Unfolding Community Structure in Rainfall Network of Germany Using Complex Network-Based Approach

Many natural systems can be represented as networks of dynamical units with a modular structure in the form of communities of densely interconnected nodes. Unfolding structure of such densely interconnected nodes in hydro-climatology is essential for reliable parameter transfer, model inter-comparison, prediction in ungauged basins, and estimating missing information. This study presents the application of complex network-based approach for regionalization of rainfall patterns in Germany. As a test case study, daily rainfall records observed at 1,229 rain gauges were selected throughout Germany. The rainfall data, when represented as a complex network using event synchronization, exhibits small-world and scale-free network topology which are a class of stable and efficient networks common in nature. In total, eight communities were identified using Louvain community detection algorithm. Each of the identified communities has a sufficient number of rain gauges which show distinct statistical and physical rainfall characteristics. The method used has wide application in most of the real systems which can be represented by network enabling to understand modular patterns through time series analysis.