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The regulating role of glaciers in catchment run-off is of fundamental importance in sustaining people living in low-lying areas. The reduction in glacierized areas under the effect of climate change disrupts the distribution and amount of run-off, threatening water supply, agriculture and hydropower. The prediction of these changes requires models that integrate hydrological, nivological and glaciological processes. In this work we propose a local model that combines the nivological and glaciological scales. The model describes the formation and evolution of the snowpack and the firn below it, under the influence of temperature, wind speed and precipitation. The model has been implemented in two versions: (1) a multi-layer one that considers separately each firn layer and (2) a single-layer one that models firn and underlying glacier ice as a single layer. The model was applied at the site of Colle Gnifetti (Monte Rosa massif, 4400–4550 ma.s.l.). We obtained an average reduction in annual snow accumulation due to wind erosion of 2×103 kgm-2yr-1 to be compared with a mean annual precipitation of about 2.7×103 kgm-2yr-1. The conserved accumulation is made up mainly of snow deposited between April and September, when temperatures above the melting point are also observed. End-of-year snow density, instead, increased an average of 65 kgm-3 when the contribution of wind to snow compaction was added. Observations show a high spatial and interannual variability in the characteristics of snow and firn at the site and a correlation of net balance with radiation and the number of melt layers. The computation of snowmelt in the model as a sole function of air temperature may therefore be one of the reasons for the observed mismatch between model and observations. [ABSTRACT FROM AUTHOR]

The capability of time-lapse photography to retrieve snow depth time series was tested. Historically, snow depth has been measured manually by rulers, with a temporal resolution of once per day, and it is a time-consuming activity. In the last few decades, ultrasonic and/or optical sensors have been developed to obtain automatic and regular measurements with higher temporal resolution and accuracy. The Finnish Meteorological Institute Image Processing Toolbox (FMIPROT) has been used to retrieve the snow depth time series from camera images of a snow stake on the ground by implementing an algorithm based on the brightness difference and contour detection. Three case studies have been illustrated to highlight potentialities and pitfalls of time-lapse photography in retrieving the snow depth time series: Sodankylä peatland, a boreal forested site in Finland, and Gressoney-La-Trinité Dejola and Careser Dam, two alpine sites in Italy. This study presents new possibilities and advantages in the retrieval of snow depth in general and snow depth time series specifically, which can be summarized as follows: (1) high temporal resolution – hourly or sub-hourly time series, depending on the camera's scan rate; (2) high accuracy levels – comparable to the most common method (manual measurements); (3) reliability and visual identification of errors or misclassifications; (4) low-cost solution; and (5) remote sensing technique – can be easily extended in remote and dangerous areas. The proper geometrical configuration between camera and stake, highlighting the main characteristics which each single component must have, has been proposed. Root mean square errors (RMSEs) and Nash–Sutcliffe efficiencies (NSEs) were calculated for all three case studies comparing with estimates from both the FMIPROT and visual inspection of images directly. The NSE values were 0.917, 0.963 and 0.916, while RMSEs were 0.039, 0.052 and 0.108 m for Sodankylä, Gressoney and Careser, respectively. In terms of accuracy, the Sodankylä case study gave better results. The worst performances occurred at Careser Dam located at 2600 m a.s.l., where extreme weather conditions and a low temporal resolution of the camera occur, strongly affecting the clarity of the images. [ABSTRACT FROM AUTHOR]

Data of liquid water flow around a capillary barrier in snow are still limited. To gain insight into this process, we carried out observations of dyed water infiltration in layered snow at 0°C during cold laboratory experiments. We considered three different finer-over-coarser textures and three different water input rates. By means of visual inspection, horizontal sectioning, and measurements of liquid water content (LWC), capillary barriers and associated preferential flow were characterized. The flow dynamics of each sample were also simulated solving the Richards equation within the 1-D multi-layer physically based snow cover model SNOWPACK. Results revealed that capillary barriers and preferential flow are relevant processes ruling the speed of water infiltration in stratified snow. Both are marked by a high degree of spatial variability at centimeter scale and complex 3-D patterns. During unsteady percolation of water, observed peaks in bulk volumetric LWC at the interface reached ~33- 36 vol% when the upper layer was composed by fine snow (grain size smaller than 0.5 mm). However, LWC might locally be greater due to the observed heterogeneity in the process. Spatial variability in water transmission increases with grain size, whereas we did not observe a systematic dependency on water input rate for samples containing fine snow. The comparison between observed and simulated LWC profiles revealed that the implementation of the Richards equation reproduces the existence of a capillary barrier for all observed cases and yields a good agreement with observed peaks in LWC at the interface between layers. [ABSTRACT FROM AUTHOR]

We investigate snow depth distribution at peak accumulation over a small Alpine area (~0.3 km²) using photogrammetry-based surveys with a fixed-wing unmanned aerial system (UAS). These devices are growing in popularity as inexpensive alternatives to existing techniques within the field of remote sensing, but the assessment of their performance in Alpine areas to map snow depth distribution is still an open issue. Moreover, several existing attempts to map snow depth using UASs have used multi-rotor systems, since they guarantee higher stability than fixed-wing systems. We designed two field campaigns: during the first survey, performed at the beginning of the accumulation season, the digital elevation model of the ground was obtained. A second survey, at peak accumulation, enabled us to estimate the snow depth distribution as a difference with respect to the previous aerial survey. Moreover, the spatial integration of UAS snow depth measurements enabled us to estimate the snow volume accumulated over the area. On the same day, we collected 12 probe measurements of snow depth at random positions within the case study to perform a preliminary evaluation of UAS-based snow depth. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073m and a RMSE equal to 0.14 m. We have also explored how some basic snow depth statistics (e.g., mean, standard deviation, minima and maxima) change with sampling resolution (from 5 cm up to ~100 m): for this case study, snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m. This provides a possible indication of sampling resolution in similar conditions. [ABSTRACT FROM AUTHOR]