Spatio-Temporal Variability and Trend Analysis of Rainfall Over Kerala, India, Using Persiann-Ccs Gridded Satellite Data (2003–2024)

DOI

https://www.doi.org/10.59256/ijrtmr.20260604035

Published In

Volume 6, Issue 4 (2026)

Published Online

July-August 2026

Pages

320-329

Article Type

Original Article

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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.