The hydrological cycle is strongly influenced by the accumulation and melting of seasonal snow. For this reason, mountains are often claimed to be the “water towers” of the world. In this context, a key variable is the snow water equivalent (SWE). However, the complex processes of snow accumulation, redistribution, and ablation make its quantification and prediction very challenging. In this work, we explore the use of multi-source data to reconstruct SWE at a high spatial resolution (HR) of 25 m. To this purpose, we propose a novel approach based on (i) in situ snow depth or SWE observations, temperature data and synthetic aperture radar (SAR) images to determine the pixel state, i.e., whether it is undergoing an SWE increase (accumulation) or decrease (ablation), (ii) a daily HR time series of snow cover area (SCA) maps derived by high- and low-resolution multispectral optical satellite images to define the days of snow presence, and (iii) a degree-day model driven by in situ temperature to determine the potential melting. Given the typical high spatial heterogeneity of snow in mountainous areas, the use of HR images represents an important novelty that allows us to sample its distribution more adequately, thus resulting in highly detailed spatialized information. The proposed SWE reconstruction approach also foresees a novel SCA time series regularization technique that models impossible transitions based on the pixel state, i.e., the erroneous change in the pixel class from snow to snow-free when it is expected to be in accumulation or equilibrium and, vice versa, from snow-free to snow when it is expected to be in ablation or equilibrium. Furthermore, it reconstructs the SWE for the entire hydrological season, including late snowfall. The approach does not require spatialized precipitation information as input, which is usually affected by uncertainty. The method provided good results in two different test catchments: the South Fork of the San Joaquin River, California, and the Schnals catchment, Italy. It obtained good agreement when evaluated against HR spatialized reference maps (showing an average bias of −22 mm, a root mean square error – RMSE – of 212 mm, and a correlation of 0.74), against a daily dataset at coarser resolution (showing an average bias of −44 mm, an RMSE of 127 mm, and a correlation of 0.66), and against manual measurements (showing an average bias of −5 mm, an RMSE of 191 mm, and a correlation of 0.35). The main sources of error are discussed to provide insights into the main advantages and disadvantages of the method that may be of interest for several hydrological and ecological applications.

The systematic monitoring of glaciers is essential to both evaluate water resource availability and better understand the effects of climate change. The increased speed of glacier changes observed in the past few years requires a more frequent update of the glacier inventories than in the past; however, the high human supervision required by the state-of-the-art techniques is discouraging their systematic application over large areas. This article proposes a novel approach to exploit the large volume of data provided by Copernicus Sentinel missions for detecting glacier outlines, including debris-covered glaciers. In detail, our method exploits the Sentinel-1 and Sentinel-2 multitemporal images to build a composite image representing the glacier conditions during the yearly maximum ablation period. The Sentinel-2 multispectral images are classified with a support vector machine and composed to a mosaic that represents the information of the maximum glacier ablation. At the same time, the Sentinel-1 time series are exploited to build a multitemporal coherence composite that represents all the snow-covered and glaciated areas together with all the moving surfaces. This information is used together with the Sentinal-2 composite to detect the debris-covered part of the glaciers. The proposed method was tested in the Central East Alps and presented an overall accuracy of 92% with respect to a reference inventory over South Tyrol and an agreement of 90% with respect to the latest glacier inventory of the Alps from Sentinel-2. The proposed approach enables to assist glacier experts in identifying glacier outlines over large areas and in short time

Melting calorimetry, a classic experiment often conducted in high-school chemistry laboratories, holds significant untapped potential for scientific applications beyond its educational context. Traditionally, this technique has been applied to measure the liquid water content in snow using two different formulations: melting calorimetry and freezing calorimetry. In contrast to freezing calorimetry, which is considered the reference method for measuring liquid water content, melting calorimetry has been perceived as prone to generating significant inaccuracies. This research revisits the formulations for both melting and freezing calorimeters to assess volumetric liquid water content in snow. By incorporating the calorimetric constant, we account for heat exchange with the calorimeter, a critical factor often neglected in melting-calorimetry experiments. This paper identifies the most effective and least uncertain method for determining this constant. A central contribution of this work is the introduction of a framework for estimating uncertainty in volumetric liquid water content measurements, adhering to established guidelines for uncertainty expression. This novel framework allows us to revisit past mathematical analyses and demonstrate that melting calorimetry delivers reliable measurements with an uncertainty 0.25 % greater than freezing calorimetry. Notably, despite this slightly higher uncertainty, melting calorimetry offers significant practical advantages for field applications. Moreover, we show how the proposed uncertainty framework can be expanded beyond instrumental uncertainty and also take into account the variability from environmental factors and operators, providing a more comprehensive characterization of the uncertainty. By exploiting the proposed uncertainty framework, we finally conduct an in-depth analysis for the optimal tuning of the experiment parameters. This analysis culminates in a robust field protocol for melting calorimetry that transcends commonsense procedural guidelines. Strict adherence to this protocol will maximize measurement accuracy. Applied in field tests in Italy and Switzerland, the melting calorimetry was demonstrated to accurately track the wet front penetration in the snowpacks, producing results comparable to independent dielectric measurements. These findings highlight the accuracy and the practical advantages of melting calorimetry as a reliable field tool for quantifying snowpack liquid water content. Melting calorimetry can potentially serve as a valuable tool for the independent calibration and validation of proximal and remote sensing techniques used for liquid water content retrieval.

Glaciers represent an important part of the hydrologic cycle in the Alps and they are very sensitive to climate change. Satellite remote sensing is an efficient tool for glacier monitoring because it provides a synoptic view over large areas. In literature, well-established methods for glacier delineation based on the Red and Short Wave Infrared (SWIR) ratio have been presented. These methods depend on a manual selection for each glacier of the “best scene”, i.e. absence of cloud coverage and minimum snow cover. A further manual refinement step is needed to handle possible errors, mainly due to cloud cover or shadows, and to include debris covered ice.A manual approach for glacier outline extraction, especially if applied over large areas and beside the respective extraordinary amount of work, may be inadequate for at least two reasons:1) The increased amount of available satellite data provided by the recently launched Sentinel-2 mission, which ensure at least one acquisition every 5 days on a given area;2) The need for a more frequent update of the glacier outlines i.e. few years, due to the faster changes affecting glaciers during the last years.In this work, we present an automatic method for glacier mapping, including bare ice and debris covered ice through the synergetic use of Sentinel-1 and Sentinel-2. The information of the Sentinel-2 time series is first classified with a Support Vector Machine (SVM) to detect cloud and snow. The snow and cloud masks are then used to select the non-cloudy pixels with the lowest snow coverage in the surrounding area. This is done by applying a moving window on the entire multi-temporal classified stack. The selected pixels for each band compose a multi-temporal cloud free mosaic, which represents the glaciers with the minimum snow cover for the current ablation season i.e., the “best scene”. If we compose the mosaic with classified pixels instead of the reflectance, we obtain the glacier – non glacier map that we use for outlines extraction. On the other hand, the Sentinel-1 coherence is used to detect the debris-covered ice over the areas classified as non-glacier from Sentinel-2. In detail, the Sentinel-1 time series is exploited to generate a multi-temporal coherence mosaic, which is representative of the loss of coherence due to the movement of the debris only. By properly thresholding this mosaic and considering the topographic information, the outlines of debris covered glaciers can be extracted.The results obtained with the proposed method are compared with the recent official glacier inventory of South Tyrol (Italy) and Tyrol (Austria), which was derived from the manual interpretation of aerial orthophotos and lidar data by glacier experts.

In this work, we present two datasets for specific areas located on the Alpine arc that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas over the South Tyrol (Italy) and Tyrol (Austria) areas in alpine environments. The snow cover fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC), respectively, covering winter seasons for SCF and spring–summer seasons for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at a global scale, but here, to obtain more insights into the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case, an MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m³·m⁻³ were achieved. The datasets presented can be used as input for hydrological models and to hydrologically characterize the study alpine area starting from different sources of information.

In this work, we present two datasets for specific areas located in South Tyrol (Italy) and in Tyrol (Austria) that can be exploited to monitor and understand water resource dynamics in mountain regions. The idea is to provide the reader with information about the different sources of water supply over five defined test areas. The Snow Cover Fraction (SCF) and Soil Moisture Content (SMC) datasets are derived from machine learning algorithms based on remote sensing data. Both SCF and SMC products are characterized by a spatial resolution of 20 m and are provided for the period from October 2020 to May 2023 (SCF) and from October 2019 to September 2022 (SMC) respectively, each covering 3 seasons of interest, winter for SCF and spring-summer for SMC. For SCF maps, the validation with very high-resolution images shows high correlation coefficients of around 0.9. The SMC products were originally produced with an algorithm validated at global scale, but here, to obtain more insight in the specific alpine mountain environment, the values estimated from the maps are compared with ground measurements of automatic stations located at different altitudes and characterized by different aspects in the Val Mazia catchment in South Tyrol (Italy). In this case a MAE between 0.05 and 0.08 and an unbiased RMSE between 0.05 and 0.09 m3·m-3 were achieved. The datasets presented can be used as input for hydrological models as well as to hydrologically characterize the study alpine area starting from different sources of information.