Exploring Lag-Dependent Spatial Connectivity Between Climate Oscillations and Agricultural Drought: A Complex Network Approach

Large-scale climate oscillations influence agricultural drought by altering atmospheric circulation, moisture transport, and land–atmosphere interactions. Understanding how climate oscillations organize the spatial connectivity of agricultural drought across different time lags remains a key challenge for regional drought assessment and predictability. To address this challenge, this study investigates the lag-dependent spatial connectivity between major climate oscillations and agricultural drought using an event-based complex network framework. For implementation, agricultural droughts in India are studied. Drought events are identified using a standardized soil moisture index for the period 1951–2014. Using these identified drought events, Event Coincidence Analysis is first applied to identify statistically significant lagged relationships between drought occurrence and the phases of major climate oscillations, including the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO), across multiple lead times (τ = 1, 3, 6, 9, and 12 months). Subsequently, these lag-specific relationships are used to construct complex networks that explicitly represent spatial connections between climate oscillations and drought events. The network analysis reveals clear and systematic regional patterns. Arid, semi-arid, and sub-humid regions consistently exhibit high network degree values, indicating strong connectivity with multiple climate oscillations, particularly at short to intermediate time lags. This suggests that droughts in these regions are driven by compound climate influences originating from different ocean basins. In contrast, humid regions display lower network degree values across all time lags, indicating weaker sensitivity to large-scale climate variability. Overall, the results demonstrate that agricultural drought across India is governed by lag-dependent and spatially organized climate influences rather than by a single dominant climate driver. The proposed framework provides a direct link between temporal climate signals and spatial drought connectivity, offering a robust basis for improving drought monitoring and early warning systems.