Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp019880vt84z
 Title: Calibrated VIC Model Parameters over CONUS Contributors: Yang, YuanPan, MingBeck, HylkeFisher, ColbyBeighley, R. EdwardKao, Shih-ChiehHong, YangWood, Eric Issue Date: 4-Sep-2019 Related Publication: Yang, Y., M. Pan, H. E. Beck, C. K. Fisher, R. E. Beighley, S.-C. Kao, Y. Hong, and E. F. Wood, 2019: "In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics", Water Resources Research, DOI:10.1029/2018WR024178 Abstract: Conventional basin-by-basin approaches to calibrate hydrologic models are limited to gauged basins and typically result in spatially discontinuous parameter fields. Moreover, the consequent low calibration density in space falls seriously behind the need from present-day applications like high resolution river hydrodynamic modeling. In this study we calibrated three key parameters of the Variable Infiltration Capacity (VIC) model at every 1/8° grid-cell using machine learning-based maps of four streamflow characteristics for the conterminous United States (CONUS), with a total of 52,663 grid-cells. This new calibration approach, as an alternative to parameter regionalization, applied to ungauged regions too. A key difference made here is that we tried to regionalize physical variables (streamflow characteristics) instead of model parameters whose behavior may often be less well understood. The resulting parameter fields no longer presented any spatial discontinuities and the patterns corresponded well with climate characteristics, such as aridity and runoff ratio. The calibrated parameters were evaluated against observed streamflow from 704/648 (calibration/validation period) small-to-medium-sized catchments used to derive the streamflow characteristics, 3941/3809 (calibration/validation period) small-to-medium-sized catchments not used to derive the streamflow characteristics) as well as five large basins. Comparisons indicated marked improvements in bias and Nash-Sutcliffe efficiency. Model performance was still poor in arid and semiarid regions, which is mostly due to both model structural and forcing deficiencies. Although the performance gain was limited by the relative small number of parameters to calibrate, the study and results here served as a proof-of-concept for a new promising approach for fine-scale hydrologic model calibrations. Description: Download the README.txt file for a detailed description of this dataset's content. URI: http://arks.princeton.edu/ark:/88435/dsp019880vt84z Referenced By: DOI:10.1029/2018WR024178 Appears in Collections: CEE Research Data Sets

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lai_uw_0.0625d.txtVIC vegetation parameter file80.57 MBText
soil_calibV4_0.0625d.txtVIC soil parameter (calibrated) file106.79 MBText
veglib_ldas.txtVIC vegetation library file3.85 kBText