Articles | Volume 10, issue 2
https://doi.org/10.5194/ascmo-10-195-2024
https://doi.org/10.5194/ascmo-10-195-2024
26 Nov 2024
 | 26 Nov 2024

A non-stationary climate-informed weather generator for assessing future flood risks

Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, Lukas Brunner, and Bruno Merz

Related authors

Assessment of coastal inundation triggered by multiple drivers in Ca Mau Peninsula, Vietnam
Hung Nghia Nguyen, Quan Quan Le, Viet Dung Nguyen, Hai Dac Do, Hung Duc Pham, Tan Hong Cao, Toan Quang To, Melissa Wood, and Ivan D. Haigh
Nat. Hazards Earth Syst. Sci., 25, 4227–4246, https://doi.org/10.5194/nhess-25-4227-2025,https://doi.org/10.5194/nhess-25-4227-2025, 2025
Short summary
Influence of groundwater recharge projections on climate-driven subsurface warming: insights from numerical modeling
Mikhail Tsypin, Viet Dung Nguyen, Mauro Cacace, Guido Blöcher, Magdalena Scheck-Wenderoth, Elco Luijendijk, and Charlotte Krawczyk
EGUsphere, https://doi.org/10.5194/egusphere-2025-4335,https://doi.org/10.5194/egusphere-2025-4335, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
The ability of a stochastic regional weather generator to reproduce heavy-precipitation events across scales
Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 25, 3075–3086, https://doi.org/10.5194/nhess-25-3075-2025,https://doi.org/10.5194/nhess-25-3075-2025, 2025
Short summary
It could have been much worse: spatial counterfactuals of the July 2021 flood in the Ahr Valley, Germany
Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 25, 2007–2029, https://doi.org/10.5194/nhess-25-2007-2025,https://doi.org/10.5194/nhess-25-2007-2025, 2025
Short summary
Beyond Observed Extremes: Can Hybrid Deep Learning Models Improve Flood Prediction?
Xiaoxiang Guan, Baoying Shan, Viet Dung Nguyen, and Bruno Merz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1509,https://doi.org/10.5194/egusphere-2025-1509, 2025
Preprint archived
Short summary

Cited articles

Ailliot, P., Allard, D., Monbet, V., and Naveau, P.: Stochastic weather generators: an overview of weather type models, Journal de La Société Française de Statistique, 156, 101–113, 2015. 
Akaike, H.: Information Theory as an Extension of the Maximum Likelihood Principle, in: Second International Symposium on Information Theory, edited by: Petrov, B. N. and Csaki, F., 267–281 pp., Akademiai Kiado, Budapest, 1973. 
Bárdossy, A. and Plate, E. J.: Space-time model for daily rainfall using atmospheric circulation patterns, Water Resour. Res., 28, 1247–1259, 1992. 
Baxevani, A. and Lennartsson, J.: A spatiotemporal precipitation generator based on a censored latent Gaussian field, Water Resour. Res., 51, 4338–4358, https://doi.org/10.1002/2014WR016455, 2015. 
Beck, C. and Philipp, A.: Evaluation and comparison of circulation type classifications for the European domain, Phys. Chem. Earth, 35, 374–387, https://doi.org/10.1016/j.pce.2010.01.001, 2010. 
Download
Short summary
We present a novel stochastic weather generator conditioned on circulation patterns and regional temperature, accounting for dynamic and thermodynamic atmospheric changes. We extensively evaluate the model for the central European region.  It statistically downscales precipitation for future periods, generating long, spatially and temporally consistent series. Results suggest an increase in extreme precipitation over the region, offering key benefits for hydrological impact studies.
Share