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.
Highlights
Methods for quantifying the flood risk of civil infrastructure systems such as road and rail networks require considerably more information compared to traditional methods that focus on flood risk at a point
Successful estimation of flood risk in these systems requires recognition both of the networked nature of the civil infrastructure system across a spatial domain, as well as the spatial and temporal structure of flood-producing mechanisms that can lead to system failure (e.g., Leonard et al, 2014; Seneviratne et al, 2012; Zscheischler et al, 2018)
Annual maximum streamflow at two locations might be assumed to follow a bivariate generalized extreme-value (GEV) distribution (Favre et al, 2004; Wang, 2001; Wang et al, 2009), which can be used to estimate both conditional probabilities and joint probabilities
Summary
Methods for quantifying the flood risk of civil infrastructure systems such as road and rail networks require considerably more information compared to traditional methods that focus on flood risk at a point. Successful estimation of flood risk in these systems requires recognition both of the networked nature of the civil infrastructure system across a spatial domain, as well as the spatial and temporal structure of flood-producing mechanisms (e.g., storms and extreme rainfall) that can lead to system failure (e.g., Leonard et al, 2014; Seneviratne et al, 2012; Zscheischler et al, 2018). One way to estimate such flood probabilities is to directly use information contained in historical streamflow data. Several frameworks have been demonstrated based directly on streamflow observations, including functional regression (Requena et al, 2018), multisite copulas (Renard and Lang, 2007), and spa-
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