Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts

  1. Zaherpour, Jamal ORCID logo
  2. Gosling, Simon ORCID logo
  3. Mount, Nick
  4. Müller Schmied, Hannes ORCID logo
  5. Veldkamp, Ted ORCID logo
  6. Dankers, Rutger ORCID logo
  7. Eisner, Stephanie ORCID logo
  8. Gerten, Dieter ORCID logo
  9. Gudmundsson, Lukas ORCID logo
  10. Haddeland, Ingjerd ORCID logo
  11. Hanasaki, Naota ORCID logo
  12. Kim, Hyungjun
  13. Leng, Guoyong
  14. Liu, Junguo
  15. Masaki, Yoshimitsu
  16. Oki, Taikan ORCID logo
  17. Pokhrel, Yadu ORCID logo
  18. SATOH, Yusuke ORCID logo
  19. Schewe, Jacob ORCID logo
  20. Wada, Yoshihide ORCID logo

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Zaherpour_2018_Environ._Res._Lett._13_065015.pdf 3,94MB
WeightNameValue
1000 Titel
  • Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts
1000 Autor/in
  1. Zaherpour, Jamal |
  2. Gosling, Simon |
  3. Mount, Nick |
  4. Müller Schmied, Hannes |
  5. Veldkamp, Ted |
  6. Dankers, Rutger |
  7. Eisner, Stephanie |
  8. Gerten, Dieter |
  9. Gudmundsson, Lukas |
  10. Haddeland, Ingjerd |
  11. Hanasaki, Naota |
  12. Kim, Hyungjun |
  13. Leng, Guoyong |
  14. Liu, Junguo |
  15. Masaki, Yoshimitsu |
  16. Oki, Taikan |
  17. Pokhrel, Yadu |
  18. SATOH, Yusuke |
  19. Schewe, Jacob |
  20. Wada, Yoshihide |
1000 Erscheinungsjahr 2018
1000 LeibnizOpen
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2018-06-12
1000 Erschienen in
1000 Quellenangabe
  • 13(6):065015
1000 FRL-Sammlung
1000 Copyrightjahr
  • 2018
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1088/1748-9326/aac547 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.
1000 Sacherschließung
lokal extreme events
lokal global hydrological models
lokal model validation
lokal human impacts
lokal land surface models
lokal model evaluation
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0001-5497-4589|https://orcid.org/0000-0001-5973-6862|https://frl.publisso.de/adhoc/uri/TW91bnQsIE5pY2s=|https://orcid.org/0000-0001-5330-9923|https://orcid.org/0000-0002-2295-8135|https://orcid.org/0000-0003-2375-5468|https://orcid.org/0000-0002-0157-1636|https://orcid.org/0000-0002-6214-6991|https://orcid.org/0000-0003-3539-8621|https://orcid.org/0000-0002-3847-6229|https://orcid.org/0000-0002-5092-7563|https://frl.publisso.de/adhoc/uri/S2ltLCBIeXVuZ2p1bg==|https://frl.publisso.de/adhoc/uri/TGVuZywgR3VveW9uZw==|https://frl.publisso.de/adhoc/uri/TGl1LCBKdW5ndW8=|https://frl.publisso.de/adhoc/uri/TWFzYWtpLCBZb3NoaW1pdHN1|https://orcid.org/0000-0003-4067-4678|https://orcid.org/0000-0002-1367-216X|https://orcid.org/0000-0001-6419-7330|https://orcid.org/0000-0001-9455-4159|https://orcid.org/0000-0003-4770-2539
1000 Label
1000 Förderer
  1. Bundesministerium für Bildung und Forschung |
  2. Islamic Development Bank |
  3. University of Nottingham |
  4. Norges Forskningsråd |
  5. Leibniz-Gemeinschaft |
  6. Seventh Framework Programme |
  7. National Natural Science Foundation of China |
  8. Beijing Municipal Natural Science Foundation |
  9. Southern University of Science and Technology |
  10. U.S. Department of Energy |
  11. Ministry of the Environment |
  12. Japan Society for the Promotion of Science |
1000 Fördernummer
  1. 01LS1201A1
  2. -
  3. -
  4. 243803/E10
  5. SAW-2013 PIK-5
  6. 603864
  7. 41625001; 41571022
  8. 8151002
  9. G01296001
  10. DE-AC05-76RLO1830
  11. S-10
  12. 16H06291
1000 Förderprogramm
  1. Inter-Sectoral Impact Model Intercomparison Project, phase 2a (ISIMIP2a)
  2. -
  3. -
  4. -
  5. -
  6. HELIX
  7. -
  8. -
  9. -
  10. -
  11. Environment Research and Technology Development Fund
  12. KAKENHI
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Bundesministerium für Bildung und Forschung |
    1000 Förderprogramm Inter-Sectoral Impact Model Intercomparison Project, phase 2a (ISIMIP2a)
    1000 Fördernummer 01LS1201A1
  2. 1000 joinedFunding-child
    1000 Förderer Islamic Development Bank |
    1000 Förderprogramm -
    1000 Fördernummer -
  3. 1000 joinedFunding-child
    1000 Förderer University of Nottingham |
    1000 Förderprogramm -
    1000 Fördernummer -
  4. 1000 joinedFunding-child
    1000 Förderer Norges Forskningsråd |
    1000 Förderprogramm -
    1000 Fördernummer 243803/E10
  5. 1000 joinedFunding-child
    1000 Förderer Leibniz-Gemeinschaft |
    1000 Förderprogramm -
    1000 Fördernummer SAW-2013 PIK-5
  6. 1000 joinedFunding-child
    1000 Förderer Seventh Framework Programme |
    1000 Förderprogramm HELIX
    1000 Fördernummer 603864
  7. 1000 joinedFunding-child
    1000 Förderer National Natural Science Foundation of China |
    1000 Förderprogramm -
    1000 Fördernummer 41625001; 41571022
  8. 1000 joinedFunding-child
    1000 Förderer Beijing Municipal Natural Science Foundation |
    1000 Förderprogramm -
    1000 Fördernummer 8151002
  9. 1000 joinedFunding-child
    1000 Förderer Southern University of Science and Technology |
    1000 Förderprogramm -
    1000 Fördernummer G01296001
  10. 1000 joinedFunding-child
    1000 Förderer U.S. Department of Energy |
    1000 Förderprogramm -
    1000 Fördernummer DE-AC05-76RLO1830
  11. 1000 joinedFunding-child
    1000 Förderer Ministry of the Environment |
    1000 Förderprogramm Environment Research and Technology Development Fund
    1000 Fördernummer S-10
  12. 1000 joinedFunding-child
    1000 Förderer Japan Society for the Promotion of Science |
    1000 Förderprogramm KAKENHI
    1000 Fördernummer 16H06291
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6424776.rdf
1000 Erstellt am 2020-12-17T11:00:59.949+0100
1000 Erstellt von 270
1000 beschreibt frl:6424776
1000 Bearbeitet von 122
1000 Zuletzt bearbeitet Wed Feb 17 09:20:56 CET 2021
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1000 Vgl. frl:6424776
1000 Oai Id
  1. oai:frl.publisso.de:frl:6424776 |
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