Extremes on river networks

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

doi:10.1214/15-aoas863

Cited by 118 publications

(192 citation statements)

References 50 publications

Supporting

Mentioning

191

Contrasting

Order By: Relevance

“…However, our study as well as the one by Asadi et al (2015) for the Danube river has shown that the Euclidean distance between stations has low explanatory power in the case of regional floods. We found that river distance is a suitable dependence measure for describing the change in dependence along the river network.…”

Section: Discussioncontrasting

confidence: 60%

“…The Euclidean distance is used as a standard distance measure when looking at spatial extremes such as precipitation, temperature, and wind (Asadi et al, 2015). However, our study as well as the one by Asadi et al (2015) for the Danube river has shown that the Euclidean distance between stations has low explanatory power in the case of regional floods.…”

Section: Discussionmentioning

confidence: 73%

“…Max-stable models do usually not allow for the consideration of the network structure. An exception is the model of Asadi et al (2015) which distinguishes between flow-connected and flowunconnected stations. Conditional exceedance models and spatial vine copulas require the fitting of several models.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Modeling the spatial dependence of floods using the Fisher copula

Brunner¹,

Furrer²,

Favre³

2018

Preprint

View full text Add to dashboard Cite

Abstract. Floods do often not only affect a single location but a whole region. Flood frequency analysis should therefore be undertaken at a regional scale which requires the considerations of the dependence of events at different locations. This dependence is often neglected even though its consideration is essential to derive reliable flood estimates. A model used in regional multivariate frequency analysis should ideally consider the dependence of events at multiple sites which might show dependence in the lower and/or upper tail of the distribution. We here seek at proposing a simple model that on the one hand 5 considers this dependence with respect to the network structure of the region and on the other hand, allows for the simulation of stochastic event sets at both gauged and ungauged locations. The new Fisher copula model is used for representing the spatial dependence of flood events in the nested Thur catchment in Switzerland. Flood event samples generated for the gauged stations using the Fisher copula are compared to samples generated by other dependence models allowing for modeling multivariate data including elliptical copulas, R-vine copulas, and max-stable models. The comparison of the dependence structures of 10 the generated samples shows that the Fisher copula is a suitable model for capturing the spatial dependence in the data. We therefore use the copula in a way such that it can be used in an interpolation context to simulate event sets comprising gauged and ungauged locations. The spatial event sets generated using the Fisher copula well capture the general dependence structure in the data and the upper tail dependence, which is of particular interest when looking at extreme flood events and when extrapolating to higher return periods. The Fisher copula is therefore a suitable model for the stochastic simulation of flood 15 event sets at multiple gauged and ungauged locations.

show abstract

Section: Discussioncontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 73%

See 1 more Smart Citation

Modeling the spatial dependence of floods using the Fisher copula

Brunner¹,

Furrer²,

Favre³

2018

Preprint

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

“…Exploring one potential approach, some recent research has explored the development of models based on max-stable processes for river network data [1]. However max-stable processes are only suitable for spatial extreme data sets that exhibit asymptotic dependence [1], a property where the largest values in each variable can occur concurrently. Therefore they are practically infeasible for higher-dimension problems such as models of national scale river flow or rain gauge networks, because simultaneous extreme flows are unlikely in any one event over all sites.…”

Section: Introductionmentioning

confidence: 99%

Improving statistical models for flood risk assessment

et al. 2016

View full text Add to dashboard Cite

Abstract.Widespread flooding, such as the events in the winter of 2013/2014 in the UK and early summer 2013 in Central Europe, demonstrate clearly how important it is to understand the characteristics of floods in which multiple locations experience extreme river flows. Recent developments in multivariate statistical modelling help to place such events in a probabilistic framework. It is now possible to perform joint probability analysis of events defined in terms of physical variables at hundreds of locations simultaneously, over multiple variables (including river flows, rainfall and sea levels), combined with analysis of temporal dependence to capture the evolution of events over a large domain. Critical constraints on such data-driven methods are the problems of missing data, especially where records over a network are not all concurrent, the joint analysis of several different physical variables, and the choice of suitable time scales when combining information from those variables. T his paper presents new developments of a high-dimensional conditional probability model for extreme river flow events conditioned on flow and r ainfall observations. T hese are: a new computationally efficient parametric approach to account for missing data in the joint analysis of ex tremes over a large hydrometric network; a robust approach for the spatial interpolation of extreme events throughout a large river network,; generation of realistic estimates of extremes at ungauged locations; and, exploiting rainfall information rationally within the statistical model to help improve efficiency. T hese methodological advances will be illustrated with data from the UK river network and recent events to show how they contribute to a flexible and effective framework for flood risk assessment, with applications in the insurance sector and for national-scale emergency planning.

show abstract

Decreasing spatial dependence in extreme snowfall in the French Alps since 1958 under climate change

et al. 2016

View full text Add to dashboard Cite

Whereas changes in magnitude of geophysical extremes under climate change have received significant attention, potential concomitant changes in spatial dependence structures have remained unexplored so far. Here we provide first evidence of such an effect, highlighting a significant trend in the spatial dependence structure of snowfall extremes in the French Alps at decadal time scale. Specifically, we process a comprehensive data set of winter maximum snowfall from all over the French Alps collected in 90 stations from 1958 to 2012. We estimate extremal dependence over 20 year moving estimation windows taking into account possible anisotropy potentially related to orographic effects and/or patterns in atmospheric flows. For each window, we derive a range representing the distance above which extremes are almost independent. We show that snowfall extremes tended to become less spatially dependent over time, with the dependence range reduced roughly by half during the study period. We demonstrate the connection between this trend and local and synoptic climatic variables associated with the current climate change context. In details, the decreasing pattern in extremal dependence is concomitant with a trend toward less harsh winter conditions. It is attributable at first to the increase in temperature and its major control on the snow/rain partitioning. Yet a magnitude effect, with less dependent extremes due to a decrease in intensity of precipitation, also exists. Finally, we show that our results are largely insensitive to the minimal modeling assumptions necessary to our data‐based approach. This robustness makes it potentially suitable for various other studies in the field of geophysical extremes.

show abstract

Extremes on river networks

Cited by 118 publications

References 50 publications

Modeling the spatial dependence of floods using the Fisher copula

Modeling the spatial dependence of floods using the Fisher copula

Improving statistical models for flood risk assessment

Decreasing spatial dependence in extreme snowfall in the French Alps since 1958 under climate change

Contact Info

Product

Resources

About