WRAPHydro Data Model: Finding Input Parameters for the Water Rights Analysis Package
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The Water Availability Model requires geospatial parameters to be used as inputs into the WRAP model. Previously these parameters were developed in ArcView 3.2 and processing suffered from performance and data management issues. This thesis presents a new hydro data model, WRAP Hydro, developed specifically for the WRAP project. A new method of determining watershed parameters for the Guadalupe basin using the Arc Hydro and WRAP Hydro toolsets is discussed. The parameter processing is done in three stages, getting base data, preprocessing and the actual processing. This provides a systematic and structured approach to determine watershed parameters. This work also validates the division of a basin into sub basins for a more efficient processing of parameters. It is found that both these methods give identical results. The values obtained by these two methods for upstream area for each control point were compared with the United States Geological Survey area values and it was observed that they matched well. The process of finding parameters when new stream segments and control points are added without having to redo the whole process again is also discussed in the thesis. The WRAP Hydro toolset provides functions that help to add and remove control points from the network. It is also possible to incorporate a new stream edit without having to process the grids for the whole basin again. Notes: This report was originally posted on the Web site of the University of Texas Center for Research in Water Resources as CRWR Online Report 03-3. This research is a component of The Watershed Consortium, a program the Texas Water Resources Institute administers for the US Army Corps of Engineers to support advances in water resources computing efforts. Consortium members include the Texas Water Resources Institute, the Center for Research in Water Resources, the Texas A&M Spatial Sciences Laboratory, and the Texas A&M Civil Engineering Department.