Flood Probability Mapping and Zoning in the Tanguiyeh Basin, SirjanAbstract

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشگاه شهید باهنر کرمان

2 دانشجوی کارشناسی ارشد دانشگاه شهید باهنر کرمان

10.22052/jdee.2025.256426.1102

چکیده

Flooding is recognized as one of the most complex and destructive natural hazards, causing more annual damage globally than any other natural disaster. Flood hazard zoning maps are crucial tools for identifying vulnerable and at-risk areas, thereby facilitating the implementation of effective management strategies. This study aimed to map flood probability within the Tanguiyeh Basin. To achieve this, key variables including vegetation cover, land use, drainage network density, distance from the drainage network, lithology, rainfall, topography, and slope were identified from various sources and integrated as informational layers into ArcGIS. Subsequently, a panel of 30 experts and flood hazard specialists weighted these variables in two rounds. The flood hazard zoning map of the Tanguiyeh Basin was then generated by integrating these weighted layers. The results indicate that areas with very low flood probability constitute 1.36% (15.48 km²) of the basin, low probability areas cover 8.79% (100.064 km²), medium probability areas represent 67.47% (767.82 km²), high probability areas comprise 22.37% (254.553 km²), and very high probability areas account for 0.01% (0.121 km²). Furthermore, the northern, eastern, and southern sections of the region show higher flood potential, primarily attributed to increased rainfall, lower permeability, and steeper slopes.

کلیدواژه‌ها

عنوان مقاله [English]

Flood Probability Mapping and Zoning in the Tanguiyeh Basin, SirjanAbstract

نویسندگان [English]

  • Mohsen Pourkhosravani 1
  • Hossein Ghazanfarpour 1
  • Fatemeh Karamiyan 2
1 Associate Professor, Department of Geography Shahid Bahonar University of Kerman, Kerman, Iran
2 M.A Student, Department of Geography Shahid Bahonar University of Kerman, Kerman, Iran;
چکیده [English]

Flooding is recognized as one of the most complex and destructive natural hazards, causing more annual damage globally than any other natural disaster. Flood hazard zoning maps are crucial tools for identifying vulnerable and at-risk areas, thereby facilitating the implementation of effective management strategies. This study aimed to map flood probability within the Tanguiyeh Basin. To achieve this, key variables including vegetation cover, land use, drainage network density, distance from the drainage network, lithology, rainfall, topography, and slope were identified from various sources and integrated as informational layers into ArcGIS. Subsequently, a panel of 30 experts and flood hazard specialists weighted these variables in two rounds. The flood hazard zoning map of the Tanguiyeh Basin was then generated by integrating these weighted layers. The results indicate that areas with very low flood probability constitute 1.36% (15.48 km²) of the basin, low probability areas cover 8.79% (100.064 km²), medium probability areas represent 67.47% (767.82 km²), high probability areas comprise 22.37% (254.553 km²), and very high probability areas account for 0.01% (0.121 km²). Furthermore, the northern, eastern, and southern sections of the region show higher flood potential, primarily attributed to increased rainfall, lower permeability, and steeper slopes.

کلیدواژه‌ها [English]

  • Drainage Network Density
  • Flood Hazard Zoning
  • Land use
  • Lithology
  • Tanguiyeh Basin
  1. Abedini, A., & Fathi Joukdan, F. (2016). Lithological conditions and flood potential in river basins: An integrated assessment. Journal of Hydrology, 537, 1012–1023. https://doi.org/10.1016/j.jhydrol.2016.04.012
  2. Ahmad, S., & Kumar, P. (2023). Flood risk mapping and hazard assessment using multi‐criteria decision analysis and GIS: A case study from South Asia. Environmental Earth Sciences, 82(2), 1–15. https://doi.org/10.1007/s12665-023-10123-4.
  3. Ajjur, S. B., Mogheir, Y. K. (2020). Flood hazard mapping using a multi-criteria decision analysis and GIS: Case study Gaza Governorate, Palestine. Arabian Journal of Geosciences, 13(2), 44. https://doi.org/10.1007/s12517-019-5024-6
  4. Arji, A., Azizi, R., & Rezaei, S. (2021). Evaluating flood risk across the Gorganroud watershed using GIS. Journal of Hydrological Studies, 18(2), 112–125. https://doi.org/10.1016/j.jhydro.2021.04.003
  5. Asadi, M., Jabbari, I. and Hesadi, H. (2022). Evaluation and Assessment of Capability of Hydrograph Model of Instantaneous Geomorphology Unit in Simulating Flood Hydrograph of Minab River Basin. Geography and Development, 20(68), 116-137. doi:10.22111/gdij10.22111.2022.7005 [In Persian]
  6. Bahrami, A., Karami, A., & Jahanbakhshi, S. (2008). The influence of drainage density on time of concentration and flood hydrograph formation. Journal of Hydrology, 360(1-2), 112–121. https://doi.org/10.1016/j.jhydrol.2008.03.001 [In Persian]
  7. Bui, Q.T., Q. H. Nguyen, X. L. Nguyen, V. D. Pham, H. D. Nguyen and V.M. Pham.(2020). Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology 581: 124371-9. DOI: 1016/j.jhydrol.2019.124379
  8. Esfandiary Darabad, F., Kheirizadeh, M. and rahimi, M. (2022). Evaluation of Morphological Changes and Flood Hazard of Kivi Chay River Using Geomorphometric Indices and HEC-RAS Model. Quantitative Geomorphological Research, 11(1), 19-43. doi: 10.22034/gmpj.2021.290177.1281
  9. Fagunloye, O.C. (2024). Mapping of Flood Risk Zones Using Multi-Criteria Approach and Radar: A Case Study of Ala and Akure-Ofosu Communities, Ondo State, Nigeria. International Journal of Geosciences, 15(8), 605-631. DOI: 4236/ijg.2024.158035. 
  10. Green, J., Haigh, I. D., Quinn, N., Neal, J., Wahl, T., Wood, M., Eilander, D., de Ruiter, M., Ward, P., & Camus, P. (2025). Review article: A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions. Natural Hazards and Earth System Sciences, 25(2), 747–816. https://doi.org/10.5194/nhess-25-747-2025
  11. Hughes, T., & Lopez, D. (2024). Advancements in flood zoning: Integrating GIS, remote sensing, and empirical data for enhanced risk mapping. International Journal of Flood Risk Management, 17(1), 33–50. https://doi.org/10.1080/19408625.2023.1976543
  12. Li, X., Peng, Y., & Ma, Y. (2022). Flood‐induced microbial contamination in built environments: A case study of urban and rural impacts. Science of the Total Environment, 806, 150672. https://doi.org/10.1016/j.scitotenv.2021.150672
  13. Mishra, K., Sinha, R. (2020). Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology, 350: 1-19. https://doi.org/10.1016/j.geomorph.2019.106861
  14. Ogato, G. S.; Bantider, A., Abebe, K., Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. Journal of Hydrology: Regional Studies, 27(6): 100659-100687.
  15. Peiris, M. T. O. V. (2024). Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities. Sustainability, 16(20), 9117. https://doi.org/10.3390/su16209117
  16. Pirzadeh, B. and Asvar, T. (2020). Determining Spatial and Temporal Variations of Groundwater Quality Parameters Using GIS and Interpolation Methods (Case Study: Sirjan Plain). Irrigation and Water Engineering, 11(2), 266-275. doi: 10.22125/iwe.2020.120736 [In Persian]
  17. Roohi, M., Ghafouri, H. R., & Ashrafi, S. M. (2025). Advancing flood disaster management: Leveraging deep learning and remote sensing technologies. Acta Geophysica, 73, 557–575. https://doi.org/10.1007/s11600-024-01481-6
  18. Sharifi, F., Saghafian, B., and Telvari, A. (2002). The Great 2001 Flood in Golestan Province. Iran: Causes &Consequences. International Conference on Flood Estimation, Berne, Switzerland, 263-271.
  19. Youssef, A. M., Pradhan, B., & Sefry, S. A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(1), 12. DOI: 1007/s12665-015-4830-8
  20. Zarei, M., Zandi, R., Naimi Tabar, M. (2022). Evaluation of flood potential using data mining models of support vector machine, chaid and random forest (case study: Farizi area Watershed). Watershed Management Journal, 13(25): pp. 133-144. doi:52547/jwmr.13.25.133 [In Persian]
  21. Ziari, K., Rajai, S. A. and Darabkhani, R. (2021). Flood Zoning Using Hierarchical Analysis andFuzzy Logic in GISCase Study: Ilam City. Emergency Management, 10(1), 21-30. [In Persian]

مراجع

  1. Abedini, A., & Fathi Joukdan, F. (2016). Lithological conditions and flood potential in river basins: An integrated assessment. Journal of Hydrology, 537, 1012–1023. https://doi.org/10.1016/j.jhydrol.2016.04.012
  2. Ahmad, S., & Kumar, P. (2023). Flood risk mapping and hazard assessment using multi‐criteria decision analysis and GIS: A case study from South Asia. Environmental Earth Sciences, 82(2), 1–15. https://doi.org/10.1007/s12665-023-10123-4.
  3. Ajjur, S. B., Mogheir, Y. K. (2020). Flood hazard mapping using a multi-criteria decision analysis and GIS: Case study Gaza Governorate, Palestine. Arabian Journal of Geosciences, 13(2), 44. https://doi.org/10.1007/s12517-019-5024-6
  4. Arji, A., Azizi, R., & Rezaei, S. (2021). Evaluating flood risk across the Gorganroud watershed using GIS. Journal of Hydrological Studies, 18(2), 112–125. https://doi.org/10.1016/j.jhydro.2021.04.003
  5. Asadi, M., Jabbari, I. and Hesadi, H. (2022). Evaluation and Assessment of Capability of Hydrograph Model of Instantaneous Geomorphology Unit in Simulating Flood Hydrograph of Minab River Basin. Geography and Development, 20(68), 116-137. doi:10.22111/gdij10.22111.2022.7005 [In Persian]
  6. Bahrami, A., Karami, A., & Jahanbakhshi, S. (2008). The influence of drainage density on time of concentration and flood hydrograph formation. Journal of Hydrology, 360(1-2), 112–121. https://doi.org/10.1016/j.jhydrol.2008.03.001 [In Persian]
  7. Bui, Q.T., Q. H. Nguyen, X. L. Nguyen, V. D. Pham, H. D. Nguyen and V.M. Pham.(2020). Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. Journal of Hydrology 581: 124371-9. DOI: 1016/j.jhydrol.2019.124379
  8. Esfandiary Darabad, F., Kheirizadeh, M. and rahimi, M. (2022). Evaluation of Morphological Changes and Flood Hazard of Kivi Chay River Using Geomorphometric Indices and HEC-RAS Model. Quantitative Geomorphological Research, 11(1), 19-43. doi: 10.22034/gmpj.2021.290177.1281
  9. Fagunloye, O.C. (2024). Mapping of Flood Risk Zones Using Multi-Criteria Approach and Radar: A Case Study of Ala and Akure-Ofosu Communities, Ondo State, Nigeria. International Journal of Geosciences, 15(8), 605-631. DOI: 4236/ijg.2024.158035. 
  10. Green, J., Haigh, I. D., Quinn, N., Neal, J., Wahl, T., Wood, M., Eilander, D., de Ruiter, M., Ward, P., & Camus, P. (2025). Review article: A comprehensive review of compound flooding literature with a focus on coastal and estuarine regions. Natural Hazards and Earth System Sciences, 25(2), 747–816. https://doi.org/10.5194/nhess-25-747-2025
  11. Hughes, T., & Lopez, D. (2024). Advancements in flood zoning: Integrating GIS, remote sensing, and empirical data for enhanced risk mapping. International Journal of Flood Risk Management, 17(1), 33–50. https://doi.org/10.1080/19408625.2023.1976543
  12. Li, X., Peng, Y., & Ma, Y. (2022). Flood‐induced microbial contamination in built environments: A case study of urban and rural impacts. Science of the Total Environment, 806, 150672. https://doi.org/10.1016/j.scitotenv.2021.150672
  13. Mishra, K., Sinha, R. (2020). Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology, 350: 1-19. https://doi.org/10.1016/j.geomorph.2019.106861
  14. Ogato, G. S.; Bantider, A., Abebe, K., Geneletti, D. (2020). Geographic information system (GIS)-Based multicriteria analysis of flooding hazard and risk in Ambo Town and its watershed, West shoa zone, oromia regional State, Ethiopia. Journal of Hydrology: Regional Studies, 27(6): 100659-100687.
  15. Peiris, M. T. O. V. (2024). Assessment of Urban Resilience to Floods: A Spatial Planning Framework for Cities. Sustainability, 16(20), 9117. https://doi.org/10.3390/su16209117
  16. Pirzadeh, B. and Asvar, T. (2020). Determining Spatial and Temporal Variations of Groundwater Quality Parameters Using GIS and Interpolation Methods (Case Study: Sirjan Plain). Irrigation and Water Engineering, 11(2), 266-275. doi: 10.22125/iwe.2020.120736 [In Persian]
  17. Roohi, M., Ghafouri, H. R., & Ashrafi, S. M. (2025). Advancing flood disaster management: Leveraging deep learning and remote sensing technologies. Acta Geophysica, 73, 557–575. https://doi.org/10.1007/s11600-024-01481-6
  18. Sharifi, F., Saghafian, B., and Telvari, A. (2002). The Great 2001 Flood in Golestan Province. Iran: Causes &Consequences. International Conference on Flood Estimation, Berne, Switzerland, 263-271.
  19. Youssef, A. M., Pradhan, B., & Sefry, S. A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental Earth Sciences, 75(1), 12. DOI: 1007/s12665-015-4830-8
  20. Zarei, M., Zandi, R., Naimi Tabar, M. (2022). Evaluation of flood potential using data mining models of support vector machine, chaid and random forest (case study: Farizi area Watershed). Watershed Management Journal, 13(25): pp. 133-144. doi:52547/jwmr.13.25.133 [In Persian]
  21. Ziari, K., Rajai, S. A. and Darabkhani, R. (2021). Flood Zoning Using Hierarchical Analysis andFuzzy Logic in GISCase Study: Ilam City. Emergency Management, 10(1), 21-30. [In Persian]
مهندسی اکوسیستم بیابان
دوره 14، شماره 47
مهر 1404
صفحه 1-12

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