Cost–benefit analysis of flood mitigation measures: a case study employing high-performance hydraulic and damage modelling

Daniela Molinari (), Susanna Dazzi, Edoardo Gattai, Guido Minucci, Giulia Pesaro, Alessio Radice and Renato Vacondio
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Daniela Molinari: Politecnico di Milano
Susanna Dazzi: University of Parma
Edoardo Gattai: Politecnico di Milano
Guido Minucci: Politecnico di Milano
Giulia Pesaro: Politecnico di Milano
Alessio Radice: Politecnico di Milano
Renato Vacondio: University of Parma

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2021, vol. 108, issue 3, No 31, 3084 pages

Abstract: Abstract This paper shows a detailed, advanced procedure to implement cost–benefit analyses (CBAs) in order to assess the effectiveness of flood mitigation measures. The town of Lodi (North of Italy) has been selected as a case study for the research work, as it was hit by a large flood in 2002 for which several data are available. In order to compute the benefits, in terms of avoided damage with the mitigation measure in place, micro-scale damage models developed within the Flood-IMPAT + project were used. The great amount of input data for such models comes from results of a two-dimensional river modelling, for what concern the hazard parameters, and from open-source database, to evaluate the vulnerability and the exposure of the hit area. The research highlights that technological-advanced, high-performance hydraulic models allow taking into account a variety of hazard scenarios, with reasonable computational time, supporting the proper accounting of the probabilistic nature of risk in CBAs. Nonetheless, such high-resolution tools support the implementation of micro-scale damage assessment models, which can provide information on the distribution of benefits in the investigated area, increasing the effectiveness of CBAs for policy making.

Keywords: Flood risk; Cost–benefit analysis; River modelling; Flood damage; Urban flooding (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11069-021-04814-6

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