Global high-resolution parameter maps for HBV
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 (0.05°), 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 info see the following open-access publication:
- Beck, H., M. Pan, P. Lin, J. Seibert, A. van Dijk, and E. Wood, Global fully-distributed parameter regionalization based on observed streamflow from 4229 headwater catchments, Journal of Geophysical Research: Atmospheres, in review.
The latest version (0.8) of the parameter maps, including the HBV model code used to derive the maps, can be downloaded here. 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 above-mentioned paper.
Frequently asked questions
Can the parameter maps be used for climate change impact assessments?
The regionalized parameters were optimized to achieve better runoff simulations using historic climate conditions, and the validity of the parameters may be compromised when the model is subjected to climate conditions it has never experienced before. Runoff projections produced using HBV with regionalized parameters are therefore not necessarily more plausible than those produced using HBV with unoptimized parameters. See Vaze et al. (2010) and Fowler et al. (2020) for further discussion.
Which of the ten sets of global parameter maps should I use?
The ten sets of global parameter maps represent different cross-validation folds, meaning they were derived from slightly different (but overlapping) subsets of catchments. All ten sets of parameter maps are equally uncertain and therefore should produce equally “good” simulations. Ideally, all ten sets of parameter maps are used in an ensemble modeling framework to obtain an ensemble of outputs, the spread of which provides an indication of the uncertainty in the parameters. See McIntyre et al. (2005) and Yang et al. (2011) for more discussion on ensemble modeling.
Can I use the parameter maps at another spatial resolution or only 0.05°?
The interactions between model parameters and simulation results are generally highly complex and nonlinear. The outputs of a low-resolution model with resampled parameter maps will therefore likely significantly differ from the resampled outputs of a high-resolution model with 0.05° parameter maps. Thus, we do not recommend using the parameter maps at other spatial resolutions. See Beven (1995), Kling and Gupta (2009), and Freund et al. (2020) for further discussion.
The parameter maps were produced by Hylke Beck (Princeton University and Princeton Climate Analytics, Inc.). The following organizations are thanked for providing streamflow and/or catchment boundary data: the United States Geological Survey (USGS), the Global Runoff Data Centre (GRDC), the Brazilian Agencia Nacional de Aguas, EURO-FRIEND-Water, the European Commission Joint Research Centre (JRC), the Water Survey of Canada (WSC), the Australian Bureau of Meteorology (BoM), and the Chilean Center for Climate and Resilience Research (CR2).