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dc.contributor.author
Gudmundsson, Lukas
dc.contributor.author
Seneviratne, Sonia I.
dc.date.accessioned
2019-04-18T10:30:02Z
dc.date.available
2017-06-11T18:23:40Z
dc.date.available
2019-04-18T10:30:02Z
dc.date.issued
2015-06-02
dc.identifier.issn
1027-5606
dc.identifier.issn
1607-7938
dc.identifier.other
10.5194/hess-19-2859-2015
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/102543
dc.identifier.doi
10.3929/ethz-b-000102543
dc.description.abstract
Terrestrial water variables are the key to understanding ecosystem processes, feed back on weather and climate, and are a prerequisite for human activities. To provide context for local investigations and to better understand phenomena that only emerge at large spatial scales, reliable information on continental-scale freshwater dynamics is necessary. To date streamflow is among the best-observed variables of terrestrial water systems. However, observation networks have a limited station density and often incomplete temporal coverage, limiting investigations to locations and times with observations. This paper presents a methodology to estimate continental-scale runoff on a 0.5° spatial grid with monthly resolution. The methodology is based on statistical upscaling of observed streamflow from small catchments in Europe and exploits readily available gridded atmospheric forcing data combined with the capability of machine learning techniques. The resulting runoff estimates are validated against (1) runoff from small catchments that were not used for model training, (2) river discharge from nine continental-scale river basins and (3) independent estimates of long-term mean evapotranspiration at the pan-European scale. In addition it is shown that the produced gridded runoff compares on average better to observations than a multi-model ensemble of comprehensive land surface models (LSMs), making it an ideal candidate for model evaluation and model development. In particular, the presented machine learning approach may help determining which factors are most relevant for an efficient modelling of runoff at regional scales. Finally, the resulting data product is used to derive a comprehensive runoff climatology for Europe and its potential for drought monitoring is illustrated.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus Publications
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Towards observation-based gridded runoff estimates for Europe
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2015-06-22
ethz.journal.title
Hydrology and Earth System Sciences
ethz.journal.volume
19
en_US
ethz.journal.issue
6
en_US
ethz.journal.abbreviated
Hydrol. earth syst. sci.
ethz.pages.start
2859
en_US
ethz.pages.end
2879
en_US
ethz.size
21 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
001881462
ethz.publication.place
Göttingen
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03778 - Seneviratne, Sonia / Seneviratne, Sonia
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03778 - Seneviratne, Sonia / Seneviratne, Sonia
ethz.date.deposited
2017-06-11T18:24:04Z
ethz.source
ECIT
ethz.identifier.importid
imp5936535303f4c98344
ethz.ecitpid
pub:160683
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-13T01:50:37Z
ethz.rosetta.lastUpdated
2019-04-18T10:30:31Z
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Towards%20observation-based%20gridded%20runoff%20estimates%20for%20Europe&rft.jtitle=Hydrology%20and%20Earth%20System%20Sciences&rft.date=2015-06-02&rft.volume=19&rft.issue=6&rft.spage=2859&rft.epage=2879&rft.issn=1027-5606&1607-7938&rft.au=Gudmundsson,%20Lukas&Seneviratne,%20Sonia%20I.&rft.genre=article&
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