Recurrence networks-based analysis of soil moisture dynamics in global land areas

Soil moisture is a complex nonlinear hydroclimatic variable playing a significant role in the hydrologic cycle. Soil moisture has great influence on land-atmospheric interactions, including triggering and shaping extreme events, especially under climate change. Therefore, identifying hidden patterns and structures in soil moisture dynamics and understanding their evolution are critical for a wide range of applications. In this study, we aim to explore the temporal dynamics of soil moisture systems across global land areas using recurrence network analysis, bridging complex networks concept and recurrence plots. We use GLDAS soil moisture data in global land areas for 1948–2024 at a spatial resolution of 1 degree x 1 degree. Recurrence networks of soil moisture systems are constructed from soil moisture time series at individual grids. The nodes of the network represent the states of the system, and the links represent the recurrence of states. The nodes of the recurrence networks are identified by delay embedding of the soil moisture time series (i.e., phase space reconstruction) in an optimum embedding dimension identified using the False Nearest Neighbour algorithm. Thereafter, the links of the networks are established by considering the closeness of the nodes in terms of Euclidean distances. Several complex network measures, such as degree centrality, betweenness centrality, shortest path length, and clustering coefficient, are used to interpret the soil moisture systems from a recurrence perspective. The results from this study suggest that the soil moisture system behavior is similar for regions characterized with similar precipitation regime. This understanding is highly relevant for identifying regions with similar land-atmospheric coupling processes.