ALBERT

Description: On 10 October 2011, a rain-on-snow flood occurred in the Bernese Alps, Switzerland, and caused significant damage. As this flood peak was unpredicted by the flood forecast system, questions were raised concerning what has caused this flood and whether it was predictable at all. In this study, we focused on one valley that was heavily hit by the event, the Loetschen valley (160 km2), and aimed to reconstruct the anatomy of this rain-on-snow flood from the synoptic conditions represented by European Centre for Medium-Range Weather Forecasts ECWMF analysis data, and the local meteorology within the valley recorded by an extensive met-station network. In addition, we applied the hydrological model WaSiM-ETH to improve our hydrological process understanding about this event and to demonstrate the predictability of this rain-on-snow flood. We found an atmospheric river bringing moist and warm air to Switzerland that followed an anomalous cold front with sustained snowfall to be central for this rain-on-snow event. Intensive rainfall (average 100 mm day−1) was accompanied by a drastic temperature increase (+8 K) that shifted the zero degree line from 1500 m a.s.l. to 3200 m a.s.l. in 12 h. The northern flank of the valley received significantly more precipitation than the southern flank, leading to an enormous flood in tributaries along the northern flank, while the tributaries along the southern flank remained nearly unchanged. We hypothesized that the reason for this was a cavity circulation combined with a seeder-feeder-cloud system enhancing both local rainfall and snow melt by condensation of the warm, moist air on the snow. Applying and adjusting the hydrological model, we show that both the latent and the sensible heat fluxes were responsible for the flood and that locally large amounts of precipitation (up to 160 mm rainfall in 12 h) was necessary to produce the estimated flood peak. With considerable adjustments to the model and meteorological input data, we were able to reproduce the flood peak, demonstrating the ability of the model to reproduce the flood. However, driving the optimized model with COSMO-2 forecast data, we still failed to simulate the flood precisely because COSMO-2 forecast data underestimated both the local precipitation peak and the temperature increase. Thus, this rain-on-snow flood was predictable, but requires a special model set up and extensive and locally precise meteorological input data, especially in terms of both precipitation and temperature.

Description: An urban climate and air pollution study in the small town of Biel (Switzerland; population: 85000 including suburbs) aims not only at the analysis of a very dense distribution of emissions, dispersion climate and depositions, but also attempts to correlate these factors with selected effects on man. These effects include the impacts on children's respiratory organs and the growth of plants (liehen). The findings of this study will have direct application in land use planning and related political decisions for new highway Systems, selection of new housing developments and industrial districts, as well as future fuel supply options. Initial results show that future activities should concentrate on case studies during anticyclonic wintertime weather types with north-northeasterly synoptic flow. These weather types are associated with cold air advection, weak winds, strong temperature inversions and consequently, high air pollution rates in the Biel area. A first draft of a very dense analysis of the air pollution potential for a similar weather type has been worked out (figure 5). This analysis will be used for the design of additional mobile measurements and modeling studies.

Description: 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 km2) 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 rain-on-snow configuration can provide useful information when rain-on-snow events are likely to occur.