Goolge Earth Engine-based Rainfall-Runoff Modelling for Hydrological Assessment in Rasulpur River Basin

Abstract

The hydrological assessment of surface rainfall-runoff is crucial for effective water resource management and flood risk mitigation. Using the SCS-CN method, this study combined remote sensing GIS, and Google Earth Engine (GEE) methods to give an occupied hydrological assessment. GEE facilitated the processing of large-scale environmental data and facilitated large details of run-off properties, land used dynamics, and rainfall patterns. Following the study, there was an overall rise in runoff with rainfall for the years 2019 to 2022. For watersheds I, II, and III, the AMC results were 71.220, 74.990, and 33.330 for normal condition (CNII), dry condition (CNI), 50.965, 55.739, 17.353, and wet condition (CNIII), 85.056, 87.336, and 53.485, respectively. From 2019 to 2021, the average annual rainfall, volume of run-off, and run-off co-efficient are 10040.371 mm, 7728.371 mm3, and 0.764 mm respectively. The annual rainfall-run-off ratio is significantly increased in the years 2019 and 2021, but in 2022, are decreased. There is a strong relationship between total rainfall and run-off in the contain, with a correlation coefficient (r) value is 0.99. The Rasulpur River basin's hydrological behaviour was able to be accurately and recently assessed thanks to the use of high-resolution, real-time satellite data and it is a valuable tool for informed decision-making and the development of sustainable water resource management strategies.