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Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

Urheber*innen
/persons/resource/Anne.Gaedeke

Gädeke,  Anne
Potsdam Institute for Climate Impact Research;

/persons/resource/Valentina.Krysanova

Krysanova,  Valentina
Potsdam Institute for Climate Impact Research;

/persons/resource/aryal.ashutosh

Aryal,  Aashutosh
Potsdam Institute for Climate Impact Research;

Chang,  Jinfeng
External Organizations;

Grillakis,  Manolis
External Organizations;

Hanasaki,  Naota
External Organizations;

Koutroulis,  Aristeidis
External Organizations;

Pokhrel,  Yadu
External Organizations;

Satoh,  Yusuke
External Organizations;

/persons/resource/Sibyll.Schaphoff

Schaphoff,  Sibyll
Potsdam Institute for Climate Impact Research;

Müller Schmied,  Hannes
External Organizations;

Stacke,  Tobias
External Organizations;

Tang,  Qiuhong
External Organizations;

Wada,  Yoshihide
External Organizations;

/persons/resource/Kirsten.Thonicke

Thonicke,  Kirsten
Potsdam Institute for Climate Impact Research;

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24775oa.pdf
(Verlagsversion), 3MB

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Zitation

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): Performance evaluation of global hydrological models in six large Pan-Arctic watersheds. - Climatic Change, 163, 3, 1329-1351.
https://doi.org/10.1007/s10584-020-02892-2


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_24775
Zusammenfassung
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.