Abstract
Abstract. Blowing snow processes are crucial in shaping the strongly heterogeneous spatiotemporal distribution of snow and in regulating subsequent snowpack evolution in mountainous terrain. Although empirical formulae and constant threshold wind speeds have been widely used to estimate the occurrence of blowing snow in regions with sparse observations, the scarcity of in situ observations in mountainous regions contrasts with the demands of models for reliable observations at high spatiotemporal resolution. Therefore, these methods struggle to accurately capture the high local variability of blowing snow. This study investigated the potential capability of the decision tree model (DTM) to detect blowing snow in the European Alps. The DTMs were constructed based on routine meteorological observations (mean wind speed, maximum wind speed, air temperature and relative humidity) and snow measurements (including in situ snow depth observations and satellite-derived products). Twenty repetitions of a random sub-sampling validation test with an optimal size ratio (0.8) between the training and validation subsets were applied to train and assess the DTMs. Results show that the maximum wind speed contributes most to the classification accuracy, and the inclusion of more predictor variables improves the overall accuracy. However, the spatiotemporal transferability of the DTM might be limited if the divergent distribution of wind speed exists between stations. Although both the site-specific DTMs and site-independent DTM show great ability in detecting blowing snow occurrence and are superior to commonly used empirical parameterizations, specific assessment indicators varied between stations and surface conditions. Events for which blowing snow and snowfall occurred simultaneously were detected the most reliably. Although models failed to fully reproduce the high frequency of local blowing snow events, they have been demonstrated to be a promising approach requiring limited meteorological variables and have the potential to scale to multiple stations across different regions.
Highlights
Wind plays a key role in the snow distribution in mountainous terrain, as it shapes both the spatial heterogeneity of snowfall and the erosion, transport and deposition of surface snow via blowing snow processes
The accurate classification of blowing snow events is important in numerical models which include blowing snow, as it determines whether or not to invoke the parameterizations for sublimation and transport associated with blowing snow processes in the model (Lenaerts et al, 2012b; Xie et al, 2019)
decision tree model (DTM) were trained with routine meteorological observations (WS, WSMAX, T and relative humidity (RH))
Summary
Wind plays a key role in the snow distribution in mountainous terrain, as it shapes both the spatial heterogeneity of snowfall and the erosion, transport and deposition of surface snow via blowing snow processes. In addition to their impacts on the strong spatiotemporal heterogeneity of the surface snow distribution, blowing snow processes have important consequences for the subsequent evolution of the snowpack (Déry and Yau, 2002; Leonard and Maksym, 2011) and the surface water and energy budgets (Lenaerts et al, 2012a; Liston, 2004; Pomeroy and Gray, 1995; Sexstone et al, 2018). Wind-driven snow redistribution is widely recognized as driving patterns in snow accumulation and snowpack evolution in alpine basins and represents an important interaction between the land and the overlying atmosphere
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