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

Schellekens, Jaap; Dutra, Emanuel; la Torrej, Alberto Martinez-de; Balsamo, Gianpaolo; Van Dijk, Albert; Sperna Weiland, F.C.; Minvielle, Marie; Calvet, J C; Decharme, B; Eisner, Stephanie L; Fink, Gabriel; Flörke, Martina

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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...[Show more]

dc.contributor.authorSchellekens, Jaap
dc.contributor.authorDutra, Emanuel
dc.contributor.authorla Torrej, Alberto Martinez-de
dc.contributor.authorBalsamo, Gianpaolo
dc.contributor.authorVan Dijk, Albert
dc.contributor.authorSperna Weiland, F.C.
dc.contributor.authorMinvielle, Marie
dc.contributor.authorCalvet, J C
dc.contributor.authorDecharme, B
dc.contributor.authorEisner, Stephanie L
dc.contributor.authorFink, Gabriel
dc.contributor.authorFlörke, Martina
dc.date.accessioned2021-04-28T03:52:22Z
dc.date.available2021-04-28T03:52:22Z
dc.identifier.issn1866-3508
dc.identifier.urihttp://hdl.handle.net/1885/231081
dc.description.abstractThe 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.
dc.description.sponsorshipThis research received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 603608, “Global Earth Observation for integrated water resource assessment”: eartH2Observe
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherCopernicus Publications
dc.rights© Author(s) 2017
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/
dc.sourceEarth System Science Data
dc.source.urihttps://essd.copernicus.org/articles/9/389/2017/
dc.titleA global water resources ensemble of hydrological models: The eartH2Observe Tier-1 dataset
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume9
dc.date.issued2017
local.identifier.absfor090509 - Water Resources Engineering
local.identifier.absfor040608 - Surfacewater Hydrology
local.identifier.ariespublicationa383154xPUB7407
local.publisher.urlhttps://essd.copernicus.org
local.type.statusPublished Version
local.contributor.affiliationSchellekens, Jaap, WL/Delft Hydraulics
local.contributor.affiliationDutra, Emanuel, European Centre for Medium-Range Weather Forecasts
local.contributor.affiliationla Torrej, Alberto Martinez-de, Centre for Ecology and Hydrology
local.contributor.affiliationBalsamo, Gianpaolo, European Centre for Medium-Range Weather Forecast
local.contributor.affiliationVan Dijk, Albert, College of Science, ANU
local.contributor.affiliationSperna Weiland, F.C., Deltares
local.contributor.affiliationMinvielle, Marie, CNRM/GAME, Météo-France,
local.contributor.affiliationCalvet, J C , CNRM-GAME UMR3589
local.contributor.affiliationDecharme , B , CNRM-GAME UMR3589
local.contributor.affiliationEisner, Stephanie L, University of Kassel
local.contributor.affiliationFink, Gabriel, University of Kassel
local.contributor.affiliationFlörke, Martina, University of Kassel
local.bibliographicCitation.issue2
local.identifier.doi10.5194/essd-9-389-2017
local.identifier.absseo960913 - Water Allocation and Quantification
dc.date.updated2020-11-23T10:05:45Z
local.identifier.scopusID2-s2.0-85021752394
local.identifier.thomsonID000404539700001
dcterms.accessRightsopen access
dc.provenanceThis work is distributed under the Creative Commons Attribution 3.0 License.
dc.rights.licenseCreative Commons Attribution licence
CollectionsANU Research Publications

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