GloH2O

HBV

Global high-resolution parameter maps

Region­alized parameters for the HBV hydro­logical model to enhance streamflow estimates at global scale. 

Overview

All hydrological models are simplifications of a complex reality and therefore need to be calibrated to obtain satisfactory streamflow simulations. Here we present high-resolution (0.05°) optimized parameter maps for the conceptual hydrological model HBV covering the entire land surface including ungauged regions.

The maps were produced using 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, instead of at lumped catchment scale, using an unprece­dented database of daily observed streamflow from 4229 small catch­ments worldwide.

The region­alized parameters resulted in a median Kling-Gupta Efficiency (KGE) improvement of +0.29 (relative to uncal­i­brated parameters) in 4229 fully independent validation catch­ments. Improve­ments were obtained for 88 % of the validation catch­ments. Substantial improve­ments were obtained even for validation catch­ments located far from the catch­ments used for optimization, under­scoring the value of the derived parameters for poorly gauged regions. For more infor­mation, see the following open-access publication: 

Beck, H. E., Pan, M., Lin, P., Seibert, J., van Dijk, A. I. J. M., and Wood, E. F. (2020). Global fully distributed parameter region­al­ization based on observed streamflow from 4,229 headwater catch­ments. Journal of Geophysical Research: Atmos­pheres, 125, e2019JD031485. 

Data access

The latest version (0.9) of the parameter maps, including the HBV model code used to derive the maps, can be downloaded here. If the dataset forms a key component of your research, we kindly ask that you give us the oppor­tunity to comment on your results prior to publi­cation. The data and code are released under the CC BY-NC 4.0 license and thus may not be used for commercial purposes. By using the data in any publi­cation you agree to cite the corre­sponding paper.