Articles | Volume 24, issue 5
https://doi.org/10.5194/nhess-24-1681-2024
© 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-24-1681-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The value of multi-source data for improved flood damage modelling with explicit input data uncertainty treatment: INSYDE 2.0
Mario Di Bacco
Department of Civil and Environmental Engineering, University of Florence, 50139 Florence, Italy
Daniela Molinari
Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy
Anna Rita Scorzini
CORRESPONDING AUTHOR
Department of Civil, Environmental and Architectural Engineering, University of L'Aquila, 67100 L'Aquila, Italy
Related authors
Sara Rrokaj, Chiara Arrighi, Marta Ballocci, Gabriele Bertoli, Francesca da Porto, Claudia De Lucia, Mario Di Bacco, Paola Di Fluri, Alessio Domeneghetti, Marco Donà, Alice Gallazzi, Andrea Gennaro, Gianluca Lelli, Sara Mozzon, Natasha Petruccelli, Elisa Saler, Anna Rita Scorzini, Simone Sterlacchini, Gaia Treglia, Debora Voltolina, Marco Zazzeri, and Daniela Molinari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-358, https://doi.org/10.5194/essd-2025-358, 2025
Preprint under review for ESSD
Short summary
Short summary
Flood damage data are key to understanding territorial risks and supporting the design of mitigation measures. However, such data are scarce, and the available ones often lack a high level of detail. We conducted a field survey of residential, commercial, and industrial buildings affected by the record-breaking flood event that hit Italy’s Marche region in 2022. The resulting datasets cover 256 assets and include detailed information on damage, building features, and mitigation measures.
Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
EGUsphere, https://doi.org/10.5194/egusphere-2025-1413, https://doi.org/10.5194/egusphere-2025-1413, 2025
Short summary
Short summary
INSYDE-content is a novel probabilistic model designed to estimate flood damage to household items with a component-based approach. By incorporating multiple variables and addressing uncertainties, the model enables more comprehensive and insightful damage assessments by accounting for an often-overlooked asset
Sara Rrokaj, Chiara Arrighi, Marta Ballocci, Gabriele Bertoli, Francesca da Porto, Claudia De Lucia, Mario Di Bacco, Paola Di Fluri, Alessio Domeneghetti, Marco Donà, Alice Gallazzi, Andrea Gennaro, Gianluca Lelli, Sara Mozzon, Natasha Petruccelli, Elisa Saler, Anna Rita Scorzini, Simone Sterlacchini, Gaia Treglia, Debora Voltolina, Marco Zazzeri, and Daniela Molinari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-358, https://doi.org/10.5194/essd-2025-358, 2025
Preprint under review for ESSD
Short summary
Short summary
Flood damage data are key to understanding territorial risks and supporting the design of mitigation measures. However, such data are scarce, and the available ones often lack a high level of detail. We conducted a field survey of residential, commercial, and industrial buildings affected by the record-breaking flood event that hit Italy’s Marche region in 2022. The resulting datasets cover 256 assets and include detailed information on damage, building features, and mitigation measures.
Pradeep Acharya, Mario Di Bacco, Daniela Molinari, and Anna Rita Scorzini
EGUsphere, https://doi.org/10.5194/egusphere-2025-1413, https://doi.org/10.5194/egusphere-2025-1413, 2025
Short summary
Short summary
INSYDE-content is a novel probabilistic model designed to estimate flood damage to household items with a component-based approach. By incorporating multiple variables and addressing uncertainties, the model enables more comprehensive and insightful damage assessments by accounting for an often-overlooked asset
Marta Ballocci, Daniela Molinari, Giovanni Marin, Marta Galliani, Alessio Domeneghetti, Giovanni Menduni, Simone Sterlacchini, and Francesco Ballio
EGUsphere, https://doi.org/10.5194/egusphere-2024-3017, https://doi.org/10.5194/egusphere-2024-3017, 2024
Short summary
Short summary
This study estimates flood direct damage to businesses in Italy using 812 damage records from five riverine flood case studies. A multiple regression model predicts economic damage based on business size, water depth, and economic sectors. The results show that damage increases non-proportionally with firm size, while water depth mainly affects stock damage. Healthcare, commercial, and manufacturing sectors are most vulnerable to building, stock, and equipment damage, respectively.
Cristina Prieto, Dhruvesh Patel, Dawei Han, Benjamin Dewals, Michaela Bray, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 24, 3381–3386, https://doi.org/10.5194/nhess-24-3381-2024, https://doi.org/10.5194/nhess-24-3381-2024, 2024
Natasha Petruccelli, Luca Mantecchini, Alice Gallazzi, Daniela Molinari, Mohammed Hammouti, Marco Zazzeri, Simone Sterlacchini, Francesco Ballio, Armando Brath, and Alessio Domeneghetti
Proc. IAHS, 385, 407–413, https://doi.org/10.5194/piahs-385-407-2024, https://doi.org/10.5194/piahs-385-407-2024, 2024
Short summary
Short summary
The study illustrates the methodology developed for flood risk assessment for road and railway infrastructures. Through the creation of a detailed database, using different data sources, and the definition of a risk matrix, a risk level (High, Medium, Low and Null) is assigned to each section, considering the physical and functional characteristics of the infrastructure, as well as its relevance and the magnitude of the expected event.
Panagiotis Asaridis and Daniela Molinari
Adv. Geosci., 61, 1–21, https://doi.org/10.5194/adgeo-61-1-2023, https://doi.org/10.5194/adgeo-61-1-2023, 2023
Short summary
Short summary
This paper presents a conceptual model for the estimation of flood damage to power grids and reviews the available methodologies, to better understand current modelling approaches, challenges, and limitations. The model adopts an interdisciplinary and multi-scale evaluation approach to handle the complex damage mechanisms and capture the cascading effects. In doing so, it adapts to different geographical and economic contexts, allowing stakeholders to implement comprehensive damage assessments.
Tommaso Simonelli, Laura Zoppi, Daniela Molinari, and Francesco Ballio
Nat. Hazards Earth Syst. Sci., 22, 1819–1823, https://doi.org/10.5194/nhess-22-1819-2022, https://doi.org/10.5194/nhess-22-1819-2022, 2022
Short summary
Short summary
The paper discusses challenges (and solutions) emerged during a collaboration among practitioners, stakeholders, and scientists in the definition of flood damage maps in the Po River District. Social aspects were proven to be fundamental components of the risk assessment; variety of competences in the working group was key in finding solutions and revealing weaknesses of intermediate proposals. This paper finally highlights the need of duplicating such an experience at a broader European level.
Anna Rita Scorzini, Benjamin Dewals, Daniela Rodriguez Castro, Pierre Archambeau, and Daniela Molinari
Nat. Hazards Earth Syst. Sci., 22, 1743–1761, https://doi.org/10.5194/nhess-22-1743-2022, https://doi.org/10.5194/nhess-22-1743-2022, 2022
Short summary
Short summary
This study presents a replicable procedure for the adaptation of synthetic, multi-variable flood damage models among countries that may have different hazard and vulnerability features. The procedure is exemplified here for the case of adaptation to the Belgian context of a flood damage model, INSYDE, for the residential sector, originally developed for Italy. The study describes necessary changes in model assumptions and input parameters to properly represent the new context of implementation.
Animesh K. Gain, Yves Bühler, Pascal Haegeli, Daniela Molinari, Mario Parise, David J. Peres, Joaquim G. Pinto, Kai Schröter, Ricardo M. Trigo, María Carmen Llasat, and Heidi Kreibich
Nat. Hazards Earth Syst. Sci., 22, 985–993, https://doi.org/10.5194/nhess-22-985-2022, https://doi.org/10.5194/nhess-22-985-2022, 2022
Short summary
Short summary
To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated to the public discussion and open-access publication of high-quality studies and original research on natural hazards and their consequences, we highlight 11 key publications covering major subject areas of NHESS that stood out within the past 20 years.
Daniela Molinari, Anna Rita Scorzini, Chiara Arrighi, Francesca Carisi, Fabio Castelli, Alessio Domeneghetti, Alice Gallazzi, Marta Galliani, Frédéric Grelot, Patric Kellermann, Heidi Kreibich, Guilherme S. Mohor, Markus Mosimann, Stephanie Natho, Claire Richert, Kai Schroeter, Annegret H. Thieken, Andreas Paul Zischg, and Francesco Ballio
Nat. Hazards Earth Syst. Sci., 20, 2997–3017, https://doi.org/10.5194/nhess-20-2997-2020, https://doi.org/10.5194/nhess-20-2997-2020, 2020
Short summary
Short summary
Flood risk management requires a realistic estimation of flood losses. However, the capacity of available flood damage models to depict real damages is questionable. With a joint effort of eight research groups, the objective of this study was to compare the performances of nine models for the estimation of flood damage to buildings. The comparison provided more objective insights on the transferability of the models and on the reliability of their estimations.
Marta Galliani, Daniela Molinari, and Francesco Ballio
Nat. Hazards Earth Syst. Sci., 20, 2937–2941, https://doi.org/10.5194/nhess-20-2937-2020, https://doi.org/10.5194/nhess-20-2937-2020, 2020
Short summary
Short summary
INSYDE is a multivariable synthetic model for flood damage assessment of dwellings. The analysis and use of this model highlighted some weaknesses, linked to its complexity, that can undermine its usability and correct implementation. This study proposes a simplified version of INSYDE which maintains its multivariable and synthetic nature but has simpler mathematical formulations permitting an easier use and a direct analysis of the relation between damage and its explanatory variables.
Cited articles
Albano, R., Sole, A., Adamowski, J., Perrone, A., and Inam, A.: Using FloodRisk GIS freeware for uncertainty analysis of direct economic flood damages in Italy, Int. J. Appl. Earth. Obs., 73, 220–229, https://doi.org/10.1016/j.jag.2018.06.019, 2018.
Amadio, M., Scorzini, A. R., Carisi, F., Essenfelder, A. H., Domeneghetti, A., Mysiak, J., and Castellarin, A.: Testing empirical and synthetic flood damage models: the case of Italy, Nat. Hazards Earth Syst. Sci., 19, 661–678, https://doi.org/10.5194/nhess-19-661-2019, 2019.
Apel, H., Merz, B., and Thieken, A. H.: Quantification of uncertainties in flood risk assessments, Int. J. River Basin Manag., 6, 149–162, https://doi.org/10.1080/15715124.2008.9635344, 2008.
Autorità di Bacino del Fiume Po: Aggiornamento e revisione del Piano di Gestione del Rischio di Alluvione – II ciclo (2021–2027), Final report, https://pianoalluvioni.adbpo.it/piano-gestione-rischio-alluvioni-2021/ (last access: 9 October 2023), 2022.
Bhuyan, K., Van Westen, C., Wang, J., and Meena, S. R.: Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence, Nat. Hazards, 119, 805–835, https://doi.org/10.1007/s11069-022-05612-4, 2023.
Cammerer, H., Thieken, A. H., and Lammel, J.: Adaptability and transferability of flood loss functions in residential areas, Nat. Hazards Earth Syst. Sci., 13, 3063–3081, https://doi.org/10.5194/nhess-13-3063-2013, 2013.
Clausen, L. and Clark, P. B.: The development of criteria for predicting dam break flood damages using modelling of historical dam failures, in: International Conference on River Flood Hydraulics, edited by: White, W. R., Hydraulics Research Limited, John Wiley & Sons Ltd., Wallingford, UK, 369–380, ISBN 0471927139, 1990.
Di Bacco, M., Rotello, P., Suppasri, A., and Scorzini, A. R.: Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event, Environ. Modell. Softw., 160, 105604, https://doi.org/10.1016/j.envsoft.2022.105604, 2023.
Di Bacco, M., Molinari, D., and Scorzini, A. R.: INSYDE 2.0, Mendeley Data [code], https://doi.org/10.17632/jpdb89gxn5.1, 2024.
Dottori, F., Figueiredo, R., Martina, M. L. V., Molinari, D., and Scorzini, A. R.: INSYDE: a synthetic, probabilistic flood damage model based on explicit cost analysis, Nat. Hazards Earth Syst. Sci., 16, 2577–2591, https://doi.org/10.5194/nhess-16-2577-2016, 2016.
Galliani, M., Molinari, D., and Ballio, F.: Brief Communication: Simple-INSYDE, development of a new tool for flood damage evaluation from an existing synthetic model, Nat. Hazards Earth Syst. Sci., 20, 2937–2941, https://doi.org/10.5194/nhess-20-2937-2020, 2020.
Gómez Zapata, J. C., Pittore, M., Cotton, F., Lilienkamp, H., Shinde, S., Aguirre, P., and Santa María, H.: Epistemic uncertainty of probabilistic building exposure compositions in scenario-based earthquake loss models, B. Earthq. Eng., 20, 2401–2438, https://doi.org/10.1007/s10518-021-01312-9, 2022.
Huayra Mena, G. C.: Flood damage model: development of INSYDE in the Po River basin, Master thesis, Politecnico di Milano, Milano, 137 pp., https://www.politesi.polimi.it/handle/10589/187358 (last access: 6 May 2024), 2022.
Kelman, I. and Spence, R.: An overview of flood actions on buildings, Eng. Geol., 73, 297–309, https://doi.org/10.1016/j.enggeo.2004.01.010, 2004.
Malgwi, M. B., Schlögl, M., and Keiler, M.: Expert-based versus data-driven flood damage models: A comparative evaluation for data-scarce regions, Int. J. Disast. Risk Re., 57, 102148, https://doi.org/10.1016/j.ijdrr.2021.102148, 2021.
Marvi, M. T.: A review of flood damage analysis for a building structure and contents, Nat. Hazards, 102, 967–995, https://doi.org/10.1007/s11069-020-03941-w, 2020.
Merz, B., Kreibich, H., and Apel, H.: Flood risk analysis: uncertainties and validation, Österreichische Wasser- und Abfallwirtschaft, 60, 89–94, https://doi.org/10.1007/s00506-008-0001-4, 2008.
Merz, B., Kreibich, H., Schwarze, R., and Thieken, A.: Review article “Assessment of economic flood damage”, Nat. Hazards Earth Syst. Sci., 10, 1697–1724, https://doi.org/10.5194/nhess-10-1697-2010, 2010.
Merz, B., Vorogushyn, S., Lall, U., Viglione, A., and Blöschl, G.: Charting unknown waters – On the role of surprise in flood risk assessment and management, Water Resour. Res., 51, 6399–6416, https://doi.org/10.1002/2015WR017464, 2015.
Mohor, G. S., Hudson, P., and Thieken, A. H.: A Comparison of Factors Driving Flood Losses in Households Affected by Different Flood Types, Water Resour. Res., 56, e2019WR025943, https://doi.org/10.1029/2019WR025943, 2020
Molinari, D. and Scorzini, A. R.: On the influence of input data quality to flood damage estimation: The performance of the INSYDE model, Water, 9, 688, https://doi.org/10.3390/w9090688, 2017.
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.
Molinari, D., Scorzini, A. R., Arrighi, C., Carisi, F., Castelli, F., Domeneghetti, A., Gallazzi, A., Galliani, M., Grelot, F., Kellermann, P., Kreibich, H., Mohor, G. S., Mosimann, M., Natho, S., Richert, C., Schroeter, K., Thieken, A. H., Zischg, A. P., and Ballio, F.: Are flood damage models converging to “reality”? Lessons learnt from a blind test, Nat. Hazards Earth Syst. Sci., 20, 2997–3017, https://doi.org/10.5194/nhess-20-2997-2020, 2020.
Morgan, M. G., Henrion, M., and Small, M.: Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy analysis, Cambridge University Press, Cambridge, UK, ISBN 0521365422, 1990.
Nofal, O. M., van de Lindt, J. W., and Do, T. Q.: Multi-variate and single-variable flood fragility and loss approaches for buildings, Reliab. Eng. Syst. Safe, 202, 106971, https://doi.org/10.1016/j.ress.2020.106971, 2020.
Papathoma-Köhle, M., Neuhäuser, B., Ratzinger, K., Wenzel, H., and Dominey-Howes, D.: Elements at risk as a framework for assessing the vulnerability of communities to landslides, Nat. Hazards Earth Syst. Sci., 7, 765–779, https://doi.org/10.5194/nhess-7-765-2007, 2007.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Paulik, R., Wild, A., Zorn, C., and Wotherspoon, L.: Residential building flood damage: Insights on processes and implications for risk assessments, J. Flood Risk Manag., 15, e12832, https://doi.org/10.1111/jfr3.12832, 2022.
Penning-Rowsell, E., Johnson, C., Tunstall, S., Morris, J., Chatterton, J., Green, C., Koussela, K., and Fernandez-Bilbao, A.: The Benefits of Flood and Coastal Risk Management: a Handbook of Assessment Techniques, Middlesex Univ. Press, Middlesex, Hydraulic Engineering Reports, ISBN 1904750516, 2005.
Pinelli, J. P., Da Cruz, J., Gurley, K., Paleo-Torres, A. S., Baradaranshoraka, M., Cocke, S., and Shin, D.: Uncertainty reduction through data management in the development, validation, calibration, and operation of a hurricane vulnerability model, Int. J. Disast. Risk Sc., 11, 790–806, https://doi.org/10.1007/s13753-020-00316-4, 2020.
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J.H.A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., and Maier, H. R.: The future of sensitivity analysis: An essential discipline for systems modeling and policy support, Environ. Modell. Softw., 137, 104954, https://doi.org/10.1016/j.envsoft.2020.104954, 2021.
Rözer, V., Kreibich, H., Schröter, K., Müller, M., Sairam, N., Doss-Gollin, J., Lall, U., and Merz, B.: Probabilistic models significantly reduce uncertainty in Hurricane Harvey pluvial flood loss estimates, Earths Future, 7, 384–394, https://doi.org/10.1029/2018EF001074, 2019.
Ruggieri, S., Cardellicchio, A., Leggieri, V., and Uva, G.: Machine-learning based vulnerability analysis of existing buildings, Autom. Constr., 132, 103936, https://doi.org/10.1016/j.autcon.2021.103936, 2021.
Sairam, N., Schröter, K., Rözer, V., Merz, B., and Kreibich, H.: Hierarchical Bayesian approach for modeling spatiotemporal variability in flood damage processes, Water Resour. Res., 55, 8223–8237, https://doi.org/10.1029/2019WR025068, 2019.
Sayers, P. B., Hall, J. W., and Meadowcroft, I. C.: Towards risk-based flood hazard management in the UK, in: Proceedings of the Institution of Civil Engineers-Civil Engineering, Thomas Telford Ltd., Vol. 150, 36–42, https://doi.org/10.1680/cien.2002.150.5.36, 2002.
Schröter, K., Kreibich, H., Vogel, K., Riggelsen, C., Scherbaum, F., and Merz, B.: How useful are complex flood damage models?, Water Resour. Res., 50, 3378–3395, https://doi.org/10.1002/2013WR014396, 2014.
Schröter, K., Lüdtke, S., Redweik, R., Meier, J., Bochow, M., Ross, L., Nagel, C., and Kreibich, H.: Flood loss estimation using 3D city models and remote sensing data, Environ. Modell. Softw., 105, 118–131, https://doi.org/10.1016/j.envsoft.2018.03.032, 2018.
Scorzini, A. R., Di Bacco, M., and Manella, G.: Regional flood risk analysis for agricultural crops: Insights from the implementation of AGRIDE-c in central Italy, Int. J. Disast. Risk Re., 53, 101999, https://doi.org/10.1016/j.ijdrr.2020.101999, 2021.
Scorzini, A. R., Dewals, B., Rodriguez Castro, D., Archambeau, P., and Molinari, D.: INSYDE-BE: adaptation of the INSYDE model to the Walloon region (Belgium), Nat. Hazards Earth Syst. Sci., 22, 1743–1761, https://doi.org/10.5194/nhess-22-1743-2022, 2022.
Sieg, T., Kienzler, S., Rözer, V., Vogel, K., Rust, H., Bronstert, A., Kreibich, H., Merz, B., and Thieken, A. H.: Toward an adequate level of detail in flood risk assessments, J. Flood Risk Manag., 16, e12889, https://doi.org/10.1111/jfr3.12889, 2023.
Taramelli, A., Righini, M., Valentini, E., Alfieri, L., Gatti, I., and Gabellani, S.: Building-scale flood loss estimation through vulnerability pattern characterization: application to an urban flood in Milan, Italy, Nat. Hazards Earth Syst. Sci., 22, 3543–3569, https://doi.org/10.5194/nhess-22-3543-2022, 2022.
Velez, R., Calderon, D., Carey, L., Aime, C., Hultquist, C., Yetman, G., Kruczkiewicz, A., Gorokhovich, Y. and Chen, R. S.: Advancing Data for Street-Level Flood Vulnerability: Evaluation of Variables Extracted from Google Street View in Quito, Ecuador, IEEE Open Journal of the Computer Society, 3, 51–61, https://doi.org/10.1109/OJCS.2022.3166887, 2022.
Wagenaar, D. J., de Bruijn, K. M., Bouwer, L. M., and de Moel, H.: Uncertainty in flood damage estimates and its potential effect on investment decisions, Nat. Hazards Earth Syst. Sci., 16, 1–14, https://doi.org/10.5194/nhess-16-1-2016, 2016.
Wagenaar, D., de Jong, J., and Bouwer, L. M.: Multi-variable flood damage modelling with limited data using supervised learning approaches, Nat. Hazards Earth Syst. Sci., 17, 1683–1696, https://doi.org/10.5194/nhess-17-1683-2017, 2017.
Wagenaar, D., Lüdtke, S., Schröter, K., Bouwer, L. M., and Kreibich, H.: Regional and temporal transferability of multivariable flood damage models, Water Resour. Res., 54, 3688–3703, https://doi.org/10.1029/2017WR022233, 2018.
Winter, B., Schneeberger, K., Huttenlau, M., and Stötter, J.: Sources of uncertainty in a probabilistic flood risk model, Nat. Hazards, 91, 431–446, https://doi.org/10.1007/s11069-017-3135-5, 2018.
Zarekarizi, M., Srikrishnan, V., and Keller, K.: Neglecting uncertainties biases house-elevation decisions to manage riverine flood risks, Nat. Commun., 11, 1–11, https://doi.org/10.1038/s41467-020-19188-9, 2020.
Short summary
INSYDE 2.0 is a tool for modelling flood damage to residential buildings. By incorporating ultra-detailed survey and desk-based data, it improves the reliability and informativeness of damage assessments while addressing input data uncertainties.
INSYDE 2.0 is a tool for modelling flood damage to residential buildings. By incorporating...
Altmetrics
Final-revised paper
Preprint