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ADDING GLOBAL SOILS DATA TO THE AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT TOOL (AGWA)
Citation:
LEVICK, L. R., D. J. SEMMENS, D. P. GUERTIN, I. S. BURNS, S. N. SCOTT, C. L. UNKRICH, AND D. C. GOODRICH. ADDING GLOBAL SOILS DATA TO THE AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT TOOL (AGWA). Presented at 2nd International Symposium on Transboundary Waters Management, Tucson, AZ, November 16 - 19, 2004.
Impact/Purpose:
The primary objectives of this research are to:
Develop methodologies so that landscape indicator values generated from different sensors on different dates (but in the same areas) are comparable; differences in metric values result from landscape changes and not differences in the sensors;
Quantify relationships between landscape metrics generated from wall-to-wall spatial data and (1) specific parameters related to water resource conditions in different environmental settings across the US, including but not limited to nutrients, sediment, and benthic communities, and (2) multi-species habitat suitability;
Develop and validate multivariate models based on quantification studies;
Develop GIS/model assessment protocols and tools to characterize risk of nutrient and sediment TMDL exceedence;
Complete an initial draft (potentially web based) of a national landscape condition assessment.
This research directly supports long-term goals established in ORDs multiyear plans related to GPRA Goal 2 (Water) and GPRA Goal 4 (Healthy Communities and Ecosystems), although funding for this task comes from Goal 4. Relative to the GRPA Goal 2 multiyear plan, this research is intended to "provide tools to assess and diagnose impairment in aquatic systems and the sources of associated stressors." Relative to the Goal 4 Multiyear Plan this research is intended to (1) provide states and tribes with an ability to assess the condition of waterbodies in a scientifically defensible and representative way, while allowing for aggregation and assessment of trends at multiple scales, (2) assist Federal, State and Local managers in diagnosing the probable cause and forecasting future conditions in a scientifically defensible manner to protect and restore ecosystems, and (3) provide Federal, State and Local managers with a scientifically defensible way to assess current and future ecological conditions, and probable causes of impairments, and a way to evaluate alternative future management scenarios.
Description:
The Automated Geospatial Watershed Assessment Tool (AGWA) is a GIS-based hydrologic modeling tool that is available as an extension for ArcView 3.x from the USDA-ARS Southwest Watershed Research Center (www.tucson.ars.ag.gov/agwa). AGWA is designed to facilitate the assessment of land-use and climate-change impacts on water yield and quality at multiple scales. It parameterizes two watershed runoff and erosion models, the Kinematic Runoff and Erosion
Model (KINERGS2) and the Soil and Water Assessment Tool (SWAT), using readily available topographic, soils, and land-cover data. After parameterization, the selected model is run through the interface, and results are imported back into the GIS for display and analysis. AGW A was originally designed to obtain hydrologic parameters from the State Soil Geographic (STATSGG) and Soil Survey Geographic (SSURGG) databases, which are only available for the United States. The latest version of AGW A (1.4x) has incorporated the ability to derive inputs from the internationally available Food and Agriculture Organization of the United Nations (FAG) digital soil map of the world. The ability to use FAG soils in AGWA facilitates the analysis of trans-boundary watersheds by avoiding difficulties associated with different classification schemes on either side of the border. When used with existing global, classified land-cover maps it is now easy to rurJ hydrologic simulations for trans-border watersheds. The structure and organization of the F AG soils dataset is fundamentally different from the STATSGG and S.SURGG datasets since it covers the entire globe and must describe a wider range of soils with a more generalized classification scheme. In addition, some of the variables required for model parameterization were not available in the FAG database and had to be acquired from other sources. The methodology used to translate information from the FAG soils dataset and other sources into input parameters for hydrologic models will be presented, along with a comparison of model results and parameters using the FAG, SSURGG and STATSGG soils datasets.