Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin

This study evaluates GPM-IMERG (Global Precipitation Measurement - Integrated Multi-satellite Retrievals) and CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations) satellite precipitation data in Turkey’s Akçay Sub-Basin by comparing them with rain gage observations from the Finike and Elmali meteorological stations. Statistical metrics, such as Pearson’s correlation coefficient, Nash-Sutcliffe Efficiency (NSE), and Root Mean Square Error (RMSE), were used to assess performance. The study also examines distributional fit via the Kolmogorov-Smirnov (K-S) test and evaluates extreme rainfall detection accuracy using metrics like Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). Results indicate that GPM-IMERG agrees well with rain gage observations at the monthly scale, but less so at daily scale. The K-S test shows that the Beta distribution best fits monthly data, while the Weibull distribution better represents daily data. By comparing satellite and ground observations, this study offers insights for flood risk analysis and climate resilience strategies in regions with limited infrastructure. The evaluation of satellite precipitation in the Akçay Sub-Basin supports improved water management and strengthens regional flood monitoring. Correlating satellite and ground data enhances the utility of high-resolution satellite precipitation in data-scarce areas, enabling more accurate hydrological modeling and resilience planning.