Land-Atmosphere Drivers of Cloudburst Events

Cloudbursts, defined as sudden, intense rainfall episodes, are increasingly frequent extreme weather events in the Indo-Himalayan region, causing widespread devastation to human life and property; yet understanding their causal mechanisms and improving predictability remains constrained by incomplete knowledge of atmospheric and land-based precursors. Particularly, the role of soil moisture as a vital land-surface component has been underexplored in the context of cloudburst formation. This study hypothesizes that increased soil moisture from agricultural irrigation amplifies atmospheric moisture fluxes via land-atmosphere coupling and contributes to enhanced cloudburst risk. The objective here is to attribute moisture source locations, identify critical pre-event land-atmospheric indicators, and assess soil–atmosphere coupling through the analysis of IMD-specified cloudburst events from 1991 to 2020 using the Indian Land Data Assimilation System (ILDAS) dataset. We employ NOAA's Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back-trajectory model and create Integrated Vapor Transport (IVT) maps, composited with winds, surface pressure, and sea level pressure, to trace moisture source locations. Pre-event anomaly detection and change-point analysis are performed using the Pruned Exact Linear Time (PELT) algorithm on soil moisture, precipitation, evaporation, and runoff variables across nine spatially proximate grid cells per event. Additionally, extreme percentile threshold exceedances and non-parametric persistence metrics quantify the early-warning potential. Decadal NDVI trends contextualize Land Use/Land Cover (LULC) influences. Results reveal moisture source hotspots in regions undergoing land-use transitions, with steep pressure gradients establishing strong circulation patterns that contribute moisture to multiple cloudburst events. Significant temporal anomalies occur across all four variables, with threshold exceedances and change-point detections ranging from 2 to 10 occurrences per event and anomaly persistence spanning 2 to 8 days for soil moisture. Early warning lead times of 15 to 120 days are identified for soil moisture, precipitation, evaporation, and runoff anomalies preceding the cloudburst events. These findings suggest that further quantifying the causal links among these variables can better help understand soil–atmosphere coupling and substantially improve early warning systems for detecting extreme rainfall events.