Abstract.A rain-on-snow flood occurred in the Bernese Alps, Switzerland, on 10 October 2011, and caused significant damage. As the flood peak was unpredicted by the flood forecast system, questions were raised concerning the causes and the predictability of the event. Here, we aimed to reconstruct the anatomy of this rain-on-snow flood in the Lötschen Valley (160 km 2 ) by analyzing meteorological data from the synoptic to the local scale and by reproducing the flood peak with the hydrological model WaSiM-ETH (Water Flow and Balance Simulation Model). This in order to gain process understanding and to evaluate the predictability.The atmospheric drivers of this rain-on-snow flood were (i) sustained snowfall followed by (ii) the passage of an atmospheric river bringing warm and moist air towards the Alps. As a result, intensive rainfall (average of 100 mm day −1 ) was accompanied by a temperature increase that shifted the 0 • line from 1500 to 3200 m a.s.l. (meters above sea level) in 24 h with a maximum increase of 9 K in 9 h. The south-facing slope of the valley received significantly more precipitation than the north-facing slope, leading to flooding only in tributaries along the south-facing slope. We hypothesized that the reason for this very local rainfall distribution was a cavity circulation combined with a seeder-feeder-cloud system enhancing local rainfall and snowmelt along the south-facing slope.By applying and considerably recalibrating the standard hydrological model setup, we proved that both latent and sensible heat fluxes were needed to reconstruct the snow cover dynamic, and that locally high-precipitation sums (160 mm in 12 h) were required to produce the estimated flood peak.However, to reproduce the rapid runoff responses during the event, we conceptually represent likely lateral flow dynamics within the snow cover causing the model to react "oversensitively" to meltwater.Driving the optimized model with COSMO (Consortium for Small-scale Modeling)-2 forecast data, we still failed to simulate the flood because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus we conclude that this rain-on-snow flood was, in general, predictable, but requires a special hydrological model setup and extensive and locally precise meteorological input data. Although, this data quality may not be achieved with forecast data, an additional model with a specific rainon-snow configuration can provide useful information when rain-on-snow events are likely to occur.

The importance of soil moisture anomalies on airmass convection over semiarid regions has been recognized in several studies. The underlying mechanisms remain partly unclear. An open question is why wetter soils can result in either an increase or a decrease of precipitation (positive or negative soil moistureprecipitation feedback, respectively). Here an idealized cloud-resolving modeling framework is used to explore the local soil moisture-precipitation feedback. The approach is able to replicate both positive and negative feedback loops, depending on the environmental parameters.The mechanism relies on horizontal soil moisture variations, which may develop and intensify spontaneously. The positive expression of the feedback is associated with the initiation of convection over dry soil patches, but the convective cells then propagate over wet patches where they strengthen and preferentially precipitate. The negative feedback may occur when the wind profile is too weak to support the propagation of convective features from dry to wet areas. Precipitation is then generally weaker and falls preferentially over dry patches. The results highlight the role of the midtropospheric flow in determining the sign of the feedback. A key element of the positive feedback is the exploitation of both low convective inhibition (CIN) over dry patches (for the initiation of convection) and high CAPE over wet patches (for the generation of precipitation).