Field Observations and Modelling of the Spatial and Temporal Variability of Snow Processes in the Intermittent and Transitional Snow Zone

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The first phase of the project has proven that there are considerable spatial and temporal differences in the snow cover of the Black Forest, an intermediate elevation mountain area in a moderate climate. This variability has a huge impact on hydrologic (flood forecasting, fresh water availability, hydropower generation etc.) and ecologic (length of vegetation period, habitat for animals) processes. It also significantly affects the surface atmosphere energy exchange. The winters in the area are characterized by frequent snow accumulation and melt events which contribute to the great snow cover variability and make model predictions of the snow cover evolution and of winter floods resulting from rapid snowmelt especially challenging. The first phase of the project has developed a comprehensive field observation network to monitor the spatial and temporal variability of the snow cover as well as the different factors of the surface energy balance responsible for the melt of the snow. The program includes the installation of numerous (>100) newly developed snow monitoring sensors (SnoMoS) that monitor snow depth and most factors of the snowmelt energy balance. Additionally, time lapse cameras provide information on snow depth, snow albedo, snow interception in the canopy and precipitation phase. Finally, several newly installed stream gauges allow the determination of spatially variable runoff generation processes during winter floods. The study identified topography (especially elevation and exposure) and land cover (especially different types of forest vegetation) as having the most influence on the snow cover evolution. Additionally, sheltering effects from vegetation or adjacent topography were shown to have great impacts on snow accumulation and melt. The second project phase plans to maintain the current observational network. The additional data will be used to better understand patterns of snow cover and energy variability in relation to topography and vegetation. Furthermore, additional data will help to establish reliable relationships between climate variables and snow processes like albedo decay and snow interception in the vegetation canopy. These results will enable a rigorous testing and the improvement of existing (snow-) model algorithms for central European areas. It is also planned to expand the network into areas identified as being particular variable during the first phase of the project to study the small scale processes responsible for this variability in more detail. Finally, the second model phase will focus even more on using the acquired data to test and improve individual model algorithms and hydrologic models of different complexities from relatively simple flood forecast models to fully distributed research models.