Development and Evaluation of TMDL Planning and Assessment
Tools and Processes
Bruce Wilson, Professor
John Nieber, Professor
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:
1. Develop, improve, and evaluate watershed models
and other approaches for TMDL development and implementation.
2. Assess potential/likely economic benefits and
costs and equity issues associated with TMDL implementation
at the
watershed and individual landowner scale.
3. 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.
Work is also
underway to compile a comprehensive
data base for TMDL work.
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