Dependence properties of spatial rainfall extremes and areal reduction factors

Le, Phuong Dong; Davison, A. C.; Engelke, Sebastian; Leonard, Michael; Westra, Seth

doi:10.1016/j.jhydrol.2018.08.061

Cited by 25 publications

(33 citation statements)

References 39 publications

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“…This study uses an asymptotically independent model, of which multiple types are valid including the Gaussian copula (Davison et al, 2012) and inverted max-stable processes (Wadsworth and Tawn, 2012). The inverted maxstable model was ultimately selected in this study to provide consistency with earlier research (Le et al, 2018a), in which it was demonstrated to preserve the spatial properties of extreme rainfall in an Australian context, including the property of asymptotic independence. Thibaud et al (2013) also compared the inverted max-stable model with a Gaussian copula in a case study in Switzerland, and they identified that the inverted max-stable model was appropriate.…”

Section: Dependence Model For Spatial Rainfallmentioning

confidence: 98%

“…To construct IDF maps across the region, the parameters of the GPD are interpolated across the region using a thin plate spline with covariates of longitude and latitude. Though more detailed modeling of covariates could be used to improve estimates (Le et al, 2018b), the interpolation used here is sufficient for demonstrating the overall method.…”

Section: Marginal Model For Rainfallmentioning

confidence: 99%

“…The spatial dependence of rainfall "events" both for single durations and also across multiple different durations. This was addressed by Le et al (2018b), who linked a max-stable model with the duration-dependent model of Koutsoyiannis et al (1998), to create a model that could be used to reflect dependencies between nearby catchments of different sizes.…”

Section: Introductionmentioning

confidence: 99%

“…The asymptotic properties of spatial dependence as the events become increasingly extreme, given the focus of many flood risk estimation methods on rare flood events. Recent evidence is emerging that rainfall has an asymptotically independent characteristic (Le et al, 2018a;Thibaud et al, 2013), which means that the level of the rainfall's dependence reduces with an increasing return period (Wadsworth and Tawn, 2012). The requirement of asymptotic independence indicates that inverted max-stable models are preferable over max-stable models.…”

mentioning

confidence: 99%

“…This study adapts the methods developed by Le et al (2018b) to inverted max-stable models to derive spatially dependent IDF estimates and ARFs as the basis for transforming rainfall into flood flows. The approach is demonstrated on a highway system spanning 20 km with five separate river crossings.…”

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confidence: 99%

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

Leonard

Westra

2019

Hydrol. Earth Syst. Sci.

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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.

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Section: Dependence Model For Spatial Rainfallmentioning

confidence: 98%

Section: Marginal Model For Rainfallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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

Leonard

Westra

2019

Hydrol. Earth Syst. Sci.

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Space–Time Simulations of Extreme Rainfall: Why and How?

Toulemonde¹,

Carreau²,

Guinot³

2020

Mathematical Modeling of Random and Deterministic Phenomena

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Spatiotemporal patterns of satellite precipitation extremes in the Xijiang River Basin: From statistical characterization to stochastic behaviour modelling

Xiong

et al. 2020

Intl Journal of Climatology

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Given the threats from rainstorm events that have been intensified due to climate change, the investigation of heavy/extreme precipitation is attracting increasing attention. The investigation of extreme precipitation, with its restricted tools and data, is quite different from that of nonextreme precipitation. Satellite precipitation estimates (SPEs) are promising precipitation measurements due to fine spatial resolution. However, the spatiotemporal patterns of SPE extremes remain unclear. Therefore, this study attempted to systematically analyse the spatiotemporal patterns of extreme SPEs provided by the Tropical Rainfall Measurement Mission (TRMM) research product from statistical characterization to stochastic behaviour modelling. The statistical characterization was depicted using comprehensive extreme precipitation indices (EPIs), while the stochastic behaviour was modelled using the generalized Pareto distribution (GPD) to fit the 'peak over threshold' samples and employing the max-stable process to fit the 'block maximum' samples (Max: annual 1-day maximum, RX1day: monthly 1-day maximum, and RX5day: monthly 5-day maximum) of the TRMM data. The TRMM extremes were analysed across the Xijiang River Basin, China. The results of the statistical

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Dependence properties of spatial rainfall extremes and areal reduction factors

Cited by 25 publications

References 39 publications

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

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

Space–Time Simulations of Extreme Rainfall: Why and How?

Spatiotemporal patterns of satellite precipitation extremes in the Xijiang River Basin: From statistical characterization to stochastic behaviour modelling

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