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Research article - Peer-reviewed, 2020

Downsizing parameter ensembles for simulations of rare floods

Sikorska-Senoner, Anna E.; Schaefli, Bettina; Seibert, Jan

Abstract

For extreme-flood estimation, simulation-based approaches represent an interesting alternative to purely statistical approaches, particularly if hydrograph shapes are required. Such simulation-based methods are adapted within continuous simulation frameworks that rely on statistical analyses of continuous streamflow time series derived from a hydrological model fed with long precipitation time series. These frameworks are, however, affected by high computational demands, particularly if floods with return periods > 1000 years are of interest or if modelling uncertainty due to different sources (meteorological input or hydrological model) is to be quantified. Here, we propose three methods for reducing the computational requirements for the hydrological simulations for extreme-flood estimation so that long streamflow time series can be analysed at a reduced computational cost. These methods rely on simulation of annual maxima and on analysing their simulated range to downsize the hydrological parameter ensemble to a small number suitable for continuous simulation frameworks. The methods are tested in a Swiss catchment with 10 000 years of synthetic streamflow data simulated thanks to a weather generator. Our results demonstrate the reliability of the proposed downsizing methods for robust simulations of rare floods with uncertainty. The methods are readily transferable to other situations where ensemble simulations are needed.

Published in

Natural Hazards and Earth System Sciences
2020, volume: 20, number: 12, pages: 3521-3549
Publisher: COPERNICUS GESELLSCHAFT MBH

Authors' information

Sikorska-Senoner, Anna E.
University of Zurich
Schaefli, Bettina
University of Bern
University of Zürich
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment

UKÄ Subject classification

Oceanography, Hydrology, Water Resources

Publication Identifiers

DOI: https://doi.org/10.5194/nhess-20-3521-2020

URI (permanent link to this page)

https://res.slu.se/id/publ/110090