Abstract
Landscape and climate change interactions are considerably interrelated in mountainous area, 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. 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, and then used to estimate SDY for a longer period earlier than 1931. The extended series (1836–2017) showed a significant decrease of SDY passing from about 36 days year−1 in 1836–1943 to 29.9 days year−1 in 1944–2017, on average. This indicates that while warming is the major factor driving the SDY decrease recently observed in the study area, other processes related to local precipitation and large-scale climatic patterns emerge from our century-long perspective as important drivers of SDY variability in the Swiss pre-alpine region.
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Diodato, N., Fratianni, S. & Bellocchi, G. Reconstruction of snow days based on monthly climate indicators in the Swiss pre-alpine region. Reg Environ Change 20, 55 (2020). https://doi.org/10.1007/s10113-020-01639-0
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DOI: https://doi.org/10.1007/s10113-020-01639-0