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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Online Access: | https://oa.tib.eu/renate/handle/123456789/6864 https://doi.org/10.34657/5911 |
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ftleibnizopen:oai:oai.leibnizopen.de:9Yf6pIkBdbrxVwz6AcYG 2023-08-20T04:03:51+02:00 Performance evaluation of global hydrological models in six large Pan-Arctic watersheds Gädeke, Anne Krysanova, Valentina Aryal, Aashutosh Chang, Jinfeng Grillakis, Manolis Hanasaki, Naota Koutroulis, Aristeidis Pokhrel, Yadu Satoh, Yusuke Schaphoff, Sibyll Müller Schmied, Hannes Stacke, Tobias Tang, Qiuhong Wada, Yoshihide Thonicke, Kirsten 2020 application/pdf https://oa.tib.eu/renate/handle/123456789/6864 https://doi.org/10.34657/5911 eng eng Dordrecht [u.a.] : Springer Science + Business Media B.V CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ Climatic change 163 (2020), Nr. 3 Arctic watersheds Boruta feature selection Global Water Models Model evaluation Model performance 550 article Text 2020 ftleibnizopen https://doi.org/10.34657/5911 2023-07-30T23:12:36Z 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. © 2020, The Author(s). publishedVersion Article in Journal/Newspaper Arctic Climate change permafrost Yukon Basin Yukon LeibnizOpen (The Leibniz Association) 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) |
institution |
Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
Arctic watersheds Boruta feature selection Global Water Models Model evaluation Model performance 550 |
spellingShingle |
Arctic watersheds Boruta feature selection Global Water Models Model evaluation Model performance 550 Gädeke, Anne Krysanova, Valentina Aryal, Aashutosh Chang, Jinfeng Grillakis, Manolis Hanasaki, Naota Koutroulis, Aristeidis Pokhrel, Yadu Satoh, Yusuke Schaphoff, Sibyll Müller Schmied, Hannes Stacke, Tobias Tang, Qiuhong Wada, Yoshihide Thonicke, Kirsten Performance evaluation of global hydrological models in six large Pan-Arctic watersheds |
topic_facet |
Arctic watersheds Boruta feature selection Global Water Models Model evaluation Model performance 550 |
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. © 2020, The Author(s). publishedVersion |
format |
Article in Journal/Newspaper |
author |
Gädeke, Anne Krysanova, Valentina Aryal, Aashutosh Chang, Jinfeng Grillakis, Manolis Hanasaki, Naota Koutroulis, Aristeidis Pokhrel, Yadu Satoh, Yusuke Schaphoff, Sibyll Müller Schmied, Hannes Stacke, Tobias Tang, Qiuhong Wada, Yoshihide Thonicke, Kirsten |
author_facet |
Gädeke, Anne Krysanova, Valentina Aryal, Aashutosh Chang, Jinfeng Grillakis, Manolis Hanasaki, Naota Koutroulis, Aristeidis Pokhrel, Yadu Satoh, Yusuke Schaphoff, Sibyll Müller Schmied, Hannes Stacke, Tobias Tang, Qiuhong Wada, Yoshihide Thonicke, Kirsten |
author_sort |
Gädeke, Anne |
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 |
Dordrecht [u.a.] : Springer Science + Business Media B.V |
publishDate |
2020 |
url |
https://oa.tib.eu/renate/handle/123456789/6864 https://doi.org/10.34657/5911 |
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 Climate change permafrost Yukon Basin Yukon |
genre_facet |
Arctic Climate change permafrost Yukon Basin Yukon |
op_source |
Climatic change 163 (2020), Nr. 3 |
op_rights |
CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.34657/5911 |
_version_ |
1774714279709114368 |