Probabilistic models significantly reduce uncertainty in Hurricane Harvey pluvial flood loss estimates | Viktor Roezer

Date: 13 Feb 2019 12:30 pm — 2:00 pm
Venue: Fawcett House (Formerly Tower 2), Room 9.04, London School of Economics

Viktor will be discussing his paper: Probabilistic models significantly reduce uncertainty in Hurricane Harvey pluvial flood loss estimates


Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multi-variable modelling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multi-variable zero-inflated beta models reduce the 90% prediction intervals for Hurricane Harvey building structure loss estimates on average by 78% (totaling US$ 3.8 billion) compared to commonly used models.

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