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 Türkiye’s Akçay Sub-Basin by comparing them with rain gauge observations from the Finike and Elmali meteorological stations. Statistical metrics including 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 gauge observations at the monthly scale (Pearson = 0.943; RMSE = 50.81 mm), but shows reduced accuracy at the daily scale (Pearson = 0.592; RMSE = 12.45 mm). The K-S test showed that the Beta distribution best fits monthly rainfall (threshold = 253.39 mm), while the Weibull distribution suits daily rainfall (threshold = 5.34 mm). GPM-IMERG achieved a POD of 0.778 and FAR of 0.222 for monthly extremes, while daily performance was lower (POD = 0.478; FAR = 0.388). These findings highlight the value of comparing satellite and ground-based data to improve flood risk assessment and enhance climate resilience in data-scarce basins.