Optimal regionalization of extreme value distributions for flood estimation

Asadi, Peiman; Engelke, Sebastian; Davison, A. C.

doi:10.1016/j.jhydrol.2017.10.051

Cited by 37 publications

(38 citation statements)

References 36 publications

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“…At all nine stations, recording high rainfall levels at one day is often followed by measures from rainy days latterly since an extreme weather condition can last numerous hours. This extremal dependence in time is well illustrated by the temporal extremogram 1 introduced in [12] as can be seen in Figure 1. In our case study, the multivariate aspect comes from the spatial dependence among daily rainfall measured at nearby weather stations in France from 1976 to 2015.…”

Section: Introductionmentioning

confidence: 55%

“…Although marginal behaviors of heavy rainfall is today well modeled, the temporal dynamic is rarely taken in account in applied studies, especially for multivariate time series [see, e.g. 20,40,1,9]. To produce accurate high return level estimates from multivariate time series of extreme daily precipitation, we propose an approach to jointly incorporate the temporal dependence and the multidimensional structure among heavy rainfall.…”

Section: Introductionmentioning

confidence: 99%

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Stable sums to infer high return levels of multivariate rainfall time series

Buriticá¹,

Naveau²

2021

Preprint

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We introduce the stable sums method for inference of extreme return levels for multivariate stationary time series. This new method is based on large deviation principles for regularly varying time series which allows for incorporation of time and space extreme dependencies in the analysis. It avoids classical declustering steps as implementation coincides for both independent and dependent observations from the stationary model. A comparison with the main estimators from extreme value theory, where detecting clustering in time is required, shows improvement of the coverage probabilities of confidence intervals obtained from our method against its competitors. Numerical experiments also point to a smaller mean squared error with the multivariate stable sums method compared to an univariate approach. We apply our method for inference of high return levels of multivariate daily fall precipitation measurements in France.

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Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Stable sums to infer high return levels of multivariate rainfall time series

Buriticá¹,

Naveau²

2021

Preprint

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“…an estimation of what could happen in terms of floods and droughts and their associated probabilities (called return periods in hydrology). Work in this field continues to be based on statistical analyses and extrapolation of observed streamflow time series (Brunner et al, 2018a;Asadi et al, 2018), but hydrological models play an ever-increasing role to complement missing or insufficient streamflow data.…”

Section: Characterization and Quantification Of Floods And Droughtsmentioning

confidence: 99%

Why do we have so many different hydrological models? A review based on the case of Switzerland

Horton¹,

Schaefli²,

Kauzlaric³

2022

Preprint

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Hydrology plays a central role in applied as well as fundamental environmental sciences, but it is well known to suffer from an overwhelming diversity of models, in particular to simulate streamflow. Based on Switzerland's example, we discuss here in detail how such diversity did arise even at the scale of such a small country. The case study's relevance stems from the fact that Switzerland shows a relatively high density of academic and research institutes active in the field of hydrology, which led to an evolution of hydrological models that stands exemplarily for the diversification that arose at a larger scale. Our analysis summarizes the main driving forces behind this evolution, discusses drawbacks and advantages of model diversity and depicts possible future evolutions. Although convenience seems to be the main driver so far, we see potential change in the future with the advent of facilitated collaboration through open sourcing and code sharing platforms. We anticipate that this review, in particular, helps researchers from other fields to understand better why hydrologists have so many different models.

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“…Regionalisations are common in flood frequency analysis and studies of hydrological extremes. Examples of different approaches to regionalisation based on extreme rainfall are given in Hosking and Wallis (1997), Carreau et al (2017), Asadi et al (2018) and Rohrbeck and Tawn (2020). For Australia, a regionalisation specific to rainfall extremes does not exist.…”

Section: Introductionmentioning

confidence: 99%

A regionalisation approach for rainfall based on extremal dependence

2020

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To mitigate the risk posed by extreme rainfall events, we require statistical models that reliably capture extremes in continuous space with dependence. However, assuming a stationary dependence structure in such models is often erroneous, particularly over large geographical domains. Furthermore, there are limitations on the ability to fit existing models, such as max-stable processes, to a large number of locations. To address these modelling challenges, we present a regionalisation method that partitions stations into regions of similar extremal dependence using clustering. To demonstrate our regionalisation approach, we consider a study region of Australia and discuss the results with respect to known climate and topographic features. To visualise and evaluate the effectiveness of the partitioning, we fit max-stable models to each of the regions. This work serves as a prelude to how one might consider undertaking a project where spatial dependence is non-stationary and is modelled on a large geographical scale.

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Optimal regionalization of extreme value distributions for flood estimation

Cited by 37 publications

References 36 publications

Stable sums to infer high return levels of multivariate rainfall time series

Stable sums to infer high return levels of multivariate rainfall time series

Why do we have so many different hydrological models? A review based on the case of Switzerland

A regionalisation approach for rainfall based on extremal dependence

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