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The traditional flood estimation methods rely on statistical techniques based on observed streamflow and precipitation. However, the short length of observational records is often a limiting factor that leads to considerable uncertainties in flood estimates, especially for rare floods. An alternative approach that circumvents this limitation is the combination of stochastic weather generators with hydrological models using long continuous simulations. The advantage of this approach is that it avoids assumptions about antecedent catchment states (eg, soil moisture, snowpack, storage levels of lakes and reservoirs) and simplified representations of the underlying physical flooding processes.

Here, we use an elaborate framework based on continuous simulations with a hydrometeorological modeling chain (Viviroli et al., 2022) to estimate rare floods for large catchments in Switzerland (larger than~ 450 km²). The modeling chain starts with a multi-site stochastic weather generator (GWEX), focusing on generating extremely high precipitation events. Then, a bucket-type hydrological model (HBV) is used to simulate discharge time series. Finally, the RS Minerve (RSM) model is employed to implement simplified representations of river channel hydraulics and floodplain inundations.