Development and Evaluation
of TMDL Planning and Assessment Tools and Processes
S-1004 Regional Research
Project
Objective
- Develop, improve, and evaluate
watershed models and other approaches for TMDL development and implementation.
Approach
Evaluate existing watershed assessment
models (FHANTM, EAAMOD, ACRU2000, etc.) for their applicability for TMDL development
in agricultural watersheds. The ultimate goal of this task is to identify
tools that assess the impact of agricultural practices on the hydrologic,
chemical, biological, and economic response of a watershed and that are
appropriate for TMDL development.
Collect and assemble comprehensive databases to facilitate development
and evaluation of models used for TMDL development. Reliable, comprehensive,
and complete data sets are necessary for the development, testing and validation
of comprehensive watershed models. Computer development has driven
the development of detailed distributed models that take into consideration
variability in land use, geography and climate. Data to derive some
input parameters required for comprehensive models used for TMDL development
are often limited. By utilizing existing data wherever possible, this
project will provide data from plot, field, and watershed experiments for
use in the model development and testing activities. The data will
be used to test/evaluate the various watershed assessment and TMDL development
tools to determine their performance and adaptability for various water quality
impairments and under the variety of geological and land-use conditions represented
in the project. We will monitor management practices in the Lake Okeechobee
basin in support of the Lake Okeechobee basin TMDL development effort and
these data will be made available to project cooperators for model development
and evaluation.
Develop better guidelines for calibration and estimation of model uncertainty.
A critical component of this task will be to determine what is the minimum
amount of data needed to develop sound TMDLs. This may vary with the
particular modeling approach used for TMDL development. This task
is essential because the lack of water quality and flow data has been identified
as one of the most severe problems facing the TMDL program. We will
work toward evaluating the impacts of errors or uncertainty in model predictions
and resulting uncertainty in TMDL allocations.
Extend capability of models for TMDL development. If problems are
identified with the existing models with respect to TMDL development, efforts
will be made to improve the models where feasible. We will continue
the development of algorithms to predict the impact of agricultural practices
on the physical and chemical constituents in water and their delivery
out of the watershed.