The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments

Classical approaches to flood hazard are obtained by the concatenation of a recurrence model for the events (i.e., an extreme river discharge) and an inundation model that propagates the discharge into a flood extent. The classical approach, however, uses “best-fit” models that do not include uncert...

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Main Authors: Balbi, Mariano, Lallemant, David
Other Authors: Earth Observatory of Singapore
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/173814
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spelling sg-ntu-dr.10356-1738142024-03-05T15:36:55Z The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments Balbi, Mariano Lallemant, David Earth Observatory of Singapore Earth and Environmental Sciences Epistemic uncertainty Flood hazard maps Classical approaches to flood hazard are obtained by the concatenation of a recurrence model for the events (i.e., an extreme river discharge) and an inundation model that propagates the discharge into a flood extent. The classical approach, however, uses “best-fit” models that do not include uncertainty from incomplete knowledge or limited data availability. The inclusion of these, so called epistemic uncertainties, can significantly impact flood hazard estimates and the corresponding decision-making process. We propose a simulation approach to robustly account for uncertainty in model's parameters, while developing a useful probabilistic output of flood hazard for further risk assessments via the Bayesian predictive posterior distribution of water depths. A Peaks-Over-Threshold Bayesian analysis is performed for future events simulation, and a pseudo-likelihood probabilistic approach for the calibration of the inundation model is used to compute uncertain water depths. The annual probability averaged over all possible models’ parameters is used to develop hazard maps that account for epistemic uncertainties. Results are compared to traditional hazard maps, showing that not including epistemic uncertainties can underestimate the hazard and lead to non-conservative designs, and that this trend increases with return period. Results also show that the influence of the uncertainty in the future occurrence of discharge events is predominant over the inundation simulator uncertainties for the case study. National Research Foundation (NRF) Published version This project is supported by the National Research Foundation, Prime Minister's Office, Singapore under the NRF-NRFF2018-06 award. 2024-02-28T08:03:53Z 2024-02-28T08:03:53Z 2023 Journal Article Balbi, M. & Lallemant, D. (2023). The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments. Water Resources Research, 59(11), e2023WR035685-. https://dx.doi.org/10.1029/2023WR035685 0043-1397 https://hdl.handle.net/10356/173814 10.1029/2023WR035685 2-s2.0-85177066883 11 59 e2023WR035685 en NRF-NRFF2018-06 Water Resources Research © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Earth and Environmental Sciences
Epistemic uncertainty
Flood hazard maps
spellingShingle Earth and Environmental Sciences
Epistemic uncertainty
Flood hazard maps
Balbi, Mariano
Lallemant, David
The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
description Classical approaches to flood hazard are obtained by the concatenation of a recurrence model for the events (i.e., an extreme river discharge) and an inundation model that propagates the discharge into a flood extent. The classical approach, however, uses “best-fit” models that do not include uncertainty from incomplete knowledge or limited data availability. The inclusion of these, so called epistemic uncertainties, can significantly impact flood hazard estimates and the corresponding decision-making process. We propose a simulation approach to robustly account for uncertainty in model's parameters, while developing a useful probabilistic output of flood hazard for further risk assessments via the Bayesian predictive posterior distribution of water depths. A Peaks-Over-Threshold Bayesian analysis is performed for future events simulation, and a pseudo-likelihood probabilistic approach for the calibration of the inundation model is used to compute uncertain water depths. The annual probability averaged over all possible models’ parameters is used to develop hazard maps that account for epistemic uncertainties. Results are compared to traditional hazard maps, showing that not including epistemic uncertainties can underestimate the hazard and lead to non-conservative designs, and that this trend increases with return period. Results also show that the influence of the uncertainty in the future occurrence of discharge events is predominant over the inundation simulator uncertainties for the case study.
author2 Earth Observatory of Singapore
author_facet Earth Observatory of Singapore
Balbi, Mariano
Lallemant, David
format Article
author Balbi, Mariano
Lallemant, David
author_sort Balbi, Mariano
title The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
title_short The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
title_full The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
title_fullStr The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
title_full_unstemmed The cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
title_sort cost of imperfect knowledge: how epistemic uncertainties influence flood hazard assessments
publishDate 2024
url https://hdl.handle.net/10356/173814
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