Articles | Volume 26, issue 3
https://doi.org/10.5194/nhess-26-1573-2026
© Author(s) 2026. 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-26-1573-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2026
Research article |
| 31 Mar 2026
| 31 Mar 2026
Development and validation of an Early Warning System for coastal flooding operating on a Mediterranean urban beach
Antonis Chatzipavlis
CORRESPONDING AUTHOR
Department of Physics and Earth Sciences, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy
Daniele Trogu
Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, 09042, Italy
Andrea Ruju
Department of Meteorology and Climatology, Regional Agency for Environmental Protection, Sassari, 07100, Italy
Juan Montes
Earth Sciences Department, University of Cadiz INMAR, Avda. República Saharaui s/n, Puerto Real, 11510, Spain
Antonio Usai
Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, 09042, Italy
Marco Porta
Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, 09042, Italy
Giovanni Coco
Institut de Ciències del Mar, CSIC, Barcelona, 08003, Spain
Sandro De Muro
Department of Chemical and Geological Sciences, University of Cagliari, Monserrato, 09042, Italy
Paolo Ciavola
Department of Physics and Earth Sciences, University of Ferrara, Via Saragat 1, Ferrara, 44122, Italy
CNR-IAS Oristano, Institute for Study of Anthropogenic Impacts and Sustainability in Marine Environment, Torregrande, 09072, Italy
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-157, https://doi.org/10.5194/essd-2026-157, 2026
Preprint under review for ESSD
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Rivers carry sediment that shapes landscapes and coasts, but in Italy these data are scattered and often incomplete. We created a national database and online platform to gather and organize past and present sediment measurements in one place. By linking data with clear information about where, when, and how they were collected, we made them easier to explore and compare over time.
Paola Emilia Souto-Ceccon, Juan Montes, Enrico Duo, Paolo Ciavola, Tomás Fernández-Montblanc, and Clara Armaroli
Earth Syst. Sci. Data, 17, 1041–1054, https://doi.org/10.5194/essd-17-1041-2025, https://doi.org/10.5194/essd-17-1041-2025, 2025
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This dataset supports the growing need for information on coastal storm impacts. It covers different European countries and is an open-access tool that can be exploited, updated, or complemented by different users and for different purposes. Via labelling with unique identifiers, the database allows for a quick and consistent retrieval of all of the resources associated with a storm event. The adopted approach can be easily exported to all European countries and beyond.
Enrico Duo, Juan Montes, Marine Le Gal, Tomás Fernández-Montblanc, Paolo Ciavola, and Clara Armaroli
Nat. Hazards Earth Syst. Sci., 25, 13–39, https://doi.org/10.5194/nhess-25-13-2025, https://doi.org/10.5194/nhess-25-13-2025, 2025
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The present work, developed within the EU H2020 European Coastal Flood Awareness System (ECFAS) project, presents an approach used to estimate direct impacts of coastal flood on population, buildings, and roads along European coasts. The findings demonstrate that the ECFAS impact approach offers valuable estimates for affected populations, reliable damage assessments for buildings and roads, and improved accuracy compared to traditional grid-based approaches.
Marine Le Gal, Tomás Fernández-Montblanc, Enrico Duo, Juan Montes Perez, Paulo Cabrita, Paola Souto Ceccon, Véra Gastal, Paolo Ciavola, and Clara Armaroli
Nat. Hazards Earth Syst. Sci., 23, 3585–3602, https://doi.org/10.5194/nhess-23-3585-2023, https://doi.org/10.5194/nhess-23-3585-2023, 2023
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Assessing coastal hazards is crucial to mitigate flooding disasters. In this regard, coastal flood databases are valuable tools. This paper describes a new coastal flood map catalogue covering the entire European coastline, as well as the methodology to build it and its accuracy. The catalogue focuses on frequent extreme events and relies on synthetic scenarios estimated from local storm conditions. Flood-prone areas and regions sensitive to storm duration and water level peak were identified.
Eduardo Gomez-de la Peña, Giovanni Coco, Colin Whittaker, and Jennifer Montaño
Earth Surf. Dynam., 11, 1145–1160, https://doi.org/10.5194/esurf-11-1145-2023, https://doi.org/10.5194/esurf-11-1145-2023, 2023
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Predicting how shorelines change over time is a major challenge in coastal research. We here have turned to deep learning (DL), a data-driven modelling approach, to predict the movement of shorelines using observations from a camera system in New Zealand. The DL models here implemented succeeded in capturing the variability and distribution of the observed shoreline data. Overall, these findings indicate that DL has the potential to enhance the accuracy of current shoreline change predictions.
Wagner L. L. Costa, Karin R. Bryan, and Giovanni Coco
Nat. Hazards Earth Syst. Sci., 23, 3125–3146, https://doi.org/10.5194/nhess-23-3125-2023, https://doi.org/10.5194/nhess-23-3125-2023, 2023
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For predicting flooding events at the coast, topo-bathymetric data are essential. However, elevation data can be unavailable. To tackle this issue, recent efforts have centred on the use of satellite-derived topography (SDT) and bathymetry (SDB). This work is aimed at evaluating their accuracy and use for flooding prediction in enclosed estuaries. Results show that the use of SDT and SDB in numerical modelling can produce similar predictions when compared to the surveyed elevation data.
Charline Dalinghaus, Giovanni Coco, and Pablo Higuera
Nat. Hazards Earth Syst. Sci., 23, 2157–2169, https://doi.org/10.5194/nhess-23-2157-2023, https://doi.org/10.5194/nhess-23-2157-2023, 2023
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Wave setup is a critical component of coastal flooding. Consequently, understanding and being able to predict wave setup is vital to protect coastal resources and the population living near the shore. Here, we applied machine learning to improve the accuracy of present predictors of wave setup. The results show that the new predictors outperform existing formulas demonstrating the capability of machine learning models to provide a physically sound description of wave setup.
Yizhang Wei, Yining Chen, Jufei Qiu, Zeng Zhou, Peng Yao, Qin Jiang, Zheng Gong, Giovanni Coco, Ian Townend, and Changkuan Zhang
Earth Surf. Dynam., 10, 65–80, https://doi.org/10.5194/esurf-10-65-2022, https://doi.org/10.5194/esurf-10-65-2022, 2022
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The barrier tidal basin is increasingly altered by human activity and sea-level rise. These environmental changes probably lead to the emergence or disappearance of islands, yet the effect of rocky islands on the evolution of tidal basins remains poorly investigated. Using numerical experiments, we explore the evolution of tidal basins under varying numbers and locations of islands. This work provides insights for predicting the response of barrier tidal basins in a changing environment.
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Short summary
This study evaluates the performance of an EWS (Early Warning System) for coastal flooding operating at a beach scale. The system captures total water level exceedances based on predefined morphological thresholds and trigger timely warnings, particularly under energetic conditions. Its forecasts are found to align well with selected overtopping events of varying magnitude and duration, leading to flooding of the berm-dune zone, which have been monitored by on-site coastal videocameras.
This study evaluates the performance of an EWS (Early Warning System) for coastal flooding...
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