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

16883 10 92350 2 267985 document 92350 Gädeke2020_Article_PerformanceEvaluationOfGlobalH.pdf application/pdf b7011fa2e97afc76c3efa476a950f765 MD5 2171085 2020-11-30 07:54:48 https://pure.iiasa.ac.at/id/eprint/16883/1/G%C3%A4deke2020_Article_PerformanceEvaluationOfGlobalH.pdf 16883 1 1 application/pdf text en public cc_by_4

Gädeke2020_Article_PerformanceEvaluationOfGlobalH.pdf

published 92352 1 268001 document 92352 indexcodes.txt text/x-c ff84a1f6256564f2de75bfe9ea8a9053 MD5 13785 2020-11-30 08:04:40 https://pure.iiasa.ac.at/id/eprint/16883/2/indexcodes.txt 16883 2 2 text/x-c other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/92350 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/92350 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/92350 archive 353 disk0/00/01/68/83 2020-11-30 07:57:37 2021-08-27 17:33:58 2020-11-30 07:57:37 article show Gädeke A. Krysanova V. Aryal A. Chang J. 2233 0000-0003-4463-7778 Grillakis M. Hanasaki N. Koutroulis A. Pokhrel Y. Satoh Y. 2080 Schaphoff S. Müller Schmied H. Stacke T. Tang Q. Wada Y. 8940 0000-0003-4770-2539 Thonicke K. Performance evaluation of global hydrological models in six large Pan-Arctic watersheds pub prog_esm prog_wat Arctic watersheds; Boruta feature selection; Global Water Models; Model evaluation; Model performance 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. 2020-11-24 published Springer 10.1007/s10584-020-02892-2 3116 268 2601 public Climatic Change 163 1329-1351 TRUE 1573-1480 FALSE no info:eu-repo/semantics/article