Preprints
https://doi.org/10.5194/nhess-2022-99
https://doi.org/10.5194/nhess-2022-99
 
22 Mar 2022
22 Mar 2022
Status: this preprint is currently under review for the journal NHESS.

Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin

Daniel Viviroli1, Anna E. Sikorska-Senoner1, Guillaume Evin2, Maria Staudinger1, Martina Kauzlaric3,4, Jérémy Chardon5, Anne-Catherine Favre5, Benoit Hingray5, Gilles Nicolet5, Damien Raynaud5, Jan Seibert1,6, Rolf Weingartner3,4, and Calvin Whealton7,a Daniel Viviroli et al.
  • 1Department of Geography, University of Zürich, Zürich, Switzerland
  • 2Université Grenoble Alpes, INRAE, UR ETNA, Grenoble, France
  • 3Mobiliar Lab for Natural Risks, University of Bern, Bern, Switzerland
  • 4Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
  • 5Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble, France
  • 6Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden
  • 7Paul Scherrer Institute, Villigen, Switzerland
  • anow at: Booz Allen Hamilton, Lexington Park, Maryland, United States

Abstract. Estimates for rare to very rare floods are limited by the relatively short streamflow records available. Often, pragmatic conversion factors are used to quantify such events based on extrapolated observations, or simplifying assumptions are made about extreme precipitation and resulting flood peaks. Continuous simulation (CS) is an alternative approach that better links flood estimation with physical processes and avoids assumptions about antecedent conditions. However, long-term CS has hardly been implemented to estimate rare floods (i.e., return periods considerably larger than 100 years) at multiple sites in a large river basin to date. Here we explore the feasibility and reliability of the CS approach for 19 sites in the Aare River basin in Switzerland (area: 17 700 km2) with exceedingly long simulations in a hydrometeorological model chain. The chain starts with a multi-site stochastic weather generator used to generate 30 realisations of hourly precipitation and temperature scenarios of 10 000 years each. These realisations were then run through a bucket-type hydrological model for 79 sub-catchments and finally routed downstream with a simplified representation of main river channels, major lakes and relevant floodplains in a hydrologic routing system. Comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations are well represented, and that meaningful information on low-probability floods can be inferred. Although uncertainties are still considerable, the explicit consideration of important processes of flood generation and routing (snow accumulation, snowmelt, soil moisture storage, bank overflow, lake and floodplain retention) is a substantial advantage. The approach allows to comprehensively explore possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is of particular value in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow observations. The framework is also suitable for estimating more frequent floods, as often required in engineering and hazard mapping.

Daniel Viviroli et al.

Status: open (extended)

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Daniel Viviroli et al.

Daniel Viviroli et al.

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Short summary
It is difficult to estimate the magnitude of rare to very rare floods due to a lack of sufficiently long observations. The challenge is even greater in large river basins, where precipitation patterns and precipitation amounts vary considerably over the course of an event, and floods from different parts of the basin coincide. We show that computer models can provide plausible results in this setting, and can thus inform flood risk and safety assessments for critical infrastructure.
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