IAHS-AISH Scientific Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Robustness of precipitation and streamflow extremes in the surrogate world of seasonal forecasts

Peter Berg1, Katharina Klehmet1, Denica Bozhinova1, Louise Crochemore2, Ilias Pechlivanidis1, Christiana Photiadou3, and Wei Yang1
Peter Berg et al.
  • 1SMHI, Hydrology Research Unit, Norrköping, Sweden (peter.berg@smhi.se)
  • 2Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, F-38000 Grenoble, France
  • 3European Environment Agency, Kongens Nytorv 6, 1050 Copenhagen, Denmark

Water and disaster risk management require accurate information about hydrometeorological extremes. Estimation of rare events by extreme value analysis is hampered by short observational records. Probabilistic seasonal forecasts allow assessing the uncertainty in the estimation of extremes. From meteorological seasonal reforecasts and therewith driven hydrological simulations, we create hundred- to thousand-year-long surrogate timeseries across Europe. We identify independent samples based on the assessment of the forecast skill, and extract precipitation and streamflow extremes to explore the impact of sample size on return period estimations. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is reduced at 100-year samples, and stabilizes at very low uncertainty around 500 years. We discuss the benefits and limitations of this method, and how it can be applied to study climate change and multiple extremes.

How to cite: Berg, P., Klehmet, K., Bozhinova, D., Crochemore, L., Pechlivanidis, I., Photiadou, C., and Yang, W.: Robustness of precipitation and streamflow extremes in the surrogate world of seasonal forecasts, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-191, https://doi.org/10.5194/iahs2022-191, 2022.