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.
Highlights
The rapid environmental changes occurring in the Pan-Arctic have triggered increased attention from the scientific community
No climate forcing data set consistently outperforms the other for all statistical metrics in all basins, though our analysis suggests that Global Water Models (GWMs) forced by Global Soil Wetness Project Phase 3 (GSWP3) show better results for bias in SD and percent bias (PBIAS) compared to when forced by the other climate data sets
Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes
Summary
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 glaciers (Zemp et al 2019), and changing partitioning of surface and groundwater (Walvoord and Striegl 2007), all of which affect the hydrological processes in Pan-Arctic watersheds. To increase our understanding of Pan-Arctic hydrological processes, Global Water Models (GWMs), here including Global Hydrological Models (GHMs), Land Surface Models (LSMs), and Dynamic Global Vegetation model (DGVMs), could provide valuable tools for obtaining estimates of hydrological variables where data availability is poor both spatially and temporally. A thorough performance evaluation is essential prior to applying models for climate change impact assessments in this region
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