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Evaluation of modelled snow depth and snow water equivalent at three contrasting sites in Switzerland using SNOWPACK simulations driven by different meteorological data input

机译:使用由不同气象数据输入驱动的SNOWPACK模拟,评估瑞士三个对比点的模拟雪深和雪水当量

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The knowledge of certain snow indices such as the number of snow days, maximum snow depth and snow water equivalent or the date of snow disappearance is important for many economical and ecological applications. However, snow data are frequently not available at the required locations and therefore have to be modelled. In this study we analyse the performance of the physically based snow model SNOWPACK to calculate the snow cover evolution with input data commonly available from automatic weather stations. We validated the model over several years at three very diverse stations in Switzerland: Weissfluhjoch (2540 m a.s.l.), Davos (1590 m a.s.l.) and Payerne (490 m a.s.l.), where snow depth and the full radiation balance are measured in order to assess the uncertainties induced by the parameterizations of radiation fluxes and by the use of uncorrect-ed precipitation measurements. In addition, we analysed the snow water equivalent at the high-alpine station Weissfluhjoch. The results demonstrate that the radiation balance, which is often measured incompletely, can successfully be parameterized and has an unexpectedly small impact on the modelled snow depth. A detailed analysis demonstrates that an adequate precipitation correction decreases the mean absolute percentage error by 14% for snow depth at the alpine and high-alpine stations and by 19% for snow water equivalent at Weissfluhjoch. The low altitude station Payerne (ephemeral snow conditions) revealed a high sensitivity with regard to the temperature threshold to distinguish solid from liquid precipitation. The analysis further suggested a high sensitivity to ground heat fluxes for ephemeral snow covers. Overall, the daily snow depth could be modelled with a mean bias error of less than - 8 cm at all sites, whereas the mean bias error for the snow water equivalent was less than -55 mm w.e. at Weissfluhjoch.
机译:某些降雪指数(例如降雪天数,最大降雪深度和雪水当量或降雪日期)的知识对于许多经济和生态应用至关重要。但是,降雪数据通常无法在所需的位置获得,因此必须进行建模。在这项研究中,我们分析了基于物理的降雪模型SNOWPACK的性能,并使用自动气象站通常提供的输入数据来计算积雪的演变。我们在瑞士的三个非常不同的站点上对模型进行了数年的验证:Weissfluhjoch(2540 m asl),Davos(1590 m asl)和Payerne(490 m asl),在这里测量了雪深和全部辐射平衡以评估由辐射通量的参数化和使用未经校正的降水测量结果引起的不确定性。此外,我们分析了高高山站Weissfluhjoch的雪水当量。结果表明,经常被不完全测量的辐射平衡可以成功地参数化,并且对模拟的积雪深度影响很小。详尽的分析表明,适当的降水校正可以使高山和高高山站的积雪深度的平均绝对百分比误差降低14%,而在魏斯富勒峰的雪水当量则使平均绝对百分比误差降低19%。低海拔气象站Payerne(短暂的雪况)显示出对温度阈值的高灵敏度,可区分固体沉淀物和液体沉淀物。分析还表明,对于短暂的积雪,地热通量具有较高的敏感性。总体而言,每天的积雪深度可以在所有地点的平均偏差误差小于-8 cm进行建模,而雪水当量的平均偏差误差小于-55 mm w.e.在Weissfluhjoch。

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