Articles | Volume 9, issue 2
Earth Syst. Sci. Data, 9, 389–413, 2017
Earth Syst. Sci. Data, 9, 389–413, 2017

Review article 03 Jul 2017

Review article | 03 Jul 2017

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

Jaap Schellekens1, Emanuel Dutra2,a, Alberto Martínez-de la Torre3, Gianpaolo Balsamo2, Albert van Dijk4, Frederiek Sperna Weiland1, Marie Minvielle5, Jean-Christophe Calvet5, Bertrand Decharme5, Stephanie Eisner6, Gabriel Fink6, Martina Flörke6, Stefanie Peßenteiner7, Rens van Beek7, Jan Polcher8, Hylke Beck9,b, René Orth10, Ben Calton11, Sophia Burke12, Wouter Dorigo13, and Graham P. Weedon14 Jaap Schellekens et al.
  • 1Deltares, Rotterdamseweg 185, 2629 HD Delft, the Netherlands
  • 2European Centre for Medium-Range Weather Forecasts, Shinfield Park, Reading RG2 9AX, UK
  • 3Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
  • 4Fenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia
  • 5CNRM/GAME, Météo-France, CNRS, UMR 3589, 42 avenue Coriolis, 31057 Toulouse CEDEX 1, France
  • 6Center for Environmental Systems Research (CESR), University of Kassel, Wilhelmshöher Allee 47, 34117 Kassel, Germany
  • 7Department of Physical Geography, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, the Netherlands
  • 8Laboratoire de Météorologie Dynamique (LMD, CNRS), Ecole Polytechnique, 91128 Palaiseau, France
  • 9European Commission, Institute for Environment and Sustainability, Joint Research Centre, Ispra, VA, Italy,
  • 10Institute for Atmospheric and Climate Science, ETH Zurich, Universitätstrasse 16, 8092 Zurich, Switzerland
  • 11PML Applications Ltd, Prospect Place, The Hoe, Plymouth, UK
  • 12AmbioTEK Community Interest Company, Essex, UK
  • 13Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria
  • 14Met Office, Joint Centre for Hydrometeorological Research, Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford, OX10 8BB, UK
  • anow at: Instituto Dom Luiz, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisbon, Portugal
  • bnow at: Princeton University, Civil and Environmental Engineering, Princeton, NJ, USA

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,, 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

Short summary
The dataset combines the results of 10 global models that describe the global continental water cycle. The data can be used as input for water resources studies, flood frequency studies etc. at different scales from continental to medium-scale catchments. We compared the results with earth observation data and conclude that most uncertainties are found in snow-dominated regions and tropical rainforest and monsoon regions.