Articles | Volume 17, issue 6
https://doi.org/10.5194/essd-17-2963-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-2963-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
27 Jun 2025
Data description paper |
| 27 Jun 2025
| 27 Jun 2025
Benchmark dataset for hydraulic simulations of flash floods in the French Mediterranean region
Juliette Godet
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France
RECOVER, INRAE, Université d'Aix-Marseille, Aix En Provence, France
Pierre Nicolle
CORRESPONDING AUTHOR
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France
Nabil Hocini
Cerema Méditerranée, 13290 Aix-en-Provence, France
Eric Gaume
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France
Philippe Davy
Géosciences Rennes, Université Rennes 1, CNRS, UMR 6118, 35042 Rennes, France
Frederic Pons
Cerema Méditerranée, 13290 Aix-en-Provence, France
Pierre Javelle
RECOVER, INRAE, Université d'Aix-Marseille, Aix En Provence, France
Pierre-André Garambois
RECOVER, INRAE, Université d'Aix-Marseille, Aix En Provence, France
Dimitri Lague
Géosciences Rennes, Université Rennes 1, CNRS, UMR 6118, 35042 Rennes, France
Olivier Payrastre
GERS-LEE, Univ Gustave Eiffel, IFSTTAR, 44344 Bouguenais, France
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Both landslide mapping and volume estimation accuracies are crucial to quantify landscape evolution and manage such a natural hazard. We developed a method to robustly detect landslides and measure their volume from repeat 3D point cloud lidar data. This method detects more landslides than classical 2D inventories and resolves known issues of indirect volume measurement. Our results also suggest that the number of small landslides classically detected from 2D imagery is underestimated.
Nabil Hocini, Olivier Payrastre, François Bourgin, Eric Gaume, Philippe Davy, Dimitri Lague, Lea Poinsignon, and Frederic Pons
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Efficient flood mapping methods are needed for large-scale, comprehensive identification of flash flood inundation hazards caused by small upstream rivers. An evaluation of three automated mapping approaches of increasing complexity, i.e., a digital terrain model (DTM) filling and two 1D–2D hydrodynamic approaches, is presented based on three major flash floods in southeastern France. The results illustrate some limits of the DTM filling method and the value of using a 2D hydrodynamic approach.
Maxime Jay-Allemand, Pierre Javelle, Igor Gejadze, Patrick Arnaud, Pierre-Olivier Malaterre, Jean-Alain Fine, and Didier Organde
Hydrol. Earth Syst. Sci., 24, 5519–5538, https://doi.org/10.5194/hess-24-5519-2020, https://doi.org/10.5194/hess-24-5519-2020, 2020
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Pierre Nicolle, François Besson, Olivier Delaigue, Pierre Etchevers, Didier François, Matthieu Le Lay, Charles Perrin, Fabienne Rousset, Dominique Thiéry, François Tilmant, Claire Magand, Timothée Leurent, and Élise Jacob
Proc. IAHS, 383, 381–389, https://doi.org/10.5194/piahs-383-381-2020, https://doi.org/10.5194/piahs-383-381-2020, 2020
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Cited articles
Albano, R., Samela, C., Craciun, I., Manfreda, S., Adamowski, J., Sole, A., Sivertun, A., and Ozunu, A.: Large scale flood risk mapping in data scarce environments: an application for Romania, Water-Sui., 12, 1834, https://doi.org/10.3390/w12061834, 2020. a
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., and Feyen, L.: Advances in pan-European flood hazard mapping, Hydrol. Process., 28, 4067–4077, https://doi.org/10.1002/hyp.9947, 2014. a, b
Andreadis, K. M., Wing, O. E. J., Colven, E., Gleason, C. J., Bates, P. D., and Brown, C. M.: Urbanizing the floodplain: global changes of imperviousness in flood-prone areas, Environ. Res. Lett., 17, 104024, https://doi.org/10.1088/1748-9326/ac9197, 2022. a
Apel, H., Vorogushyn, S., and Merz, B.: Brief communication: Impact forecasting could substantially improve the emergency management of deadly floods: case study July 2021 floods in Germany, Nat. Hazards Earth Syst. Sci., 22, 3005–3014, https://doi.org/10.5194/nhess-22-3005-2022, 2022. a
Arnaud, P., Aubert, Y. Y., Organde, D., Cantet, P., Fouchier, C., and Folton, N.: Estimation de l'aléa hydrométéorologique par une méthode par simulation : la méthode SHYREG : présentation – performances – bases de données., La Houille Blanche – Revue internationale de l'eau, 2, 20, https://doi.org/10.1051/lhb/2014012, 2014. a
Aubert, Y., Arnaud, P., Ribstein, P., and Fine, J.-A.: The SHYREG flow method – application to 1605 basins in metropolitan France, Hydrolog. Sci. J., 59, 993–1005, https://doi.org/10.1080/02626667.2014.902061, 2014. a
Bates, P. D.: Flood Inundation Prediction, Annu. Rev. Fluid Mech., 54, 287–315, https://doi.org/10.1146/annurev-fluid-030121-113138, 2022. a, b
Bechon, P.-M., Le Coz, J., Leleu, I., and Renard, B.: Des outils du réseau Etat en hydrométrie et leur ouverture aux notions d'incertitude, Journées de l'hydraulique, 35, 1–7, 2013. a
Bösmeier, A. S., Himmelsbach, I., and Seeger, S.: Reliability of flood marks and practical relevance for flood hazard assessment in southwestern Germany, Nat. Hazards Earth Syst. Sci., 22, 2963–2979, https://doi.org/10.5194/nhess-22-2963-2022, 2022. a
Caruso, A., Guillot, A., and Arnaud, P.: Notice sur les indices de confiance de laméthode Shyreg-Débit – Définitions et calculs, Tech. rep., IRSTEA, 2013. a
Caumont, O., Mandement, M., Bouttier, F., Eeckman, J., Lebeaupin Brossier, C., Lovat, A., Nuissier, O., and Laurantin, O.: The heavy precipitation event of 14–15 October 2018 in the Aude catchment: a meteorological study based on operational numerical weather prediction systems and standard and personal observations, Nat. Hazards Earth Syst. Sci., 21, 1135–1157, https://doi.org/10.5194/nhess-21-1135-2021, 2021. a
CCR: Les catastrophes naturelles en France: bilan 1982–2020, Tech. rep., CCR, https://side.developpement-durable.gouv.fr/ACCIDR/doc/SYRACUSE/795441/les-catastrophes-naturelles-en-france-bilan-1982-2020 (last access: 25 June 2025), 2021. a
Champeaux, J.-L., Dupuy, P., Laurantin, O., Soulan, I., Tabary, P., and Soubeyroux, J.-M.: Les mesures de précipitations et l'estimation des lames d'eau à Météo-France : état de l'art et perspectives, La Houille Blanche, 95, 28–34, https://doi.org/10.1051/lhb/2009052, 2009. a
Davy, P., Croissant, T., and Lague, D.: A precipiton method to calculate river hydrodynamics, with applications to flood prediction, landscape evolution models, and braiding instabilities, J. Geophys. Res.-Earth, 122, 1491–1512, https://doi.org/10.1002/2016JF004156, 2017. a, b, c
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009. a
Dottori, F., Kalas, M., Salamon, P., Bianchi, A., Alfieri, L., and Feyen, L.: An operational procedure for rapid flood risk assessment in Europe, Nat. Hazards Earth Syst. Sci., 17, 1111–1126, https://doi.org/10.5194/nhess-17-1111-2017, 2017. a
Dottori, F., Alfieri, L., Bianchi, A., Skoien, J., and Salamon, P.: A new dataset of river flood hazard maps for Europe and the Mediterranean Basin, Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, 2022. a, b
Ducrocq, V., Boudevillain, B., Bouvier, C., Braud, I., Fourrie, N., Lebeaupin-Brossier, C., Javelle, P., Nuissier, O., Payrastre, O., Roux, H., Ruin, I., and Vincendon, B.: Le programme HYMEX – Connaissances et prévision des pluies intenses et crues rapides en région méditerranéenne, La Houille Blanche, 105, 5–12, https://doi.org/10.1051/lhb/2019048, 2019. a
Environment Agency: LIDAR Composite Digital Surface Model (DSM) – 1m, https://www.data.gov.uk/dataset/cf3f1137-c12b-44a1-a835-e80fe4a60b92/lidar-composite-digital-surface-model-dsm-1m (last access: 25 June 2025), 2024. a
Fleischmann, A., Paiva, R., and Collischonn, W.: Can regional to continental river hydrodynamic models be locally relevant? A cross-scale comparison, J. Hydrol., 3, 100027, https://doi.org/10.1016/j.hydroa.2019.100027, 2019. a, b
Frizzle, C., Trudel, M., Daniel, S., Pruneau, A., and Noman, J. : LiDAR topo-bathymetry for riverbed elevation assessment: A review of approaches and performance for hydrodynamic modelling of flood plainsew of flood plains, Earth Surf. Proc. Land., 49, 2585–2600, https://doi.org/10.1002/esp.5808, 2024. a
Gaume, E., Livet, M., Desbordes, M., and Villeneuve, J. P.: Hydrological analysis of the river Aude, France, flash flood on 12 and 13 November 1999, J. Hydrol., 286, 135–154, https://doi.org/10.1016/j.jhydrol.2003.09.015, 2004. a
Gaume, E., Borga, M., Llasat, M. C., Maouche, S., Lang, M., and Diakakis, M.: Sub-chapter 1.3.4. Mediterranean extreme floods and flash floods, in: The Mediterranean Region Under Climate Change: A Scientific Update, edited by: Moatti, J.-P. and Thiébault, S., Synthèses, IRD Éditions, Marseille, https://doi.org/10.4000/books.irdeditions.23181, 133–144, 2016. a
Gebrehiwot, A. and Hashemi-Beni, L.: 3D Inundation mapping: a comparison between deep learning image classification and geomorphic flood index approaches, Frontiers in Remote Sensing, 3, 868104, https://doi.org/10.3389/frsen.2022.868104, 2022. a
Godet, J., Gaume, E., Javelle, P., Nicolle, P., and Payrastre, O.: Technical note: Comparing three different methods for allocating river points to coarse-resolution hydrological modelling grid cells, Hydrol. Earth Syst. Sci., 28, 1403–1413, https://doi.org/10.5194/hess-28-1403-2024, 2024. a, b
Hocini, N., Payrastre, O., Bourgin, F., Gaume, E., Davy, P., Lague, D., Poinsignon, L., and Pons, F.: Performance of automated methods for flash flood inundation mapping: a comparison of a digital terrain model (DTM) filling and two hydrodynamic methods, Hydrol. Earth Syst. Sci., 25, 2979–2995, https://doi.org/10.5194/hess-25-2979-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
IGN: LiDAR HD, https://www.data.gouv.fr/fr/datasets/lidar-hd/ (last access: 25 June 2025), 2024. a
Kalsron, J., Jolivet, L., and Payrastre, O.: Construction de MNE adaptés à la simulation d'inondations, Thèse de master, Université de Lyon, https://documentation.ensg.eu/index.php?lvl=notice_display&id=99151 (last access: 25 June 2025), 2019. a
Lague, D. and Feldmann, B.: Chapter 2 – Topo-bathymetric airborne LiDAR for fluvial-geomorphology analysis, in: Developments in Earth Surface Processes, edited by: Tarolli, P. and Mudd, S. M., vol. 23 of Remote Sensing of Geomorphology, Elsevier, https://doi.org/10.1016/B978-0-444-64177-9.00002-3, 25–54, 2020. a
Lamichhane, N. and Sharma, S.: Effect of input data in hydraulic modeling for flood warning systems, Hydrolog. Sci. J., 63, 938–956, https://doi.org/10.1080/02626667.2018.1464166, 2018. a
Le Bihan, G., Payrastre, O., Gaume, E., Moncoulon, D., and Pons, F.: The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data, Hydrol. Earth Syst. Sci., 21, 5911–5928, https://doi.org/10.5194/hess-21-5911-2017, 2017. a, b, c
Le Coz, J., Renard, B., Bonnifait, L., Branger, F., and Le Boursicaud, R.: Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach, J. Hydrol., 509, 573–587, https://doi.org/10.1016/j.jhydrol.2013.11.016, 2014. a
Lumbroso, D. and Gaume, E.: Reducing the uncertainty in indirect estimates of extreme flash flood discharges, J. Hydrol., 414–415, 16–30, https://doi.org/10.1016/j.jhydrol.2011.08.048, 2012. a
Luo, H., Fytanidis, D. K., Schmidt, A. R., and García, M. H.: Comparative 1D and 3D numerical investigation of open-channel junction flows and energy losses, Adv. Water Resour., 117, 120–139, https://doi.org/10.1016/j.advwatres.2018.05.012, 2018. a
Merz, B., Kuhlicke, C., Kunz, M., Pittore, M., Babeyko, A., Bresch, D. N., Domeisen, D. I. V., Feser, F., Koszalka, I., Kreibich, H., Pantillon, F., Parolai, S., Pinto, J. G., Punge, H. J., Rivalta, E., Schröter, K., Strehlow, K., Weisse, R., and Wurpts, A.: Impact forecasting to support emergency management of natural hazards, Rev. Geophys., 58, e2020RG000704, https://doi.org/10.1029/2020RG000704, 2020. a
Molinari, D., De Bruijn, K. M., Castillo-Rodríguez, J. T., Aronica, G. T., and Bouwer, L. M.: Validation of flood risk models: current practice and possible improvements, Int. J. Disast. Risk. Re., 33, 441–448, https://doi.org/10.1016/j.ijdrr.2018.10.022, 2019. a
Naulin, J. P., Payrastre, O., and Gaume, E.: Spatially distributed flood forecasting in flash flood prone areas: application to road network supervision in Southern France, J. Hydrol., 486, 88–99, https://doi.org/10.1016/j.jhydrol.2013.01.044, 2013. a
Nicolle, P., Payrastre, O., and Hocini, N.: Production d'un catalogue d'emprises inondées mono-fréquence sur les petits cours d'eau de l'arc méditerranéen (rapport d'étape). Action 8.1, Research Report, IFSTTAR – Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux, https://hal.archives-ouvertes.fr/hal-03327095 (last access: 25 June 2025), 2021. a
Nicolle, P., Payrastre, O., and Hocini, N.: Méthode de prétraitement des MNT pour l'amélioration des calculs automatisés de zones inondables, Research Report, Université Gustave Eiffel; DGPR (Direction Générale de la Prévention des Risques – Ministère de la Transition Ecologique et Solidaire), https://hal.science/hal-04159337 (last access: 25 June 2025), 2023. a
Nicolle, P., Hocini, N., Godet, J., Pons, F., Payrastre, O., Arnaud, P., and Lague, D.: Benchmark dataset for hydraulic simulations of flash floods in the French Mediterranean region, Recherche Data Gouv [data set], https://doi.org/10.57745/IXXNAY, 2024. a, b
Nobre, A. D., Cuartas, L. A., Hodnett, M., Rennó, C. D., Rodrigues, G., Silveira, A., Waterloo, M., and Saleska, S.: Height above the nearest drainage – a hydrologically relevant new terrain model, J. Hydrol., 404, 13–29, https://doi.org/10.1016/j.jhydrol.2011.03.051, 2011. a
Payrastre, O., Nicolle, P., Bonnifait, L., Brigode, P., Astagneau, P., Baise, A., Belleville, A., Bouamara, N., Bourgin, F., Breil, P., Brunet, P., Cerbelaud, A., Courapied, F., Devreux, L., Dreyfus, R., Gaume, E., Nomis, S., Poggio, J., Pons, F., Rabab, Y., and Sevrez, D.: Tempête Alex du 2 octobre 2020 dans les Alpes-Maritimes : une contribution de la communauté scientifique à l'estimation des débits de pointe des crues, LHB Hydroscience Journal, 108, 2082891, https://doi.org/10.1080/27678490.2022.2082891, 2022. a
Pricope, N. G. and Bashit, M. S.: Emerging trends in topobathymetric LiDAR technology and mapping, Int. J. Remote Sens., 44, 7706–7731, https://doi.org/10.1080/01431161.2023.2287564, 2023. a
Ritter, J., Berenguer, M., Park, S., and Sempere-Torres, D.: Real-time assessment of flash flood impacts at pan-European scale: the ReAFFINE method, J. Hydrol., 603, 127022, https://doi.org/10.1016/j.jhydrol.2021.127022, 2021. a
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and Freer, J. E.: A high-resolution global flood hazard model, Water Resour. Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015. a, b
Tramblay, Y., Mimeau, L., Neppel, L., Vinet, F., and Sauquet, E.: Detection and attribution of flood trends in Mediterranean basins, Hydrol. Earth Syst. Sci., 23, 4419–4431, https://doi.org/10.5194/hess-23-4419-2019, 2019. a
Wimmer, M. H., Hollaus, M., Blöschl, G., Buttinger-Kreuzhuber, A., Komma, J., Waser, J., and Pfeifer, N.: Processing of nationwide topographic data for ensuring consistent river network representation, J. Hydrol., 13, 100106, https://doi.org/10.1016/j.hydroa.2021.100106, 2021. a, b, c
Wing, O. E. J., Bates, P. D., Quinn, N. D., Savage, J. T. S., Uhe, P. F., Cooper, A., Collings, T. P., Addor, N., Lord, N. S., Hatchard, S., Hoch, J. M., Bates, J., Probyn, I., Himsworth, S., Rodríguez González, J., Brine, M. P., Wilkinson, H., Sampson, C. C., Smith, A. M., Neal, J. C., and Haigh, I. D.: A 30 m global flood inundation model for any climate scenario, Water Resour. Res., 60, e2023WR036460, https://doi.org/10.1029/2023WR036460, 2024. a, b, c, d
Short summary
This paper describes a dataset that includes input, output, and validation data for the simulation of flash flood hazards and three specific flash flood events in the French Mediterranean region. This dataset is particularly valuable as flood mapping methods often lack sufficient benchmark data. Additionally, we demonstrate how the hydraulic method we used, named Floodos, produces highly satisfactory results.
This paper describes a dataset that includes input, output, and validation data for the...
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