Enhancing Rainfall Measurement Using Remote Sensing Data in Sparse Rain Gauge Networks: A Case Study in White Nile State, Sudan †

Abstract

Monitoring rainfall is essential to understanding hydrological processes, managing water resources, and mitigating drought and flood risks. Many regions, particularly in developing countries, have sparse rain gauge networks, which limit spatial coverage and result in inaccurate rainfall estimates. By combining remote sensing data with rain gauge measurements, rainfall estimates can be improved, and spatial coverage can be enhanced. Remote sensing techniques provide a valuable resource for supplementing and enhancing rainfall monitoring in such areas. This study leverages Global Precipitation Measurement (GPM) satellite data to enhance rainfall estimation in White Nile State, Sudan, where only two rain gauge stations are operational and the state’s total area is 39.600 km2. GPM data, well-known for its high temporal and spatial resolution, offers a promising alternative to mitigate the limitations of sparse ground-based networks. The study integrates GPM satellite data with ground-based measurements through statistical and geostatistical techniques, as well as validation, to improve rainfall accuracy. The results show that, on average, GPM data and rain gauge measurements exhibit a strong correlation of 0.87, with an annual RMSE of 10.23 mm and an AME of 8.25 mm. These findings demonstrate that GPM data effectively complements traditional rain gauge observations by accurately capturing spatial rainfall distributions and extreme precipitation events. The findings underscore the potential of remote sensing to provide reliable rainfall information in data-scarce regions, contributing to better water resource management and disaster risk reduction strategies.