Geographically distributed trend and variability analysis of rainfall over the state of Haryana, India, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data dataset

Abstract

Climate change is impacting global rainfall patterns, subsequently affecting food security worldwide. This study aims to analyse long-term rainfall trends at finer spatial resolutions to understand the effects of changing rainfall patterns on agriculture and other socioeconomic activities. Point rain-gauge data presents limitations, such as non-uniform geographic coverage, issues of data continuity and availability, and lack of data in remote regions. These challenges make high-resolution spatial trend analysis using point gauge observations difficult. To overcome these limitations, the study utilizes the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, which merges remote sensing data (radar and satellite observations) calibrated with rain-gauge datasets. The CHIRPS dataset, available at three temporal scales with a 5-km spatial resolution, is used for trend analysis and drought monitoring. The research evaluates rainfall trends over Haryana and its 77 tehsils (talukas) at seasonal and annual scales from 1981 to 2020. Trend analysis was conducted using the Mann–Kendall Test, Spearman’s coefficient test, and the magnitude of the trends was assessed using Sen’s slope estimate and linear regression. The findings reveal an increasing trend in both annual and monsoonal rainfall across Haryana and its 77 tehsils, except for Naraingarh tehsil in northern Haryana. The western part of Haryana shows a statistically significant increase in annual and monsoon rainfall. Conversely, winter and post-monsoon seasons exhibit a decreasing trend across almost all tehsils, while pre-monsoon rainfall shows a significant rising trend in the southern and southeastern tehsils. The annual and monsoon rainfall patterns have shifted, increasing by 6.26% and 6.68% respectively from the 1981–2010 WMO standard period to 1991–2020. These changing patterns suggest potential impacts on agricultural and economic activity management.