The present study applies the concept of community structure to classify catchments in two large regions: Australia and the United States. Specifically, the edge betweenness method is applied to monthly streamflow data from a network of 218 stations across Australia and from a network of 639 stations across the United States. The influence of streamflow correlation threshold (i.e. spatial correlation in streamflow between streamflow stations) on catchment classification is examined, through use of different thresholds, suitable for each region, as appropriate. The results reveal that, for both regions, a very small number of communities have a large number of catchments within them (for instance, considering both regions as small as 16–18% of the largest communities combine to represent as much as 70–75% of the catchments), and a significantly large number of communities have only a very few catchments within them (for instance, almost 70% of the communities have only one or two stations within them, and thus represent only about 20% and 10% of the catchments in Australia and the US, respectively). An interpretation of the identified catchment communities in terms of catchment characteristics (station drainage area, station stream length, and station elevation) and flow properties (mean and coefficient of variation) is also made. The catchment classification is also explained using the correlation–distance relationship between the stations.

本研究运用社区结构的概念对澳大利亚和美国两个大区域的集水区进行分类。具体来说，边缘中间性该方法应用于来自澳大利亚218个站点的网络和美国639个站点的网络的月流量数据。通过使用适用于每个区域的不同阈值，适当地检查了水流相关性阈值（即水流站之间的水流中的空间相关性）对集水区分类的影响。结果表明，对于这两个地区，只有极少数的社区内有大量流域（例如，考虑到两个地区的最大社区中，只有16％至18％的社区相加，代表了多达70至75个社区） ％的集水区），并且相当多的社区内部只有很少的集水区（例如，几乎70％的社区中只有一个或两个站点，因此分别仅占澳大利亚和美国流域的20％和10％）。还根据流域特征（站点排水面积，站点溪流长度和站点海拔）和流量特性（均值和变异系数）对确定的流域群落进行了解释。还使用站点之间的相关性-距离关系来解释流域分类。