2002 Annual Report
Research
Development and Evaluation of TMDL Planning and Assessment Tools and
Processes
Bruce Wilson, Associate Professor
John Nieber, Professor
Mary Renwick, Senior Fellow in Economics and Water Policy, Water Resources
Center
26 additional personnel from 17 different states
Funding Source
Minnesota Agricultural Experiment Station-Regional Project S-1004
Objective
Total Maximum Daily Loads (TMDLs) is being used nationwide to assess
the amount of a pollutant that a waterbody can receive and still meet
applicable state water quality standards. This is a multi-state region
project to improve the science and tools that are used in TMDL work. The
specific objectives are to:
- Develop, improve, and evaluate watershed models and other approaches for TMDL development and implementation.
- Assess potential/likely economic benefits and costs and equity issues associated with TMDL implementation at the watershed and individual landowner scale.
- Assess the potential ecological benefits/implications of TMDL implementation
at watershed level.
Project Description
The TMDL program has become a national issue because lawsuits have forced
the USEPA to develop rules that require every state to develop and submit
TMDL plans for all waterways in the United States that fail to meet state
water quality standards. Current public and private costs associated with
this effort are estimated to be $1.035 billion for development of TMDL
plans, $255 million for additional monitoring to support TMDLs, and $13.5
to $64.5 billion for implementation of TMDL plans over the next fifteen
years. According to the USEPA, agriculture is the largest source of water
quality impairment in the United States. As a consequence, agriculture
is the focus of many TMDL studies. The regional project brings together
expertise in agriculture, agricultural economics, water quality monitoring
and modeling, agricultural pollution control, and the TMDL planning process
to address TMDLs.
Results
Our contributions to the regional project in the past year include better understanding of the fundamental processes of soil erosion and in the calibration methods. Detailed information has been collected on shear partitioning processes. A framework for calibrating models has been proposed using a Bayesian framework. This framework allows experiential information to be combined with data in the calibration. The usefulness of the approach was examined by calibrating a drainage model. The Bayesian approach was shown to be superior to traditional least square methods.