Seminario Ing. Carisi: "Damage models: critical issues and perspectives on the basis of the Secchia flood event in 2014"

  • Data: 17 dicembre 2018 dalle 09:30 alle 11:30

  • Luogo: Aula 5.4 - Scuola di Ingegneria e Architettura - Viale del Risorgimento, 2 - Bologna

Damage models: critical issues and perspectives on the basis of the Secchia flood event in 2014

Ing. Francesca Carisi
DICAM, University of Bologna

Thursday, January 17th, 2019
h. 9:30
Room 5.4 (viale Risorgimento 2, Bologna)

Abstract: Flood loss models (which estimate the amount of damage suffered by exposed assets during a flood event) are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data, which is often rooted in a lack of protocols and reference procedures for compiling loss datasets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia River flood event of January 2014, when a sudden levee breach caused the inundation of nearly 52 km2 in northern Italy. After this event local authorities collected a comprehensive flood loss dataset of affected private households including building footprints and structures and damages to buildings and contents. The dataset was enriched with further information compiled by us, including economic building values, maximum water depths, velocities and flood durations for each building. By analyzing this dataset, we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multivariable loss models for residential buildings. The accuracy of the proposed models is compared with that of several flood damage models reported in the literature, providing additional insights into the transferability of the models among different contexts. Our results show that (1) even simple univariable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multivariable models that consider several explanatory variables outperform univariable models, which use only water depth. However, multivariable models can only be effectively developed and applied if enough and detailed information is available.