Assessing Watershed Conditions with EMDS

The US EPA Office of Research and Development and the Forest Service (US Department of Agriculture) have cooperatively developed a new analytical technique for watershed assessment, using knowledge-based processing of landscape databases that enable environmental managers to make better decisions (Reynolds et al. 2000). Much of the knowledge base design concerns assessment of stream characteristics to provide decision support for the Total Maximum Daily Load (TMDL) program of the US EPA. Section 303(d) of the Clean Water Act, identifies sources of pollution remaining after end-of-pipe discharges are regulated and best available technology has been applied. Remaining sources of pollutants are termed non-point sources (NPS). Under requirements of the Act, states develop lists of waters that do not meet state water quality standards, even after point sources of pollution have installed required levels of pollution control technology. States must establish priority rankings based on severity of pollution and beneficial uses of water bodies, such as recreation or fishing, and must develop TMDLs for waters on the lists. TMDLs specify amounts of pollutants that need to be reduced to meet state water quality standards and allocate pollution control responsibilities among pollution sources in a watershed.

The Ecosystem Management Decision Support (EMDS) system supports ecological assessments, usually at regional, provincial, or watershed scales (Table III). It provides a general software environment for building knowledge bases that describe logical relations among ecosystem states and processes of interest in an assessment. Once these knowledge bases are constructed by specialists, the system provides tools for analyzing the logical structure and the importance of missing information. EMDS provides a formal logic-based approach to assessment analysis that integrates diverse topics into a single set of analyses. It also provides robust methods for handling incomplete information. A variety of maps, tables, and graphs provide useful information about what data are missing, the influence of missing data, and how data are distributed in the landscape. EMDS also provides support for exploring alternative future conditions.

• EMDS Example Introduction
• Knowledge base structure
• Example EMDS Analysis
• EMDS Case Study Discussion