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

• Published in: Climatic change Vol. 163; no. 3; pp. 1329 - 1351
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-12-2020; Springer Nature B.V; Springer Verlag
• Subjects: Algorithms; Atmospheric Sciences; Climate and vegetation; Climate change; Climate change models; Climate Change/Climate Change Impacts; Climate models; Climatic data; Climatology; Discharge; Earth and Environmental Science; Earth Sciences; Environmental impact; Fluid dynamics; Hydrologic models; Hydrologic processes; Hydrology; Learning algorithms; Low flow; Machine learning; Performance evaluation; Performance indices; Permafrost; Physical simulation; Sciences of the Universe; Snow; Snow-water equivalent; Statistical analysis; Watersheds; Arctic region; Model evaluation; Model performance; Global Water Models; Boruta feature selection; Arctic watersheds
• ISSN: 0165-0009; 1573-1480
• DOI: 10.1007/s10584-020-02892-2

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 API
discharge
(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 API
discharge
is 43%. WATERGAP2 and MATSIRO present the highest (API
discharge
 > 55%) while ORCHIDEE and JULES-W1 the lowest (API
discharge
 ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average API
extreme
is 35% and 26%, respectively, and over six GWMs API
SWE
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.