Answers are grounded in this paper and cite their sources.
Understanding the spatial and temporal distribution of rainfall is fundamental to water resource planning and disaster preparedness in monsoon-driven regions. This study investigated spatio-temporal variability and long-term trends of annual rainfall across the 14 administrative districts of Kerala, India, over 2003–2024, using satellite-derived estimates from PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks — Cloud Classification System) at a native resolution of approximately 0.04° (~4 km). Rasters were reprojected to UTM Zone 43N and clipped to the Kerala boundary prior to analysis. The coefficient of variation (CV), annual anomaly, Mann-Kendall trend test, Sen’s slope estimator and Moran’s I spatial autocorrelation were applied. The state-wide long-term mean annual rainfall was 1,832.0 mm (CV = 23.7%). District-level CV ranged from 17.4% (Idukki) to 30.5% (Kasaragod), with 12 of 14 districts classified as moderately variable. Moran’s I (I = 0.1692, p = 0.112) indicated no significant spatial clustering. The year 2018 recorded the highest positive anomaly (+79.5%), coinciding with the catastrophic Kerala floods, while 2016 was the driest year (−43.1%). Five southern and central districts — Alappuzha, Ernakulam, Thiruvananthapuram, Kottayam and Kollam — exhibited statistically significant increasing trends (p < 0.05), with Sen’s slope values of +29.9 to +36.3 mm/yr; no district showed a decreasing trend. A pronounced decadal shift was observed, with the state mean rising from 1,677.1 mm (2003–2013) to 1,987.0 mm (2014–2024), an increase of 18.5%. The findings underscore rainfall intensification across Kerala’s southern districts and the need for adaptive water resource strategies.
No takes yet. Share an insight, caveat, or question.
Anjitha et al. (2026) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: