Utilizing Post‐Disaster Surveys to Understand the Social Context of Floods–Experiences from Northern Australia

King, David; Gurtner, Yetta

doi:10.1002/9781119217930.ch8

Cited by 1 publication

(1 citation statement)

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“…It must be acknowledged that the weakness in data availability has not gone unnoticed in science and constantly more efforts to collect flood (damage) data and to develop standardised methods are demanded by many authors and organisations (Elmer 2012); see, for example, Ramirez et al (1988), Mileti (1999) Fortunately, several attempts to improve data collection and availability have been implemented already (see Molinari et al 2017, for a review): standardised procedures have been developed to collect data by means of telephone interviews (Thieken et al, 2005;Kreibich et al, 2007;Kienzler et al, 2015, Thieken et al, 2017 or field surveys (Molinari et al, 2014;;Berni et al, 2017;King and Gurtner, 2017;Ballio et al, 2018) after flood events. Other possibilities like crowd sourcing or volunteered information approaches, data collection via dedicated web-sites, analysing information from social networks and similar have been explored (see e.g.…”

Section: Research and Policy Prioritiesmentioning

confidence: 99%

Validation of flood risk models: Current practice and possible improvements

Molinari

Bruijn

Castillo-Rodríguez

et al. 2019

International Journal of Disaster Risk Reduction

107

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Although often neglected, model validation is a key topic in flood risk analysis, as flood risk estimates are used to underpin large investments and important decisions. In this paper, we discuss the state of the art of flood risk model validation, using as input the discussion among more than 50 experts at two scientific workshop events. The events aimed at identifying policy and research recommendations towards promoting more common practice of validation, and an improvement of flood risk model reliability. We pay specific attention to different components of the risk modelling chain (i.e. flood hazard, defence failure, and flood damage analysis) as well as to their role in risk estimates, to highlight specificities and commonalities with respect to implemented techniques and research needs. The main conclusions from this analysis can be summarised as the need of higher quality data to perform validation and of benchmark solutions to be followed in different contexts, along with a greater involvement of end-users in the application on flood risk model validation.

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