Extremes on river networks
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Abstract (arXiv)
Max-stable processes are the natural extension of the classical extreme-value distributions to the functional setting, and they are increasingly widely used to estimate probabilities of complex extreme events. In this paper we broaden them from the usual situation in which dependence varies according to functions of Euclidean distance to situations in which extreme river discharges at two locations on a river network may be dependent because the locations are flow-connected or because of common meteorological events. In the former case dependence depends on river distance, and in the second it depends on the hydrological distance between the locations, either of which may be very different from their Euclidean distance. Inference for the model parameters is performed using a multivariate threshold likelihood, which is shown by simulation to work well. The ideas are illustrated with data from the upper Danube basin.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jan 2015.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 3; 2026: 1.
9 papers.
- 2022Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Riskcited by 19
- 2023A VAE Approach to Sample Multivariate Extremescited by 9
- 2021Estimating a Directed Tree for Extremescited by 5
- 2025Extreme Conformal Prediction: Reliable Intervals for High-Impact Eventscited by 5
- 2024Extremal graphical modeling with latent variables via convex optimizationcited by 1
- 2025Inference for max-linear Bayesian networks with noisecited by 1
- 2021Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networkscited by 0
- 2025A PC Algorithm for Max-Linear Bayesian Networkscited by 0
- 2026Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approachcited by 0
- 2026Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach29 May 2026
- 2025A PC Algorithm for Max-Linear Bayesian Networks19 Aug 2025
- 2025Extreme Conformal Prediction: Reliable Intervals for High-Impact Events13 May 2025
- 2025Inference for max-linear Bayesian networks with noise1 May 2025
- 2024Extremal graphical modeling with latent variables via convex optimization14 Mar 2024
- 2023A VAE Approach to Sample Multivariate Extremes19 Jun 2023
- 2022Neural Networks for Extreme Quantile Regression with an Application to Forecasting of Flood Risk16 Aug 2022
- 2021Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks30 Oct 2021
- 2021Estimating a Directed Tree for Extremes11 Feb 2021