Comparing post-event and pre-event damage assessment: Information gaps and lessons learnt

Abstract

Post event damage and needs assessment can supply fundamental information to feed risk models, i.e. data to define, calibrate and validate risk models. The lack or low quality of information regarding damage and losses collected in the aftermath of events conditions the quality of pre-event scenarios, thus affecting also the significance and the relevance of cost benefit analyses on mitigation measures to reduce the severity and magnitude of damage that are expected. Data collected in the aftermath of disasters are usually not suitable to this aim. Mostly, data on damage explicative variables (i.e. hazard, exposure, vulnerability and mitigation actions) are missing; damage data themselves can be also unsuitable as they refer to different spatial or temporal scales than those at which damage models work. In such a context, this paper presents results from the European Project IDEA (Improving Damage assessments to Enhance cost-benefit Analyses). The project is a response to the very limited reliability of data currently used to support cost-benefit analyses for natural hazards mitigation. The main objective of IDEA is an improvement of both damage data quality and procedures to collect and manage them. The paper focus in detail on the investigation of how improved damage data can better support the risk-modelling process. To this aim, the flood hitting the Umbria Region (Italy) in 2012 and the earthquake event that stuck the municipality of Lorca (Spain) in 2011 were investigated. Observed damages and damage predictions based on data that were available before the disaster have been compared. The comparison had several objectives:

- to verify the reliability of damage models that are currently used for damage estimation and that are proposed in literature; - to identify data gaps in pre-event assessment that could be narrowed by better damage data. This is relevant for showing what data are currently missing in risk modelling but could be obtained at reasonable costs; - to identify sectors for which pre-event damage assessment cannot be carried out or is carried out at the expense of large uncertainties and/or roughness; - to show how improved risk modelling could better feed cost benefit analyses of pre-event mitigation measures.