A Comprehensive Review of the HEC-HMS Rainfall–Runoff Simulation Model and Its Hydrological Applications
Rakesh Kumar Turkar, R.N. Shrivastava, M.K. Awasthi, A.S. Lodhi
Journal of Agriculture and Ecology Research International · pp. 128–139 · Published 27 Oct 2025
10.9734/jaeri/2025/v26i5711Abstract
Effective water resources management requires a thorough understanding of the hydrological cycle and its response to natural and human-induced factors. The HEC-HMS model is a versatile tool for simulating rainfall–runoff processes under diverse hydrological and climatic conditions. This study reviews and applies HEC-HMS, integrated with GIS and remote sensing, to assess key watershed parameters such as runoff depth, rainfall–runoff relationships, and peak discharge. Validation from multiple studies demonstrates its reliability for event-based and continuous simulations. Results show that SCS-CN and Green-Ampt methods are effective for event-based modeling, while SMA is preferred for continuous simulations. Sensitivity analyses highlight curve number, infiltration rate, lag time, and base flow as critical parameters. Integration with geospatial tools improves parameter extraction and spatial representation, enabling better analysis in gauged and ungauged basins. HEC-HMS reproduces observed flows accurately, though performance may decline in data-scarce areas or during extreme events. Using integrated hydro-meteorological and GIS-derived datasets, calibration and validation confirmed strong agreement between simulated and observed flows, supporting reliable flood estimation. Overall, HEC-HMS is precise in runoff simulation, applicable to ungauged watersheds, and valuable for water resource assessment, planning, and management. Careful calibration, model selection, and data integration enhance prediction accuracy, establishing HEC-HMS as a robust tool for future hydrological modeling and water management studies.
Cited by 2
Hayatuddin Tuasikal, Pitojo Tri Juwono, Moh. Sholichin · Engineering Technology & Applied Science Research · 2026
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