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Model parameter influence on probabilistic flood risk analysis
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DOI：10.1016/j.ijdrr.2023.104215.png)
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摘要
En 中文
Flood risk models are critical for effective evidence-based flood management interventions. Risk models typically analyse flood hazards, elements (e.g., buildings) exposed, and their susceptibility to monetary losses. These model parameters are inherently uncertain and their interactions within a risk model create an uncertain loss outcome. This study constructed an object-level probabilistic flood risk model for Westport (New Zealand) to analyse parameter uncertainty and sensitivities for residential building monetary loss prediction. Several model parameter combinations were assessed: 1) water depth spatial sampling method, 2) replacement valuation method, and 3) univariable and multivariable vulnerability model. Monetary loss uncertainty was analysed using test models that varied parameter combinations. Sensitivity to parameter uncertainty distributions were assessed using a global sensitivity analysis based on Sobol's method. Monetary loss estimates ranged between +/- NZD 1 million to +/- NZD 6 million (90 % confidence interval) for test models by varying parameter combinations. Loss estimates ranged from 2 % to 100 % across the analysed test models. Replacement valuation and vulnerability models were the primary drivers of loss uncertainty. Sobol first-order indices confirmed vulnerability models had the largest parameter influence on uncertainty while total-order indices showed replacement value has a larger overall contribution. The study outcomes emphasise flood risk modellers' need to investigate model uncertainties to inform accuracy improvement solutions.
Keyword:
Flooding
Buildings
Vulnerability
Risk models
Uncertainty
Sensitivity analysis
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6.1K
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2.1W
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