Performance evaluation of global hydrological models in six large Pan-Arctic watersheds
Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in...
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fthzgzmk:oai:publications.hereon.de:39328 2023-06-11T04:07:43+02:00 Performance evaluation of global hydrological models in six large Pan-Arctic watersheds Gädeke, A. Krysanova, V. Aryal, A. Chang, J. Grillakis, M. Hanasaki, N. Koutroulis, A. Pokhrel, Y. Satoh, Y. Schaphoff, S. Müller Schmied, H. Stacke, T. Tang, Q. Wada, Y. Thonicke, K. 2020 https://publications.hereon.de/id/39328 https://publications.hzg.de/id/39328 https://doi.org/10.1007/s10584-020-02892-2 en eng Springer https://dx.doi.org/10.1007/s10584-020-02892-2 urn:issn:0165-0009 https://publications.hereon.de/id/39328 https://publications.hzg.de/id/39328 info:eu-repo/semantics/openAccess open_access oa_allgemein_hybrid issn:0165-0009 Gädeke, A.; Krysanova, V.; Aryal, A.; Chang, J.; Grillakis, M.; Hanasaki, N.; Koutroulis, A.; Pokhrel, Y.; Satoh, Y.; Schaphoff, S.; Müller Schmied, H.; Stacke, T.; Tang, Q.; Wada, Y.; Thonicke, K.: Performance evaluation of global hydrological models in six large Pan-Arctic watersheds. In: Climatic Change. Vol. 163 (2020) 1329 - 1351. (DOI: /10.1007/s10584-020-02892-2) info:eu-repo/semantics/article Zeitschrift Artikel 2020 fthzgzmk https://doi.org/10.1007/s10584-020-02892-2 2023-05-28T23:25:08Z Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. Article in Journal/Newspaper Arctic Arctic Climate change permafrost Yukon Basin Yukon Hereon Publications (Helmholtz-Zentrum) Arctic Jules ENVELOPE(140.917,140.917,-66.742,-66.742) Kolyma ENVELOPE(161.000,161.000,69.500,69.500) Yukon Yukon Basin ENVELOPE(-135.000,-135.000,64.282,64.282) Climatic Change 163 3 1329 1351 |
institution |
Open Polar |
collection |
Hereon Publications (Helmholtz-Zentrum) |
op_collection_id |
fthzgzmk |
language |
English |
description |
Global Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds. |
format |
Article in Journal/Newspaper |
author |
Gädeke, A. Krysanova, V. Aryal, A. Chang, J. Grillakis, M. Hanasaki, N. Koutroulis, A. Pokhrel, Y. Satoh, Y. Schaphoff, S. Müller Schmied, H. Stacke, T. Tang, Q. Wada, Y. Thonicke, K. |
spellingShingle |
Gädeke, A. Krysanova, V. Aryal, A. Chang, J. Grillakis, M. Hanasaki, N. Koutroulis, A. Pokhrel, Y. Satoh, Y. Schaphoff, S. Müller Schmied, H. Stacke, T. Tang, Q. Wada, Y. Thonicke, K. Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
author_facet |
Gädeke, A. Krysanova, V. Aryal, A. Chang, J. Grillakis, M. Hanasaki, N. Koutroulis, A. Pokhrel, Y. Satoh, Y. Schaphoff, S. Müller Schmied, H. Stacke, T. Tang, Q. Wada, Y. Thonicke, K. |
author_sort |
Gädeke, A. |
title |
Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
title_short |
Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
title_full |
Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
title_fullStr |
Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
title_full_unstemmed |
Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
title_sort |
performance evaluation of global hydrological models in six large pan-arctic watersheds |
publisher |
Springer |
publishDate |
2020 |
url |
https://publications.hereon.de/id/39328 https://publications.hzg.de/id/39328 https://doi.org/10.1007/s10584-020-02892-2 |
long_lat |
ENVELOPE(140.917,140.917,-66.742,-66.742) ENVELOPE(161.000,161.000,69.500,69.500) ENVELOPE(-135.000,-135.000,64.282,64.282) |
geographic |
Arctic Jules Kolyma Yukon Yukon Basin |
geographic_facet |
Arctic Jules Kolyma Yukon Yukon Basin |
genre |
Arctic Arctic Climate change permafrost Yukon Basin Yukon |
genre_facet |
Arctic Arctic Climate change permafrost Yukon Basin Yukon |
op_source |
issn:0165-0009 Gädeke, A.; Krysanova, V.; Aryal, A.; Chang, J.; Grillakis, M.; Hanasaki, N.; Koutroulis, A.; Pokhrel, Y.; Satoh, Y.; Schaphoff, S.; Müller Schmied, H.; Stacke, T.; Tang, Q.; Wada, Y.; Thonicke, K.: Performance evaluation of global hydrological models in six large Pan-Arctic watersheds. In: Climatic Change. Vol. 163 (2020) 1329 - 1351. (DOI: /10.1007/s10584-020-02892-2) |
op_relation |
https://dx.doi.org/10.1007/s10584-020-02892-2 urn:issn:0165-0009 https://publications.hereon.de/id/39328 https://publications.hzg.de/id/39328 |
op_rights |
info:eu-repo/semantics/openAccess open_access oa_allgemein_hybrid |
op_doi |
https://doi.org/10.1007/s10584-020-02892-2 |
container_title |
Climatic Change |
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163 |
container_issue |
3 |
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1329 |
op_container_end_page |
1351 |
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