1.College of Hydraulic Engineering,Faculty of Infrastructure Engineering,Dalian University of Technology,Dalian;2.Nanjing Hydraulic Research Institute,Nanjing;3.Reclaimed water north survey,design and Research Co,Ltd Tianjin
气象产品与气候模式预测数据与地面观测数据仍有偏差,通常需要对其进行偏差修正以保证数据可靠性；常用的偏差修正方法的修正效果受区域特征和气象要素等的影响,对于覆盖多个气候带、空间异质性大且多气象要素综合影响的区域,单一偏差修正方法效果不甚理想。为此,本文提出一种广义联合偏差修正方法,该方法根据降雨、气温双要素间时空相关性及两者对区域水文过程的作用程度,耦合了Quantile Mapping(QM)法和Joint Bias Correction(JBC)法联合修正。将该方法应用于澜沧江湄公河流域,结果表明：与Quantile Mapping(QM)法相比,广义联合偏差修正考虑了降水气温相关性改善了降水和温度极值的修正效果,纳什系数明显提升,尤其是5、6月份,纳什系数提升了0.5以上；与Joint Bias Correction(JBC)法相比,该方法考虑了气象-水文的动态关系降低了降水和温度频率分布及均值的偏差,使修正数据分布更贴近实测数据分布；利用修正后的气象数据驱动分布式水文模型时,径流模拟精度提升了91.2%；在此基础上将该方法修正气象数据用于流域未来气象水文预测。广义联合偏差修正方法实现了传统方法间的互补优势,推广了适用范围,提升气象数据修正精度,为后续流域未来径流预测和水资源规划管理提供可靠依据。
There are still deviations between meteorological products and climate model prediction data and ground observation data, which usually need to be corrected to ensure data reliability; The correction effect of commonly used deviation correction methods is affected by regional characteristics and meteorological elements. For areas covering multiple climate zones, large spatial heterogeneity and comprehensive influence of multiple meteorological elements, the effect of single deviation correction method is not ideal. Therefore, a generalized joint deviation correction method is proposed in this paper. According to the temporal and spatial correlation between rainfall and temperature and their effect on regional hydrological process, The quantitative mapping (QM) method and joint bias correction (JBC) method are coupled for joint correction. The method is applied to the Lancang Mekong River Basin, and the results show that Compared with the (QM) method, the generalized joint deviation correction takes into account the correlation between precipitation and temperature, improves the correction effect of precipitation and temperature extreme values, and the Nash coefficient increases significantly, especially in May and June, the Nash coefficient increases by more than 0.5; compared with joint bias correction Compared with (JBC) method, this method considers the dynamic relationship between meteorology and hydrology, reduces the deviation of precipitation and temperature frequency distribution and mean value, and makes the modified data distribution closer to the measured data distribution; when the modified meteorological data is used to drive the distributed hydrological model, the accuracy of runoff simulation is improved by 91.2%; on this basis, the modified meteorological data of this method is applied to the future meteorology and hydrology of the basin forecast. The generalized joint deviation correction method realizes the complementary advantages between traditional methods, extends the scope of application, improves the accuracy of meteorological data correction, and provides a reliable basis for future runoff prediction and water resources planning and management of subsequent basins.