Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1419-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Apr 2022
Research article |
| 21 Apr 2022
| 21 Apr 2022
Real-time coastal flood hazard assessment using DEM-based hydrogeomorphic classifiers
Keighobad Jafarzadegan
CORRESPONDING AUTHOR
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
David F. Muñoz
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Hamed Moftakhari
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Joseph L. Gutenson
Coastal and Hydraulics Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA
Gaurav Savant
Coastal and Hydraulics Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA
Hamid Moradkhani
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Related authors
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 24, 2647–2665, https://doi.org/10.5194/nhess-24-2647-2024, https://doi.org/10.5194/nhess-24-2647-2024, 2024
Short summary
Short summary
This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
Short summary
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
Soheil Radfar, Hamed Moftakhari, David F. Muñoz, Avantika Gori, Ferdinand Diermanse, Ning Lin, and Amir AghaKouchak
EGUsphere, https://doi.org/10.5194/egusphere-2025-4623, https://doi.org/10.5194/egusphere-2025-4623, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Flooding in coastal areas often occurs when several mechanisms act together, causing compound flooding. Researchers increasingly use hybrid models that combine numerical models with statistical tools to study these events. Yet, the term “hybrid model” has been used inconsistently. This paper provides a clear definition and classification system, along with examples and technical challenges.
Soheil Radfar, Georgios Boumis, Hamed R. Moftakhari, Wanyun Shao, Larisa Lee, and Alison N. Rellinger
Geosci. Commun., 8, 237–250, https://doi.org/10.5194/gc-8-237-2025, https://doi.org/10.5194/gc-8-237-2025, 2025
Short summary
Short summary
Our study presents a method to visualize how variations in the relationship of flood drivers like discharge and surge evolve over time. This method simplifies complex relationships, making it easier to understand evolving flood risks, especially as climate change increases these threats. By surveying a diverse group, we found that this visual approach could improve communication between scientists and non-experts, helping communities better prepare for compound flooding in a changing climate.
Peyman Abbaszadeh, Fatemeh Gholizadeh, Keyhan Gavahi, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 29, 2407–2427, https://doi.org/10.5194/hess-29-2407-2025, https://doi.org/10.5194/hess-29-2407-2025, 2025
Short summary
Short summary
The Hybrid Ensemble and Variational Data Assimilation framework for Environmental Systems (HEAVEN) enhances flood predictions by refining hydrologic models through improved data integration and uncertainty management. Tested in three southeastern US watersheds during hurricanes, HEAVEN assimilates real-time United States Geological Survey (USGS) streamflow data, boosting forecast accuracy.
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 24, 2647–2665, https://doi.org/10.5194/nhess-24-2647-2024, https://doi.org/10.5194/nhess-24-2647-2024, 2024
Short summary
Short summary
This study utilizes the global copula Bayesian model averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 28, 2531–2553, https://doi.org/10.5194/hess-28-2531-2024, https://doi.org/10.5194/hess-28-2531-2024, 2024
Short summary
Short summary
Linking hydrodynamics with machine learning models for compound flood modeling enables a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking their evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analyses.
Gaurav Savant and Tate O. McAlpin
EGUsphere, https://doi.org/10.5194/egusphere-2022-199, https://doi.org/10.5194/egusphere-2022-199, 2022
Preprint archived
Short summary
Short summary
The knowledge of timescales of flushing processes within an estuary is essential for the health, and productivity of the estuary, as well as for optimum estuarine management. The flushing times were directly related to the magnitude of freshwater inputs to the system, with the bay exhibiting an average flushing time of 16.5 days for average river inflows.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
Short summary
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
Ian E. Floyd, Alejandro Sanchez, Stanford Gibson, and Gaurav Savant
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-509, https://doi.org/10.5194/hess-2020-509, 2020
Publication in HESS not foreseen
Cited articles
Afshari, S., Tavakoly, A. A., Rajib, M. A., Zheng, X., Follum, M. L., Omranian, E., and Fekete, B. M.:
Comparison of new generation low-complexity flood inundation mapping tools with a hydrodynamic model, J. Hydrol., 556, 539–556, https://doi.org/10.1016/j.jhydrol.2017.11.036, 2018.
Alizad, K., Hagen, S. C., Medeiros, S. C., Bilskie, M. V., Morris, J. T., Balthis, L., and Buckel, C. A.:
Dynamic responses and implications to coastal wetlands and the surrounding regions under sea level rise, PLOS ONE, 13, e0205176, https://doi.org/10.1371/journal.pone.0205176, 2018.
Arcement, G. J. and Schneider, V. R.:
Guide for selecting Manning's roughness coefficients for natural channels and flood plains, U.S. Geological Survey, https://ton.sdsu.edu/usgs_report_2339.pdf (last access: 23 September 2021), 1989.
Barbier, E. B.:
Chapter 27 – The Value of Coastal Wetland Ecosystem Services, in: Coastal Wetlands, edited by: Perillo, G. M. E., Wolanski, E., Cahoon, D. R., and Hopkinson, C. S., Elsevier, 947–964, https://doi.org/10.1016/B978-0-444-63893-9.00027-7, 2019.
Bass, B. and Bedient, P.:
Surrogate modeling of joint flood risk across coastal watersheds, J. Hydrol., 558, 159–173, https://doi.org/10.1016/j.jhydrol.2018.01.014, 2018.
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.:
A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010.
Bates, P. D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., Giustarini, L., Coxon, G., Porter, J. R., Amodeo, M. F., Chu, Z., Lewis-Gruss, S., Freeman, N. B., Houser, T., Delgado, M., Hamidi, A., Bolliger, I., McCusker, K. E., Emanuel, K., Ferreira, C. M., Khalid, A., Haigh, I. D., Couasnon, A., Kopp, R. E., Hsiang, S., and Krajewski, W. F.:
Combined Modeling of US Fluvial, Pluvial, and Coastal Flood Hazard Under Current and Future Climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020WR028673, 2021.
Bracken, C., Holman, K. D., Rajagopalan, B., and Moradkhani, H.:
A Bayesian hierarchical approach to multivariate nonstationary hydrologic frequency analysis, Water Resour. Res., 54, 243–255, 2018.
Brunner, M. I., Seibert, J., and Favre, A.-C.:
Bivariate return periods and their importance for flood peak and volume estimation, WIREs Water, 3, 819–833, https://doi.org/10.1002/wat2.1173, 2016.
Carlston, C. W.:
Longitudinal Slope Characteristics of Rivers of the Midcontinent and the Atlantic East Gulf Slopes, Int. Assoc. Sci. Hydrol. Bull., 14, 21–31, https://doi.org/10.1080/02626666909493751, 1969.
Chow Ven, T.:
Open channel hydraulics, McGraw-Hill Kogakusha, Tokyo, 680 pp., 1959.
Cowardin, L. M., Carter, V., Golet, F. C., and Laroe, E. T.: Classification of Wetlands and Deepwater Habitats of the United States, in: Water Encyclopedia, edited by: Lehr, J. H. and Keeley, J., John Wiley & Sons, Inc., Hoboken, NJ, USA, sw2162, https://doi.org/10.1002/047147844X.sw2162, 2013.
Davidson, N. C.:
How much wetland has the world lost? Long-term and recent trends in global wetland area, Mar. Freshwater Res., 65, 934–941, https://doi.org/10.1071/MF14173, 2014.
Degiorgis, M., Gnecco, G., Gorni, S., Roth, G., Sanguineti, M., and Taramasso, A. C.:
Classifiers for the detection of flood-prone areas using remote sensed elevation data, J. Hydrol., 470–471, 302–315, https://doi.org/10.1016/j.jhydrol.2012.09.006, 2012.
Degiorgis, M., Gnecco, G., Gorni, S., Roth, G., Sanguineti, M., and Taramasso, A. C.:
Flood Hazard Assessment Via Threshold Binary Classifiers: Case Study of the Tanaro River Basin, Irrig. Drain., 62, 1–10, https://doi.org/10.1002/ird.1806, 2013.
Delft3D Flexible Mesh Suite: User’s manual,
https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/ (last access: 15 November 2021), 2021.
Dodov, B. A. and Foufoula-Georgiou, E.:
Floodplain morphometry extraction from a high-resolution digital elevation model: a simple algorithm for regional analysis studies, IEEE Geosci. Remote S., 3, 410–413, https://doi.org/10.1109/LGRS.2006.874161, 2006.
Fagherazzi, S., Mariotti, G., Banks, A. T., Morgan, E. J., and Fulweiler, R. W.:
The relationships among hydrodynamics, sediment distribution, and chlorophyll in a mesotidal estuary, Estuar. Coast. Shelf S., 144, 54–64, https://doi.org/10.1016/j.ecss.2014.04.003, 2014.
Familkhalili, R., Talke, S. A., and Jay, D. A.:
Tide-Storm Surge Interactions in Highly Altered Estuaries: How Channel Deepening Increases Surge Vulnerability, J. Geophys. Res.-Oceans, 125, e2019JC015286, https://doi.org/10.1029/2019JC015286, 2020.
Fawcett, T.:
An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Forbes, C., Rhome, J., Mattocks, C., and Taylor, A.:
Predicting the Storm Surge Threat of Hurricane Sandy with the National Weather Service SLOSH Model, J. Mar. Sci. Eng., 2, 437–476, https://doi.org/10.3390/jmse2020437, 2014.
Ghanbari, M., Arabi, M., Kao, S.-C., Obeysekera, J., and Sweet, W.:
Climate Change and Changes in Compound Coastal-Riverine Flooding Hazard Along the U.S. Coasts, Earths Future, 9, e2021EF002055, https://doi.org/10.1029/2021EF002055, 2021.
Gharari, S., Hrachowitz, M., Fenicia, F., and Savenije, H. H. G.: Hydrological landscape classification: investigating the performance of HAND based landscape classifications in a central European meso-scale catchment, Hydrol. Earth Syst. Sci., 15, 3275–3291, https://doi.org/10.5194/hess-15-3275-2011, 2011.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Gutenson, J.:
A Review of Current and Future NWS Services, 2020 Interagency Flood Risk Management Training Seminars,
25–28 February 2020,
St. Louis, Missouri, 2020.
Gutenson, J. L., Tavakoly, A. A., Massey, T. C., Savant, G., Tritinger, A. S., Owensby, M. B., Wahl, M. D., and Islam, M. S.:
Investigating Modeling Strategies to Couple Inland Hydrology and Coastal Hydraulics to Better Understand Compound Flood Risk, in: World Environmental and Water Resources Congress 2021: Planning a Resilient Future along America's Freshwaters 2021, 7–11 June 2021, Virtual Conference, 64–75, https://doi.org/10.1061/9780784483466.006, 2021.
Helton, J. C. and Davis, F. J.:
Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems, Reliab. Eng. Syst. Safe, 81, 23–69, 2003.
IWRSS:
Requirements for the National Flood Inundation Mapping Services, National Oceanic and Atmospheric Administration United States Army Corps of Engineers United States Geological Survey, https://water.usgs.gov/osw/iwrss/IWRSS_FIM_Requirements_Report_09-2013.pdf (last access: 23 September 2021), 2013.
IWRSS:
Design for the National Flood Inundation Mapping Services, National Oceanic and Atmospheric Administration United States Army Corps of Engineers United States Geological Survey, https://water.usgs.gov/osw/iwrss/DesignforIWRSSFIMServices_RevisedMAY2016.pdf (last access: 23 September 2021), 2015.
Jafarzadegan, K. and Merwade, V.:
A DEM-based approach for large-scale floodplain mapping in ungauged watersheds, J. Hydrol., 550, 650–662, https://doi.org/10.1016/j.jhydrol.2017.04.053, 2017.
Jafarzadegan, K. and Merwade, V.:
Probabilistic floodplain mapping using HAND-based statistical approach, Geomorphology, 324, 48–61, https://doi.org/10.1016/j.geomorph.2018.09.024, 2019.
Jafarzadegan, K., Merwade, V., and Saksena, S.:
A geomorphic approach to 100-year floodplain mapping for the Conterminous United States, J. Hydrol., 561, 43–58, https://doi.org/10.1016/j.jhydrol.2018.03.061, 2018.
Jafarzadegan, K., Merwade, V., and Moradkhani, H.:
Combining clustering and classification for the regionalization of environmental model parameters: Application to floodplain mapping in data-scarce regions, Environ. Model. Softw., 125, 104613, https://doi.org/10.1016/j.envsoft.2019.104613, 2020.
Jafarzadegan, K., Abbaszadeh, P., and Moradkhani, H.: Sequential data assimilation for real-time probabilistic flood inundation mapping, Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, 2021.
Jelesnianski, C., Chen, J., Shaffer, W., and Gilad, A.:
SLOSH – A Hurricane Storm Surge Forecast Model, in: OCEANS 1984, Washington, DC, USA, 10–12 September 1984, 314–317, https://doi.org/10.1109/OCEANS.1984.1152341, 1984.
Khojasteh, D., Chen, S., Felder, S., Heimhuber, V., and Glamore, W.:
Estuarine tidal range dynamics under rising sea levels, PLOS ONE, 16, e0257538, https://doi.org/10.1371/journal.pone.0257538, 2021a.
Khojasteh, D., Glamore, W., Heimhuber, V., and Felder, S.:
Sea level rise impacts on estuarine dynamics: A review, Sci. Total Environ., 780, 146470, https://doi.org/10.1016/j.scitotenv.2021.146470, 2021b.
Kirwan, M. L. and Megonigal, J. P.:
Tidal wetland stability in the face of human impacts and sea-level rise, Nature, 504, 53–60, https://doi.org/10.1038/nature12856, 2013.
Kulp, S. A. and Strauss, B. H.:
New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding, Nat. Commun., 10, 4844, https://doi.org/10.1038/s41467-019-12808-z, 2019.
Kumbier, K., Carvalho, R. C., Vafeidis, A. T., and Woodroffe, C. D.: Investigating compound flooding in an estuary using hydrodynamic modelling: a case study from the Shoalhaven River, Australia, Nat. Hazards Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/nhess-18-463-2018, 2018.
Land, M., Tonderski, K., and Verhoeven, J. T. A.:
Wetlands as Biogeochemical Hotspots Affecting Water Quality in Catchments, in: Wetlands: Ecosystem Services, Restoration and Wise Use, edited by: An, S. and Verhoeven, J. T. A., Springer International Publishing, Cham, 13–37, https://doi.org/10.1007/978-3-030-14861-4_2, 2019.
Liu, Z., Merwade, V., and Jafarzadegan, K.:
Investigating the role of model structure and surface roughness in generating flood inundation extents using one-and two-dimensional hydraulic models, J. Flood Risk Manag., 12, e12347, 2019.
Longenecker, H. E., Graeden, E., Kluskiewicz, D., Zuzak, C., Rozelle, J., and Aziz, A. L.:
A rapid flood risk assessment method for response operations and nonsubject-matter-expert community planning, J. Flood Risk Manag., 13, e12579, https://doi.org/10.1111/jfr3.12579, 2020.
Luettich Jr., R. A., Westerink, J. J., and Scheffner, N. W.:
ADCIRC: an advanced three-dimensional circulation model for shelves, coasts, and estuaries. Report 1, Theory and methodology of ADCIRC-2DD1 and ADCIRC-3DL, This Digit. Resour. Was Creat. Scans Print Resour., US Army Corps of Engineers,
Technical Report DRP-92-6,
141 pp., 1992.
Maidment, D. R.:
Conceptual Framework for the National Flood Interoperability Experiment, J. Am. Water Resour. As., 53, 245–257, https://doi.org/10.1111/1752-1688.12474, 2017.
Maidment, D. R., Clark, E., Hooper, R., and Ernest, A.:
National Flood Interoperability Experiment, in: AGU Fall Meeting Abstracts, 2014.
Manfreda, S., Di Leo, M., and Sole, A.:
Detection of Flood-Prone Areas Using Digital Elevation Models, J. Hydrol. Eng., 16, 781–790, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000367, 2011.
Manfreda, S., Nardi, F., Samela, C., Grimaldi, S., Taramasso, A. C., Roth, G., and Sole, A.:
Investigation on the use of geomorphic approaches for the delineation of flood prone areas, J. Hydrol., 517, 863–876, https://doi.org/10.1016/j.jhydrol.2014.06.009, 2014.
Manfreda, S., Samela, C., Gioia, A., Consoli, G. G., Iacobellis, V., Giuzio, L., Cantisani, A., and Sole, A.:
Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models, Nat. Hazards, 79, 735–754, https://doi.org/10.1007/s11069-015-1869-5, 2015a.
Manfreda, S., Samela, C., Gioia, A., Consoli, G. G., Iacobellis, V., Giuzio, L., Cantisani, A., and Sole, A.:
Flood-prone areas assessment using linear binary classifiers based on flood maps obtained from 1D and 2D hydraulic models, Nat. Hazards, 79, 735–754, https://doi.org/10.1007/s11069-015-1869-5, 2015b.
McGlynn, B. L. and McDonnell, J. J.:
Quantifying the relative contributions of riparian and hillslope zones to catchment runoff, Water Resour. Res., 39, https://doi.org/10.1029/2003WR002091, 2003.
McGlynn, B. L. and Seibert, J.:
Distributed assessment of contributing area and riparian buffering along stream networks, Water Resour. Res., 39, https://doi.org/10.1029/2002WR001521, 2003.
McGrath, H., Bourgon, J.-F., Proulx-Bourque, J.-S., Nastev, M., and Abo El Ezz, A.:
A comparison of simplified conceptual models for rapid web-based flood inundation mapping, Nat. Hazards, 93, 905–920, https://doi.org/10.1007/s11069-018-3331-y, 2018.
Medeiros, S., Hagen, S., Weishampel, J., and Angelo, J.:
Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density, Remote Sens.-Basel, 7, 3507–3525, https://doi.org/10.3390/rs70403507, 2015.
Morton, R. A. and Barras, J. A.:
Hurricane Impacts on Coastal Wetlands: A Half-Century Record of Storm-Generated Features from Southern Louisiana, J. Coastal Res., 27, 27–43, https://doi.org/10.2112/JCOASTRES-D-10-00185.1, 2011.
Muis, S., Lin, N., Verlaan, M., Winsemius, H. C., Ward, P. J., and Aerts, J. C. J. H.:
Spatiotemporal patterns of extreme sea levels along the western North-Atlantic coasts, Sci. Rep., 9, 3391, https://doi.org/10.1038/s41598-019-40157-w, 2019.
Muñoz, D. F., Cissell, J. R., and Moftakhari, H.:
Adjusting Emergent Herbaceous Wetland Elevation with Object-Based Image Analysis, Random Forest and the 2016 NLCD, Remote Sens.-Basel, 11, 2346, https://doi.org/10.3390/rs11202346, 2019.
Muñoz, D. F., Moftakhari, H., and Moradkhani, H.:
Compound effects of flood drivers and wetland elevation correction on coastal flood hazard assessment, Water Resour. Res., 56, e2020WR027544, https://doi.org/10.1029/2020WR027544, 2020.
Muñoz, D. F., Muñoz, P., Moftakhari, H., and Moradkhani, H.:
From local to regional compound flood mapping with deep learning and data fusion techniques, Sci. Total Environ., 782, 146927, 2021.
Muñoz, D. F., Abbaszadeh, P., Moftakhari, H., and Moradkhani, H.:
Accounting for uncertainties in compound flood hazard assessment: The value of data assimilation, Coast. Eng., 171, 104057, https://doi.org/10.1016/j.coastaleng.2021.104057, 2022.
Nardi, F., Vivoni, E. R., and Grimaldi, S.:
Investigating a floodplain scaling relation using a hydrogeomorphic delineation method, Water Resour. Res., 42, W09409, https://doi.org/10.1029/2005WR004155, 2006.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
NWS (National Weather Service), OAR (Oceanic and Atmospheric Research), and Eastern Research Group, Inc.: Hurricane Forecast Improvement Program: Socio-Economic Research and Recommendations, Final Report, https://repository.library.noaa.gov/view/noaa/28751 (last access: 23 September 2021), 2013.
Roelvink, J. A. and Banning, G. K. F. M. V.:
Design and development of DELFT3D and application to coastal morphodynamics, Oceanogr. Lit. Rev., 11, 925, 1995.
Rogers, J. N., Parrish, C. E., Ward, L. G., and Burdick, D. M.:
Improving salt marsh digital elevation model accuracy with full-waveform lidar and nonparametric predictive modeling, Estuar. Coast. Shelf S., 202, 193–211, https://doi.org/10.1016/j.ecss.2017.11.034, 2018.
Samela, C., Manfreda, S., Paola, F. D., Giugni, M., Sole, A., and Fiorentino, M.:
DEM-Based Approaches for the Delineation of Flood-Prone Areas in an Ungauged Basin in Africa, J. Hydrol. Eng., 21, 06015010, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001272, 2016.
Samela, C., Troy, T. J., and Manfreda, S.:
Geomorphic classifiers for flood-prone areas delineation for data-scarce environments, Adv. Water Resour., 102, 13–28, https://doi.org/10.1016/j.advwatres.2017.01.007, 2017.
Schieder, N. W., Walters, D. C., and Kirwan, M. L.:
Massive Upland to Wetland Conversion Compensated for Historical Marsh Loss in Chesapeake Bay, USA, Estuaries Coasts, 41, 940–951, https://doi.org/10.1007/s12237-017-0336-9, 2018.
Schile, L. M., Callaway, J. C., Morris, J. T., Stralberg, D., Parker, V. T., and Kelly, M.: Modeling Tidal Marsh Distribution with Sea-Level Rise: Evaluating the Role of Vegetation, Sediment, and Upland Habitat in Marsh Resiliency, PLOS ONE, 9, e88760, https://doi.org/10.1371/journal.pone.0088760, 2014.
Sea, Lake, and Overland Surges from Hurricanes (SLOSH):
https://www.nhc.noaa.gov/surge/slosh.php (last access: 18 January 2022), 2022.
Sullivan, J. C., Torres, R., and Garrett, A.:
Intertidal Creeks and Overmarsh Circulation in a Small Salt Marsh Basin, J. Geophys. Res.-Earth, 124, 447–463, https://doi.org/10.1029/2018JF004861, 2019.
Teng, J., Vaze, J., Dutta, D., and Marvanek, S.:
Rapid Inundation Modelling in Large Floodplains Using LiDAR DEM, Water Resour. Manag., 29, 2619–2636, https://doi.org/10.1007/s11269-015-0960-8, 2015.
Thomas, A., Dietrich, J., Asher, T., Bell, M., Blanton, B., Copeland, J., Cox, A., Dawson, C., Fleming, J., and Luettich, R.:
Influence of storm timing and forward speed on tides and storm surge during Hurricane Matthew, Ocean Model., 137, 1–19, https://doi.org/10.1016/j.ocemod.2019.03.004, 2019.
USGS Surface Water Information:
https://water.usgs.gov/osw/iwrss/ (last access: 16 November 2021), 2021.
U.S. Army Corps of Engineers: Current Channel Condition Survey Reports and Charts, Savannah Harbor, 2017.
Wamsley, T. V., Cialone, M. A., Smith, J. M., Atkinson, J. H., and Rosati, J. D.:
The potential of wetlands in reducing storm surge, Ocean Eng., 37, 59–68, https://doi.org/10.1016/j.oceaneng.2009.07.018, 2010.
Williams, W. A., Jensen, M. E., Winne, J. C., and Redmond, R. L.:
An Automated Technique for Delineating and Characterizing Valley-Bottom Settings, in: Monitoring Ecological Condition in the Western United States: Proceedings of the Fourth Symposium on the Environmental Monitoring and Assessment Program (EMAP), San Franciso, CA, 6–8 April 1999, edited by: Sandhu, S. S., Melzian, B. D., Long, E. R., Whitford, W. G., and Walton, B. T., Springer Netherlands, Dordrecht, 105–114, https://doi.org/10.1007/978-94-011-4343-1_10, 2000.
Wing, O. E. J., Sampson, C. C., Bates, P. D., Quinn, N., Smith, A. M., and Neal, J. C.:
A flood inundation forecast of Hurricane Harvey using a continental-scale 2D hydrodynamic model, J. Hydrol. X, 4, 100039, https://doi.org/10.1016/j.hydroa.2019.100039, 2019.
Wu, W., Zhou, Y., and Tian, B.:
Coastal wetlands facing climate change and anthropogenic activities: A remote sensing analysis and modelling application, Ocean Coast. Manag., 138, 1–10, https://doi.org/10.1016/j.ocecoaman.2017.01.005, 2017.
Yan, H. and Moradkhani, H.:
A regional Bayesian hierarchical model for flood frequency analysis, Stoch. Env. Res. Risk A., 29, 1019–1036, 2015.
Zheng, X., Maidment, D. R., Tarboton, D. G., Liu, Y. Y., and Passalacqua, P.:
GeoFlood: Large-Scale Flood Inundation Mapping Based on High-Resolution Terrain Analysis, Water Resour. Res., 54, 10,013-10,033, https://doi.org/10.1029/2018WR023457, 2018a.
Zheng, X., Tarboton, D. G., Maidment, D. R., Liu, Y. Y., and Passalacqua, P.:
River Channel Geometry and Rating Curve Estimation Using Height above the Nearest Drainage, J. Am. Water Resour. As., 54, 785–806, https://doi.org/10.1111/1752-1688.12661, 2018b.
Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A., and Sharp, J. L.: Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine, Int. J. Appl. Earth Obs., 69, 175–185, https://doi.org/10.1016/j.jag.2017.12.006, 2018.
Short summary
The high population settled in coastal regions and the potential damage imposed by coastal floods highlight the need for improving coastal flood hazard assessment techniques. This study introduces a topography-based approach for rapid estimation of flood hazard areas in the Savannah River delta. Our validation results demonstrate that, besides the high efficiency of the proposed approach, the estimated areas accurately overlap with reference flood maps.
The high population settled in coastal regions and the potential damage imposed by coastal...
Altmetrics
Final-revised paper
Preprint