Abstract

Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.

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