Performance evaluation of global hydrological models in six large Pan-Arctic watersheds
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Performance evaluation of global hydrological models in six large Pan-Arctic watersheds Anne Gädeke 1 & Valentina Krysanova 1 & Aashutosh Aryal 1 & Jinfeng Chang 2,3,4 & Manolis Grillakis 5,6 & Naota Hanasaki 7 & Aristeidis Koutroulis 5 & Yadu Pokhrel 8 & Yusuke Satoh 3,7 & Sibyll Schaphoff 1 & Hannes Müller Schmied 9,10 & Tobias Stacke 11 & Qiuhong Tang 12 & Yoshihide Wada 3 & Kirsten Thonicke 1 Received: 15 January 2020 / Accepted: 12 October 2020/ # The Author(s) 2020
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.
This article is part of a Special Issue on “How evaluation of hydrological models influences results of climate impact assessment,” edited by Valentina Krysanova, Fred Hattermann, and Zbigniew Kundzewicz
* Anne Gädeke [email protected] Extended author information available on the last page of the article
Climatic Change
Keywords Global Water Models . Model performance . Model evaluation . Arctic watersheds . Boruta feature selection
1 Introduction The rapid environmental changes occurring in the Pan-Arctic have triggered increased attention from the scientific community. Such changes include observed decreasing extent and duration of snow cover (Pulliainen et al. 2020), permafrost thaw (Biskaborn et al. 2019), and related changes in soil active layer depth (Walvoord and Kurylyk 2016), increased melting rates of gl
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