Bias correction techniques are widely used to bridge the gap between climate model outputs and input requirements of hydrological models to assess the climate change impacts on hydrology. In addition to univariate bias correction methods, several multivariate bias correction methods were proposed recently, which can not only correct the biases in marginal distributions of individual climate variables but also properly adjust the biased intervariable correlations simulated by climate models. Due to the diversities of climate regime and climate model bias, hydrological simulation for watersheds under different climate conditions may show various sensitivities to the correction of intervariable correlations. Therefore, it is of great importance to investigate (1) whether the correction of intervariable correlations has impacts on the hydrological modeling and (2) how these impacts vary with watersheds under different climate conditions. To achieve these goals, this study evaluates behaviors and their spatial variability of multiple state‐of‐the‐art multivariate bias correction methods in hydrological modeling over 2,840 watersheds distributed in different climate regimes in North America. The results show that, compared to using a quantile mapping univariate bias correction method, applying multivariate methods can improve the simulation of snow proportion, snowmelt, evaporation, and several streamflow variables. In addition, this improvement is more clear for watersheds with arid and warm temperate climates in southern regions, while it is limited for northern snow‐characterized watersheds. Overall, this study demonstrates the importance of using multivariate bias correction methods instead of univariate methods in hydrological climate change impact studies, especially for watersheds with arid and warm temperate climates.

中文翻译：

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使用最先进的多元偏差校正方法对北美水文建模的影响

偏差校正技术被广泛用于弥合气候模型输出和水文模型输入要求之间的差距，以评估气候变化对水文的影响。除了单变量偏差校正方法外，最近还提出了几种多变量偏差校正方法，它们不仅可以校正单个气候变量边际分布的偏差，而且可以适当调整气候模型模拟的有偏差的变量间相关性。由于气候制度和气候模式偏差的多样性，不同气候条件下的流域水文模拟可能对变量相关性的校正表现出不同的敏感性。所以，调查 (1) 变量相关性的校正是否对水文模型产生影响以及 (2) 这些影响如何在不同气候条件下随流域而变化，这一点非常重要。为了实现这些目标，本研究评估了分布在北美不同气候状况的 2,840 个流域的水文建模中多种最先进的多元偏差校正方法的行为及其空间变异性。结果表明，与使用分位数映射单变量偏差校正方法相比，应用多变量方法可以提高对积雪比例、融雪、蒸发和几个水流变量的模拟。此外，这种改善对于南部地区干旱和暖温带气候的流域更为明显，而它仅限于北部以雪为特征的流域。总的来说，这项研究证明了在水文气候变化影响研究中使用多元偏差校正方法代替单变量方法的重要性，尤其是对于干旱和暖温带气候的流域。