TY - JOUR
T1 - Global Fully Distributed Parameter Regionalization Based on Observed Streamflow From 4,229 Headwater Catchments
AU - Beck, Hylke E.
AU - Pan, Ming
AU - Lin, Peirong
AU - Seibert, Jan
AU - van Dijk, Albert I.J.M.
AU - Wood, Eric F.
N1 - Generated from Scopus record by KAUST IRTS on 2023-02-14
PY - 2020/9/16
Y1 - 2020/9/16
N2 - 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°), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4,229 headwater catchments (
AB - 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°), instead of at lumped catchment scale, using an unprecedented database of daily observed streamflow from 4,229 headwater catchments (
UR - https://onlinelibrary.wiley.com/doi/10.1029/2019JD031485
UR - http://www.scopus.com/inward/record.url?scp=85091218167&partnerID=8YFLogxK
U2 - 10.1029/2019JD031485
DO - 10.1029/2019JD031485
M3 - Article
SN - 2169-897X
VL - 125
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 17
ER -