Grantee Research Project Results
2003 Progress Report: Adaptive Implementation Modeling and Monitoring for TMDL Refinement
EPA Grant Number: R830883Title: Adaptive Implementation Modeling and Monitoring for TMDL Refinement
Investigators: Reckhow, Kenneth H. , Stow, Craig A. , Shabman, Leonard A. , Arhonditsis, George B. , Borsuk, Mark E. , Qian, Song S. , Roessler, Chris , McMahon, Gerard
Current Investigators: Reckhow, Kenneth H. , Stow, Craig A. , Shabman, Leonard A. , Borsuk, Mark E. , Roessler, Chris , McMahon, Gerard
Institution: Duke University , University of South Carolina at Columbia , United States Geological Survey , Resources for the Future
Current Institution: Duke University , Resources for the Future , United States Geological Survey
EPA Project Officer: Packard, Benjamin H
Project Period: June 1, 2003 through May 31, 2006 (Extended to October 30, 2007)
Project Period Covered by this Report: June 1, 2003 through May 31, 2004
Project Amount: $660,171
RFA: Developing Regional-Scale Stressor-Response Models for Use in Environmental Decision-making (2002) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Aquatic Ecosystems
Objective:
The overall objective of this research project is to develop an adaptive implementation modeling and monitoring strategy (AIMMS) for total maximum daily load (TMDL) improvement. AIMMS will allow us to analytically integrate TMDL modeling with postimplementation monitoring to refine and improve the TMDL over time. Two probabilistic models with the ability to support error propagation (i.e., the NeuBERN Bayes network estuary model and the U.S. Geological Survey Neuse Spatially Referenced Regressions on Watershed Attributes [SPARROW] Model) will be linked in AIMMS. The case study for application and evaluation of AIMMS would be the Neuse Estuary nitrogen TMDL in North Carolina.
The specific objectives of this research project are to: (1) assess the value of information (value of additional monitoring) for TMDL compliance evaluation using the linked models in AIMMS; (2) conduct additional monitoring to update the TMDL forecast using AIMMS; and (3) develop and test a process for engaging stakeholder decisionmakers in refining the format of the model outputs and endpoints. The anticipated result from the third specific objective is to assure the model’s utility and credibility as an adaptive management decision support tool. In a general sense, this research project should have broad applicability as a framework to update and improve model forecasts (and management actions) over time. In a specific sense, the Neuse application of AIMMS will provide an informative and useful case study, which will serve as a basis for the required 2006 North Carolina Division of Water Quality reevaluation of designated use support in the Neuse Estuary.
Progress Summary:
We worked to relate watershed processes to estuarine dynamics by establishing an integrated modeling construction. The two components of this approach were the Neuse SPARROW (McMahon, et al., 2003) and the NeuBERN (Borsuk, et al., 2004a,b) Models. These two models were developed independently, and our primary focus was to evaluate the existing results, exploit the experience gained during their application, and consider possible areas of enhancement/refinement. During this evaluation process, two issues were raised where modifications were warranted. The first problem was the unaccounted spatial autocorrelation within the basin area by the SPARROW Model. The second problem was the need to resolve the temporal resolution mismatch between the two models.
Bayesian Analysis of SPARROW
The current SPARROW Model formulation does not capture several potentially important sources of spatial autocorrelation (i.e., similarity in soil and land use characteristics and physical interactions among neighboring sub-basins) (McMahon, et al., 2003). Another aspect of the model that requires modification is the counterintuitive use of observed data to predict future, unrealized states (Qian, et al., 2004). Although the premise is error propagation control among nested basins (Smith, et al., 1997), this assumption undermines the use of the SPARROW Model for actual predictive applications. To address these issues, a Bayesian parameter estimation using the conditional autoregressive model (Besag and Kooperberg, 1995) was adopted, with the intention of allowing dynamic modeling of nutrient transport between subwatersheds and reducing spatial correlations. The Bayesian approach has significantly improved model performance (Qian, et al., 2004). One additional problem that needs to be addressed is the temporal resolution “incompatibility” between the watershed and the estuarine-response model. The SPARROW Model provides annual nutrient loading estimates, whereas the NeuBERN water quality model aims to describe estuarine ecological processes with a finer time scale (days or residence time for each of the Neuse Estuary segments modeled). Several assumptions or simplifications usually are adopted in the modeling practice for addressing the temporal resolution mismatch when developing composite models (e.g., averaging outputs or running the models at different time scales from those that can be supported reliably). Our study explicitly acknowledges the predictive uncertainty of these simplifications, and we now are testing a hierarchical Bayesian methodological framework that can resolve this problem.
Water Quality Model
In the original version of the estuarine model NeuBERN, predictions of algal response to changes in nutrient loading were based on a statistical fit to historical data. Although such a model may be useful in predicting the response to minor changes over short time scales, it is unlikely to be very accurate for more severe reductions or long-term system development. For this purpose, we developed a more process-based model that has a strong theoretical basis and yet is mathematically simple enough to be parameterized using available data and to allow for a complete uncertainty analysis. Model construction and uncertainty analysis are being performed using the program packages AQUASIM and UNCSIM. AQUASIM facilitates the construction and calibration of process-based models, whereas UNCSIM, to which it is easily linked, facilitates Bayesian parameter inference and uncertainty analysis. Initial parameter estimates yield a reasonably good fit to data, which should be improved after site-specific parameter estimation. We currently are in the process of improving model fit using a combination of sensitivity analysis and frequentistic parameter estimation. The next step will be to conduct Bayesian inference on the most important parameters so that we have distributions to use in the Bayes net model. This model then will be used as part of the linked NeuBERN-SPARROW Model for developing AIMMS.
The Role of Stakeholders in Adaptive Implementation of TMDLs
Significant progress has been made in synthesizing the legal, policy science, and economics literature relevant to TMDL decisionmaking, including the role of stakeholders in adaptive implementation of TMDLs. A published paper suggests that adaptive implementation of TMDLs will require new protocols for selecting initial regulatory and investment actions, securing and targeting postimplementation monitoring resources, establishing the time steps when additional actions will be evaluated, and clarifying the decisionmaking roles and responsibilities of regulatory agencies and stakeholders in all of the above. These and other ideas will be explored further during a series of workshops on adaptive implementation (that rely partly on EPA Science To Achieve Results funds) that have been scheduled for late October 2004, mid-January 2005, and early April 2005.
References:
Besag J, Kooperberg CL. On conditional and intrinsic autoregressions. Biometrika 1995;82:733-746.
Borsuk, ME, Stow CA, Reckhow KH. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling, 2004a;173(2-3):219-239.
Borsuk ME, Stow CA, Reckhow KH. The confounding effect of flow on estuarine response to nitrogen loading. Journal of Environmental Engineering 2004b;130(6):605-614.
McMahon G, Alexander RB, Qian S. Support of total maximum daily load programs using spatially referenced regression models. Journal of Water Resources Planning and Management 2003;129(4):315-329.
Qian SS, Reckhow KH, Zhai J, McMahon G. A Bayesian analysis of SPARROW. Water Resources Research (submitted, 2004).
Smith RA, Schwarz GE, Alexander RB. Regional interpretation of water-quality monitoring data. Water Resources Research 1997;33:2781-2798.
Future Activities:
We will test the optimal way to link the watershed and estuarine models. Once the models are linked, the integrated watershed-waterbody response model will be updated with the use of our extensive historic database (covers both TMDL pre- and postimplementation periods). Using data from the postimplementation period will allow us to assess the TMDL effectiveness and make refinements to attain the criterion (chlorophyll) and meet designated uses. Furthermore, we intend to use the linked model as a means to optimize the sampling network and identify needs for additional monitoring in the Neuse drainage network and the Neuse Estuary. Based on this assessment of the value of information (value of additional monitoring), we will design a sampling network that focuses on sites where the greatest forecast uncertainty occurs but also minimizes sample redundancy caused by spatial or temporal correlation. The collection of this additional information (if necessary) will allow us to update the linked model, update the TMDL forecast, and reassess the TMDL effectiveness. The role of decision-makers and stakeholders is very important for refining the model’s credibility. We expect that their suggestions will help to extend the tools of analysis to better characterize the certainty and uncertainties about stressor-response relationships and stressor reduction-response relationships for complex water bodies. Finally, the stakeholder contribution would be vital in selecting meaningful water quality (and modeling) endpoints, and one of our future objectives would be to use our modeling framework to support decisions on setting water quality standards by stakeholders.
Journal Articles on this Report : 3 Displayed | Download in RIS Format
Other project views: | All 9 publications | 6 publications in selected types | All 5 journal articles |
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Borsuk ME, Stow CA, Reckhow KH. A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 2004;173(2-3):219-239. |
R830883 (2003) R830883 (2004) |
Exit Exit Exit |
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Borsuk ME, Stow CA, Reckhow KH. Confounding effect of flow on estuarine response to nitrogen loading. Journal of Environmental Engineering 2004;130(6):605-614. |
R830883 (2003) R830883 (2004) |
Exit |
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McMahon G, Alexander RB, Qian S. Support of total maximum daily load programs using spatially referenced regression models. Journal of Water Resources Planning and Management 2003;129(4):315-329. |
R830883 (2003) R830883 (2004) |
Exit |
Supplemental Keywords:
water, watersheds, risk, integrated assessment, ecological effects, Bayesian, modeling, Southeast, ecosystem protection/environmental exposure and risk, ecology and ecosystems, economics and decision-making, environmental monitoring, monitoring/modeling, regional/scaling, water and watershed, Bayesian approach, Bayesian classifiers, total maximum daily load, TMDL, adaptive implementation modeling, aquatic ecosystem, assessment endpoint mechanistic research, decision analysis, decisionmaking, decision support tool, ecological indicators, ecological models, ecological variation, ecology, ecology assessment models, ecosystem assessment, ecosystem modeling, ecosystem stress, environmental decision-making, environmental risk assessment, regional-scale impacts, risk assessment, water monitoring, watershed, Economic, Social, and Behavioral Science Research Program,, RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Water, Ecosystem Protection/Environmental Exposure & Risk, Water & Watershed, Monitoring/Modeling, Regional/Scaling, Environmental Monitoring, decision-making, Ecology and Ecosystems, Economics & Decision Making, Watersheds, risk assessment, ecosystem modeling, aquatic ecosystem, watershed, ecology, ecosystem assessment, Bayesian approach, decision analysis, decision making, environmental decision making, ecological variation, TMDL, regional scale impacts, water quality, assessment endpoint mechanistic research, ecological indicators, ecology assessment models, ecosystem stress, watershed assessment, ecological models, decision support tool, environmental risk assessment, Bayesian classifiers, water monitoringProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.