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Best Management Practices (BMPs) have become the most effective way to mitigate non-point source pollution (NPS) issues. Much attention has been paid to NPS in rural areas, where agricultural activities increase nutrients, toxics, and sediments in surface water. Stormwater from urban areas is also a major contributor to NPS pollution. For watersheds bearing various soil types and land uses, a single type of BMP cannot be the panacea to all stormwater problems. To solve these problems, a Diagnostic Decision Support System (DDSS) was developed in this research. The DDSS can identify and locate the most critical NPS areas (hotspots) within a watershed in high spatial resolution. The DDSS can provide a series of spatially distributed small-scale BMPs which are effective in treating the NPS and are suitable for the physical environment. The BMPs, varying in types and locations, are recommended at HRU (Hydrologic Response Unit) level. The DDSS was tested in Watts Branch, a small urban watershed of the Anacostia River in metropolitan Washington D.C., USA. The process-based hydrologic model, Soil and Water Assessment Tool (SWAT), was used to simulate watershed responses. The simulation results were then used by the DDSS for BMP recommendation. Hotspots of different NPS were successfully located and prescribed with spatially distributed BMPs. The DDSS serves as a useful tool to better understand urban watersheds and to make proper stormwater management plans. [ABSTRACT FROM AUTHOR]

A decision support system in the framework of the geographic information system (GIS) and subsurface flow model, Hydrosub, were used to identify critical areas from simulated spatial distributions of relative nitrogen export. Diagnosis and prescription Expert Systems (ES) are developed and applied to the identification of probable causes of excessive nitrogen export and selection of appropriate Best Management Practices (BMPs). The result is a spatially distributed set of recommended Best Management Practices that are feasible economically and environmentally. For the study watershed, using catch crops and rhizobium-legume (instead of using conventional commercial fertilizers) were the most recommended Best Management Practices. [ABSTRACT FROM AUTHOR]

Phosphorus (P) is one of the main nutrients controlling algal production in aquatic systems. Proper management of P in agricultural production systems can greatly enhance our ability to combat pollution of aquatic environments. To address this issue, a decision support system (DSS) consisting of the Maryland Phosphorus Index (PI), diagnosis expert system (ES), prescription ES, and a nonpoint‐source pollution model, Ground Water Loading Effects of Agricultural Management Systems (GLEAMS), was developed and applied to an agricultural watershed in southern Sweden. This system can identify critical source areas (CSAs) regarding phosphorus losses within the watershed, make a diagnosis of probable causes, prescribe the most appropriate best management practices (BMPs), and test the environmental effects of the applied BMPs. The PI calculations identified small parts of the watershed as CSAs. Only 10.4% of the total watershed area in 1995 and 5.2% of the total watershed area in 1996 were classed as "high potential P movement." Four probable causes (high P level in soil, excessive P fertilization, stream proximity, and subsurface drainage) and three BMPs (riparian buffer strips, reduced P fertilizer application, and P fertilizer incorporation) were identified by a diagnosis and prescription expert system. The GLEAMS simulations conducted for one selected CSA field for a 24‐yr period showed that the recommended BMP reduced runoff P losses by 55% and sediment P losses by 71%, if applied from the first year. Results showed that using DSS may enable us to select a proper BMP implementation strategy and to realize the beneficial effect of BMPs on a long‐term basis. [ABSTRACT FROM AUTHOR]

Climate variability and farmers' desire to improve the crop yield have resulted in an increase in irrigated agriculture in the mid-Atlantic region. However, the huge initial capital cost associated with the installation and operation of irrigation systems is generally prohibitive, with most farmers finding difficulty in justifying the expenditure, and uncertainty of the overall return on their investment. The objective of this study was to develop a decision tool for farmers in temperate regions to evaluate the cost-benefit of irrigation installations. The developed irrigation economic model involved the development of an economic component that balances the expected economic return, based on anticipated crop yield increases due to supplemental irrigation, versus the water, maintenance, and capital costs associated with the irrigation system. Model development included the input of relevant data and required local calibration. Soil and Water Assessment Tool (SWAT) output files were used as the basis for data input into the irrigation economic model. An irrigation-scheduling component was incorporated into the model to prescribe irrigation volumes for each agricultural field defined within the area of interest. The economic component of the model identifies and prioritizes those fields in which supplemental irrigation will result in the greatest economic return in terms of increased agricultural production and revenue. The study is conducted on the Pocomoke river basin in the Coastal Plain of Maryland's eastern shore. Results showed that irrigation system selection was mainly influenced by cost of water and irrigation installation costs, and to a lesser extent by physical characteristics of the terrain and the associated properties. [ABSTRACT FROM AUTHOR]