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Research Article Open access CC BY 4.0

Statistical Analysis of Climate Variability and its Impacts on Tank Irrigation: A Case Study of the Agaramar Sub-Basin, India

Abisha C.S., Carolin Arul, Melba Respina B

International Journal of Environment and Climate Change · pp. 140–158 · Published 7 Apr 2025

10.9734/ijecc/2025/v15i44799

Abstract

Analyzing climatic behaviour at the relevant time scales is crucial in countries that rely on rain-fed agriculture. This study was conducted in the Thippasamudram tank within the Agaramar sub-basin, Vellore district.  Historical rainfall and rainy-day data from 1981 to 2020 were acquired from the IMD data portal, Pune. This study used statistical analysis to detect trends in the temperature, rainfall, and rainy days at monthly, seasonal and annual timescales (1981-2020) at Thippasamudram village. The results revealed that the minimum temperature got significantly increased during the Southwest and the Northeast monsoon seasons, summer season and yearly timescales at rates of 0.017℃, 0.037℃, 0.024℃, and 0.018℃ respectively. The Mann Whitney Pettitt test revealed that the year 2001 was the change point on the annual scale. The highest positive temperature anomaly was recorded in the year 2012, at a rate of 0.86℃. From the Precipitation Concentration Index (PCI) and Rainfall Anomaly Index (RAI) results, the year 2012 was characterized as very dry and had an irregular distribution of rainfall. The tank filling levels were triangulated with the actual data and found below 50 %. This study found high variability in monthly and seasonal rainfall from the Coefficient of Variation (CV) statistical analysis. It showed a significant variation in seasonal and yearly rainy days exhibiting reduced variation. No significant annual precipitation trend was detected. The analysis revealed that the rainfed Thippasamudram tank experiences inconsistent filling due to erratic rainfall. The findings of this study enable policymakers and farmers to improve irrigation infrastructure and optimize irrigation scheduling, leading to enhanced efficiency.

Climate variability precipitation concentration index rainfall anomaly index statistical analysis temperature anomaly

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