Comparing Remotely Sensed and Weather Station Measured Rainfall Data for Flood Prediction in KwaZulu Natal, South Africa

Extreme rainfall events frequently trigger flash floods, which cause widespread damage to ecosystems, infrastructure, and livelihoods. Accurate rainfall estimation is therefore essential for flood modeling and early warning systems. This study assessed the performance of the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement Integrated Multi‐Satellite Retrievals for GPM (GPM‐IMERG) against weather station rainfall observations collected across KwaZulu‐Natal, South Africa, between 2000 and 2022. Satellite‐derived precipitation estimates were evaluated against field measured data using the correlation coefficient ( CC ), percentage bias (PBIAS), mean absolute error (MAE), and root mean square error (RMSE). The results showed that GPM‐IMERG outperformed TRMM, exhibiting stronger agreement with weather station observations ( CC = 0.97 compared with 0.86), a lower PBIAS (−3.76% vs. −5.94%), and smaller estimation errors (MAE = 5.48 mm and RMSE = 7.59 mm) than TRMM (MAE = 13.07 mm and RMSE = 15.33 mm). Furthermore, GPM‐IMERG exhibited a statistically significant agreement with observed rainfall ( p < 0.05), demonstrating improved precipitation estimation over its predecessor, TRMM. These findings demonstrate that GPM‐IMERG (GPM_3IMERGM v07) provides reliable precipitation estimates for KwaZulu‐Natal and has the potential to improve flood forecasting, strengthen early warning systems, and enhance disaster preparedness in regions vulnerable to extreme rainfall events.