Spatially dependent flood probabilities to support the design of civil infrastructure systems

Abstract

Abstract. Conventional flood risk methods typically focus on estimation at a single location, which can be inadequate for civil infrastructure systems such as road or railway infrastructure. This is because rainfall extremes are spatially dependent; to understand overall system risk, it is necessary to assess the interconnected elements of the system jointly. For example, when designing evacuation routes it is necessary to understand the risk of one part of the system failing given that another region is flooded or exceeds the level at which evacuation becomes necessary. Similarly, failure of any single part of a road section (e.g., a flooded river crossing) may lead to the wider system's failure (i.e., the entire road becomes inoperable). This study demonstrates a spatially dependent intensity–duration–frequency (IDF) framework that can be used to estimate flood risk across multiple catchments, accounting for dependence both in space and across different critical storm durations. The framework is demonstrated via a case study of a highway upgrade comprising five river crossings. The results show substantial differences in conditional and unconditional design flow estimates, highlighting the importance of taking an integrated approach. There is also a reduction in the estimated failure probability of the overall system compared with the case where each river crossing is treated independently. The results demonstrate the potential uses of spatially dependent intensity–duration–frequency methods and suggest the need for more conservative design estimates to take into account conditional risks.

Challenges in modeling and predicting floods and droughts: A review

“…3. Continuous stochastic models: We need to develop continuous stochastic simulation approaches for joint drought and flood assessments because event-based approaches currently only focus on one type of extreme (Diederen et al, 2019;Le, Leonard, & Westra, 2019). Such continuous approaches can be indirect or direct.…”

A systematic review of climate change science relevant to Australian design flood estimation

“…The information available to characterise this variability is very limited and this dearth of evidence poses problems for design flood estimation under stationarity assumptions and limits our ability to estimate the impacts of climate change on flood risks. With climate change, it is important to correctly reflect changes in spatial and temporal correlation structures and transition probabilities, particularly for large catchments, which are sensitive to spatial variability in rainfalls, or for such applications as the design of linear infrastructure such as railways and major highways (Le et al, 2019). It can be expected that the only way the impacts of climate change can be considered on the spatio-temporal patterns of extreme rainfall is through a combination of physical modelling (e.g., Chang et al 2016) and careful regional pooling (e.g., Visser et al 2023).…”

How Probable Is Widespread Flooding in the United States?

“…The latter is crucial to make them useful for the analysis of widespread events and their probabilities. So far, three main modeling approaches have been proposed, which enable consideration of spatial dependencies: (1) indirect, continuous modeling approaches Water Resources Research 10.1029/2020WR028096 (corresponding to discrete-time models in the stochastic literature) simulating continuous streamflow series by combining a stochastic weather generator with a hydrological model (Winter et al, 2019); (2) indirect, event-based modeling approaches simulating flood events for specific rainfall events generated using spatially dependent intensity-duration-frequency curves allowing for extreme rainfall of different durations at different locations (Le et al, 2019); and (3) direct, event-based approaches enabling the direct generation of flood events by employing spatial extreme value models such as the conditional exceedance model by Heffernan and Tawn (2004) (Diederen et al, 2019;Keef et al, 2013), hierarchical Bayesian models (Yan & Moradkhani, 2015), the multivariate skew-t distribution (Ghizzoni et al, 2010;, or copula-based approaches including pair-copula constructions (Bevacqua et al, 2017;Schulte & Schumann, 2015), Student-t copulas (Ghizzoni et al, 2012), dynamical conditional copulas (Serinaldi & Kilsby, 2017), or the Fisher copula (Brunner, Furrer, & Favre, 2019). All types of approaches have their advantages and disadvantages.…”