Spatio-temporal analysis of rainfall variability across drought prone districts of Odisha under changing climate conditions

Climate change has altered rainfall patterns globally, increasing the frequency of extremes and posing significant risks to agriculture, water resources, and disaster management. The agrarian economy of Odisha, eastern India, is highly dependent on monsoon rainfall, making it particularly vulnerable to rainfall variability. This study analyses long-term rainfall trends over five drought-prone western districts of Odisha—Nuapada, Balangir, Bargarh, Kalahandi, and Kandhamal—using 32 years (1990–2021) of daily gridded rainfall data at a 0.25° × 0.25° spatial resolution. Rainfall trends were assessed at monthly, seasonal, and annual scales using the Mann–Kendall test and Sen’s slope estimator.
Results indicate non-significant declining annual rainfall trends in Balangir, Bargarh, Kalahandi, and Kandhamal, while Nuapada exhibited a non-significant increasing trend. Monsoon rainfall showed non-significant negative trends across most districts, with no substantial increase observed in Nuapada. During the post-monsoon season, non-significant decreasing trends were noted in Nuapada, Balangir, and Kandhamal, whereas Bargarh and Kalahandi showed increasing tendencies. Winter rainfall exhibited non-significant increasing trends in all districts except Kandhamal, which recorded a declining trend. Summer rainfall showed non-significant decreasing trends in Nuapada, Balangir, and Kandhamal; however, Kalahandi experienced a statistically significant decline (Z = −2.06, p = 0.04) with a Sen’s slope of −1.40 mm yr⁻¹. Monthly rainfall trends displayed considerable spatial variability across the study area. Nevertheless, strong natural climatic variability, particularly monsoon dynamics, poses challenges in clearly distinguishing long-term climate change signals from natural fluctuations. The findings highlight pronounced spatial and temporal rainfall variability in western Odisha, emphasizing the need for climate-resilient agricultural planning and adaptive water resource management.