Uncertainty in flood damage estimates and its potential effect on investment decisions

Abstract

Abstract. This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Sources of uncertainty in a probabilistic flood risk model

“…Thus, the selection of an appropriate damage model has the comparably highest effect on the final model results. This is in accordance with other studies, which also point out that especially the damage functions are a major source of uncertainty in flood risk modelling (Wagenaar et al 2016;Apel et al 2008;Achleitner et al 2016;De Moel and Aerts 2011). The findings of De show an even larger range with a factor of four in damage modelling.…”

“…The inundation maps and building stock values are not calculated explicitly in the risk model and serve only as input data. Even when not considered here, several studies showed that both components are associated with uncertainties (Merwade et al 2008;Bales and Wagner 2009;Wagenaar et al 2016). In addition to each variation in the Hazard and Flood Impact Module, the uncertainty associated with the random process in the HT Model is reflected by the 5th and 95th percentiles of multiple model runs.…”

“…Practical applications of flood risk models are, for example, cost-benefit analysis of flood mitigation measures or solvency considerations in the (re)insurance business. Various recent studies showed that results are associated with considerable uncertainties (Merz and Thieken 2009;Beven and Hall 2014;Wagenaar et al 2016). Hence, the topic of uncertainty analysis in flood risk modelling is gaining greater attention (Beven et al 2015).…”

“…By introducing Monte Carlo methods to sample the model parameters of interest, the distribution of possible model outcomes can be created empirically. This technique was used for example by Wagenaar et al (2016) to investigate the influence of different assumptions in the damage estimation process. They showed that the associated uncertainties have a significant effect on investment decisions for flood protection measures.…”

Flood fatality hazard and flood damage hazard: combining multiple hazard characteristics into meaningful maps for spatial planning

“…Flood probabilities are especially difficult to establish in protected areas, where they depend not only on exceedance probabilities of water levels, but also on the strength of the defences. The damage functions also contribute significantly to the uncertainty (Wagenaar et al, 2015). If the damage functions do not relate to the most relevant parameters, or have a shape which does not adequately reflect reality, the hazard maps will also be less accurate.…”