Watershed Quality Index
To address the need for simplified resource assessment tools that use existing databases, Tennessee Technological University (TTU), in cooperation with the Tennessee Department of Environment and Conservation (TDEC), initiated this project with funding from the U.S. Environmental Protection Agency (EPA). The project was titled "Demonstrating a Holistic Approach to Identifying and Costing Needs on a Watershed Basis." The primary goal of the project was to use universally available data to develop a user-friendly tool for conceptual assessment of environmental and financial costs associated with changes in land- and water-resource practices. A watershed quality index (WQI) was developed to illustrate, in simplistic terms, the positive and/or negative impacts on receiving-water quality and quantity. Although the WQI model was designed for use on watersheds throughout the United States, it was developed based upon data for the Richland Creek watershed, a relatively large (1254 km2) system in south central Tennessee with multiple land uses.
WQI Model Components
The WQI model is a GIS-based model that consists of three watershed-process models:
- the Agricultural Nonpoint Source (AGNPS) model developed by the U.S. Department of Agriculture, which simulates surface water runoff;
- the United States Geological Survey's Modular Three-Dimensional Finite-Difference Groundwater Flow model (MODFLOW);
- the RCHRES module of the Hydrological Simulation Program-Fortran (HSPF) model developed by the EPA, which simulates instream processes.
The model also includes the fish biodiversity (FISHDIV) model, which was developed at TTU. The integration of these models provides comprehensive analysis of watershed quality, which is depicted by the WQI score. The WQI score is based on physical and chemical parameters such as sediments, nitrogen and phosphorus. The integration of model output also provides the basis for costing of alternative land uses and point-source-effluent treatment methods. With the WQI model, single-event and time-series simulations are available.
WQI Model Data Collection
The WQI model was developed using the following universally available data sets. The data sets, which were acquired from numerous federal and state agencies, provided input to the component watershed process models.
- Digital Elevation Model (U.S. Geological Survey [USGS])
- Stream Reach File 1 (USEPA)
- Land Use/Land Cover (USGS)
- Soils (USDA-Natural Resources Conservation Service [NRCS])
- Groundwater (USGS, TDEC)
- Meteorological Conditions (USDA-NRCS)
- Stream Discharge (USGS)
- Fish Biodiversity (Tennessee Valley Authority-River Action Team [TVA-RAT], Tennessee Wildlife Resources Agency [TWRA], TTU)
- Cost (USDA-Farm Services Agency [FSA], NRCS) Numerous watershed-specific data parameters were also required for WQI model execution. These included, but were not limited to, stream channel geomorphology, stream water temperature, suspended sediment concentrations, soil moisture conditions, hydraulic conductivity, wastewater effluent quality and quantity, and water withdrawals.
Computer Hardware and Software
The WQI model was developed in a workstation computing environment using Arc/Info and ArcView software. The Arc/Info Grid module permitted cell-based modeling for the overland flow and groundwater flow components. The ArcView software served as the foundation for WQI model interface user interaction. The use of ArcView allows users to easily visualize the effects of land use and water resource modifications on instream water quality and quantity.
Two documents, including a user's guide and a technical guide, have been written to provide technical assistance and support for WQI model users. A User's Guide for a Universal Watershed Quality Index (WQI) was written for the user who executes the WQI model, whereas A Universal Watershed Quality Index (WQI) was written for a system administrator, who is responsible for establishing the WQI model in a particular watershed. The technical guide was written as an instruction book that contains the sources of data and methods of acquisition and processing.