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Spatial Decision Support for Optimizing Placement of BMPs
Citation:
Sinshaw, T., C. Surbeck, D. Shields, A. Hossain, AND K. Forshay. Spatial Decision Support for Optimizing Placement of BMPs. American Geophysical Union Annual Meeting, Washington, District Of Columbia, December 10 - 13, 2018.
Impact/Purpose:
Nutrient pollution is causing a severe water quality degradation in the U.S. water bodies that resulted fish kill and loss of sensitive species. Currently, efforts are under way to restore nutrient impaired waters to regain the socio-economic and ecological benefits. Such restoration planning is supported by a watershed tools and data. However, in watersheds where data is scarce, the use of watershed tools is limited. In this research, an alternative tool was developed to support restoration plans, such as best management practice choice and placement, for a data scarce watershed. This approach will assist watershed managers and researchers to perform restoration studies when resources are not available for monitoring.
Description:
Nutrient reduction efforts are planned based on spatially complex watershed information. These efforts encompass a series of activities, such as identifying sources, quantifying source yields, estimating exported load, and establishing source reducing best management practices (BMPs). The choice and placement of BMPs requires a decision on three conflicting objectives: performance, site suitability, and establishment cost. The present study applied a spatial decision support system for the Beasley Lake Watershed to optimize a nitrogen (N) source reduction plan. The watershed information required to assess N pollution was stored as a database pool and served as an updatable data view. The nutrient movement on the landscape was tracked from sources to the receiving Beasley Lake using a distance-decay method. The critical N source locations and suitable sites for establishing buffer strips and wetlands were identified. This information served as a decision guide for choice and placement of BMPs within the watershed. Three BMP scenarios were identified through an iterative BMP placement process. With these BMP scenarios, it was possible to reduce up to 25% of the N load. The best BMP scenario was found at a cost to performance ratio of 168 $/kg. The approach presented in this study can be an alternative N assessment method when the availability of data and resources limit the use of existing watershed models for water quality assessment.