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.

中文翻译：

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六大泛北极流域全球水文模型的性能评估

全球水模型 (GWM) 包括全球水文、地表和动态全球植被模型，为量化气候变化对数据稀缺的高纬度地区水文过程的影响提供了宝贵的工具。在这里，我们通过新的综合性能指数 (API) 对六个主要泛北极流域的不同水文指标（每月和季节性排放、极端情况、趋势（或缺乏）和雪水当量 (SWE)）进行了系统的模型性能评估) 基于常用的统计评估指标。机器学习 Boruta 特征选择算法用于评估 API 属性的解释能力。我们的结果表明，研究中包含的九个 GWM 中的大多数在真实地表示泛北极水文过程方面表现出相当大的困难。九个 GWM 的平均 API 排放（每月和季节性排放）> 50%，仅在科雷马盆地（55%），育空盆地低至 30%，所有流域的平均 API 排放为 43%。WATERGAP2 和 MATSIRO 表现出最高（APIdischarge > 55%），而 ORCHIDEE 和 JULES-W1 最低（APIdischarge ≤ 25%）在所有流域中执行 GWM。对于高流量和低流量，平均 APIextreme 分别为 35% 和 26%，超过 6 个 GWM 的 APISWE 为 57%。Boruta 算法表明，使用不同的基于观测的气候数据集不会影响所有流域中 API 的总分。最终，