Adaptive Implementation Modeling and Monitoring for TMDL Refinement

EPA Grant Number: R830883
Title: Adaptive Implementation Modeling and Monitoring for TMDL Refinement
Investigators: Reckhow, Kenneth H. , Stow, Craig A. , Shabman, Leonard A. , Borsuk, Mark E. , Roessler, Chris , McMahon, Gerard
Institution: Duke University , United States Geological Survey [USGS] , Resources for the Future
Current Institution: Duke University , Resources for the Future , United States Geological Survey [USGS]
EPA Project Officer: Packard, Benjamin H
Project Period: June 1, 2003 through May 31, 2006 (Extended to October 30, 2007)
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


The objectives are: 1) To develop an adaptive implementation modeling and monitoring strategy (AIMMS) for TMDL improvement. For this study, the models in AIMMS will be the NeuBERN Bayes network estuary model linked with the USGS Neuse SPARROW model. AIMMS will allow us to analytically integrate TMDL modeling with post-implementation monitoring to refine and improve the TMDL over time. 2) To apply and evaluate AIMMS on the recently approved Neuse Estuary nitrogen TMDL in North Carolina. This will be accomplished through two sub-objectives: a) to assess the value of information (value of additional monitoring) for TMDL compliance assessment using the linked models in AIMMS; b) to conduct the additional monitoring and use these data to update the TMDL forecast using AIMMS. 3) To develop and test a process for engaging stakeholder decision makers in refining the format of the model outputs and endpoints in order to assure the model’s utility and credibility when the model is used as an adaptive management decision support tool.


AIMMS will build upon recent work of the investigators in the development of the Neuse nitrogen TMDL, using (1) NeuBERN, a Bayesian network model characterizing the relationships between multiple stressors and ecological endpoints in the Neuse Estuary, and (2) SPARROW, a spatially-referenced USGS model implemented in the Neuse watershed. NeuBERN and Neuse-SPARROW will be linked in AIMMS: both models are probabilistic and support error propagation. We will use these linked models in AIMMS to assess the value of sample information and then once the data have been collected, analytically examine the improvements in the Neuse nitrogen TMDL forecast. The Neuse stakeholders group will be briefed and consulted throughout on the credibility and value of adaptive implementation.

Expected Results:

The general lack of forecast uncertainty estimates in TMDL modeling leads to risky TMDL decisions with often unanticipated ecosystems responses. The ubiquitous and often substantial uncertainty that characterizes the TMDL process was a key reason that the 2001 NRC TMDL panel proposed adaptive implementation of TMDLs. Yet while the concept of adaptive implementation makes sense as a pragmatic solution to scientific uncertainty, practical matters related to technical approaches and policy issues remain. We believe AIMMS serves the technical need for analytic approaches for adaptive TMDLs and for other environmental assessments hampered by substantial uncertainty. So in a general sense the results of this 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, and will serve as a basis for the required 2006 NC DWQ re-evaluation of designated use support in the Neuse Estuary.

Publications and Presentations:

Publications have been submitted on this project: View all 9 publications for this project

Journal Articles:

Journal Articles have been submitted on this project: View all 5 journal articles for this project

Supplemental Keywords:

water, watersheds, risk, ecological effects, Bayesian, modeling., RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Water, Ecosystem Protection/Environmental Exposure & Risk, Water & Watershed, Monitoring/Modeling, Regional/Scaling, Environmental Monitoring, decision-making, Ecology and Ecosystems, Watersheds, Economics & Decision Making, 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 monitoring

Progress and Final Reports:

  • 2003 Progress Report
  • 2004 Progress Report
  • 2005 Progress Report
  • 2006
  • Final Report