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.

中文翻译：

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根据瑞士高山前地区的每月气候指标重建下雪天

在山区，景观和气候变化的相互作用是密切相关的，在山区，不合适或不连续的地面气象变量阻碍了均匀生态和水文数据的产生，并可能阻碍长期的环境研究。我们开发了一个非线性多元回归模型（NLMRM），用于估算瑞士北部阿尔卑斯山前山区（SPAR）重点地区的每年下雪天（SDY）。通过使用1931-2006年期间测得的SDY数据和其他气候变量对模型进行校准和评估，然后用于估计比1931年更早的时间的SDY。扩展系列（1836-2017年）显示SDY显着降低约36天一年比一年^-1的一年1836年至1943年，以29.9天^-1平均在1944年至2017年。这表明，虽然变暖是最近在研究区观测到的SDY下降的主要因素，但从我们长达一个世纪的角度来看，与局部降水和大规模气候模式有关的其他过程也是瑞士前SDY变异的重要驱动力。高山地区。