Simulation and Zonation of Flood Potential by Integrating WMS and HEC-RAS Models (Case Study: Savadkuh County)

سلیمانی, بهناز; Ilanloo, Maryam; Ghorobi, Majid

doi:10.22059/ije.2026.411871.1908

Simulation and Zonation of Flood Potential by Integrating WMS and HEC-RAS Models (Case Study: Savadkuh County)

Document Type : Research Article

Authors

¹ Department of Mathematics, Mahs.C. Islamic Azad University, Mahshahr, Iran

² Department of Geography, Mahs.C. Islamic Azad University, Mahshahr, Iran

³ Department of Environment, El.C., Islamic Azad University, Tehran, Iran.

10.22059/ije.2026.411871.1908

Abstract

Objective: This study aimed to analyze the hydrological behavior of the Savadkouh watershed in Savadkouh County, simulate flood flow, and perform flood risk zoning using hydrological and hydraulic models, considering the region’s mountainous conditions, steep slopes, heavy rainfall, and extensive land use changes.
Method: Topographic data, land use maps, soil hydrological groups, and rainfall information were collected and processed. The Watershed Modeling System (WMS) model and the Soil Conservation Service (SCS) method were applied to estimate runoff curve number (CN), moisture retention capacity, runoff depth and volume, and flood hydrograph. Hydraulic flow simulation was then conducted using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) based on 21 river cross-sections. Finally, flood zoning and flow depth maps were generated.
Results: The findings indicated that topography, soil permeability, land use type, and rainfall intensity are the most influential factors in runoff generation and flood risk increase. The average curve number (CN) of the basin was estimated at approximately 78.3, reflecting a relatively high runoff potential. Areas with hydrological soil groups C and D, intensive agricultural lands, residential areas, and steep slopes showed the highest runoff volume and flood depth. Hydrographic analysis revealed a rapid watershed response, short concentration time, and high peak discharge, increasing the probability of flash floods. Flood zoning results were consistent with locations of previous flood events.
Conclusions: The study concludes that land use change and reduction of vegetation cover have significantly increased flood risk in the basin. Effective flood risk mitigation strategies include vegetation conservation, controlling development within floodplains, reforming land use management practices, and implementing comprehensive watershed management measures.

Keywords

Main Subjects

Surface water resources

References

Ebrahimi, L., Ilanloo, M. (2024). "Flood Susceptibility Zoning of the Shahrestanak Drainage Basin Using the WMS Hydrological Model and GIS Integration." Journal of Environmental Hazards Management, 11(1), 15–29. [In persian].
Esfandiari Darabad, F., Abedini, M., Fakhripour, N., Nazafat Takleh, B. (2023). "Flood Hazard Zoning Using the Hec–Ras Hydrodynamic Model: A Case Study of the Qareh Su Watershed, Kermanshah Province." Journal of Environmental Science Studies, 8(3), 6952–6961. [In persian].
Arab, N., Salmanmahini, A., Micaeli Tabrizi, A., Vitte, T. (2023). "Flood Risk Analysis Using the Random Forest Machine Learning Method: A Case Study of Mashhad City." Journal of Ecohydrology, 10(1), 1–15. [In persian].
Saffari, A., Ahmadabadi, A., Sedighifar, Z. (2020). "Flood Risk Analysis Based on the WMS Model in Urban Catchments: A Case Study of the Darband, Golabdarreh, and Sa'adabad Catchments in the Tehran Metropolis." Quarterly Journal of Agriculture and Natural Resources, 20(57), 317–334. [In persian].
Abdi, K., Kamiabi, S., & Zandmoghadam, M. R. (2019). Integrated assessment of vulnerability, resilience, and spatial risk against floods in Sari city. Journal of Natural Geographical Research, 51(3), 431–445. [In Persian] https://doi.org/10.1007/s10707-020-00461-x
Adeniyi, O., Perera, S., Ginige, K., & Feng, Y. (2019). Developing maturity levels for flood resilience of businesses using built environment flood resilience capability areas. Sustainable Cities and Society, 51, 101778. https://doi.org/10.1016/j.scs.2019.101778
Arab, N., Salmanmahiny, A. R., Mikaeili Tabrizi, A., & Witte, T. (2023). Flood hazard analysis using random forest machine learning: A case study of Mashhad city. Iranian Journal of Eco Hydrology, 10(1), 1–15.
Bertilsson, L., Wiklund, K., de Moura Tebaldi, I., Rezende, O. M., Veról, A. P., & Miguez, M. G. (2019). Urban flood resilience–A multi-criteria index to integrate flood resilience into urban planning. Journal of Hydrology, 573, 970–982. https://doi.org/10.1016/j.jhydrol.2018.06.052
Chen, Y., Liu, T., Chen, R., & Zhao, M. (2020). Influence of the built environment on community flood resilience: Evidence from Nanjing City, China. Sustainability, 12(6), 2401. https://doi.org/10.3390/su12062401
Ebrahimi, L., & Ilanloo, M. (2024). Flood occurrence zoning in Shahrestanak watershed using WMS hydrological model and GIS integration. Journal of Environmental Hazards Management, 11(1), 15–29. [In Persian]
Echogdali, F. Z., Boutaleb, S., Elmouden, A., & Ouchchen, M. (2018). Assessing flood hazard at river basin scale: Comparison between HECRAS-WMS and flood hazard index (FHI) methods applied to el maleh basin, Morocco. Journal of Water Resource and Protection, 10(9), 957–977.
Esfandyari, F., Abedini, M., Fakheripour, N., & Nezafat Takle, B. (2023). Flood risk zoning using Hec-Ras hydrodynamic model: A case study of Qarahsou Basin, Kermanshah Province. Journal of Environmental Studies, 8(3), 6952–6961. https://doi.org/10.22034/jess.2022.344023.1795
Hegazy, M. N., El-Fakharany, M. A. A., Abdo, A. M., & Mansour, N. M. (2023). Estimation of expected peak discharge and flood volume of the Heliopolis basin, East Cairo, Egypt, using RS and WMS program. The Egyptian Journal of Remote Sensing and Space Sciences, 26(3), 676–690.
Hosseini, S., Mohammadi, S., Hassanzadeh, R., & Honarmand, M. (2023). Flood hazard assessment using GIS and RS in southern Kerman province: A case study of the Hamun-Jazmourian watershed. Iranian Journal of Eco Hydrology, 10(1), 33–47. https://doi.org/10.22059/ije.2023.354747.1712
Ikirri, M., Faik, F., Boutaleb, S., Echogdali, F. Z., Abioui, M., & Al-Ansari, N. (2021). Application of HEC-RAS/WMS and FHI models for extreme hydrological events under climate change in the Ifni River arid watershed from Morocco. In Climate and Land Use Impacts on Natural and Artificial Systems (pp. 251–270). Elsevier.
Kabenge, M., Elaru, J., Wang, H., & Li, F. (2017). Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Natural Hazards, 89(3), 1369–1387. https://doi.org/10.1007/s11069-017-3024-y
Khaledi, S., Ghahroodi, M., & Farahmand, G. (2021). Assessment and evaluation of urban flood resilience: A case study of Urmia city. Journal of Sustainable Development of Geographical Environment, 2(3), 169–182. [In Persian]
Kumar, V., Sharma, K. V., Caloiero, T., Mehta, D. J., & Singh, K. (2023). Comprehensive overview of flood modeling approaches: A review of recent advances. Hydrology, 10(7), 141. https://doi.org/10.3390/hydrology10070141
Mannucci, S., Rosso, F., D’Amico, A., Bernardini, G., & Morganti, M. (2022). Flood resilience and adaptation in the built environment: How far along are we?. Sustainability, 14(7), 4096. https://doi.org/10.3390/su14074096
McClymont, K., Morrison, D., Beevers, L., & Carmen, E. (2020). Flood resilience: A systematic review. Journal of Environmental Planning and Management, 63(7), 1151–1176. https://doi.org/10.1080/09640568.2019.1675140
Meng, M., Dabrowski, M., & Stead, D. (2020). Enhancing flood resilience and climate adaptation: The state of the art and new directions for spatial planning. Sustainability, 12(19), 7864. https://doi.org/10.3390/su12197864
Munawar, H. S., Hammad, A. W., & Waller, S. T. (2022). Remote sensing methods for flood prediction: A review. Sensors, 22(3), 960. https://doi.org/10.3390/s22030960
Nofal, O. M., & Van De Lindt, J. W. (2022). Understanding flood risk in the context of community resilience modeling for the built environment: Research needs and trends. Sustainable and Resilient Infrastructure, 7(3), 171–187. https://doi.org/10.1080/23789689.2021.1931945
Okasha, M. R., Hashem, S. M., & Azeez, E. A. B. (2024). Using the Water Modeling System (WMS) to study some morphometric and flash flood features of the drainage basins in New Cairo first Precinct. Buhūth, 4(5), 138–163.
Rathnasiri, P., Adeniyi, O., & Thurairajah, N. (2023). Data-driven approaches to built environment flood resilience: A scientometric and critical review. Advanced Engineering Informatics, 57, 102085. https://doi.org/10.1016/j.aei.2023.102085
Safari, A., Ahmadabadi, A., & Sedighifar, Z. (2020). Analysis of flood hazard using the WMS model in urban watersheds: Case study of Darband, Ghalabdar, and Sa'adabad-Kalan basins, Tehran metropolis. Journal of Applied Geographical Sciences Research, 20(57), 317–334.
Szewrański, S., Świąder, M., Kazak, J. K., Tokarczyk‑Dorociak, K., & Van Hoof, J. (2018). Socio‑environmental vulnerability mapping for environmental and flood resilience assessment: the case of ageing and poverty in the City of Wrocław, Poland. Integrated Environmental Assessment and Management, 14(5), 592–597. https://doi.org/10.1002/ieam.4085
Wang, L., Cui, S., Li, Y., Huang, H., Manandhar, B., Nitivattananon, V., … Huang, W. (2022). A review of the flood management: From flood control to flood resilience. Heliyon, 8(11). https://doi.org/10.1016/j.heliyon.2022.e11339
Wang, Z., Bi, Y., Lu, G., Zhang, X., Xu, X., Ning, Y., … Wang, A. (2024). Monitoring forest diversity under Moso bamboo invasion: A random forest approach. Forests, 15(2), 318. https://doi.org/10.3390/f15020318
Xu, W., Yu, Q., & Proverbs, D. (2023). Evaluation of factors found to influence urban flood resilience in China. Water, 15(10), 1887. https://doi.org/10.3390/w15101887
Zevenbergen, C., Gersonius, B., & Radhakrishan, M. (2020). Flood resilience. Philosophical Transactions of the Royal Society A, 378(2168), 20190212. https://doi.org/10.1098/rsta.2019.0212
Zhang, Y., Zhang, Y., Du, B., Zhang, C., Guo, X., & Tu, W. (2022). Parallel discriminative subspace for city target detection from high dimension images. GeoInformatica, 26(2), 299–322. https://doi.org/10.1007/s10707-020-00461-x