Survey data of damaged residential buildings and business premises from the 2022 record-breaking flood in the Marche region, Italy

Abstract. Accurate flood damage data are essential for developing reliable flood risk assessments and designing effective risk management strategies. However, empirical flood damage data remain limited, particularly at the object level, hindering the calibration and validation of predictive models. Existing datasets are often highly aggregated and lack the granularity required for detailed analysis. This paper presents two comprehensive, micro-scale datasets documenting flood damage to 256 buildings, comprising both residential buildings and business premises, surveyed in the aftermath of the 2022 flood event in the Marche region of Italy. The georeferenced datasets include information on hazard characteristics, buildings' vulnerability features, physical damage description across structural and non-structural components, indirect damage, and implemented mitigation measures. In addition, original survey forms are provided to support future data collections in different contexts. Datasets and survey forms are available at the link: https://doi.org/10.5281/zenodo.15591850 (Rrokaj et al., 2025). The quality and richness of these datasets make them a valuable resource for improving flood risk modelling and supporting local stakeholders in identifying intervention priorities.

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Publication Details

Journal
Earth system science data
Published
2025-11-25
DOI
https://doi.org/10.5194/essd-17-6487-2025
Primary Topic
Flood Risk Assessment and Management
Type
article
Field-Weighted Citation Impact
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article

Survey data of damaged residential buildings and business premises from the 2022 record-breaking flood in the Marche region, Italy

Marta Ballocci, Gabriele Bertoli, Simone Sterlacchini, Alessio Domeneghetti et al.
Earth system science data
Flood Risk Assessment and Management
article

Survey data of damaged residential buildings and business premises from the 2022 record-breaking flood in the Marche region, Italy

Marta Ballocci, Gabriele Bertoli, Simone Sterlacchini, Alessio Domeneghetti, Mario Di Bacco, Sara Mozzon, Marco Zazzeri, Daniela Molinari, Alice Gallazzi, Debora Voltolina, Anna Rita Scorzini, Sara Rrokaj, Natasha Petruccelli, Paola Di Fluri, Mohammed Hammouti, Francesca da Porto, Marco Donà, Chiara Arrighi, Elisa Saler, Claudia De Lucia, Gianluca Lelli, Andrea Gennaro, Gaia Treglia
article en

Abstract

Abstract. Accurate flood damage data are essential for developing reliable flood risk assessments and designing effective risk management strategies. However, empirical flood damage data remain limited, particularly at the object level, hindering the calibration and validation of predictive models. Existing datasets are often highly aggregated and lack the granularity required for detailed analysis. This paper presents two comprehensive, micro-scale datasets documenting flood damage to 256 buildings, comprising both residential buildings and business premises, surveyed in the aftermath of the 2022 flood event in the Marche region of Italy. The georeferenced datasets include information on hazard characteristics, buildings' vulnerability features, physical damage description across structural and non-structural components, indirect damage, and implemented mitigation measures. In addition, original survey forms are provided to support future data collections in different contexts. Datasets and survey forms are available at the link: https://doi.org/10.5281/zenodo.15591850 (Rrokaj et al., 2025). The quality and richness of these datasets make them a valuable resource for improving flood risk modelling and supporting local stakeholders in identifying intervention priorities.

Earth system science dataVol. 17(11)
University of Padua (IT), University of L'Aquila (IT), Institute of Environmental Geology and Geoengineering (IT), University of Florence (IT), University of Bologna (IT), Politecnico di Milano (IT)
Openalex Percentile: Top 66%
Flood Risk Assessment and Management
DOIOpen Access PDFOpenAlex
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