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Calculating Economic Flood Damage through Microscale Risk Maps and Data Generalization: A Pilot Study in Southern Italy

Author

Listed:
  • Gianna Ida Festa

    (Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Italy)

  • Luigi Guerriero

    (Department of Earth Sciences, University of Naples “Federico II”, 80132 Napoli, Italy)

  • Mariano Focareta

    (MAPSat s.r.l., 82100 Benevento, Italy)

  • Giuseppe Meoli

    (MAPSat s.r.l., 82100 Benevento, Italy)

  • Silvana Revellino

    (Department of Management & Innovation Systems, University of Salerno, 84084 Fisciano, Italy)

  • Francesco Maria Guadagno

    (Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Italy)

  • Paola Revellino

    (Department of Sciences and Technologies, University of Sannio, 82100 Benevento, Italy)

Abstract

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method—the huge amounts of input data—by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient.

Suggested Citation

  • Gianna Ida Festa & Luigi Guerriero & Mariano Focareta & Giuseppe Meoli & Silvana Revellino & Francesco Maria Guadagno & Paola Revellino, 2022. "Calculating Economic Flood Damage through Microscale Risk Maps and Data Generalization: A Pilot Study in Southern Italy," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6286-:d:820913
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    References listed on IDEAS

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    2. Fan Yang & Suwen Xiong & Jiangang Ou & Ziyu Zhao & Ting Lei, 2022. "Human Settlement Resilience Zoning and Optimizing Strategies for River-Network Cities under Flood Risk Management Objectives: Taking Yueyang City as an Example," Sustainability, MDPI, vol. 14(15), pages 1-22, August.

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