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

Anne Gädeke (), Valentina Krysanova, Aashutosh Aryal, Jinfeng Chang, Manolis Grillakis, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Yusuke Satoh, Sibyll Schaphoff, Hannes Müller Schmied, Tobias Stacke, Qiuhong Tang, Yoshihide Wada and Kirsten Thonicke
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Anne Gädeke: Potsdam Institute for Climate Impact Research, Member of the Leibniz Association
Valentina Krysanova: Potsdam Institute for Climate Impact Research, Member of the Leibniz Association
Aashutosh Aryal: Potsdam Institute for Climate Impact Research, Member of the Leibniz Association
Jinfeng Chang: Université Paris-Saclay
Manolis Grillakis: Technical University of Crete
Naota Hanasaki: National Institute for Environmental Studies
Aristeidis Koutroulis: Technical University of Crete
Yadu Pokhrel: Michigan State University
Yusuke Satoh: International Institute for Applied Systems Analysis (IIASA)
Sibyll Schaphoff: Potsdam Institute for Climate Impact Research, Member of the Leibniz Association
Hannes Müller Schmied: Goethe-University of Frankfurt
Tobias Stacke: Institute of Coastal Research
Qiuhong Tang: Chinese Academy of Sciences
Yoshihide Wada: International Institute for Applied Systems Analysis (IIASA)
Kirsten Thonicke: Potsdam Institute for Climate Impact Research, Member of the Leibniz Association

Climatic Change, 2020, vol. 163, issue 3, No 11, 1329-1351

Abstract: Abstract 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.

Keywords: Global Water Models; Model performance; Model evaluation; Arctic watersheds; Boruta feature selection (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10584-020-02892-2

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