评估英国水文气候变化影响的Euro-CORDEX区域气候模型的性能评估：不同空间分辨率和分位数映射偏差校正方法的比较
Evaluation of the performance of Euro-CORDEX RCMs for assessing hydrological climate change impacts in Great Britain: a comparison of different spatial resolutions and quantile mapping bias correction methods
摘要：
区域气候模式（RCMs）是分析区域气候变化影响的重要工具，因为它们提供的模拟比全球气候模式具有更多的小尺度细节和更小的预期误差。已经有很多努力，以提高空间分辨率和模拟技能的区域气候模型，但在何种程度上，这提高了预测的水文变化是不清楚的。在这里，我们评估的技能再分析驱动的欧洲CORDEX区域气候模型在模拟降水和温度，并作为一个水文模型的驱动程序，以模拟河流流量的四个英国集水区，涵盖不同的物理，气候和水文特征。我们测试是否高分辨率RCM提供附加值，通过分析两个RCM分辨率，50公里和12.5公里，这也是偏差校正采用参数分位数映射（QM）方法，使用正态分布的温度，和伽玛（GQM）和双伽玛（DGQM）分布的降水。在一个小流域，地形复杂，12.5公里的RCM优于其50公里版本的降水和温度，但当与水文模型结合使用时，未能捕捉到观测到的河流流量分布。在其他（较大）流域，只有一个高分辨率的RCM始终优于其低分辨率版本，这意味着在这些流域使用高分辨率的RCM一般没有增加值。GQM降低了大部分模拟偏差，但极端降水和高流量除外，DGQM进一步降低了这些偏差。偏差校正并没有改善每日的时间变化的代表性，但它的月变化，特别是当应用DGQM。总体而言，RCM分辨率的增加并不意味着更好地模拟水文和偏差校正是一种替代方案，以减轻决策。
英文摘要：
Regional Climate Models (RCMs) are an essential tool for analysing regional climate change impacts as they provide simulations with more small-scale details and expected smaller errors than global climate models. There has been much effort to increase the spatial resolution and simulation skill of RCMs, yet the extent to which this improves the projection of hydrological change is unclear. Here, we evaluate the skill of five reanalysis-driven Euro-CORDEX RCMs in simulating precipitation and temperature, and as drivers of a hydrological model to simulate river flow on four UK catchments covering different physical, climatic and hydrological characteristics. We test whether high-resolution RCMs provide added value, through analysis of two RCM resolutions, 50 km and 12.5 km, which are also bias-corrected employing the parametric quantile-mapping (QM) method, using the normal distribution for temperature, and the Gamma (GQM) and Double Gamma (DGQM) distributions for precipitation. In a small catchment with complex topography, the 12.5 km RCMs outperform their 50 km version for precipitation and temperature, but when used in combination with the hydrological model, fail to capture the observed river flow distribution. In the other (larger) catchments, only one high-resolution RCM consistently outperforms its low-resolution version, implying that in general there is no added value from using the high-resolution RCMs in those catchments. GQM decreases most of the simulation biases, except for extreme precipitation and high flows, which are further decreased by DGQM. Bias correction does not improve the representation of daily temporal variability, but it does for monthly variability, in particular when applying DGQM. Overall, an increase in RCM resolution does not imply a better simulation of hydrology and bias-correction represents an alternative to ease decision-making.
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