Stream gauging stations are important in hydrology and water science for obtaining water-related information, such as stage and discharge. However, for efficient operation and management, a more accurate grouping method is needed, which should be based on the interrelationships between stream gauging stations. This study presents a grouping method that employs community detection based on complex networks. The proposed grouping method was compared with the cluster analysis approach, which is based on statistics, to verify its adaptability. To achieve this goal, 39 stream gauging stations in the Yeongsan River basin of South Korea were investigated. The numbers of groups (clusters) in the study were two, four, six, and eight, which were determined to be suitable by fusion coefficient analysis. Ward’s method was employed for cluster analysis, and multilevel modularity optimization was applied for community detection. A higher level of cohesion between stream gauging stations was observed in the community detection method at the basin scale and the stream link scale within the basin than in the cluster analysis. This suggests that community detection is more effective than cluster analysis in terms of hydrologic similarity, persistence, and connectivity. As such, these findings could be applied to grouping methods for efficient operation and maintenance of stream gauging stations.

流量测量站在水文学和水科学中对于获取与水有关的信息（例如水位和流量）非常重要。但是，为了有效地进行操作和管理，需要一种更准确的分组方法，该方法应基于流测量站之间的相互关系。这项研究提出了一种分组方法，该方法采用基于复杂网络的社区检测。将提出的分组方法与基于统计的聚类分析方法进行了比较，以验证其适应性。为了实现这一目标，对韩国荣山河流域的39个水位测量站进行了调查。该研究中的组（簇）的数量为二，四，六和八，通过融合系数分析确定为合适。使用Ward方法进行聚类分析，多级模块化优化技术被应用于社区检测。与聚类分析相比，在流域尺度和流域内流域尺度的群落检测方法中，测流站之间的凝聚力更高。这表明就水文相似性，持久性和连通性而言，社区检测比聚类分析更有效。因此，这些发现可以应用于分组方法，以有效地操作和维护测流站。这表明就水文相似性，持久性和连通性而言，社区检测比聚类分析更有效。因此，这些发现可以应用于分组方法，以有效地操作和维护测流站。这表明就水文相似性，持久性和连通性而言，社区检测比聚类分析更有效。因此，这些发现可以应用于分组方法，以有效地操作和维护测流站。