Document Type : Research Paper
Authors
Water Engineering Department, Faculty of Agriculture, Razi University, Kermanshah, Iran.
10.22126/arww.2025.11922.1360
Abstract
Floods rank among the most destructive natural disasters worldwide, with their frequency and intensity amplified by climate change. This study presents an integrated approach combining hydraulic modeling and multi-sensor satellite data to improve floodplain mapping accuracy during the April 3, 2019 flood event in Kermanshah, western Iran. The research focuses on the confluence of the Gharasoo, Merek, and Razavar rivers, where combined flows created significant flood risks for Kermanshah city. The study makes several important methodological contributions to flood modeling. First, it demonstrates the value of simultaneous analysis of both steady and unsteady state conditions, providing a more comprehensive understanding of flood dynamics compared to conventional single-state approaches. Second, the integration of optical (Sentinel-2, Landsat) and radar (Sentinel-1) remote sensing data effectively overcomes the limitations of individual sensors, particularly in addressing cloud cover issues. Third, the implementation of Google Earth Engine enables near-real-time flood monitoring capabilities, significantly enhancing operational response potential. Finally, the development of robust validation metrics specifically adapted for flood model assessment represents an important step forward in model verification methodologies. HEC-Geo RAS simulations predicted extreme conditions with water levels rising up to 6 meters and flow velocities reaching 3m/s. Validation results showed strong agreement between unsteady state modeling and satellite observations (F1=0.73, F2=0.72), while steady-state conditions exhibited lower correlation (F1=0.41, F2=0.28). The model effectively tracked flood progression from inception to peak, while satellite imagery provided rapid regional coverage despite occasional cloud obstructions.
Keywords
Afzal, M.A. et al. (2022) ‘Flood inundation modeling by integrating HEC–RAS and satellite imagery: A case study of the Indus River basin’,
Water, 14(19), 2984. doi: https://doi.org/10.3390/w14192984
Avarand, Y. et al. (2025) ‘Multi-objective fuzzy-stochastic optimization model for agricultural water allocation by applying effective rainfall’, Sustainable Water Resources Management, 11(3), 66. doi: https://doi.org/10.1007/s40899-025-01243-2
Ayoubikia, R. et al. (2019) ‘Optimization of regional water resources allocation in Sefidroud river basin by social equity approach’, Iran-Water Resources Research, 14(5). pp. 236-252. doi: https://www.iwrr.ir/article_66470.html (Accessed date: 2 June 2025).
Bach, H. and Verhoef, W. (2003) 'Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL', in Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003). Toulouse, France, 21-25 July 2003. Piscataway, NJ: IEEE, pp. 1679-1681.
Brivio, P.A. et al. (2002) ‘Integration of remote sensing data and GIS for accurate mapping of flooded areas’, International Journal of Remote Sensing, 23(3), pp. 429–441. doi: https://doi.org/10.1080/01431160010014729
Brown, K.M., Hambridge, C.H. and Brownett, J.M. (2016) ‘Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data’, Progress in Physical Geography: Earth and Environment, 40(2), pp. 196–214. doi: https://doi.org/10.1177/0309133316633570
Gao, B. (1996) ‘NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space’, Remote Sensing of Environment, 58(3), pp. 257–266. doi: https://doi.org/10.1016/S0034-4257(96)00067-3
Cao, H. et al. (2019) ‘Operational flood detection using Sentinel-1 SAR data over large areas’, Water, 11(4), p. 786. doi: https://doi.org/10.3390/w11040786
Chen, Z. et al. (2019) ‘RFim: a real-time inundation extent model for large floodplains based on remote sensing big data and water level observations’, Remote Sensing, 11(13), p. 1585. doi: https://doi.org/10.3390/rs11131585
Chini, M. et al. (2017) ‘A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case’, IEEE Transactions on Geoscience and Remote Sensing, 55(12), pp. 6975–6988. doi: https://doi.org/10.1109/TGRS.2017.2737663
Chini, M. et al. (2019) ‘Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane Harvey as a test case’, Remote Sensing, 11(2), 107. doi: https://doi.org/10.3390/rs11020107
Cunnane, C. (1998) ‘Methods and merits of regional flood frequency analysis’, Journal of Hydrology, 100(1-3), pp.269–290. doi: https://doi.org/10.1016/0022-1694(88)90188-6
Desalegn, H. and Mulu, A. (2021) ‘Mapping flood inundation areas using GIS and HEC-RAS model at Fetam River, Upper Abbay Basin, Ethiopia’, Scientific African, 12, p. e00834. doi: https://doi.org/10.1016/j.sciaf.2021.e00834
Di Baldassarre, G., et al. (2009) ‘A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent’, Journal of Hydrology, 367(3-4), pp. 276–282. doi: https://doi.org/10.1016/j.jhydrol.2009.01.020
Elkhrachy, I. (2021) ‘Sentinel-1 remote sensing data and Hydrologic Engineering Centres River Analysis System two-dimensional integration for flash flood detection and modelling in New Cairo City, Egypt’, Journal of Flood Risk Management, 14(2), p. e12692. doi: https://doi.org/10.1111/jfr3.12692
Ezzine, A. et al. (2020) ‘Flood mapping using hydraulic modeling and Sentinel-1 image: Case study of Medjerda Basin, northern Tunisia’, The Egyptian Journal of Remote Sensing and Space Science, 23(3), pp. 303–310. doi: https://doi.org/10.1016/j.ejrs.2020.01.001
Feyisa, G.L. et al. (2014) ‘Automated water extraction index: A new technique for surface water mapping using Landsat imagery’, Remote Sensing of Environment, 140, pp. 23–35. doi: https://doi.org/10.1016/j.rse.2013.08.029
Gharbi, M. (2016) ‘Etude des crues et du transport sédimentaire associé - Application au bassin versant de la Medjerda’. Doctoral thesis. Université de Toulouse. Available at: https://oatao.univ-toulouse.fr/18120/ (Accessed: 2 April 2025).
Giustarini, L. et al. (2013) ‘A change detection approach to flood mapping in urban areas using TerraSAR-X’, IEEE Transactions on Geoscience and Remote Sensing, 51(4), pp. 2417–2430. doi: https://doi.org/10.1109/TGRS.2012.2210901
Gord, S., Hafezparast Mavaddat, M. and Ghobadian, R. (2024) ‘Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province)’, Natural Hazards, 120, pp. 8437–8457. doi: https://doi.org/10.1007/s11069-024-06514-3
Hafezparast, M., Gord, S. and Ghobadian, R. (2025) ‘Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah’, Journal of Applied Research in Water and Wastewater, 12(1), pp. 86-95, doi: https://doi.org/10.22126/arww.2025.11095.1349
Henry, J.B. et al. (2006) ‘Envisat multipolarized ASAR data for flood mapping’, International Journal of Remote Sensing, 27(9-10), pp. 1921–1929. doi: https://doi.org/10.1080/01431160500486724
Hong Quang, N. et al. (2020) ‘Hydrological/hydraulic modeling-based thresholding of multi SAR remote sensing data for flood monitoring in regions of the Vietnamese Lower Mekong River Basin’, Water, 12(1), p. 71. doi: https://doi.org/10.3390/w12010071
Horritt, M.S. et al. (2007) ‘Comparing the performance of a 2-D finite element and a 2-D finite volume model of floodplain inundation using airborne SAR imagery’, Hydrological Processes, 21(20), pp. 2745–2759. doi: https://doi.org/10.1002/hyp.6486
Horritt, M.S. et al. (2001) ‘Flood boundary delineation from synthetic aperture radar imagery using a statistical active contour model’, International Journal of Remote Sensing, 22(13), pp. 2489–2507. doi: https://doi.org/10.1080/01431160116969
ICLR (2019) Focus on flood mapping in Canada. Canada. Institute for Catastrophic Loss Reduction Available at: https://www.iclr.org/wp-content/uploads/2019/11/ CatTales _Sept-Oct-2019.pdf (Accessed: date 2 June 2025).
Li, M. et al. (2022) ‘Automated extraction of lake water bodies in complex geographical environments by fusing sentinel-1/2 Data’, Water, 14(1), p. 30. doi: https://doi.org/10.3390/w14010030
Liang, J. and Liu, D. (2020) ‘A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery’, ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp. 53–62. doi: https://doi.org/10.1016/j.isprsjprs.2019.10.017
Liu, X. et al. (2017) ‘Flood inundation mapping from optical satellite images using spatiotemporal context learning and modest AdaBoost’, Remote Sensing, 9(6), p. 617. doi: https://doi.org/10.3390/rs9060617
Martinis, S. et al. (2013) ‘A multi-scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chains’, Remote Sensing, 5(11), pp. 5598–5619. doi: https://doi.org/10.3390/rs5115598
McFeeters, S.K. (1996) ‘The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features’, International Journal of Remote Sensing, 17(7), pp. 1425–1432. doi: https://doi.org/10.1080/01431169608948714
Notti, D. et al. (2018) "Potential and limitations of open satellite data for flood mapping". Remote Sensing, 10(11), 1673. doi: https://doi.org/10.3390/rs10111673
Parida, B.R. et al. (2021) ‘Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)’, Geocarto International, 37(16), pp. 4665–4690. doi: https://doi.org/10.1080/10106049.2021.1899305
Pulvirenti, L. et al. (2010) ‘Analysis of COSMO-SkyMed observations of the 2008 flood in Myanmar’, Italian Journal of Remote Sensing, 42(2), pp. 79–90. doi: https://doi.org/10.5721/ItJRS20104227
Rosser, J.F. et al. (2017) ‘Rapid flood inundation mapping using social media, remote sensing and topographic data’, Natural Hazards, 87(1), pp. 103–120. doi: https://doi.org/10.1007/s11069-017-2755-0
Schittkowski, K. (2002) ‘EASY-FIT: a software system for data fitting in dynamical systems’, Structural and Multidisciplinary Optimization, 23(2), pp. 153–169. doi: https://doi.org/10.1007/s00158-002-0177-3
Schlaffer, S. et al. (2015) ‘Flood detection from multi-temporal SAR data using harmonic analysis and change detection’, International Journal of Applied Earth Observation and Geoinformation, 38, pp. 15–24. doi: https://doi.org/10.1016/j.jag.2014.12.001
Shen, X. et al. (2019) ‘Inundation extent mapping by synthetic aperture radar: a review’, Remote Sensing, 11(7), p. 879. doi: https://doi.org/10.3390/rs11070879
Sinha, R. et al. (2008) ‘Flood risk analysis in the Kosi river basin, north Bihar using multiparametric approach of Analytical Hierarchy Process
Water, 14(19), 2984. doi: https://doi.org/10.3390/w14192984
Avarand, Y. et al. (2025) ‘Multi-objective fuzzy-stochastic optimization model for agricultural water allocation by applying effective rainfall’, Sustainable Water Resources Management, 11(3), 66. doi: https://doi.org/10.1007/s40899-025-01243-2
Ayoubikia, R. et al. (2019) ‘Optimization of regional water resources allocation in Sefidroud river basin by social equity approach’, Iran-Water Resources Research, 14(5). pp. 236-252. doi: https://www.iwrr.ir/article_66470.html (Accessed date: 2 June 2025).
Bach, H. and Verhoef, W. (2003) 'Sensitivity studies on the effect of surface soil moisture on canopy reflectance using the radiative transfer model GeoSAIL', in Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2003). Toulouse, France, 21-25 July 2003. Piscataway, NJ: IEEE, pp. 1679-1681.
Brivio, P.A. et al. (2002) ‘Integration of remote sensing data and GIS for accurate mapping of flooded areas’, International Journal of Remote Sensing, 23(3), pp. 429–441. doi: https://doi.org/10.1080/01431160010014729
Brown, K.M., Hambridge, C.H. and Brownett, J.M. (2016) ‘Progress in operational flood mapping using satellite synthetic aperture radar (SAR) and airborne light detection and ranging (LiDAR) data’, Progress in Physical Geography: Earth and Environment, 40(2), pp. 196–214. doi: https://doi.org/10.1177/0309133316633570
Gao, B. (1996) ‘NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space’, Remote Sensing of Environment, 58(3), pp. 257–266. doi: https://doi.org/10.1016/S0034-4257(96)00067-3
Cao, H. et al. (2019) ‘Operational flood detection using Sentinel-1 SAR data over large areas’, Water, 11(4), p. 786. doi: https://doi.org/10.3390/w11040786
Chen, Z. et al. (2019) ‘RFim: a real-time inundation extent model for large floodplains based on remote sensing big data and water level observations’, Remote Sensing, 11(13), p. 1585. doi: https://doi.org/10.3390/rs11131585
Chini, M. et al. (2017) ‘A hierarchical split-based approach for parametric thresholding of SAR images: Flood inundation as a test case’, IEEE Transactions on Geoscience and Remote Sensing, 55(12), pp. 6975–6988. doi: https://doi.org/10.1109/TGRS.2017.2737663
Chini, M. et al. (2019) ‘Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and hurricane Harvey as a test case’, Remote Sensing, 11(2), 107. doi: https://doi.org/10.3390/rs11020107
Cunnane, C. (1998) ‘Methods and merits of regional flood frequency analysis’, Journal of Hydrology, 100(1-3), pp.269–290. doi: https://doi.org/10.1016/0022-1694(88)90188-6
Desalegn, H. and Mulu, A. (2021) ‘Mapping flood inundation areas using GIS and HEC-RAS model at Fetam River, Upper Abbay Basin, Ethiopia’, Scientific African, 12, p. e00834. doi: https://doi.org/10.1016/j.sciaf.2021.e00834
Di Baldassarre, G., et al. (2009) ‘A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent’, Journal of Hydrology, 367(3-4), pp. 276–282. doi: https://doi.org/10.1016/j.jhydrol.2009.01.020
Elkhrachy, I. (2021) ‘Sentinel-1 remote sensing data and Hydrologic Engineering Centres River Analysis System two-dimensional integration for flash flood detection and modelling in New Cairo City, Egypt’, Journal of Flood Risk Management, 14(2), p. e12692. doi: https://doi.org/10.1111/jfr3.12692
Ezzine, A. et al. (2020) ‘Flood mapping using hydraulic modeling and Sentinel-1 image: Case study of Medjerda Basin, northern Tunisia’, The Egyptian Journal of Remote Sensing and Space Science, 23(3), pp. 303–310. doi: https://doi.org/10.1016/j.ejrs.2020.01.001
Feyisa, G.L. et al. (2014) ‘Automated water extraction index: A new technique for surface water mapping using Landsat imagery’, Remote Sensing of Environment, 140, pp. 23–35. doi: https://doi.org/10.1016/j.rse.2013.08.029
Gharbi, M. (2016) ‘Etude des crues et du transport sédimentaire associé - Application au bassin versant de la Medjerda’. Doctoral thesis. Université de Toulouse. Available at: https://oatao.univ-toulouse.fr/18120/ (Accessed: 2 April 2025).
Giustarini, L. et al. (2013) ‘A change detection approach to flood mapping in urban areas using TerraSAR-X’, IEEE Transactions on Geoscience and Remote Sensing, 51(4), pp. 2417–2430. doi: https://doi.org/10.1109/TGRS.2012.2210901
Gord, S., Hafezparast Mavaddat, M. and Ghobadian, R. (2024) ‘Flood impact assessment on agricultural and municipal areas using Sentinel-1 and 2 satellite images (case study: Kermanshah province)’, Natural Hazards, 120, pp. 8437–8457. doi: https://doi.org/10.1007/s11069-024-06514-3
Hafezparast, M., Gord, S. and Ghobadian, R. (2025) ‘Comparing random forest flood frequency analysis with regional flood frequency, Creager and SCS in Doab-Qazanchi, Kermanshah’, Journal of Applied Research in Water and Wastewater, 12(1), pp. 86-95, doi: https://doi.org/10.22126/arww.2025.11095.1349
Henry, J.B. et al. (2006) ‘Envisat multipolarized ASAR data for flood mapping’, International Journal of Remote Sensing, 27(9-10), pp. 1921–1929. doi: https://doi.org/10.1080/01431160500486724
Hong Quang, N. et al. (2020) ‘Hydrological/hydraulic modeling-based thresholding of multi SAR remote sensing data for flood monitoring in regions of the Vietnamese Lower Mekong River Basin’, Water, 12(1), p. 71. doi: https://doi.org/10.3390/w12010071
Horritt, M.S. et al. (2007) ‘Comparing the performance of a 2-D finite element and a 2-D finite volume model of floodplain inundation using airborne SAR imagery’, Hydrological Processes, 21(20), pp. 2745–2759. doi: https://doi.org/10.1002/hyp.6486
Horritt, M.S. et al. (2001) ‘Flood boundary delineation from synthetic aperture radar imagery using a statistical active contour model’, International Journal of Remote Sensing, 22(13), pp. 2489–2507. doi: https://doi.org/10.1080/01431160116969
ICLR (2019) Focus on flood mapping in Canada. Canada. Institute for Catastrophic Loss Reduction Available at: https://www.iclr.org/wp-content/uploads/2019/11/ CatTales _Sept-Oct-2019.pdf (Accessed: date 2 June 2025).
Li, M. et al. (2022) ‘Automated extraction of lake water bodies in complex geographical environments by fusing sentinel-1/2 Data’, Water, 14(1), p. 30. doi: https://doi.org/10.3390/w14010030
Liang, J. and Liu, D. (2020) ‘A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery’, ISPRS Journal of Photogrammetry and Remote Sensing, 159, pp. 53–62. doi: https://doi.org/10.1016/j.isprsjprs.2019.10.017
Liu, X. et al. (2017) ‘Flood inundation mapping from optical satellite images using spatiotemporal context learning and modest AdaBoost’, Remote Sensing, 9(6), p. 617. doi: https://doi.org/10.3390/rs9060617
Martinis, S. et al. (2013) ‘A multi-scale flood monitoring system based on fully automatic MODIS and TerraSAR-X processing chains’, Remote Sensing, 5(11), pp. 5598–5619. doi: https://doi.org/10.3390/rs5115598
McFeeters, S.K. (1996) ‘The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features’, International Journal of Remote Sensing, 17(7), pp. 1425–1432. doi: https://doi.org/10.1080/01431169608948714
Notti, D. et al. (2018) "Potential and limitations of open satellite data for flood mapping". Remote Sensing, 10(11), 1673. doi: https://doi.org/10.3390/rs10111673
Parida, B.R. et al. (2021) ‘Estimating floodwater depth using SAR-derived flood inundation maps and geomorphic model in kosi river basin (India)’, Geocarto International, 37(16), pp. 4665–4690. doi: https://doi.org/10.1080/10106049.2021.1899305
Pulvirenti, L. et al. (2010) ‘Analysis of COSMO-SkyMed observations of the 2008 flood in Myanmar’, Italian Journal of Remote Sensing, 42(2), pp. 79–90. doi: https://doi.org/10.5721/ItJRS20104227
Rosser, J.F. et al. (2017) ‘Rapid flood inundation mapping using social media, remote sensing and topographic data’, Natural Hazards, 87(1), pp. 103–120. doi: https://doi.org/10.1007/s11069-017-2755-0
Schittkowski, K. (2002) ‘EASY-FIT: a software system for data fitting in dynamical systems’, Structural and Multidisciplinary Optimization, 23(2), pp. 153–169. doi: https://doi.org/10.1007/s00158-002-0177-3
Schlaffer, S. et al. (2015) ‘Flood detection from multi-temporal SAR data using harmonic analysis and change detection’, International Journal of Applied Earth Observation and Geoinformation, 38, pp. 15–24. doi: https://doi.org/10.1016/j.jag.2014.12.001
Shen, X. et al. (2019) ‘Inundation extent mapping by synthetic aperture radar: a review’, Remote Sensing, 11(7), p. 879. doi: https://doi.org/10.3390/rs11070879
Sinha, R. et al. (2008) ‘Flood risk analysis in the Kosi river basin, north Bihar using multiparametric approach of Analytical Hierarchy Process
)