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A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

Abstract : The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979-2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr-1 (334 kg m-2 yr-1), while the ensemble mean of total evaporation was 537 kg m-2 yr-1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.
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https://hal-insu.archives-ouvertes.fr/insu-03668340
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Submitted on : Monday, May 16, 2022 - 8:49:14 AM
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Jaap Schellekens, Emanuel Dutra, Alberto Martínez-de La Torre, Gianpaolo Balsamo, Albert van Dijk, et al.. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset. Earth System Science Data, 2017, 9, pp.389-413. ⟨10.5194/essd-9-389-2017⟩. ⟨insu-03668340⟩

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