摘要：Rain gauge network is important for collecting rainfall information effectively and efficiently. Rain gauge networks have been studied for several decades from a range of hydrological perspectives, where rain gauges with unique or non-repeating information are considered as important. However, the problem of quantification of node importance and subsequent identification of the most important nodes in rain gauge networks have not yet been extensively addressed in the literature. In this study, we use the concept of the complex networks to evaluate the Indian Meteorological Department (IMD) monitored 692 rain gauge in the Ganga River Basin. We consider the complex network theory-based Degree Centrality (DC), Clustering Coefficient (CC) and Mutual Information (MI) as the parameters to quantify the rainfall variability associated with all the rain gauges in the network. Multiple rain gauge network scenario with varying rain gauge density (i.e. Network Size (NS) = 173, 344, 519, and 692) and Temporal Resolution (i.e. TR = 3 hours, 1 day, and 1 month) are introduced to study the effect of rain gauge density, gauge location and temporal resolution on the node importance quantification. Proxy validation of the methodology was done using a hydrological model. Our results indicate that the network density and temporal resolution strongly influence a node’s importance in rain gauge network. In addition, we concluded that the degree centrality along with clustering coefficient is the preferred parameter than the mutual information for the node importance quantification. Furthermore, we observed that the network properties (spatial distribution, DC, Collapse Correlation Threshold (CCT), CC Range distributions) associated with TR = 3 hours and 1 day are comparable whereas TR = 1 month exhibit completely different trends. We also found that the rain gauges situated at high elevated areas are extremely important irrespective of the NS and TR. The encouraging results for the quantification of nodes importance in this study seem to indicate that the approach has the potential to be used in extreme rainfall forecasting, in studying changing rainfall patterns and in filling gaps in spatial data. The technique can be further helpful in the ground-based observation network design of a wide range of meteorological parameters with spatial correlation.
