Research Accomplishment Reports 2009

Modeling for TMDL Development, and Watershed Based Planning, Management and Assessment

D.R. Edwards
Department of Biosystems and Agricultural Engineering

 

Non-Technical Summary

The Clean Water Act (CWA) employs regulatory and nonregulatory tools to reduce direct pollutant discharges into waterways, finance wastewater treatment facilities, and manage polluted runoff. These tools are used to restore and maintain the chemical, physical, and biological integrity of the nation's waters.

Starting in the late 1980s, efforts to address polluted runoff have increased significantly. For "nonpoint" runoff, voluntary programs, including cost-sharing with landowners, have been used as the key tools. Evolution of CWA programs over the last decade has included a shift to more holistic watershed-based strategies, with equal emphasis placed on protecting healthy waters and restoring impaired ones. Involvement of stakeholder groups is another hallmark of this approach.

The CWA Section 303(d) fact sheet indicates a total of 38,698 impaired waters. Due to the immensity of the stream miles, lakes and estuaries involved and the jurisdictional differences within impaired watersheds, tools are needed to better understand the causes and processes that can be used to restore and protect these water bodies. Total Maximum Daily Loads (TMDLs) are quantitative objectives and strategies to achieve water quality standards. The water quality standards constitute the goals required to fully support designated uses of streams, lakes, and wetlands. In general terms, the TMDL development process involves assessing the causes and amounts of pollution, identifying the best corrective actions and a monitoring strategy to ensure effectiveness.

There is a need to evaluate existing tools and to develop new ones based on the best science available. This project will develop tools to guide the use of these policies so stakeholders can understand what practices are available and why they should implement them. An important outcome of the project will be increased knowledge of the appropriateness of various TMDL development tools for application in agricultural watersheds. In addition, existing TMDL development tools will be enhanced and some new tools may be developed. This outcome will improve models used for TMDL development.

Another important outcome of the projects is improved software interfaces. This outcome will employ advances in information technology to allow data to be entered more easily in models and to aid in the interpretation of results. Another outcome of the project is the collection of data for TMDL model evaluation and for BMP effectiveness assessment. Available data will be utilized where possible, but some additional data collection will be required.

The overall outcome of the project will be the evaluation and development of watershed models, economic, and social analysis tools that can be used for TMDL development and implementation in agricultural watersheds. Project accomplishment will ensure that techniques used for TMDL development and implementation in agricultural watersheds are based on the best science available and that proposed TMDLs are feasible. The ultimate beneficiaries will be the agricultural community, land users, home owners and other stakeholders who will be impacted by the TMDL program.

2009 Project Description

Near the end of the study period, a study was completed that investigated total uncertainty, its components of model and parameter uncertainty, and how all types of uncertainty are influenced by model selection. These topics are specifically related to Tasks 9 and 10 of Objective 1. This study being largely a work in progress through the calendar year, dissemination of the findings has been largely informal, though communications with colleagues at other institutions. The technical outputs (probability distributions of model predictions, model parameters, and model correctness for different candidate models), however, are scheduled for presentation at upcoming professional meetings followed by publication.

2009 Impact

Work to date indicates that, at under at least some conditions, there are some advantages to using relatively complex hydrologic/water quality models in terms of lesser prediction uncertainty. The relationships between parameter and model uncertainty are more subtle, however, depending greatly on the amount of model calibration data available and the number of parameters present in the model. The findings additionally indicate that prediction uncertainty is highly dependent on uncertainty in only a few key model parameters, depending on model structure and subsequent calculations involving those parameters, a result that is consistent with sensitivity analysis.

Given the very limited duration of the project, impacts are necessarily limited; however, the results will most naturally find their application in litigation involving hydrologic/water quality models and in the design of data collection projects having the goal of calibrating hydrologic/water quality models.