Bayesian Data-Driven approach enhances synthetic flood loss models. (October 2020)

• Record Type: Journal Article
• Authors: Sairam, Nivedita; Schröter, Kai; Carisi, Francesca; Wagenaar, Dennis; Domeneghetti, Alessio; Molinari, Daniela; Brill, Fabio; Priest, Sally; Viavattene, Christophe; Merz, Bruno; Kreibich, Heidi
• Is Part Of: Environmental modelling & software. Volume 132(2020)
• Publication Date: 2020-10
• Subjects: Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104798
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Flood loss estimation models are developed using synthetic or empirical approaches. The synthetic approach consists of what-if scenarios developed by experts. The empirical models are based on statistical analysis of empirical loss data. In this study, we propose a novel Bayesian Data-Driven approach to enhance established synthetic models using available empirical data from recorded events. For five case studies in Western Europe, the resulting Bayesian Data-Driven Synthetic (BDDS) model enhances synthetic model predictions by reducing the prediction errors and quantifying the uncertainty and reliability of loss predictions for post-event scenarios and future events. The performance of the BDDS model for a potential future event is improved by integration of empirical data once a new flood event affects the region. The BDDS model, therefore, has high potential for combining established synthetic models with local empirical loss data to provide accurate and reliable flood loss predictions for quantifying future risk. Highlights: Bayesian Data-Driven approach integrates knowledge from the vast compendium of synthetic models with empirical loss data. This approach improves accuracy and quantifies reliability of synthetic flood loss models using local empirical data. Using empirical flood damage data from past events, this approach improves loss predictions for a potential future event.