Reconstruction of snow days based on monthly climate indicators in the Swiss pre-alpine region

Landscape and climate change interactions are considerably interrelated in mountainous areas, where unsuitable or discontinuous surface meteorological variables constitute an impediment ​to the generation of homogeneous ecological and hydrological data and may hinder long-term environmental studies. ​To facilitate snow days’ reconstructions, we developed a non-linear multivariate regression model (NLMRM) estimating snow days per year (SDY) in a focus area, the northern Swiss Pre-Alpine Region (SPAR). The model was calibrated and assessed by using measured SDY data and other climatic variables in the period 1931-2006 were used to calibrate and asses the model​, ​which is then used to estimate SDY ​(​for a longer period earlier than 1931. The extended ​SDY ​series (1836-2017) showed a significant decrease of SDY ​passing shifting from about 36 days yr-1 in 1836-1943 to 29.9 days yr-1 in 1944-2017, on average. This indicates that​, while warming is the major factor driving the ​snow cover SDY decrease recently observed in the study area, ​in a century-long perspective ​other processes related to local precipitation and large-scale climatic patterns emerge from our century-long perspective as important drivers of ​snow cover SDY variability in the Swiss pre-alpine region.