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

该项目的第一阶段已经证明，黑森林是一个中等海拔山区，气候温和，积雪在空间和时间上有相当大的差异。这种变化对水文（洪水预报、淡水供应、水力发电等）有巨大影响。和生态（植被期的长度，动物的栖息地）过程。它还显著影响地表大气能量交换。该地区冬季的特点是频繁的积雪和融化事件，这导致了巨大的积雪变化，并使模型预测的积雪演变和冬季洪水造成的快速融雪特别具有挑战性。该项目的第一阶段建立了一个全面的实地观测网络，以监测积雪的空间和时间变化以及导致积雪融化的地表能量平衡的不同因素。该计划包括安装许多（>100）新开发的积雪监测传感器（SnoMoS），监测积雪深度和融雪能量平衡的大多数因素。此外，延时摄影机还提供了有关雪深、积雪深度、树冠中的积雪拦截和降水阶段的信息。最后，几个新安装的流计允许在冬季洪水的空间变化径流生成过程的确定。这项研究确定地形（特别是海拔和暴露）和土地覆盖（特别是不同类型的森林植被）对积雪演变的影响最大。此外，植被或邻近地形的遮蔽作用对积雪和融化有很大的影响。项目第二阶段计划维持现有的观测网络。额外的数据将用于更好地了解积雪模式和与地形和植被有关的能量变化。此外，更多的数据将有助于建立气候变量和积雪过程之间的可靠关系，如植被冠层中的积雪衰减和积雪拦截。这些结果将使严格的测试和改进现有的（雪）模型算法为中欧地区。还计划在项目第一阶段将网络扩展到确定为特别可变的区域，以更详细地研究造成这种可变性的小规模过程。最后，第二个模型阶段将更加侧重于使用获得的数据来测试和改进各个模型算法和不同复杂性的水文模型，从相对简单的洪水预报模型到完全分布式的研究模型。

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.