Abstract Bias correction is usually implemented prior to using climate model outputs for impact studies. However, bias correction methods that are commonly used treat climate variables independently and often ignore inter-variable dependencies. The effects of ignoring such dependencies on impact studies need to be investigated. This study aims to assess the impacts of correcting the inter-variable correlation of climate model outputs on hydrological modeling. To this end, a joint bias correction (JBC) method which corrects the joint distribution of two variables as a whole is compared with an independent bias correction (IBC) method; this is considered in terms of correcting simulations of precipitation and temperature from 26 climate models for hydrological modeling over 12 watersheds located in various climate regimes. The results show that the simulated precipitation and temperature are considerably biased not only in the individual distributions, but also in their correlations, which in turn result in biased hydrological simulations. In addition to reducing the biases of the individual characteristics of precipitation and temperature, the JBC method can also reduce the bias in precipitation-temperature (P-T) correlations. In terms of hydrological modeling, the JBC method performs significantly better than the IBC method for 11 out of the 12 watersheds over the calibration period. For the validation period, the advantages of the JBC method are greatly reduced as the performance becomes dependent on the watershed, GCM and hydrological metric considered. For arid/tropical and snowfall-rainfall-mixed watersheds, JBC performs better than IBC. For snowfall- or rainfall-dominated watersheds, however, the two methods behave similarly, with IBC performing somewhat better than JBC. Overall, the results emphasize the advantages of correcting the P-T correlation when using climate model-simulated precipitation and temperature to assess the impact of climate change on watershed hydrology. However, a thorough validation and a comparison with other methods are recommended before using the JBC method, since it may perform worse than the IBC method for some cases due to bias nonstationarity of climate model outputs.

中文翻译：

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修正气候模式输出变量间相关性对水文建模的影响

摘要 偏差校正通常在使用气候模型输出进行影响研究之前实施。然而，常用的偏差校正方法独立处理气候变量，往往忽略变量间的依赖关系。需要调查忽略这种依赖性对影响研究的影响。本研究旨在评估校正气候模型输出的变量间相关性对水文建模的影响。为此，将两个变量的联合分布作为一个整体进行修正的联合偏差校正（JBC）方法与独立偏差校正（IBC）方法进行了比较；考虑到这一点是从 26 个气候模型中校正降水和温度的模拟，用于水文建模的 12 个流域处于各种气候状况。结果表明，模拟的降水和温度不仅在个体分布上存在很大偏差，而且在它们的相关性上也存在很大偏差，进而导致水文模拟存在偏差。JBC 方法除了可以减少降水和温度个体特征的偏差外，还可以减少降水-温度 (PT) 相关性的偏差。在水文建模方面，对于校准期间的 12 个流域中的 11 个，JBC 方法的性能明显优于 IBC 方法。在验证期间，JBC 方法的优势大大降低，因为性能变得依赖于流域、GCM 和所考虑的水文指标。对于干旱/热带和降雪-降雨混合流域，JBC 的表现优于 IBC。然而，对于降雪或降雨为主的流域，这两种方法的行为相似，IBC 的表现略好于 JBC。总体而言，结果强调了在使用气候模型模拟的降水和温度来评估气候变化对流域水文的影响时校正 PT 相关性的优势。但是，建议在使用 JBC 方法之前进行彻底的验证并与其他方法进行比较，因为在某些情况下，由于气候模型输出的偏差非平稳性，它的性能可能比 IBC 方法差。结果强调了在使用气候模型模拟的降水和温度来评估气候变化对流域水文的影响时校正 PT 相关性的优势。但是，建议在使用 JBC 方法之前进行彻底的验证并与其他方法进行比较，因为在某些情况下，由于气候模型输出的偏差非平稳性，它的性能可能比 IBC 方法差。结果强调了在使用气候模型模拟的降水和温度来评估气候变化对流域水文的影响时校正 PT 相关性的优势。然而，在使用 JBC 方法之前，建议进行彻底的验证并与其他方法进行比较，因为在某些情况下，由于气候模型输出的偏差非平稳性，它的性能可能比 IBC 方法差。