm is to solve environmental problems and consider what actions to take effectively. However, it still has its limitations, and we must continue to transfer paradigms.

4.2 New Ecological Paradigm

When people raise their living standards, population growth will slow down and fertility will decline (Day and Dowrick 2004 ). The current global economic challenge is how to use the earth’s resources economically. With rates of fertility declining in every region of the world (Connelly 2003 ), it is now possible to begin to see the end of the limit population growth by order. For example, adopting the one-child policy in mainland China from historical judge (Feng et al. 2013 ), does not play a sustainable role from social consequences (Cai and Feng 2021 ). The increasing proportion of elderly in China is producing social pressures (Zhang and Goza 2006 ). Who will care for the elderly in China?

Of course, the idea of the Garden of Eden is a myth (Delumeau 2000 ); mankind will never return to the original state of nature. When we are getting old, we may not remember same enchanting natural world where it was, but located this time on our side of death, is described as the kingdom of heaven, does urge the age in our turns while we should be getting old.

We need to explain the issues and take action to protect high-quality air, water, soil, sunlight, and biodiversity from all generations. We believed that the elderly people will manifest a higher level of endorsement of the New Ecological Paradigm (NEP) (Costache and Sencovici 2019 ). However, the NEP scale could be limited with respect to the anachronistic wording of items (Lalonde and Jackson 2002 ). Some questions of the New Ecological Paradigm (NEP) could be starting to get into some very complicated, ethical issues that readers will believe we may support or refute. Some wording of items could be hard to capture people’s increasingly thorough understanding of the nature, severity, and scope of environmental problems (Lalonde and Jackson 2002 ). Hawcroft and Milfont ( 2010 : 143) have documented this kind of abuse among previous studies using the NEP scale (Dunlap et al. 2000 ; Cruz and Manata 2020 ). We may also claim to explain this relatively new focus with a meaningful construct toward sustainability paradigm , including increasingly more pervasive and global environmental issues, changing societal expectations, and educational reform (Hart 2013 ).

4.3 Sustainability Paradigm

Fifty years after the birth of neoliberal economic policies, the debate over how to properly address global environmental issues continues. It is worth noting that, as of now, the proponents of the Dominant Social Paradigm (DSP) and the New Ecological Paradigm (NEP) have each held their own words. Our goal is to guide on dialogue and action on environmental issues. We try to achieve sustainable development through environmental education, communication, and advocacy as a Sustainability Paradigm (see Fig.  6.10 ).

A paradigm returning from functional paradigm toward interpretive paradigm (Illustrated by Wei-Ta Fang)

Global climate change (GCC) represents a world-historical opportunity for the emergence of a common global society (Broadbent et al. 2016 ), with failure to do so likely to bring intensifying calamities for all economic developed and/or emerging economics. This is the time represented a global field to discuss our modelling for some choices in certain ways of media discourse (Broadbent et al. 2016 ). To prevent a global extinction crisis and achieve a sustainable society requires rethinking our social values. Environmental education can help learners understand the connections of living environments during a pandemic, become creative problem solvers and active environmental citizens associated with climate governance (Chen and Lee 2020 ) to participate in shaping a common future (Fig.  6.11 ). Therefore, experiential learning and critical pedagogy will provide learners with opportunities for transformative and sustainable development. While we create a worldwide community of critical thinking, we may try to remember how relative to other forms of teaching, generated by critical educational research. We will engage in critical pedagogy in diverse and creative ways and in different settings (Kincheloe 2008 ). Environmental education is a modern education paradigm that inspires civic responsibility, constructs a positive social status, and promotes a healthy lifestyle. Therefore, we are not convincing all environmental ethics from rigid lessons, but we tried to apply modelling from ethical theories to behave humans’ capacities to follow pedagogy’s notion of praxis—informed action from practical knowledge. This required to be gained through learning anything adopting by day-to-day hands-on experiences from personal theories. We may encourage that you may learn skills of “knowing-how” in all empirical condition from your effective motivation. In this praxis-based context, we gain the ability to change ourselves relative to other forms of teaching and learning.

Environmental education can help learners understand the connections of living environments (Qixingtan Beach, Hualien, Taiwan, 2019) (Photo by Dennis Woo)

Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J (eds) Action control: from cognition to behavior. Springer, Berlin Heidelberg, pp 11–39

Google Scholar  

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Proc 50(2):179–211

Ajzen I, Manstead AS (2007) Changing health-related behaviours: an approach based on the theory of planned behaviour. In: The scope of social psychology. Psychology Press, pp 55–76

Bamberg S (2003) How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. J Environ Psychol 23(1):21–32

Bamberg S, Möser G (2007) Twenty years after Hines, Hungerford, and Tomera: a new meta-analysis of psycho-social determinants of pro-environmental behaviour. J Environ Psychol 27(1):14–25

Bamberg S, Hunecke M, Blöbaum A (2007) Social context, personal norms and the use of public transportation: Two field studies. J Environ Psychol 27(3):190–203

Barnhill DL, Gottlieb RS (eds) (2010) Deep ecology and world religions: new essays on sacred ground. SUNY Press, New York

Barrett NF (2011) Wuwei and flow: comparative reflections on spirituality, transcendence, and skill in the Zhuangzi. Philosophy East and West 61(4):679–706

Bellah RN (1983) The ethical aims of social inquiry. In: Haan N, Bellah RN, Rabinow P, Sullivan WM (eds) Social science as moral inquiry. Columbia University Press, New York, pp 360–382

Bennett M (2004) Different shades of green. Coll Lit 31(3):207–212

Borgmann A (1995) The nature of reality and the reality of nature. In: Soulé ME, Lease G (eds) Reinventing nature. Island, Washington DC

Bradford G (1989) How Deep is Deep Ecology. Times Change, Ojai

Bravo-Osorio (2022) Towards an ethic of ecological resilience. Ethics, Policy Environ https://doi.org/10.1080/21550085.2022.2054648

Broadbent J, Sonnett J, Botetzagias I, Carson M, Carvalho A, Chien Y et al (2016) Conflicting climate change frames in a global field of media discourse. Socius 2:2378023116670660

Burdett MS (2015) The image of God and human uniqueness: challenges from the biological and information sciences. Exposit times 127(1):3–10

Burke E (1790) Reflections on the revolution in France and on the proceedings in certain societies in London relative to that event in a letter intended to have been sent to a gentleman in Paris. https://socialsciences.mcmaster.ca/econ/ugcm/3ll3/burke/revfrance.pdf

Cai Y, Feng W (2021) The social and sociological consequences of China’s one-child policy. Ann Rev Sociol 47:587–606

Callicott JB (1989) In defense of the land ethic: essays in environmental philosophy. Suny Press, New York

Callicott JB, Parker J, Batson J, Bell N, Brown K, Moss S (2011) The other in a sand county almanac: Aldo Leopold’s animals and his wild-animal ethic. Environ Ethics 33(2):115–146

Callicott JB (2005) Turning the whole soul: the educational dialectic of a sand county almanac. Glob Relig Cult Ecol Worldv 9(3):365–384

Callicott JB (2010) The conceptual foundations of the land ethic. Technol and Values: Essent Read 438–453

Capra F, Luisi PL (2014) The systems view of life: a unifying vision. Cambridge University Press, Cambridge

Capra F (1975) The Tao of Phys. Shambhala, Boulder

Carney M (2021) Value(s): building a better world for all. Public Affairs, New York

Chao S-H, Jiang J, Wei K-C, Ng E, Hsu C-H, Chiang Y-T, Fang W-T (2021) Understanding pro-environmental behavior of citizen science: an exploratory study of the bird survey in Taoyuan’s farm ponds project. Sustainability 13(9):5126. https://doi.org/10.3390/su13095126

Article   Google Scholar  

Chen RS, Lee HC (2020) Assessing climate governance of Tainan City through stakeholder networks and text mining. In: Chou K-T, Hasegawa K, Ku D, Kao S-F (eds) Climate change governance in Asia. Routledge, Oxfordshire, pp 256–281

Cherdymova EI, Ukolova LI, Gribkova OV, Kabkova EP, Tararina LI, Kurbanov RA, Belyalova AM, Kudrinskaya IV (2018) Projective techniques for student environmental attitudes study. Ekoloji 27(106):541–546

Connelly M (2003) Population control is history: New perspectives on the international campaign to limit population growth. Comp Stud Soc Hist 45(1):122–147

Costache A, Sencovici M (2019) Age, gender and endorsement of the New Ecological Paradigm. Int Multi Sci GeoConf: SGEM 19(5.1):11–22

Cruz SM, Manata B (2020) Measurement of environmental concern: a review and analysis. Front Psychol 11:363. https://doi.org/10.3389/fpsyg.2020.00363

D’Ambrosio PJ (2022) Non-humans in the Zhuangzi: animalism and anti-anthropocentrism. Asian Philos 32(1):1–18

Day C, Dowrick S (2004) Ageing economics: human capital, productivity and fertility. Agenda: J Pol Anal Reform 11(1):3–20

Delumeau J (2000) History of paradise: the Garden of Eden in myth and tradition. University of Illinois Press

Devall B (1980) The deep ecology movement. Nat Resour J 20:299

Devall B, Sessions G (1984) The development of nature resources and the integrity of nature. Environ Eth 6(4):293–322

Devall B, Sessions G (1985) Deep ecology: living as if nature mattered. Gibbs Smith

Disinger JF (1990) Environmental education for sustainable development? J Environ Educ 21:3–6

Droz L (2021) The concept of milieu in environmental ethics: individual responsibility within an interconnected world. Routledge, Oxfordshire

Dunlap RE (1975) The impact of political orientation on environmental attitude and action. Environ Behav 7(4):428–454

Dunlap RE (2008) The new environmental paradigm scale: from marginality to worldwide use. J Environ Educ 40(1):3–18

Dunlap RE (2022) Understanding opposition to the environmental movement: the importance of dominant American values. Edward Elgar Publishing, In Handbook of Anti-Environmentalism

Dunlap RE, Van Liere KD (1978) The new environmental paradigm. J Environ Educ 9(4):10–19

Dunlap RE, Van Liere KD (1984) Commitment to the dominant social paradigm and concern for environmental quality. Soc Sci Q 65:1013–1028

Dunlap RE, Grieneeks JK, Rokeach M (1983) Human values and pro-environmental behavior. In: Conn WD (ed) Energy and material resources: attitudes, values, and public policy. Boulder, pp 145–168

Dunlap RE, Van Liere KD, Mertig AG, Jones RE (2000) Measuring endorsement of the new ecological paradigm: a revised NEP Scale. J Soc Issues 56(3):425–442

Dunlap RE, McCright AM (2015). Challenging climate change. In RE Dunlap and RJ Brulle (eds) Climate change and society: sociological perspectives. Oxford, Cambridge

de Figueiredo MD, Marquesan FFS (2022) Back to the future: ecocentrism, organization studies, and the Anthropocene. Scand J Manag 38(2):101197

Fang W-T, Ng E, Wang C-M, Hsu M-L (2017) Normative beliefs, attitudes, and social norms: People reduce waste as an index of social relationships when spending leisure time. Sustainability 9(10):1696

Fang W-T, Chiang Y-T, Ng E, Lo J-C (2019) Using the norm activation model to predict the pro-environmental behaviors of public servants at the central and local governments in Taiwan. Sustainability 11(13):3712

Fang W-T, Huang M-H, Cheng B-Y, Chiu R-J, Chiang Y-T, Hsu C-W, Ng E (2021a) Applying a comprehensive action determination model to examine the recycling behavior of Taipei city residents. Sustainability 13(2):490

Fang W-T, Ng E, Liu S-M, Chiang Y-T, Chang M-C (2021b) Determinants of pro-environmental behavior among excessive smartphone usage children and moderate smartphone usage children in Taiwan. PeerJ 9:e11635. https://doi.org/10.7717/peerj.11635

Fang W-T (2020) Envisioning environmental literacy. Springer Singapore, p 12

Farida I, Permadi Y, Adelia T, Liviani N (2019) Considering all (Non) living things: a biocentric orientation in Blair Richmond’s the lithia trilogy. Lingua Cultura 13(2):87–92

Feliciotti A, Romice O, Porta S (2018) From system ecology to urban morphology: towards a theory of urban form resilience. In: International Forum on Urbanism. 2018-12-10–2018-12-12, UIC School of Architecture, Chicago

Feng W, Cai Y, Gu B (2013) Population, policy, and politics: how will history judge China’s one-child policy? Popul Dev Rev 38:115–129

Fios F (2019). Building awareness of eco-centrism to protect the environment. J Phys Conf Ser 1402(2):022095 (IOP Publishing, Bristol)

Flores A, Clark TW (2001) Finding common ground in biological conservation: beyond the anthropocentric vs. biocentric controversy. Yale Sch for Environ Stud Bull Ser 105:241–252

Flükiger JM (2009) The radical animal liberation movement: some reflections on its future. J Stud Radicalism 2(2):111–132

De Groot JIM, Steg L (2009) Morality and prosocial behavior: the role of awareness, responsibility and norms in the norm activation model. J Soc Psychol 149:425–449

Gladwin TN, Kennelly JJ, Krause TS (1995) Shifting paradigms for sustainable development: implications for management theory and research. Acad Manag Rev 20(4):874–907

Gollnhofer JF, Schouten JW (2017) Complementing the dominant social paradigm with sustainability. J Macromark 37(2):143–152

Gruenewald DA (2004) A foucauldian analysis of environmental education: toward the socioecological challenge of the earth charter. Curric Inq 34(1):71–107

Hansla A, Gamble A, Juliusson A, Gärling T (2008) The relationships between awareness of consequences, environmental concern, and value orientations. J Environ Psychol 28(1):1–9

Hart P (2013) Environmental education. In: Handbook of research on science education. Routledge, pp 703–740

Hawcroft LJ, Milfont TL (2010) The use (and abuse) of the new environmental paradigm scale over the last 30 years: a meta-analysis. J Environ Psychol 30:143–158

Hernández B, Martín AM, Ruiz C, Hidalgo MdC (2010) The role of place identity and place attachment in breaking environmental protection laws. J Environ Psychol 30(3):281–288

Hines JM, Hungerford HR, Tomera AN (1986/87) Analysis and synthesis of research on responsible environmental behavior: a meta-analysis. J Environ Educ 18(2):1–8

Hirose Y (1994) Determinants of environmental conscious behavior. Jap J Soc Psychol 10(1):44–55

Huang Y (2010) Confucius and Mencius on the motivation to be moral. Philos East and West 65–87

Hung C-M, Hung H-Y, Yeh C-F, Fu Y-Q, Chen D, Lei F et al (2014) Species delimitation in the Chinese bamboo partridge Bambusicola thoracica (Phasianidae; Aves). Zoolog Scr 43(6):562–575

Hurka T (1996) Perfectionism. Oxford University Press, Oxford

Johns D (1992) The practical relevance of deep ecology. Wild Earth 2:62–68

Kaiser FG, Wolfing S, Fuhrer U (1999) Environmental attitude and ecological behaviour. J Environ Psychol 19:1–19

Katz E (1987) Searching for intrinsic value: pragmatism and despair in environmental ethics. Environ Ethics 9(3):231–241

Kerkhoff LV, Lebel L (2006) Linking knowledge and action for sustainable development. Annu Rev Environ Resour 31:445–477

Kilbourne WE (2004) Sustainable communication and the dominant social paradigm: can they be integrated? Mark Theory 4(3):187–208

Kilbourne WE (2006) The role of the dominant social paradigm in the quality of life/environmental interface. Appl Res Qual Life 1(1):39–61

Kilner JF (2015) Dignity and destiny: Humanity in the image of God. Eerdmans Publishing, Grand Rapids, Wm. B

Kim T (2009) Reading Zhuangzi eco-philosophically. J Daoist Stud 2(2):1–31

Kincheloe JL (2008) Critical pedagogy primer, vol 1. Peter Lang

Kollmuss A, Agyeman J (2002) Mind the gap: why do people act environmentally and what are the barriers to pro-environmental behavior? Environ Educ Res 8(3):239–260

Kopnina H, Cherniak B (2015) Cultivating a value for non-human interests through the convergence of animal welfare, animal rights, and deep ecology in environmental education. Educat Sci 5(4):363–379

Kopnina H, Gjerris M (2015) Are some animals more equal than others? Animal rights and deep ecology in environmental education. Can J Environ Educ 20:108–122

Kopnina H, Washington H, Gray J, Taylor B (2018) The ‘future of conservation’ debate: defending ecocentrism and the nature needs half movement. Biol Cons 217:140–148

Kornai J (2002) The system paradigm. Voprosy Economiki 4. https://ideas.repec.org/a/nos/voprec/2002-4-1.html

Kuhlmann W (1996) Making the law more ecocentric: responding to leopold and conservation biology. Duke Envtl l Pol’y f 7:133

Lalonde R, Jackson EL (2002) The new environmental paradigm scale: has it outlived its usefulness? J Environ Educ 33(4):28–36

Lau DC (2004) Mencius. Penguin UK, London

Lee JH (2007) What is it like to be a butterfly? A philosophical interpretation of Zhuangzi’s butterfly dream. Asian Phil 17(2):185–202

Leopold A (1949) A sand county almanac. Oxford University Press, Oxford

Liu S, Chiang Y-T, Tseng C-C, Ng E, Yeh G-L, Fang W-T (2018) The theory of planned behavior to predict protective behavioral intentions against PM2. 5 in parents of young children from urban and rural Beijing, China. Int J Environ Res Publ Health 15(10):2215

Machlis GE, Force JE, Burch WR Jr (1997) The human ecosystem part I: the human ecosystem as an organizing concept in ecosystem management. Soc Nat Resour 10(4):347–367

Madin JS, O’Donnell MJ, Connolly SR (2008) Climate-mediated mechanical changes to post-disturbance coral assemblages. Biol Let 4(5):490–493

Marcinkowski T, Reid A (2019) Reviews of research on the attitude–behavior relationship and their implications for future environmental education research. Environ Educ Res 25(4):459–471

Mazzucato M (2020) The value of everything. Public Affairs, New York

McElroy M (2002) Deep knowledge management and sustainability. Centre for Sustainable Innovation. Retrieved on April 30, 2022. Center for Sustainable Innovation ( www.sustainableinnovation.org )

McKenzie-Mohr D (2011) Fostering Sustainable Behavior: an introduction to community-based social marketing. New Society Publishers

McLaughlin A (1993) Regarding nature: industrialism and deep ecology. Suny Press, New York

Mersereau MR (2016) The abiotic Internet: internet mediation through organizational practice at Na-Me-Res. University of Toronto, Toronto

Moeller HG (2015) Paradoxes of health and power in the Zhuangzi. New Vis Zhuangzi 70–81

Möller HG (1999). Zhuangzi’s “Dream of the Butterfly”: a daoist interpretation. Phil East and West 439–450

Næss A (1973) The shallow and the deep, long-range ecology movement: a summary. Inquiry 16(1–4):95–100

Næss A (1984) A defence of the deep ecology movement. Environ Ethics 6(3):265–270

Næss A (1986) The deep ecological movement: some philosophical aspect. Philos Inq 8:10–31

Næss A (1987) Self-realization: an ecological approach to being in the world. Trumpeter 4(3):35–42

Næss A (1989) Ecology, community, and lifestyle. Cambridge University Press, Cambridge

Næss A (1985a) Identification as a source of deep ecological attitudes. In: Deep ecology. In: Tobias M (ed) Avant books, San Diego, pp 256–270

Næss A (1985b) Ecosophy T: deep versus shallow ecology. In: Pojman (ed) Environ Ethics, pp 151–153

Næss A (1995) Self-realization. An ecological approach to being in the world. In: Sessions G (ed) Deep ecology for the twenty-first century Shambhala, Boston, pp 225–239

Næss A (2011) The deep ecological movement: Some philosophical aspects. In: Bhaskar R, Næss P, KG Høyer (eds) Ecophilosophy in a world of crisis; critical realism and the Nordic Contributions,. Routledge, Oxfordshire, pp 96–110

Noll S (2017) Climate induced migration: a pragmatic strategy for wildlife conservation on farmland. Pragmatism Today 8(2):24–40

Nyrud AQ, Roos A, Sande JB (2008) Residential bioenergy heating: a study of consumer perceptions of improved woodstoves. Energy Policy 36(8):3169–3176

Ölander F, Thøgersen J (1995) Understanding of consumer behavior as a prerequisite for environmental protection. J Consum Policy 18(4):345–385

Orton D (1996) Left biocentrism. Green Web 49:1–10

Parkes G (2013) Zhuangzi and Nietzsche on the human and nature. Environ Philos 10(1):1–24

Pepper D (2002) Eco-socialism: from deep ecology to social justice. Routledge, Oxfordshire

Piccolo J, Washington H, Kopnina H, Taylor B (2018) Why conservation scientists should re-embrace their ecocentric roots. Conserv Biol 32(4):959–961

Pienaar EF, Lew DK, Wallmo K (2013) Are environmental attitudes influenced by survey context? An investigation of the context dependency of the New Ecological Paradigm (NEP) Scale. Soc Sci Res 42(6):1542–1554

Pirages DC, Ehrlich PR (1974) Ark II: social response to environmental imperatives. Freeman, San Francisco

Regan T (1980) Utilitarianism, vegetarianism, and animal rights. Philos Public Aff 9(4):305–324

Regan T (1983) The case for animal rights. University of California Press

Rolston H III (1975) Is there an ecological ethic? Ethics 85(2):93–109

Rowe JS (1994b) Ecocentrism and traditional ecological knowledge. http://www.ecospherics.net/pages/Ro993tek_1.html

Ruzzenenti F, Font Vivanco D, Galvin R, Sorrell S, Wagner A, Walnum HJ (2019) The rebound effect and the Jevons’ paradox: beyond the conventional wisdom. Front Energy Res 90 https://doi.org/10.3389/fenrg.2019.00090

Salleh A (2022) An ecocentric epistemology for ecosocialism. the routledge handbook on ecosocialism. Routledge, Oxfordshire, pp 57–66

Sandler R (2012) Intrinsic value. Ecology, and conservation. Nat Educ Knowl 3(10):4

Sapolsky RM (2017) Behave: the biology of humans at our best and worst. Penguin

Schwartz B (1987) The battle for human nature: science. Morality and Modern Life. WW Norton & Company, New York

Sessions G (1987) The deep ecology movement: a review. Environ Rev 11(2):105–125

Shrivastava P (2008) Corporate citizenship and the environment. Handb Res Glob Corp Citizensh, 166–184

Singer P (1975) Animal Liberation. HarperCollins

Singer P (1997) Neither human nor natural: ethics and feral animals. Reprod Fertil Dev 9(1):157–162

Sorokoumova EA, Cherdymova EI (2021) Developing structural components of ecological consciousness to promote civic identity formation. Psychol Sci Educ 26(1):102–112

Sterba JP (1995) From biocentric individualism to biocentric pluralism. Environ Ethics 17(2):191–207

Sterba JP (1998) A biocentrist strikes back. Environ Ethics 20(4):361–376

Stern P (2000) Toward a coherent theory of environmentally significant behavior. J Soc Issues 56(3):407–424

Stern PC, Dietz T, Abel TD, Guagnano GA, Kalof L (1999) A value-belief-norm theory of support for social movements: the case of environmentalism. Hum Ecol Rev 6(2):81–97

Steverson BK (1991) A critique of ecocentric environmental ethics, Doctoral dissertation, Tulane University

Taylor C (2010) Aristotle. In: Skorupski J (ed) The routledge companion to ethics. Routledge, Oxfordshire, pp 67–77

Tete F, Ariche CK (2021) Virtue ethics as philosophical foundation for environmental education. GNOSI: An Interdisc J Human Theo Praxis 4(1(May)):83–90

Thøgersen J (2006) Norms for environmentally responsible behaviour: an extended taxonomy. J Environ Psychol 26(4):247–261

Thøgersen J (2009) Promoting public transport as a subscription service: effects of a free month travel card. Transp Policy 16(6):335–343

Vilkka L (2021) The intrinsic value of nature. Brill, Leiden

Washington W, Taylor B, Kopnina HN, Cryer P, Piccolo JJ (2017) Why ecocentrism is the key pathway to sustainability. Ecolog Citizen 1(1):35–41

Washington H, Chapron G, Kopnina H, Curry P, Gray J, Piccolo JJ (2018) Foregrounding ecojustice in conservation. Biol Cons 228:367–374

Wenzel CH (2003) Ethics and zhuangzi: awareness, freedom, and autonomy. J Chin Philos 30(1):115–126

Woo S-K, LePage B, Chiang Y-T, Fang W-T (2022). Predicting the protective behavioral intentions for parents with young children that possess different levels of education in Hong Kong using the theory of planned behavior for air polluted with PM2. 5. BMC Public Health 22(1):1–11

Zhang Y, Goza FW (2006) Who will care for the elderly in China? A review of the problems caused by China’s one-child policy and their potential solutions. J Aging Stud 20(2):151–164

Zimmerman ME (2020) Contesting earth’s future. University of California Press, Oakland, Contesting Earth’s Future

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Fang, WT., Hassan, A., LePage, B.A. (2023). Environmental Ethics: Modelling for Values and Choices. In: The Living Environmental Education. Sustainable Development Goals Series. Springer, Singapore. https://doi.org/10.1007/978-981-19-4234-1_6

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