As greenhouse gas concentrations continue to rise and the world enters uncharted climate conditions, climate projections at the regional scale are becoming more and more crucial for planning adequate adaptation and mitigation strategies. In particular, in mountainous regions, high spatial resolution of climate information is crucial, as climate variables often exhibit strong variability over short spatial scales. Here, we (1)...

Assessing future changes of weather-related variables in mountainous areas poses significant challenges, partly due to the inherent complexity of orographic effects and the associated atmospheric dynamics, which further amplify the difficulty. Additionally, mountains exhibit region-specific changes, making it essential to analyze single areas separately. In recent decades, elevation-dependent warming and its future projections...

Polar Organic Chemical Integrative Sampler (POCIS) is a passive sampler employed to monitor organic compounds in water (e.g. pesticides, drugs, etc.); in our case consists of a receiving phase, Oasis HLB, enclosed between two polyethersulfone (PES) membranes. In most cases, the analytes were extracted only from the receiving phase but recent works show that some compounds were also adsorbed on the PES membranes. Many aspects o...

Mountains, such as the Trentino region in the North-Eastern Italian Alps, are highly vulnerable to climate change. Their intricate topography amplifies the interaction between local atmospheric processes and regional-scale dynamics, adding complexity to the assessment of climate impacts. To devise effective adaptation strategies, it is crucial to thoroughly understand the environmental and socio-economic sectors most affected...

Data from the EURO-CORDEX ensemble of regional climate model simulations and the CORDEX-Adjust dataset were evaluated over the European Alps using multiple gridded observational datasets. Biases, which are here defined as the difference between models and observations, were assessed as a function of the elevation for different climate indices that span average and extreme conditions. Moreover, we assessed the impact of differe...

Despite the importance of snow in alpine regions, little attention has been given to the homogenization of snow depth time series. Snow depth time series are generally characterized by high spatial heterogeneity and low correlation among the time series, and the homogenization thereof is therefore challenging. In this work, we present a comparison between two homogenization methods for mean seasonal snow depth time series avai...

Time series of snow depth bear relevant information for environmental studies in Alpine areas. In addition, due to its sensitivity to air temperature, snow depth dynamic is a robust indicator of climate change effects in mountainous areas. Unfortunately, collecting accurate snow depth data is a difficult task and often time series are obtained by merging data from different sources. Especially in the past, observations were no...