Quantifying the effects of data integration algorithms on the outcomes of a subsurface-land surface processes model

摘要: Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in integration various data sources on outcomes model Process-based Adaptive Watershed Simulator coupled with Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, represented land use percentages are much closer to raw dataset, and lead a 26% difference carbon flux from that traditional dominant classes method. River bed elevations extracted using novel algorithm agree well groundwater table significantly increase baseflow contribution streams relative coarse-DEM-based model. The inclusion additional information soil pedotransfer functions drastically shifts ET, Net Primary Production recharge. These results indicate judicious treatment input has strong impacts ecosystem fluxes. We emphasize need report details procedures.