Accurate surface runoff evaluation stands critical for hydrological studies and both water resource governance and flood danger assessment together with land-use design. The current methods of runoff measurement depend on in situ data sources, although the limitations in data accessibility and availability and operational expenditure. Rainfall-runoff estimation is a difficult task in unmeasured basins because these areas lack essential devices for data collection, such as stream gauges. Water resource assessment alongside flood prediction becomes challenging because ungauged basins are extensively spread across the Earth's surface. Water availability decisions, together with flood control and sustainable water management, face challenges because rainfall-runoff models remain unspecified. The research develops a Cloud-Based Computational Model, which incorporates remote sensing data and the Natural Resources Conservation Service Curve Number method to calculate direct surface runoff. The research examines arid and semi-arid regions, taking the Tekeze-Atbra sub-basin in Sudan as a case study, which is a significant tributary of the Nile River that experiences substantial runoff variations between seasons. The model incorporates satellite data (rainfall, LULC, soil moisture, and DEM) to estimate runoff while providing better spatial accuracy. The model demonstrates enhanced performance through evaluation testing that checks observed hydrological data and validates its effectiveness in offering better real-time water management predictions. The model demonstrated reliable results through runoff assessments against hydrological records spanning from 2015 to 2020, thus proving itself as a flexible computing technique for forecasting runoff linking remote sensing approaches with cloud computing features and hydrological modeling systems to handle data-sparse regions.
Saeed et al. (Thu,) studied this question.