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Title: SPATIAL WATER QUALITY MODELING FRAMEWORK DEVELOPMENT USING ARCGIS9

Author
item Ascough Ii, James
item Green, Timothy
item Ahuja, Lajpat
item Ma, Liwang
item Vandenberg, Bruce

Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Proceedings
Publication Acceptance Date: 5/12/2005
Publication Date: 7/17/2005
Citation: Ascough II, J.C., Green, T.R., Ahuja, L.R., Ma, L., Vandenberg, B.C. 2005. Spatial water quality modeling framework development using ArcGIS9. Proceedings 05-2082. 2005 ASAE Annual International Meeting, Tampa, FL. July 17-20. http://asae/frymulti.com/techpapers.asp?confid=tfl2005.

Interpretive Summary: Most agricultural water quality models are based on lumped parameterizations of spatial processes. The MARIA-GIS (Management of Agricultural Resources through Integrated Assessment and Geographic Information Systems) water quality tool has been developed to predict space-time planning scenarios across spatially variable agricultural landscapes. The tool runs under the ArcGIS 9 environment, and consists of a multi-functional system for agricultural production and water quality simulation modeling; and spatial data storage, analysis, and display. MARIA-GIS offers a spatial framework for integrating a complex, agricultural system water quality model (modified USDA-ARS RZWQM) with interaction between simulated land areas via overland runoff and runon. MARIA-GIS also provides the increased interface sophistication necessary for distributed hydrologic modeling. This paper briefly describes the MARIA-GIS development history, with special emphasis on the spatial water quality modeling GIS framework and the incorporated simulation modeling components. We then apply MARIA-GIS to predict crop yield, nitrate (N) concentration, drainage volume, and nitrate loading from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation in response to rainfall amount, N source, N rate, and timing of N application for a long-term plot study near Nashua in northeastern Iowa, U.S.A. Predicted (simulated vs. observed) results show improved model performance considering spatially variable soil properties and overland flow generation within plots, as compared to point-based model simulation using homogeneous soils with no flow routing. Keywords. Simulation modeling, GIS, Water quality, Agricultural resource management, BMP.

Technical Abstract: Most agricultural water quality models are based on lumped parameterizations of spatial processes. The MARIA-GIS (Management of Agricultural Resources through Integrated Assessment and Geographic Information Systems) water quality tool has been developed to predict space-time planning scenarios across spatially variable agricultural landscapes. The tool runs under the ArcGIS 9 environment, and consists of a multi-functional system for agricultural production and water quality simulation modeling; and spatial data storage, analysis, and display. MARIA-GIS offers a spatial framework for integrating a complex, agricultural system water quality model (modified USDA-ARS RZWQM) with interaction between simulated land areas via overland runoff and runon. MARIA-GIS also provides the increased interface sophistication necessary for distributed hydrologic modeling. This paper briefly describes the MARIA-GIS development history, with special emphasis on the spatial water quality modeling GIS framework and the incorporated simulation modeling components. We then apply MARIA-GIS to predict crop yield, nitrate (N) concentration, drainage volume, and nitrate loading from a corn (Zea mays L.) and soybean (Glycine max (L.) Merr.) rotation in response to rainfall amount, N source, N rate, and timing of N application for a long-term plot study near Nashua in northeastern Iowa, U.S.A. Predicted (simulated vs. observed) results show improved model performance considering spatially variable soil properties and overland flow generation within plots, as compared to point-based model simulation using homogeneous soils with no flow routing. Keywords. Simulation modeling, GIS, Water quality, Agricultural resource management, BMP.