Sub-Basin Prioritization for Flood Control Based on Their Contribution to Flood Characteristics Using Satellite Remote Sensing Data: A Case Study of the Hablehroud River Basin

Extended Abstract
Background and Objective
This study prioritizes sub-basins of the Hablehroud watershed for flood risk using the HEC-HMS model, with GPM satellite rainfall data due to the lack of ground observations. Three flood events were used for calibration and validation, showing strong model performance (e.g., R² up to 0.93). A 50-year flood simulation and sub-basin elimination method identified Sub-basin 4 as the most critical, reducing outflow by 22.58% when removed. Satellite data evaluation showed reasonable accuracy. Results indicate that factors like location and concentration time matter more than just area or peak flow.
Methodology
This section focuses on modeling the Hablehroud Watershed using the HEC-HMS hydrological model. The study area, covering approximately 326,991 hectares, spans parts of Tehran, Mazandaran, and Semnan provinces. Soil data were obtained from the HWSD database, while vegetation cover information was derived from MODIS satellite imagery. Curve Numbers (CN) for sub-basins were calculated based on soil hydrologic groups and land use types using TR-55 reference tables. Due to the unavailability of high-resolution observational rainfall data, satellite-based GPM (IMERG product) precipitation data with a 30-minute temporal resolution were utilized and converted to hourly intervals via Python programming. The SCS-CN and Clark methods were applied for runoff estimation. Model performance was evaluated using R², Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) indices. The model was calibrated and validated against three selected flood events recorded at the Bankouh hydrometric station.
Findings
This study employed the HEC-HMS model to manage flood risk in the Hablehroud Basin. After determining Curve Numbers (CN) for sub-basins using land cover and soil data, the accuracy of IMERG satellite rainfall data was evaluated and deemed acceptable. The model was then calibrated and validated using these data, showing strong agreement between simulated and observed hydrographs (R² up to 93% and NSE up to 83%).
To identify flood-prone areas, the impact of individually removing each sub-basin on peak discharge and flood volume was analyzed. Sub-basin 4, which showed the highest reduction in peak discharge, was prioritized for flood control interventions. Additionally, a 30% reduction in CN values for individual sub-basins significantly decreased peak discharge and flood volume, with model performance (NSE) improving up to 89.6%.
The results highlight that land use modifications and enhanced soil infiltration can serve as effective strategies for flood mitigation. The study underscores the value of targeted sub-basin management and CN adjustment in improving watershed hydrological responses and reducing flood risk.
Conclusion
This study investigates the simulation of hydrological processes in a watershed using the HEC-HMS model, along with remote sensing and GIS data. The results indicate that the model performs well in simulating runoff and streamflow, and satellite data significantly enhance the simulation accuracy in the absence of ground-based stations. Land use maps, digital elevation models, and soil data were generated using GIS. The analysis shows that watershed area alone does not determine the amount of runoff; factors such as slope, elevation, time of concentration, and sub-basin location also play important roles. Sub-basin 4 is the largest in terms of area, while sub-basin 7 is the smallest. Sub-basin 4 also shows the greatest reduction in discharge, whereas sub-basin 15 has the least. Moreover, smaller sub-basins tend to produce higher peak discharges and are more sensitive to sudden rainfall events. Overall, the integration of remote sensing, GIS, and numerical modeling provides an effective tool for analyzing the hydrological behavior of watersheds.