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A Decision Support System for Nonpoint Source Pollution Management Using a Distributed Model-GIS-DBMS Linkage

by Jaewan Yoon, (A.M.ASCE), North Dakota State Univ, Fargo, United States,
G. Padmanabhan, (A.M.ASCE), North Dakota State Univ, Fargo, United States,



Document Type: Proceeding Paper

Part of: Computing in Civil Engineering

Abstract:

Methods were developed for directly linking the distributed parameter model, AGNPS with a GIS and a relational database management system (RDBMS) to investigate a nonpoint source pollution problem. AGNPS is an event-based model that simulates runoff and the transport of sediment and pollutants from mainly agricultural watersheds. Distributed parameter models such as AGNPS are often applied to large problem domains. Linking such models to GIS and database management system can facilitate better data storage, manipulation and analysis than conventional methods. In this study, rather than manually implementing AGNPS, extracted data are integrated in an automatic fashion through a direct linking between the AGNPS model engine and GIS. This direct linkage results in a powerful, up-to-date tool that would be capable of monitoring and instantaneously visualizing the transport of any pollutant that AGNPS can simulate. Thereby, it reduces the time required to analyze the numerical output from AGNPS, and enables users to identify critical areas of nonpoint source (NPS) pollution and furthermore, to perform various `what if' scenarios to support the decision making processes such as Best Management Practices (BMP) for the watershed. A case study was performed on a watershed of 98.1 km² (= 24240 acres) in the upper segment of the Sand Hill River Subbasin in Minnesota by using this linkage implementation. Simulated results showed that the optimal BMP scenario achieved an average reduction of about 26% from current nonpoint source pollutant levels on soluble and sediment-attached nitrogen and phosphorous. Especially, this optimal BMP scenario was most effective in controlling the erosion and sediment yield. As a result, a maximum 50% reduction of sediment yield was observed.

Subject Headings: Geographic information systems | Nonpoint pollution | Information systems | Water pollution | Management methods | Information management | Data analysis | Minnesota | United States

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