Articles | Volume 24, issue 8
https://doi.org/10.5194/nhess-24-2817-2024
© Author(s) 2024. 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-24-2817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Aug 2024
Research article |
| 22 Aug 2024
| 22 Aug 2024
Water depth estimate and flood extent enhancement for satellite-based inundation maps
Andrea Betterle
CORRESPONDING AUTHOR
European Commission, Joint Research Centre, Ispra, Italy
Peter Salamon
European Commission, Joint Research Centre, Ispra, Italy
Related authors
Lorenzo Scarpellini, Andrea Ficchì, Claudia D'Angelo, Andrea Betterle, Peter Salamon, and Andrea Castelletti
EGUsphere, https://doi.org/10.5194/egusphere-2026-2807, https://doi.org/10.5194/egusphere-2026-2807, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study shows that flood protection assumptions strongly affect risk estimates, and that flood model simulations, satellite observations, and historical records often disagree. Simulations overestimate large floods, satellites miss small ones, and historical records favour populated areas. No single data source provides a complete picture, highlighting the need to combine multiple datasets for more reliable continental-scale flood risk assessments.
Lorenzo Scarpellini, Andrea Ficchì, Claudia D'Angelo, Andrea Betterle, Peter Salamon, and Andrea Castelletti
EGUsphere, https://doi.org/10.5194/egusphere-2026-2807, https://doi.org/10.5194/egusphere-2026-2807, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
This study shows that flood protection assumptions strongly affect risk estimates, and that flood model simulations, satellite observations, and historical records often disagree. Simulations overestimate large floods, satellites miss small ones, and historical records favour populated areas. No single data source provides a complete picture, highlighting the need to combine multiple datasets for more reliable continental-scale flood risk assessments.
Peter Salamon, Tim Sperzel, Goncalo Ramos Gomes, Marco Radke-Fretz, Carina-Denise Lemke, Carlo Russo, Christoph Schweim, Ervin Zsoter, Alessandro Dosio, Mariapina Vomero, Markus Ziese, and Stefania Grimaldi
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-723, https://doi.org/10.5194/essd-2025-723, 2026
Preprint under review for ESSD
Short summary
Short summary
We introduce the European Meteorological Observations -1 (EMO-1) dataset developed to support the European Flood Awareness System. EMO-1 provides maps with interpolated meteorological observations across Europe at daily and 6-hourly time steps and with a pixel size of ~1.5 km for the period 1990–2024. The dataset uses observations from thousands of meteorological stations from 47 different data providers for interpolation. It is publicly available at the Joint Research Centre Data Catalogue.
Jesús Casado-Rodríguez, Juliana Disperati, Stefania Grimaldi, and Peter Salamon
EGUsphere, https://doi.org/10.5194/egusphere-2026-904, https://doi.org/10.5194/egusphere-2026-904, 2026
Short summary
Short summary
Dams significantly change river flows, yet they are difficult to represent in global hydrological models. We tested four different modeling methods using data from 164 reservoirs to find the most effective approach. We found that tracking water storage is better than tracking outflow for predicting reservoir behavior. This discovery enables the use of satellite information for better water management. These improvements are being implemented in the European and Global Flood Awareness Systems.
Andrea Ficchì, Davide Bavera, Stefania Grimaldi, Francesca Moschini, Alberto Pistocchi, Carlo Russo, Peter Salamon, and Andrea Toreti
EGUsphere, https://doi.org/10.5194/egusphere-2026-43, https://doi.org/10.5194/egusphere-2026-43, 2026
Short summary
Short summary
This study introduces a new metric for hydrological model calibration combining traditional model efficiency measures with a new component to better assess similarity between observed and simulated river flows. Tests on two hydrological models and large catchment samples show improved low-flow simulations without degrading high-flow accuracy. The new metric can support more reliable modeling for droughts and floods and is already being adopted in operational monitoring and forecasting systems.
Serigne Bassirou Diop, Job Ekolu, Yves Tramblay, Bastien Dieppois, Stefania Grimaldi, Ansoumana Bodian, Juliette Blanchet, Ponnambalam Rameshwaran, Peter Salamon, and Benjamin Sultan
Nat. Hazards Earth Syst. Sci., 25, 3161–3184, https://doi.org/10.5194/nhess-25-3161-2025, https://doi.org/10.5194/nhess-25-3161-2025, 2025
Short summary
Short summary
West Africa is very vulnerable to river floods. Current flood hazards are poorly understood due to limited data. This study is filling this knowledge gap using recent databases and two regional hydrological models to analyze changes in flood risk under two climate scenarios. Results show that most areas will see more frequent and severe floods, with some increasing by over 45 %. These findings stress the urgent need for climate-resilient strategies to protect communities and infrastructure.
Margarita Choulga, Francesca Moschini, Cinzia Mazzetti, Stefania Grimaldi, Juliana Disperati, Hylke Beck, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 28, 2991–3036, https://doi.org/10.5194/hess-28-2991-2024, https://doi.org/10.5194/hess-28-2991-2024, 2024
Short summary
Short summary
CEMS_SurfaceFields_2022 dataset is a new set of high-resolution maps for land type (e.g. lake, forest), soil properties and population water needs at approximately 2 and 6 km at the Equator, covering Europe and the globe (excluding Antarctica). We describe what and how new high-resolution information can be used to create the dataset. The paper suggests that the dataset can be used as input for river, weather or other models, as well as for statistical descriptions of the region of interest.
Florian Roth, Bernhard Bauer-Marschallinger, Mark Edwin Tupas, Christoph Reimer, Peter Salamon, and Wolfgang Wagner
Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, https://doi.org/10.5194/nhess-23-3305-2023, 2023
Short summary
Short summary
In August and September 2022, millions of people were impacted by a severe flood event in Pakistan. Since many roads and other infrastructure were destroyed, satellite data were the only way of providing large-scale information on the flood's impact. Based on the flood mapping algorithm developed at Technische Universität Wien (TU Wien), we mapped an area of 30 492 km2 that was flooded at least once during the study's time period. This affected area matches about the total area of Belgium.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
Short summary
Short summary
EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Francesco Dottori, Lorenzo Alfieri, Alessandra Bianchi, Jon Skoien, and Peter Salamon
Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, https://doi.org/10.5194/essd-14-1549-2022, 2022
Short summary
Short summary
We present a set of hazard maps for river flooding for Europe and the Mediterranean basin. The maps depict inundation extent and depth for flood probabilities for up to 1-in-500-year flood hazards and are based on hydrological and hydrodynamic models driven by observed climatology. The maps can identify two-thirds of the flood extent reported by official flood maps, with increasing skill for higher-magnitude floods. The maps are used for evaluating present and future impacts of river floods.
Cited articles
Bauer-Marschallinger, B., Sabel, D., and Wagner, W.: Optimisation of global grids for high-resolution remote sensing data, Comput. Geosci., 72, 84–93, 2014. a
Betterle, A.: FLEXTH – Flood extent enhancement and water depth estimation tool for satellite-derived inundation maps, EU [code], https://code.europa.eu/floods/floods-river/flexth (last access: 5 August 2024), 2024. a
Bryant, S., McGrath, H., and Boudreault, M.: Gridded flood depth estimates from satellite-derived inundations, Nat. Hazards Earth Syst. Sci., 22, 1437–1450, https://doi.org/10.5194/nhess-22-1437-2022, 2022. a
Chow, V.: Open-channel Hydraulics, McGraw-Hill civil engineering series, Blackburn Press, ISBN 9781932846188, 2009. a
Cian, F., Marconcini, M., Ceccato, P., and Giupponi, C.: Flood depth estimation by means of high-resolution SAR images and lidar data, Nat. Hazards Earth Syst. Sci., 18, 3063–3084, https://doi.org/10.5194/nhess-18-3063-2018, 2018 a
Clement, M. A., Kilsby, C., and Moore, P.: Multi-temporal synthetic aperture radar flood mapping using change detection, J. Flood Risk Manage., 11, 152–168, 2018. a
Cohen, S., Brakenridge, G. R., Kettner, A., Bates, B., Nelson, J., McDonald, R., Huang, Y.-F., Munasinghe, D., and Zhang, J.: Estimating floodwater depths from flood inundation maps and topography, J. Am. Water Resour. Assoc., 54, 847–858, 2018. a
Cohen, S., Raney, A., Munasinghe, D., Loftis, J. D., Molthan, A., Bell, J., Rogers, L., Galantowicz, J., Brakenridge, G. R., Kettner, A. J., Huang, Y.-F., and Tsang, Y.-P.: The Floodwater Depth Estimation Tool (FwDET v2.0) for improved remote sensing analysis of coastal flooding, Nat. Hazards Earth Syst. Sci., 19, 2053–2065, https://doi.org/10.5194/nhess-19-2053-2019, 2019. a, b, c, d
Cohen, S., Peter, B. G., Haag, A., Munasinghe, D., Moragoda, N., Narayanan, A., and May, S.: Sensitivity of Remote Sensing Floodwater Depth Calculation to Boundary Filtering and Digital Elevation Model Selections, Remote Sens., 14, 5313, https://doi.org/10.3390/rs14215313, 2022. a, b, c, d
Dandabathula, G., Hari, R., Ghosh, K., Bera, A. K., and Srivastav, S. K.: Accuracy assessment of digital bare-earth model using ICESat-2 photons: Analysis of the FABDEM, Model. Earth Syst. Environ., 9, 2677–2694, 2023. a
Douris, J. and Kim, G.: The Atlas of Mortality and Economic Losses from Weather, Climate and Water Extremes (1970–2019), ISBN 978-92-63-11267-5, 2021. a
EMDAT: Disasters in numbers, Tech. rep., CRED, https://www.un-spider.org/news-and-events/news/cred-publication-2022-disasters-numbers (last access: 5 August 2024), 2022. a
ESA: Copernicus DEM, https://doi.org/10.5270/esa-c5d3d65, 2022. a
Feyen, L., Ciscar Martinez, J. C., Gosling, S., Ibarreta Ruiz, D., Soria Ramirez, A., Dosio, A., Naumann, G., Russo, S., Formetta, G., Forzieri, G., Girardello, M., Spinoni, J., Mentaschi, L., Bisselink, B., Bernhard, J., Gelati, E., Adamovic, M., Guenther, S., de Roo, A., Cammalleri, C., Dottori, F., Bianchi, A., Alfieri, L., Vousdoukas, M., Mongelli, I., Hinkel, J., Ward, P., Gomes Da Costa, H., de Rigo, D., Liberta', G., Durrant, T., San-Miguel-Ayanz, J., Barredo Cano, J. I., Mauri, A., Caudullo, G., Ceccherini, G., Beck, P., Cescatti, A., Hristov, J., Toreti, A., Perez Dominguez, I., Dentener, F., Fellmann, T., Elleby, C., Ceglar, A., Fumagalli, D., Niemeyer, S., Cerrani, I., Panarello, L., Bratu, M., Després, J., Szewczyk, W., Matei, N., Mulholland, E., and Olariaga-Guardiola, M.: Climate change impacts and adaptation in Europe, JRC PESETA IV final report, JRC Research Reports JRC119178, Joint Research Centre (Seville site), https://doi.org/10.2760/171121, JRC119178, 2020. a
Fritz, H. M. and Okal, E. A.: Socotra Island, Yemen: field survey of the 2004 Indian Ocean tsunami, Nat. Hazards, 46, 107–117, 2008. a
Fritz, H. M., Blount, C. D., Thwin, S., Thu, M. K., and Chan, N.: Cyclone Nargis storm surge in Myanmar, Nat. Geosci., 2, 448–449, 2009. a
Fritz, H. M., Blount, C., Albusaidi, F. B., and Al-Harthy, A. H. M.: Cyclone Gonu storm surge in the Gulf of Oman, Indian ocean tropical cyclones and climate change, Springer, Dordrecht, https://doi.org/10.1007/978-90-481-3109-9_30, 255–263, 2010. a
Fuentes, I., Padarian, J., van Ogtrop, F., and Vervoort, R. W.: Comparison of surface water volume estimation methodologies that couple surface reflectance data and digital terrain models, Water, 11, 780, https://doi.org/10.3390/w11040780, 2019. a
Grimaldi, S., Li, Y., Pauwels, V. R., and Walker, J. P.: Remote sensing-derived water extent and level to constrain hydraulic flood forecasting models: Opportunities and challenges, Surv. Geophys., 37, 977–1034, 2016. a
Haralick, R. M. and Shapiro, L. G.: Computer and Robot Vision, Addison-Wesley Longman Publishing Co., Inc., ISBN 0201108771, 1992. a
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., and Neal, J.: A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 024016, https://doi.org/10.1088/1748-9326/ac4d4f, 2022. a
Huizinga, J., De Moel, H., and Szewczyk, W.: Global flood depth-damage functions: Methodology and the database with guidelines, Tech. rep., Joint Research Centre (Seville site), https://doi.org/10.2760/16510, 2017. a
Jo, M., Osmanoglu, B., Zhang, B., and Wdowinski, S.: Flood extent mapping using dual-polarimetric Sentinel-1 synthetic aperture radar imagery, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3, 711–713, https://doi.org/10.5194/isprs-archives-XLII-3-711-2018, 2018. a
Jongman, B., Kreibich, H., Apel, H., Barredo, J. I., Bates, P. D., Feyen, L., Gericke, A., Neal, J., Aerts, J. C., and Ward, P. J.: Comparative flood damage model assessment: Towards a European approach, Nat. Hazards Earth Syst. Sci., 12, 3733–3752, https://doi.org/10.5194/nhess-12-3733-2012, 2012. a
Khattab, M. F., Abo, R. K., Al-Muqdadi, S. W., and Merkel, B. J.: Generate reservoir depths mapping by using digital elevation model: A case study of Mosul dam lake, Northern Iraq, Adv. Remote Sens., 6, 161–174, 2017. a
Krullikowski, C., Chow, C., Wieland, M., Martinis, S., Bauer-Marschallinger, B., Roth, F., Matgen, P., Chini, M., Hostache, R., Li, Y., Salamon, P., McCormick, N., Reimer, C., Clarke, T., Bauer-Marschallinger, B., Wagner, W., Martinis, S., Chow, C., Böhnke, C., Matgen, P., Chini, M., Hostache, R., Molini, L., Fiori, E., and Walli, A.: Estimating ensemble likelihoods for the Sentinel-1 based Global Flood Monitoring product of the Copernicus Emergency Management Service, arXiv, 2304.12488, https://arxiv.org/abs/2304.12488 (last access: 5 August 2024), 2023. a
Li, B., Xie, H., Liu, S., Tong, X., Tang, H., and Wang, X.: A method of extracting high-accuracy elevation control points from ICESat-2 altimetry data, Photogram. Eng. Remote Sens., 87, 821–830, 2021. a
Li, Y., Fu, H., Zhu, J., and Wang, C.: A filtering method for ICESat-2 photon point cloud data based on relative neighboring relationship and local weighted distance statistics, IEEE Geosci. Remote Sens. Lett., 18, 1891–1895, 2020. a
Liu, P. L.-F., Lynett, P., Fernando, H., Jaffe, B. E., Fritz, H., Higman, B., Morton, R., Goff, J., and Synolakis, C.: Observations by the international tsunami survey team in Sri Lanka, Science, 308, 1595–1595, 2005. a
Nanditha, J. S., Kushwaha, A. P., Singh, R., Malik, I., Solanki, H., Chuphal, D. S., Dangar, S., Mahto, S. S., Vegad, U., and Mishra, V.: The Pakistan Flood of August 2022: Causes and Implications, Earth's Future, 11, e2022EF003230, https://doi.org/10.1029/2022EF003230, 2022. a, b
Nobre, A. D., Cuartas, L. A., Momo, M. R., Severo, D. L., Pinheiro, A., and Nobre, C. A.: HAND contour: a new proxy predictor of inundation extent, Hydrol. Process., 30, 320–333, 2016. a
opencv: opencv, GitHub [software], https://github.com/itseez/opencv (last access: 5 August 2024), 2015. a
Penton, D. J., Teng, J., Ticehurst, C., Marvanek, S., Freebairn, A., Mateo, C., Vaze, J., Yang, A., Khanam, F., Sengupta, A., and Polino, C.: The floodplain inundation history of the Murray-Darling Basin through two-monthly maximum water depth maps, Sci. Data, 10, 2052–4463 https://doi.org/10.1038/s41597-023-02559-4, 2023. a, b
Pulvirenti, L., Pierdicca, N., Chini, M., and Guerriero, L.: An algorithm for operational flood mapping from Synthetic Aperture Radar (SAR) data using fuzzy logic, Nat. Hazards Earth Syst. Sci., 11, 529–540, https://doi.org/10.5194/nhess-11-529-2011, 2011. a
Rodriguez Enriquez, A., Wahl, T., Talke, S. A., Orton, P., Booth, J. F., and Santamaria-Aguilar, S.: Matflood: An Efficient Algorithm for Mapping Flood Extent and Depth, Environ. Modell. Software, https://doi.org/10.1016/j.envsoft.2023.105829, 2023. a
Rossi, D., Zolezzi, G., Bertoldi, W., and Vitti, A.: Monitoring Braided River-Bed Dynamics at the Sub-Event Time Scale Using Time Series of Sentinel-1 SAR Imagery, Remote Sens., 15, 3622, https://doi.org/10.3390/rs15143622, 2023. a
Roth, F., Bauer-Marschallinger, B., Tupas, M. E., Reimer, C., Salamon, P., and Wagner, W.: Sentinel-1-based analysis of the severe flood over Pakistan 2022, Nat. Hazards Earth Syst. Sci., 23, 3305–3317, https://doi.org/10.5194/nhess-23-3305-2023, 2023. a, b
Salamon, P., Mctlormick, N., Reimer, C., Clarke, T., Bauer-Marschallinger, B., Wagner, W., Martinis, S., Chow, C., Böhnke, C., Matgen, P., Chini, M., Hostache, R., Molini, L., Fiori, E., and Walli, A.: The new, systematic global flood monitoring product of the copernicus emergency management service, in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 11–16 July 2021, Brussels, https://doi.org/10.1109/IGARSS47720.2021.9554214, 1053–1056, 2021. a, b
Sánchez, F. and Lastra, J.: Guía metodológica para el desarrollo del Sistema Nacional de Cartografía de Zonas Inundables, https://www.miteco.gob.es/es/agua/temas/gestion-de-los-riesgos-de-inundacion/snczi/guia-metodologica-determinacion-zonas-inundables.html (last access: 5 August 2024), 2011. a
Schumann, G. J.-P. and Moller, D. K.: Microwave remote sensing of flood inundation, Phys. Chem. Earth Pt. A/B/C, 83, 84–95, 2015. a
Spasova, T. and Nedkov, R.: On the use of SAR and optical data in assessment of flooded areas, in: Seventh International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2019), SPIE, 11174, 268–278, https://doi.org/10.1117/12.2533660, 2019. a
Stockman, G. and Shapiro, L. G.: Computer vision, Prentice Hall PTR, Provenienza dell'originale la University of California, Prentice Hall, 2001, ISBN 0130307963, 9780130307965, 580 pp., 2001. a
Teng, J., Penton, D., Ticehurst, C., Sengupta, A., Freebairn, A., Marvanek, S., Vaze, J., Gibbs, M., Streeton, N., Karim, F., and Morton, S.: A Comprehensive Assessment of Floodwater Depth Estimation Models in Semiarid Regions, Water Resour. Res., 58, e2022WR032031, 2022. a
Vanzo, D., Peter, S., Vonwiller, L., Bürgler, M., Weberndorfer, M., Siviglia, A., Conde, D., and Vetsch, D. F.: BASEMENT v3: A modular freeware for river process modelling over multiple computational backends, Environ. Model. Softw., 143, 105102, https://doi.org/10.1016/j.envsoft.2021.105102, 2021. a
Voigt, S., Giulio-Tonolo, F., Lyons, J., Kučera, J., Jones, B., Schneiderhan, T., Platzeck, G., Kaku, K., Hazarika, M. K., Czaran, L., Li, S., Pedersen, W., James, G. K., Proy, C., Muthike, D. M., Bequignon, J., and Guha-Sapir, D.: Global trends in satellite-based emergency mapping, Science, 353, 247–252, 2016. a
Wang, C., Zhu, X., Nie, S., Xi, X., Li, D., Zheng, W., and Chen, S.: Ground elevation accuracy verification of ICESat-2 data: A case study in Alaska, USA, Opt. Express, 27, 38168–38179, 2019. a
Zhang, J., Huang, Y.-F., Munasinghe, D., Fang, Z., Tsang, Y.-P., and Cohen, S.: Comparative analysis of inundation mapping approaches for the 2016 flood in the Brazos River, Texas, J. Am. Water Resour. Assoc., 54, 820–833, 2018. a
Zhu, J., Yang, P.-F., Li, Y., Xie, Y.-Z., and Fu, H.-Q.: Accuracy assessment of ICESat-2 ATL08 terrain estimates: A case study in Spain, J. Central South Univers., 29, 226–238, 2022. a
Short summary
The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve satellite-based flood monitoring using topographical data. FLEXTH is readily available as a computer code, offering a practical and scalable solution for estimating flood depth quickly and systematically over large areas. The methodology can reduce the impacts of floods and enhance emergency response efforts, particularly where resources are limited.
The study proposes a new framework, named FLEXTH, to estimate flood water depth and improve...
Altmetrics
Final-revised paper
Preprint