Quantifying the effects of data integration algorithms on the outcomes of a subsurface–land surface processes model - Shen, Chaopeng; Niu, Jie; Fang, Kuai (Environmental modelling & software : with environment data news, 2014)
Trans-disciplinary hydrologic models oriented toward practical questions must be accompanied by accurate parameterization techniques. This paper describes the effects of different choices in the integration of various data sources on outcomes of the model Process-based Adaptive Watershed Simulator coupled with the Community Land Model (PAWS + CLM). Using our Hierarchical Stochastic Selection method, the represented land use percentages are much closer to the raw dataset, and lead to a 26% difference in carbon flux from that of the traditional dominant classes method. River bed elevations extracted using a novel algorithm agree well with the groundwater table and significantly increase baseflow contribution to streams relative to a coarse-DEM-based model. The inclusion of additional information in the soil pedotransfer functions drastically shifts ET, Net Primary Production and recharge. These results indicate that judicious treatment of input data has strong impacts on hydrologic and ecosystem fluxes. We emphasize the need to report details of data integration procedures. •Novel data pre-processing and integration algorithms are presented.•We showed the impact of River Bed Elevation extraction algorithm on simulated baseflow.•We showed the impact of a novel Hierarchical Stochastic Selection method on land use representation and carbon flux.•We showed the influence of using additional data in soil pedotransfer functions on basin-wide hydrologic and ecosystem fluxes.•We emphasize the need to include details of data integration procedures in the literature.