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

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.

How evaluation of hydrological models influences results of climate impact assessment—an editorial

“…The drainage areas of 14 of the 58 basins are less than 100,000 km 2 , and 10 basins have drainage areas larger than 1 million square kilometers. The characteristics of these basins can be found in Table S1 in Krysanova et al (2020) and Table 1 in Gädeke et al (2020). The case study areas where the regional-scale models were applied ( Fig.…”

“…The following climate data were used in the research reported in the papers of this Special Issue. For evaluation of the regional-scale models in the historical period, three reanalysis datasets were used: EWEMBI Lange 2018 In turn, global hydrological models (gHMs) were evaluated using model runs driven by WFDEI and GSWP3 (Kim et al 2014) data for 57 river basins worldwide (Krysanova et al 2020) and by WATCH, WFDEI, GSWP3, and Princeton (Sheffield et al 2006) data for six Arctic basins (Gädeke et al 2020). The continental-scale model E-HYPE applied EFAS-Meteo data (Ntegeka et al 2013) with resolution of 5 km (Hundecha et al 2020).…”

“…It was set-up for the pan-European domain (Donnelly et al 2016) and is referred to as E-HYPE. Table 2 in Gädeke et al (2020) presents nine global water models evaluated for 57 or six Arctic river basins (Fig. 2).…”

“…Next, a comparison of projected impacts in terms of magnitude of change and spreads was performed for 12 selected river basins using (i) all models, applying the ensemble mean approach (EM), and (ii) only satisfactorily performing models, applying weighting coefficients (WCO) estimated based on the evaluation results. & A systematic performance evaluation of nine global water models was carried out for six major Pan-Arctic watersheds by Gädeke et al (2020), considering different hydrological indicators: monthly and long-term mean monthly discharge (for multiple gauges), high and low flow extremes (for the outlets), and snow water equivalent. For that, a similar as above aggregated performance index (API, in %, from 0 to 100) based on the usually used criteria was applied.…”

“…One paper is devoted to a systematic evaluation of global water models in the Arctic region, though impact assessment is not included (Gädeke et al 2020). The focus of three remaining papers is on other specific research questions, such as testing a new calibration method for a large region in Africa (Chawanda et al 2020), selection of climate ensemble members based on simulated streamflow (Kiesel et al 2020), and reviewing sources of uncertainty in climate impact projections (Dankers and Kundzewicz, 2020), but all three have indirect relations to the main topic of this SI.…”