Project description
Motivated by the challenging task to model extreme flood events along rivers caused by heavy precipitation and the resulting runoff, this project aims at developing a Bayesian framework to describe extreme events by the interplay of statistical and physically-driven models. More precisely, in a first step, we will establish a Bayesian statistical model for single extreme events in spatio-temporal random fields, such as heavy precipitation. In a second step, simulations from this model will serve as input for physically-driven models describing, for instance, the runoff. The flexibility of our modelling framework will allow us to tackle various challenges such as modelling on various spatio-temporal scales, inclusion of multiple data sources, combination of different models, and inclusion of various types of flexibly defined extreme events – coming at the price of high computational costs. Varying the model specification within this framework will enable us to navigate in the PN5 triangle of model accuracy, precision and the required computational resources. This will be demonstrated in a showcase example on extreme flooding in the upper Neckar basin, linked to the SimTech vision “Engineered Geosystems”. In particular, we will compare the benefits and costs of our combined approach to purely statistical benchmark models directly built from river discharge data.
Project information
Project title | Bayesian multiscale spatio-temporal modelling of extreme events |
Project leaders | Marco Oesting (Wolfgang Nowak) |
Project staff | Max Thannheimer, Student Assistant |
Project duration | April 2021 - December 2024 |
Project number | PN 5-10 |
Publications PN 5-10
2024
- M. Oesting and O. Wintenberger, “Estimation of the spectral measure from convex combinations of regularly varying random vectors,” The Annals of Statistics, vol. 52, Art. no. 6, 2024, doi: 10.1214/24-AOS2387.
2023
- M. Oesting, A. Rapp, and E. Spodarev, “Detection of long range dependence in the time domain for (in)finite-variance time series,” Statistics, pp. 1–28, Dec. 2023, doi: 10.1080/02331888.2023.2287749.
2022
- M. Oesting and K. Strokorb, “A Comparative Tour through the Simulation Algorithms for Max-Stable Processes,” Statistical Science, vol. 37, Art. no. 1, 2022, doi: 10.1214/20-STS820.

Marco Oesting
Jun.-Prof. Dr. rer. nat.Computational Statistics

Wolfgang Nowak
Prof. Dr.-Ing.Stochastic Simulation and Safety Research for Hydrosystems | Spokesperson EXC 2075
[Image: SimTech/Max Kovalnko]