基于非线性相关性和复杂网络的径流相似性分区

径流相似性分区对径流资料插补移用和区域洪水频率分析具有重要意义。为准确识别水文站网中各站径流特征的相似性和差异性, 提高径流相似性分区结果的准确性, 引入Copula熵方法估算基于互信息的R统计量, 以度量各径流序列间的非线性相关性。在此基础上, 应用复杂网络理论构建以水文站为节点、以对应径流序列间R统计量是否大于给定阈值为节点间连边存在判别依据的径流相似性分区模型, 采用基于边介数的社团检测算法(GN算法)进行径流相似性分区。以鄱阳湖水系的水文站网为实例, 研究结果表明: 径流相似性分区模型具有较高的稳定性和效率; R统计量阈值为0.80时, 径流相似性分区结果最优, 此时水文站网划分为南北两部分共12类分区, 其中北部仅含1类分区; 相比于K均值聚类方法, 复杂网络方法表现更优, 其最优分区结果更为合理。

Study on streamflow similarity regionalization based on nonlinear correlation and complex network

Streamflow similarity regionalization is significant for interpolating and transferring streamflow data and regional flood frequency analysis. To accurately identify the similarities and differences of streamflow characteristics of different hydrological stations in a hydrometric network and improve the accuracy of streamflow similarity regionalization results, we introduce the copula entropy method to estimate the mutual-information-based R statistics, aiming at measuring the nonlinear correlations between streamflow series. Subsequently, the complex network theory was applied to construct the streamflow similarity regionalization model, where the hydrological stations were set as nodes and the condition whether the R statistics of corresponding streamflow series were higher than the specified R-statistic threshold was considered as the basis for judging the existence of the linked edges between node pairs. The GN algorithm was used to conduct the streamflow similarity regionalization. Taking the hydrometric network of the Poyang Lake basin as an example, the research results showed that the streamflow similarity regionalization model was characterized by high stability and efficiency. When the R statistic was 0.8, the streamflow similarity regionalization result was the best, the hydrometric network was generally divided into two parts, i.e., north and south, and in total 12 types of regions, where the north part contained only one type of region. Compared with the K-means clustering method, the complex network method performs better, and its optimal partitioning result is more reasonable.