Spatio-temporal analysis of rainfall dataset in the Mandakini Watershed, India using Mann-Kendall, Sen’s slope, and ITA approach

Abstract

Climate change has become a global concern over the past few decades Where, rainfall plays a crucial role in understanding these changes. The spatio-temporal analysis of rainfall provides insights into variability in rainfall patterns. This study investigates the multi-temporal rainfall records from 1983-2022, by utilizing Indian Meteorological Department (IMD) gridded dataset at a resolution of 0.25° x 0.25°. The seasonal and annual rainfall variability, magnitude, and trends were estimated using the Coefficient of Variation (CV), Mann-Kendall (MK) test, Sen’s slope, and Innovative Trend Analysis (ITA) at a 5% significant level in the Mandakini watershed. This study reveals increased fluctuations in precipitation, with rising trends in monsoon and annual rainfall data. Annual rainfall is notably higher in the northern and central regions, and the Rudraprayag station has shown considerable variability in CV% (59.77-130.05) values over the past 40 years, as depicted through spatial interpolation mapping. There are no significant changes during the pre-monsoon and winter seasons. The Okhimath station, experiencing substantial rainfall, faces heightened risks of floods, soil erosion, and landslides. The consistent decline in pre-monsoon and winter rainfall may lead to water scarcity, negatively impacting agriculture and snow deposits, while increased monsoon precipitation raises the risk of flooding and landslides. This evolving situation in the Mandakini watershed underscores the importance of adaptive management strategies to mitigate potential impacts on local ecosystems.