基于信息熵的流域水文要素时空变异分析研究

潮白河流域位于海河流域的上游,也是北京市重要水源地密云水库的上游.近年来,密云水库的入流呈急剧下降的趋势,已经严重威胁北京市城市供水的安全.为深入理解密云水库来水急剧减少的影响原因,进行水文要素的时空变异分析是十分重要和必要的.传统的变异方法主要采用变异系数.结合信息熵理论与新兴的GIS技术,提出一种基于差异信息测度的普通序列和栅格序列的信息理论与方法,并应用于潮白河流域1973～2002年58测站的降雨数据和2个入库流量数据的分析.结果表明,该方法是可行有效的,同时也显示,降雨的时空变异不是造成潮白河流域流量剧减的主要原因.

Study of watershed hydrological spatio-temporal variability analysis based on information entropy

The Chaobai River Basin lies in the upper reaches of the Hai River Basin and also the upstream of Miyun Reservoir which is the Beijing municipal tap water main resources. The precipitation is one of the most important hydrological factors and its variability analysis is crucial for further understanding hydrological process and hydrological modelling. Traditional spatial variability was quantitatively described using coefficient of variation (Cv) based on statistics. Combining information entropy theory with GIS spatial analysis technology, the spatio-temporal variability of annual precipitation is analyzed. A new spatio-temporal variability analysis method which is based on the differential information entropy relative measure is also presented. This study was conducted using 58 gauge stations rainfall data and 2 gauges streamflow data of Chaobai River Basin during the period from 1973 to 2002. Results of this study not only show that the new spatio-temporal variability analysis method is feasible and effective, but also indicate that the rainfall isn t the main reasons of Miyun Reservoir's inflow rapid reducing.