Climate change will modify the spatiotemporal distribution of water resources in the future. Snow availability in alpine systems plays an important role for water dependent ecosystems, water demand supply, tourism, and hydropower. The assessment of the impact of climate change (and its uncertainty) on snow is a key subject in determining suitable adaptation strategies in these systems. In this paper, we propose a new methodology for assessing the impact of climate change on snow cover areas (SCAs). We have developed the Monte Carlo method analysis to combine several approaches to generate multiple input series and propagate them within a previously calibrated SCA cellular automata model. This generates potential future local scenarios from regional climate models. These scenarios are used to generate multiple series by using a stochastic weather generator. The methodology also includes an approach to correct the outputs bias of the stochastic weather generators when it is needed. Finally, the historical and the corrected multiple future weather series are used to simulate the impact on the SCA by using a cellular automata model. It is a novel approach that allows us to quantify the impact and uncertainty of climate change on the SCA. The methodology has been applied to the Sierra Nevada (southern Spain), which is the most southern alpine mountain range in Europe. In the horizon 2071–2100, under the RCP 8.5 emission scenario, we estimate mean reductions of SCA that will move from 42 to 66% from December to February. The reductions are higher for the rest of the year (from March to May reductions of between 47 and 95% and from September to November reductions of between 54 and 100%). These SCA changes may be roughly equivalent to an elevation shift of snow of around 400 m.

气候变化将在未来改变水资源的时空分布。高山系统中的降雪量对依赖水的生态系统，需水供应，旅游业和水力发电起着重要作用。对气候变化（及其不确定性）对降雪的影响进行评估是确定这些系统中合适的适应策略的关键主题。在本文中，我们提出了一种新的方法来评估气候变化对积雪区域（SCA）的影响。我们已经开发了蒙特卡洛方法分析，以结合多种方法来生成多个输入序列，并在先前校准的SCA细胞自动机模型中传播它们。这将从区域气候模型中得出潜在的未来本地情景。这些场景用于通过使用随机天气生成器来生成多个序列。该方法还包括一种在需要时校正随机天气生成器的输出偏差的方法。最后，通过使用元胞自动机模型，使用历史和校正后的多个未来天气序列来模拟对SCA的影响。这是一种新颖的方法，可让我们量​​化气候变化对SCA的影响和不确定性。该方法已应用于内华达山脉（西班牙南部），这是欧洲最南端的高山山脉。在2071年至2100年的地平线上，在RCP 8.5排放情景下，我们估计从12月到2月，SCA的平均减少量将从42％降至66％。一年中其余时间的减少量更高（3月至5月减少47％至95％，9月至11月减少54％至100％）。这些SCA变化可能大致相当于约400 m的降雪高度变化。