THE SPATIO-TEMPORAL CHARACTERISTICS OF SOIL MOISTURE AND ITS PERSISTENCE OVER TAMIL NADU

Abstract

Soil moisture is a key regulator of land–atmosphere interactions, crop productivity, and drought development, yet its spatial variability and persistence remain poorly characterised for Tamil Nadu, a state marked by diverse rainfall regimes across the Western Ghats, coastal plains, and interior districts. This study used district-wise, gridded rainfall (IMD, 0.25°×0.25°) and soil moisture (ESA CCI, 0.25°×0.25°) data for 1991–2020 to examine (i) the spatial distribution and variability of rainfall and soil moisture, (ii) soil moisture persistence, and (iii) the relationship between rainfall and soil moisture across the state. Monthly and annual means, coefficients of variation, seasonal soil moisture, anomaly-based autocorrelation, e-folding persistence duration, and Pearson lag correlation were computed for each district. Mean annual rainfall was 883.2 mm, highest in The Nilgiris and Kanniyakumari and lowest in Karur and Tiruppur, with annual CV ranging 18.9–34.6%. Soil moisture (0.10–0.36 m³/m³) followed a distinct seasonal cycle, peaking during the Northeast Monsoon (mean 0.296) and lowest during the Hot Weather Period (0.188), confirming the Northeast Monsoon as Tamil Nadu's principal recharge season. Lag-1 autocorrelation of soil moisture anomalies ranged from 0.338 to 0.650 (state average ≈ 0.506) and was positive in every district, with persistence durations of 1–4 months (2–3 months in 86% of districts) — longest in Tirunelveli, Chengalpattu, Thiruvallur and Vellore, and shortest in The Nilgiris. Contemporaneous rainfall–soil moisture correlation was weak and non-significant (r = 0.144, p = 0.394), while a significant positive correlation emerged only at an 11-month lag (r = 0.407, p = 0.039), indicating a delayed rather than immediate coupling. These findings demonstrate that Tamil Nadu's soil moisture is spatially heterogeneous, monsoon-dependent, and characterised by short- to intermediate-term memory, providing district-level diagnostics valuable for drought monitoring, irrigation scheduling, and agricultural planning across the state.