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

Abstract : Landscape and climate change interactions are considerably interrelated in mountainous area, where unsuitable or discontinuoussurface meteorological variables constitute an impediment to the generation of homogeneous ecological and hydrological data,and may hinder long-term environmental studies. We developed a non-linear multivariate regression model (NLMRM) estimat-ing snow days per year (SDY) in a focus area, the northern Swiss pre-alpine region (SPAR). The model was calibrated andassessed by using measured SDY data and other climatic variables in the period 1931–2006, and then used to estimate SDY for alonger period earlier than 1931. The extended series (1836–2017) showed a significant decrease of SDY passing from about36 days year−1in 1836–1943 to 29.9 days year−1in 1944–2017, on average. This indicates that while warming is the major factordriving the SDY decrease recently observed in the study area, other processes related to local precipitation and large-scaleclimatic patterns emerge from our century-long perspective as important drivers of SDYvariability in the Swiss pre-alpine region.
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Nazzareno Diodato, Simona Fratianni, Gianni Bellocchi. Reconstruction of snow days based on monthly climate indicators in the Swiss pre-alpine region. Regional Environmental Change, Springer Verlag, 2020, 20 (2), ⟨10.1007/s10113-020-01639-0⟩. ⟨hal-02899857⟩



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