Calibrated VIC Model Parameters over CONUS

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. This dataset contains the 1/8 degeree calibrated VIC
parameters over CONUS in the article "In Quest of Calibration Density
and Consistency in Hydrologic Modeling: Distributed Parameter Calibration
against Streamflow Characteristics" by Yuan Yang, Ming Pan, Hylke E.
Beck, Colby K. Fisher, R. Edwrad Beighley, Shih-Chieh Kao, Yang Hong, and
Eric F. Wood, published by Water Resources Research in 2019.
doi/10.1029/2018WR024178