Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia

Integrating satellite rainfall estimates with hydrological models significantly improves water resource management, particularly in data-scarce regions. This study demonstrates that combining Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) satellite rainfall products with the GR2M hydrological model enhances flow simulation accuracy in the Awash River Basin, Ethiopia. The findings reveal that 80% of calibrated and 60% of validated watersheds achieved lower percent bias (<±10), indicating better performance. However, certain watersheds like Melka Kuntire, Mojo, Metehara, and Kessem showed poor accuracy with higher bias (≥±25). The study underscores the potential of satellite rainfall data for effective water resource planning but emphasizes the need for ground-based validation and bias correction to improve model reliability.

Adane, G. B., Hirpa, B. A., Gebru, B. M., Song, C., & Lee, W.-K. (2021). Integrating Satellite Rainfall Estimates with Hydrological Water Balance Model: Rainfall-Runoff Modeling in Awash River Basin, Ethiopia. Water, 13(6), 800. https://doi.org/10.3390/w13060800