Lakshmi et al. (2025) Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-09-16
• Authors: Venkataraman Lakshmi, Elif Gülen KIR, Bin Fang
• DOI: 10.3390/rs17183199

Research Groups

Not explicitly stated in the provided text.

Short Summary

This study evaluates the performance of GPM-IMERG and CHIRPS satellite precipitation data against rain gauge observations in Türkiye’s Akçay Sub-Basin, finding that GPM-IMERG shows good agreement at the monthly scale but reduced accuracy at the daily scale, particularly for extreme events.

Objective

• To evaluate the performance of GPM-IMERG and CHIRPS satellite precipitation data in Türkiye’s Akçay Sub-Basin by comparing them with rain gauge observations.

Study Configuration

• Spatial Scale: Akçay Sub-Basin, Türkiye.
• Temporal Scale: Daily and monthly.

Methodology and Data

• Models used: GPM-IMERG, CHIRPS
• Data sources: GPM-IMERG (satellite), CHIRPS (satellite), rain gauge observations (Finike and Elmali meteorological stations). Statistical metrics included Pearson’s correlation coefficient, Nash-Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), Kolmogorov–Smirnov (K-S) test, Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI).

Main Results

• GPM-IMERG showed good agreement with rain gauge observations at the monthly scale (Pearson = 0.943; RMSE = 50.81 mm).
• GPM-IMERG exhibited reduced accuracy at the daily scale (Pearson = 0.592; RMSE = 12.45 mm).
• The Kolmogorov–Smirnov (K-S) test indicated that the Beta distribution best fits monthly rainfall (threshold = 253.39 mm), while the Weibull distribution best suits daily rainfall (threshold = 5.34 mm).
• For extreme rainfall detection, GPM-IMERG achieved a monthly POD of 0.778 and FAR of 0.222.
• Daily extreme rainfall detection performance for GPM-IMERG was lower (POD = 0.478; FAR = 0.388).

Contributions

• Provides a detailed performance assessment of GPM-IMERG and CHIRPS satellite precipitation data in a data-scarce basin, highlighting scale-dependent accuracy and implications for flood risk assessment and climate resilience.
• Quantifies the distributional fit of rainfall data at different temporal scales using the K-S test.

Funding

Not explicitly stated in the provided text.

Citation

@article{Lakshmi2025Precipitation,
  author = {Lakshmi, Venkataraman and KIR, Elif Gülen and Fang, Bin},
  title = {Precipitation Data Accuracy and Extreme Rainfall Detection for Flood Risk Analysis in the Akçay Sub-Basin},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17183199},
  url = {https://doi.org/10.3390/rs17183199}
}