Grantee Research Project Results

Final Report: 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 , 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 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. Another objective of this project is to apply and evaluate AIMMS on the Neuse Estuary TMDL in North Carolina.

Ecological Significance

• The Neuse River drains into Pamlico Sound, which is one of the most important fish nursery areas in the mid-Atlantic.
• The Neuse Estuary supports recreational and commercial fishing and shellfishing.

Effects of Multiple Stressors

• Nitrogen is the nutrient that generally controls algal density, although the relationship is relatively weak.
• River flow and salinity are also factors important in algal density.
• Wind and stratification affect oxygen level, which in turn influences fish and shellfish.

Summary/Accomplishments (Outputs/Outcomes):

Lessons Learned

• It is feasible to develop and update a Bayesian model for pollutant loading and water body response, that can be regularly updated with new monitoring data.
• The Neuse Estuary achieved compliance with the chlorophyll criterion far sooner than expected, due to an aggressive nitrogen control program in North Carolina.

Interactions with Potential Users

• North Carolina Division of Water Quality – evaluation of chlorophyll criterion compliance
• EPA Council for Regulatory Environmental Modeling – to develop a proposal to build upon this STAR project
• EPA OW, WERF, AMSA (and other groups) – additional support for the Adaptive Implementation White Paper

Other Project Outcomes & Benefits

• Developing a joint research proposal with the EPA Council for Regulatory Environmental Modeling to continue model development with Bayesian updating using remote sensing data
• Funded three students at Duke

Previously…

• Models are likely inadequate
• Adaptive Implementation (AI):  learning while doing
• Key to AI:  monitoring and post-implementation assessment
• Bayesian statistics as an analytical tool
• Examples:  TN, chla, and chlamodel parameter updating results

 

Post (TMDL) Implementation Questions

Has compliance with the water quality standard been achieved?

If compliance has not been achieved, what pollutant reduction actions did not respond as predicted?

Can the decline of chlaconcentrations in the estuary be attributed to actions in the watershed?

Can we quantify the changes in N loading from the watershed?

Year 3 Issues

• Use the CUAHSI “digital watershed”to efficiently link SPARROW and NeuBERN
• Represent land use (pollutant load) change in SPARROW
• Characterize prior probabilities for SPARROW parameters
• Design monitoring program (sensitivity analysis)

Coupling the HIS with the Neuse Bayesian Nitrogen Model

• A automated data retrieval system
• A reach based nitrogen loading model

Reach-Based State-Space Model

• Model framework:
  • Stream reach as the basic building block
  • Separating model and observation errors
  • Sequential updating model coefficient distributions
• Model features:
  • Using all available information
  • Accounting for spatiotemporal correlation
  • Ease of implementation

Model Description

• Example watershed with 9 stream reaches
• Used previously to illustrate SPARROW with 3 sub-watersheds

Expected Outcomes

• A coupled HIS-SPARROW (reach-based) watershed nitrogen loading model for the Neuse
• An automated modeling framework for:
  1. Re-assessment of the Neuse nitrogen TMDL for NC Division of Water Quality
  2. Development of guidelines and procedures for adaptive implementation of TMDLs

Journal Articles:

No journal articles submitted with this report: View all 9 publications for this project

Supplemental Keywords:

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 monitoring

Progress and Final Reports:

Original Abstract

2003 Progress Report

2004 Progress Report

2005 Progress Report

2006