基于信息熵的最优雨量站网动态评估

The optimal rainfall network dynamic evaluation based on information entropy

雨量站网是降水数据的最直接来源,其优化和评估至关重要。信息熵不仅可以代表降雨分布的不确定性,还可以反映雨量站之间的相关性大小。以湘江流域的子流域——湘乡流域为研究区域,基于信息熵的最大信息最小冗余准则,应用一种易于实现的贪婪排名算法,对该流域雨量站网进行站点重要性排名。同时考虑不同的滑动年际序列和不同气象条件,对排名结果进行比较分析。结果表明:不同起始日的观测序列影响了雨量站网站点的重要性排名,不同时期站点排名的变化证明了雨量站网站点的时间变异性,但随着观测序列长度的增加,这种时间变异性逐渐减小;在不同时期(雨季或旱季),最优雨量站网的站点重要性排名存在差异,旱季对最优站网站点重要性排名的时间变异性影响更显著。

Rainfall network is the most direct source of rainfall data,and its optimization and evaluation are very essential.Information entropy can not only represent the uncertainty of rainfall distribution,but also reflect the correlation between rainfall stations.In this paper,the Xiangxiang subbasin in the Xiangjiang River basin is taken as the research area.Based on the criterion called maximum information minimum redundancy(MIMR)of information entropy,the significance of the rainfall stations in the network distribution is ranked by using an easy-to-implement greedy ranking algorithm.At the same time,the ranking results are compared by considering different moving inter-annual series and meteorological conditions.The results show that observation series with different starting days can affect the ranking of stations.The temporal variability of rainfall station in a station network is proved by the ranking variation of stations during different periods,while with the increase of the length of observation series,the temporal variability decreases.The rankings of stations of optimal network are different during dry and wet seasons,and the dry season has more significant impact on the temporal variability of the ranking.