径流响应评估中基于动力与统计相结合的降尺度方法是否优于单一的降尺度方法
Does the Dynamical-Statistical Downscaling Method Perform Better than the Single Downscaling Method at the Evaluation of Hydrological Response?
DOI: 10.12677/JWRR.2019.86061, PDF,  被引量    国家自然科学基金支持
作者: 刘 寒, 陈 杰, 吴桂炀：武汉大学水资源与水电科学国家重点实验室，湖北 武汉
关键词: 动力降尺度；统计降尺度；动力–统计降尺度；气候模型；降水；径流；Dynamical Downscaling； Statistical Downscaling； Dynamical-Statistical Downscaling； Climate Model； Precipitation； Streamflow
摘要: 降尺度一般可以分为动力降尺度、统计降尺度、动力与统计降尺度相结合的方法。以上方法均被广泛应用于气候变化对流域径流的影响评估中，然而对以上不同类型的降尺度方法在径流模拟中的对比评价尚不多见。本文以崇阳溪流域、东江流域和湘江流域为研究对象，对比了以上三种不同类型的降尺度方法在径流模拟中的表现，旨在回答在径流响应中动力与统计相结合降尺度方法是否优于单一的方法。同时通过使用两种不同的统计降尺度方法(偏差校正方法)研究校正降水时序对径流模拟的影响。研究结果表明：1) 全球气候模式(GCM)和与之相对应的区域气候模式(RCM)模拟的降水和气温均具有较大的偏差，说明动力降尺度方法并不能有效的降低GCM输出变量在流域尺度上的偏差；2) 偏差校正方法可以很好的校正降水与气温的偏差，并能较好的模拟流域的径流量，同时动力与统计相结合的方法在径流模拟方面没有明显的优势，因此在径流响应评估中可以绕过RCM直接对GCM进行偏差校正；3) 考虑降水时序的偏差校正方法在日降水、月径流以及水量模拟方面具有一定的优势，但在日径流模拟方面与只考虑降水概率校正的方法类似。以上结果可为径流响应评估中降尺度方法的选择提供参考。
Abstract: The downscaling can be generally classified into dynamical, statistical and dynamical-statistical down-scaling. These downscaling methods were widely applied to evaluating the impact of climate change on streamflow, while there were few studies on the comparison and evaluation of different types of downscaling methods in runoff simulation. To figure out whether dynamical-statistical downscaling method is superior to single downscaling method in terms of runoff simulation, three downscaling methods are compared for its performance on runoff simulation over three watersheds in this study. The results show that: 1) The precipitation and temperature simulated by Global climate model (GCM) and its corresponding regional climate model (RCM) are very biased, indicating that the dynamical downscaling method does not effectively reduce the deviation between GCM outputs and observations at the watershed scale; 2) The bias correction methods are capable of correcting biases of GCM- and RCM-simulated precipitation and temperature, and can reproduce streamflow more realistically. In addition, there are no obvious advantage of using dynamical-statistical downscaling over single statistical downscaling in runoff simulation, thus it can directly apply bias correction method to GCM outputs; 3) The bias correction method taking the temporal sequence of precipitation into account has certain advantages in the simulation of daily precipitation, monthly runoff and water quantity, but it performs similarly to the method that only corrects the wet-day frequency of precipitation in the daily runoff simulation. The above results can provide references for the selection of downscaling methods in hydrological climate change impact studies.
文章引用：刘寒, 陈杰, 吴桂炀. 径流响应评估中基于动力与统计相结合的降尺度方法是否优于单一的降尺度方法[J]. 水资源研究, 2019, 8(6): 535-546. https://doi.org/10.12677/JWRR.2019.86061

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