Exceptional floods: Leveraging large-ensemble simulation approaches to study their frequency and changes

Department of Hydrology and Atmospheric Sciences
 
* SPECIAL TIME * 10 am on Thursday, April 7, 2022
Available via zoom only
 
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Manuela I. Brunner
Lecturer, Environmental Hydrological Systems, University of Freiburg

Abstract

Exceptional floods, i.e. very extreme events, are hardly observed and their frequency and magnitude difficult to study. In this talk, I discuss three methods that enable studying exceptional extreme events absent in observational records: stochastic simulation, reanalysis ensemble pooling, and single-model initialized large ensembles. I apply these different techniques, which aim to increase sample size, to (1) study the frequency of widespread floods in the United States, (2) determine the magnitude of rare floods in Europe, and (3) shed light on the relationship between future increases in extreme precipitation and flooding. These applications suggest that simulation approaches that enable increasing sample size and capture flood variability help to increase our understanding of the drivers and characteristics of exceptional extreme events.

Bio

Manuela Brunner is a lecturer in the group of Environmental Hydrological Systems at the University of Freiburg. Her research focus lies on hydrological extremes and changes in water resources. She develops novel methods to simulate and predict floods and droughts and to assess past and future changes in the water cycle under global change. Manuela Brunner has obtained her PhD from the Universities of Zurich and Grenoble-Alpes in 2018 and has since worked as a postdoc at the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) in Birmensdorf and the National Center of Atmospheric Research (NCAR) in Boulder, United States. Her current research covers regional droughts and floods, water scarcity, stochastic streamflow simulation, predictions in ungauged basins, and the impact of global change on hydrological extremes and the water cycle.