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Hydrologic modeling applications

Hydrologic Modeling of Watersheds

The areas of ongoing research studies are the following:

Non-point source pollutions from agricultural fields, grazing lands, and urban areas are leading causes of water-quality problems in the United States. We analyze complex hydrologic processes on field and watershed scales and assessing potential critical areas of non-point source pollution in Kansas and surrounding states.

Within six watershed assessment and planning projects and 10 HUC-8 watersheds in Kansas we are applying various watershed and hydrologic modeling tools, simplified like STEPL and RUSLE2 and complex like SWAT and AGNPS, to assess watershed water-quality conditions, identify potential critical areas at field and HUC-12 watershed scales, and develop a cost-effective action plan that consists of implementation of selected conservation practices statistically distributed within the watershed for the highest water-quality benefits. To evaluate capability of different hydrologic methods to identify agricultural and grazing fields with greatest soil erosion and nutrients runoff potential, we developed modeling framework that incorporates existing models and uses various input databases including NLCD and NASS land cover datasets, STATSGO and SSURGO soil databases, and high resolution topography data (10 to 30 m). The framework relies on SWAT and RUSLE models to calculate pollutant loadings for each grid-cell or Hydrologic Response Unit (HRU) and then downscale the output to individual agricultural or pasture fields. Only top 10 to 20% of the fields ranked by the highest pollutant load are considered as critical areas. Multiple scenarios with inputs from various databases were conducted and evaluated. The simulation process is automated with the use of several tools in the SWATioTools package developed as extension to ArcGIS 9.x.

Impacts of climate change and associated shifts in weather patterns and land-use conditions on sustainability of current states of watersheds are examined with the use of a stochastic weather generator WINDS, watershed model SWAT, and outputs of Global Climate Models.