Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin
Remote Sensing, cilt.17, sa.18, 2025 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 17 Sayı: 18
- Basım Tarihi: 2025
- Doi Numarası: 10.3390/rs17183199
- Dergi Adı: Remote Sensing
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Compendex, INSPEC, Veterinary Science Database, Directory of Open Access Journals
- Anahtar Kelimeler: CHIRPS, correlation of precipitation data, extreme rainfall detection, GPM-IMERG, Kolmogorov–Smirnov, statistical metrics
- Süleyman Demirel Üniversitesi Adresli: Evet
Özet
Highlights: What are the main findings? GPM-IMERG outperforms CHIRPS in the Türkiye’s Akçay Sub-Basin, with higher accuracy at the monthly scale (Pearson = 0.943; RMSE = 50.81 mm) but lower performance at the daily scale (Pearson = 0.592; RMSE = 12.45 mm). Extreme rainfall analysis indicated that the Beta distribution best fits monthly precipitation, while the Weibull distribution fits daily precipitation, improving threshold-based flood risk assessments. What is the implication of the main finding? GPM-IMERG is suitable for long-term precipitation monitoring and monthly extreme event detection in data-scarce basins, supporting hydrological modeling and flood risk management. 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.