Development and Evaluation of
TMDL Planning and Assessment Tools and Processes
Bruce Wilson, 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.
Need or Impact
The TMDL program has become a national issue because lawsuits
have forced the U.S. Environmental Protection Agency 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.
Project Status
Contributions to the regional project 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.
|
