大范围干旱的定量识别是干旱研究的基础和难点。高时空分辨率、高精度的大范围土壤含水量资料缺乏是问题的核心。本项目结合水文模型和遥感反演的优势改进土壤含水量精度，研究大范围干旱的定量识别方法。项目通过融合地面观测资料、再分析资料和卫星测雨产品的多源气象数据，改善水文模型输入资料精度，提高近65年全国范围逐日模拟土壤含水量的精度。结合模型模拟的时间连续性和遥感反演的空间分布相对合理性，融合VIC模型模拟、SMOS和ASCAT遥感反演数据，创建具有较高精度、高时空分辨率的大范围土壤含水量融合数据。并引入Box-Cox变换对其进行正态化处理，构建改进的土壤含水量距平指数（ISMAPI），据此提出大范围干旱的高时空尺度定量识别方法，并尝试分析该识别方法下的大范围干旱时空演变特征，及其与大气环流异常因子间的关系。研究成果对及时准确掌握旱情发生与发展规律意义重大，可为风险决策和抗旱减灾提供重要依据。

Large-scale drought identification has been the basic and difficult problems for drought research. Lack of high precision, high spatial-temporal resolution of soil moisture data is the core of the problem. Based on integrating high spatial-temporal resolution multi-source soil moisture data, the method of large-scale drought recognition will be studied. With the assimilation multi-source data of surface observation data, reanalysis data and data generated by satellite precipitation product serve as the input of hydrological model, the output, simulated soil moisture at daily resolution in recent 65 years, will be enhaced. Specifically, therein combining the advantage of temporal continuation of hydrology model (VIC) and relative spatial rationality of remote sensing inversion (SMOS and ASCAT), the large-scale soil moisture dataset can be generated with the characteristics of high precision, high spatial and temporal resolution. Then, ISMAPI can be specified by processing fusion soil moisture normally with Box-Cox transformation and its grades of dryness/wetness is divided. Based on the ISMAPI, the quantitative identification method is established to analyze the large-scale drought in high spatial and temporal scale, as well as its spatial and temporal evolution characteristics, and its relationships with the factors of atmospheric circulation anomalies. The research will give us a comprehensive understanding of the large-scale soil drought. It is of great significance for effective prevention and mitigation of drought disaster.