We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
The Cost of Imperfect Knowledge: How Epistemic Uncertainties Influence Flood Hazard Assessments.
- Authors
Balbi, Mariano; Lallemant, David C. B.
- Abstract
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. Plain Language Summary: Estimating the annual probability of some flood‐depth level is a key input for risk analysis and engineering design. This is typically calculated via sophisticated probability and physics‐based models that require many parameters. However, the classical approach uses a fixed set of "best parameters" for this and do not include the degree of uncertainty, even when such uncertainties may be very high. This work proposes a method to estimate the annual probability of flood‐depth including the uncertainty in the parameters used to compute it. More importantly, it shows that not including this uncertainty might severely underestimate the hazard and consequently lead to unsafe designs. Key Points: Flood hazard assessments involve sophisticated probability and physics‐based models that require the specification of many parametersWe propose a Bayesian methodology to include uncertainty in models parameters into flood hazard estimates and mappingThe inclusion of uncertainty in parameters can significantly affect hazard estimates and its omission can lead to non‐conservative planning
- Subjects
EPISTEMIC uncertainty; FLOOD warning systems; RISK assessment; BAYESIAN analysis; WATER distribution; FLOODS
- Publication
Water Resources Research, 2023, Vol 59, Issue 11, p1
- ISSN
0043-1397
- Publication type
Article
- DOI
10.1029/2023WR035685