Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-1109-2022
© 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-22-1109-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Extreme-coastal-water-level estimation and projection: a comparison of statistical methods
Maria Francesca Caruso
CORRESPONDING AUTHOR
Department of Civil, Architectural, and Environmental Engineering, University of Padua, 35131, Padua, Italy
Marco Marani
Department of Civil, Architectural, and Environmental Engineering, University of Padua, 35131, Padua, Italy
Earth and Climate Sciences Division, Duke University, Durham, NC, USA
Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
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S. Sithara, Chiara Favaretto, Piero Ruol, and Marco Marani
EGUsphere, https://doi.org/10.5194/egusphere-2026-1243, https://doi.org/10.5194/egusphere-2026-1243, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Sea level records are often too short. The traditional asymptotic extreme value analysis fails to produce accurate results with short records. By leveraging the non-asymptotic Metastatistical Extreme Value Distribution via the integration of two novel independent event selection methodologies, along with a robust cross-validation procedure, we found that our methodology can accurately estimate the likelihood of long return period extreme sea level events in data-scarce settings.
Alice Puppin, Davide Tognin, Massimiliano Ghinassi, Erica Franceschinis, Nicola Realdon, Marco Marani, and Andrea D'Alpaos
Biogeosciences, 21, 2937–2954, https://doi.org/10.5194/bg-21-2937-2024, https://doi.org/10.5194/bg-21-2937-2024, 2024
Short summary
Short summary
This study aims at inspecting organic matter dynamics affecting the survival and carbon sink potential of salt marshes, which are valuable yet endangered wetland environments. Measuring the organic matter content in marsh soils and its relationship with environmental variables, we observed that the organic matter accumulation varies at different scales, and it is driven by the interplay between sediment supply and vegetation, which are affected, in turn, by marine and fluvial influences.
Guillaume Goodwin, Marco Marani, Sonia Silvestri, Luca Carniello, and Andrea D'Alpaos
Biogeosciences, 20, 4551–4576, https://doi.org/10.5194/bg-20-4551-2023, https://doi.org/10.5194/bg-20-4551-2023, 2023
Short summary
Short summary
Seagrass meadows are an emblematic coastal habitat. Their sensitivity to environmental change means that it is essential to monitor their evolution closely. However, high costs make this endeavor a technical challenge. Here, we used machine learning to map seagrass meadows in 148 satellite images in the Venice Lagoon, Italy. We found that adding information such as depth of the seabed and known seagrass location improved our capacity to map temporal change in seagrass habitat.
T. Blount, S. Silvestri, M. Marani, and A. D’Alpaos
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 57–62, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-57-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-57-2023, 2023
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
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Short summary
We comparatively evaluate the predictive performance of traditional and new approaches to estimate the probability distributions of extreme coastal water levels. The metastatistical approach maximizes the use of observational information and provides reliable estimates of high quantiles with respect to traditional methods. Leveraging the increased estimation accuracy afforded by this approach, we investigate future changes in the frequency of extreme total water levels.
We comparatively evaluate the predictive performance of traditional and new approaches to...
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