Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments

Abstract

All hydrological models need to be calibrated to obtain satisfactory streamflow simulations. Here we present a novel parameter regionalization approach that involves the optimization of transfer equations linking model parameters to climate and landscape characteristics. The optimization was performed in a fully spatially distributed fashion at high resolution (0.05 degrees), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4,229 headwater catchments (<5,000 km(2)) worldwide. The optimized equations were subsequently applied globally to produce parameter maps for the entire land surface including ungauged regions. The approach was evaluated using the Kling-Gupta efficiency (KGE) and a gridded version of the hydrological model HBV. Tenfold cross validation was used to evaluate the generalizability of the approach and to obtain an ensemble of parameter maps. For the 4,229 independent validation catchments, the regionalized parameters yielded a median KGE of 0.46. The median KGE improvement (relative to uncalibrated parameters) was 0.29, and improvements were obtained for 88% of the independent validation catchments. These scores compare favorably to those from previous large catchment sample studies. The degree of performance improvement due to the regionalized parameters did not depend on climate or topography. Substantial improvements were obtained even for independent validation catchments located far from the catchments used for optimization, underscoring the value of the derived parameters for poorly gauged regions. The regionalized parameters-available via -should be useful for hydrological applications requiring accurate streamflow simulations.

Published in

Journal of geophysical research. Atmospheres
2020, Volume: 125, number: 17, article number: e2019JD031485
Publisher: AMER GEOPHYSICAL UNION

SLU Authors

• Seibert, Jan

  • Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
    
  • Stockholm University
  • University of Zürich

UKÄ Subject classification

Oceanography, Hydrology, Water Resources


Publication Identifiers

DOI: https://doi.org/10.1029/2019JD031485

Permanent link to this page (URI)

https://res.slu.se/id/publ/108146