Abstract. Accelerating glacier melt rates were observed during the last decades. Substantial ice loss occurs particularly during heat waves that are expected to intensify in the future. Because measuring and modelling glacier mass balance at the daily scale remains challenging, short-term mass balance variations, including extreme melt events, are poorly captured. Here, we present a novel approach based on computer-vision techniques for automatically determining daily mass balance variations at the local scale. The approach is based on the automated recognition of color-taped ablation stakes from camera images, and is tested and validated at six stations installed on three Alpine glaciers during the summers of 2019–2022. Our approach produces daily mass balance with an uncertainty of ±0.81 cm w.e d−1, which is about half of the accuracy obtained from manual read outs. The automatically retrieved daily mass balances at the six sites were compared to average daily mass balances over the last decade derived from seasonal in situ observations to detect and assess extreme melt events. This allows analyzing the impact that the summer heat waves which occurred in 2022 had on glacier melt. Our results indicate 23 days with extreme melt, showing a strong correspondence between the heat wave periods and extreme melt events. The combination of below-average winter snow fall and a suite of summer heat waves led to unprecedented glacier mass loss. The Swiss-wide glacier storage change during the 25 days of heat waves in 2022 is estimated as 1.27±0.10 km3 of water, corresponding to 35 % of the overall glacier mass loss during that summer. Compared to the average course of the past decade, the 25 days of heat waves in 2022 caused a glacier mass loss that corresponds to 56 % of the overall mass loss experienced on average during summers 2010–2020, demonstrating the relevance of heat waves for seasonal melt.

<p>Triggered by climate change, glaciers are retreating world-wide at alarming rates. Since glacier melt can contribute significant proportions to hydrological catchment runoff, it is important to know how much meltwater glaciers can still release under decreasing ice volumes. For a better water resources management, a near real-time mass balance estimate would thus be desirable. On short time scales, glacier mass balance models are usually uncertain though, and they rely heavily on field data for calibration and validation. Because acquiring field data is resource-intensive, most studies rely exclusively on annual or seasonal data sets.</p><p>To provide an improved data basis for near-real time analyses produced within the CRAMPON project (Cryospheric Monitoring and Prediction Online), we aim at measuring glacier point ablation automatically, remotely and with high temporal resolution. For this purpose, we have equipped nine ablation stakes on Rhonegletscher, Grosser Aletschgletscher, Findelengletscher and Glacier de la Plaine Morte, Switzerland, with an additional setup: attached to each ablation stake, another aluminum stake construction holds a solar-powered camera at about 1m distance. As the ice surface melts, the camera slides down the ablation stake, takes RGB images of the bottom 50cm at 20min intervals, and sends the images to a server. Colored tape markers of known width and spacing serve as a scale reference on the stake. The total sequence of markers using eight different colors is shuffled to allow for a unique identification of sub-sequences of four markers.</p><p>By means of computer vision, the distance of the ablation stake top from the ice surface is obtained automatically: the stake is identified by finding collinear points of high color saturation on an image, i.e. the tape markers. The base point at the ice surface is given, because it has a fixed relative position to the camera. Individual markers are identified by their color, while the color sub-sequences provide the total position on the stake. A pixel-to-metric scale is calculated for each image from the known marker tape width and spacing, which also accounts for the perspective skewness of the stake. A reading uncertainty estimate of 2mm is derived from noise in the scale calculation. This estimate includes the quality of the detected marker bounds, image pixel size and the precision of the actual marker positions as error sources. Images with bad weather conditions are rejected by the processing.</p><p>The so-obtained ice melt time series between subsequent image pairs is aggregated to daily values. The results show good agreement with manual readings. In addition to the suggested image processing, we discuss two alternative approaches: by detecting tape markers through a template matching and tracking their location on the images over time, the alternatives avoid the reconstruction of the stake top position while being more sensitive to longer data gaps. We conclude that the presented setup is well-suited to automatically and remotely determine real-time ablation rates with low effort.</p>

<p>Summer heat waves have a substantial impact on glacier melt as emphasized by the extreme summer of 2022 that caused unprecedented mass losses to the Swiss glaciers. Despite the dramatic impact on glaciers, the summer of 2022 offered a unique opportunity to analyze the implications that such extraordinary events have on glacier melt and related runoff release.</p> <p>This study presents a novel approach based on computer-vision techniques for automatically determining daily mass balance variations at the local scale. The approach is based on the automated recognition of color-taped ablation stakes from camera images acquired at six sites on three Alpine glaciers in the period 2019-2022. The validation of the method revealed an uncertainty of the automated readings of ±0.81 cm d<sup>-1</sup>. By comparing the automatically retrieved mass balances at the six sites with the average mass balance of the last decade derived from seasonal in situ observations, we detect extreme melt events in the summer seasons of 2019-2022.</p> <p>The in-depth analysis of summer 2022 allows us to assess the impact that the summer heat waves have on glacier melt. With our approach we detect 23 days with extreme melt over the summer, emphasizing the strong correspondence between heat waves and extreme melt events. The Swiss-wide glacier mass loss during the 25 days of heat waves in 2022 is estimated as 1.27 ± 0.10 Gt, corresponding to 35% of the overall glacier mass loss in the summer of 2022. As compared to the 2010-2020 average glacier mass change, days with extreme melt in 2022 correspond to 56% of the mass change during the summer period, thus demonstrating the significance of heat waves for seasonal melt.</p>

<p>Climate change is affecting glaciers worldwide, leading to unprecedented melt rates. In this context, establishing systems that provide near-real-time glacier information can be of high interest. However, the effort for acquiring real-time, in situ glacier observations is large.</p><p>In a previous study, we investigated the potential for automated acquisition of real-time mass balance readings by using optical cameras installed in-situ and computer vision techniques. The setup proved to be useful for obtaining melt rates with a temporal resolution of 20 minutes. However, it is not feasible to cover an extensive portion of a glacier with such a setup.</p><p>In our contribution, we present a method to acquire glacier mass balance readings with a custom drone equipped with a camera. The principle is to acquire images of a color-coded stake, from which surface mass balance can be determined via the glaciological method. To autonomously approach and read the stake, we exploit a combination of computer vision techniques and geometrical triangulation.  The results of off-glacier test flights, as well as four flights on Rhonegletscher, Switzerland, prove that the system is successful in detecting the stake in the videos captured by the drone. The determined stake position has uncertainties of 2.4 - 4.6 m, thus being sufficient to safely approach the stake. We investigate the main factors influencing the performance of the method in more detail, and discuss potential future developments of the system.</p>

Abstract. Snow exists in a wide range of temperatures and around its melting point snow becomes a three-phase material. A better understanding of wet snow and the first starting point of water percolation in the seasonal snowpack is essential for snow pack stability, snow melt run-off and remote sensing. In order to induce and measure precisely the liquid water and the corresponding dielectric properties inside a snow sample, an experimental setup was developed. Using microwave heating at 18 kHz allows the use of dielectric properties of ice to enable heat to be dissipated homogeneously through the entire volume of snow. A desired liquid water content inside the snow sample could then be created and analysed in a micro-computer tomography. Based on the electrical monitoring a promising perspective for retrieving water content and water distribution in the snowpack is given. The heating process and extraction of water content are mainly dependent on the morphological properties of snow, the temperature and the liquid water content. The experimental observation can be divided in three different heating processes affecting the dielectric properties of snow for different densities: (1) dry snow heating process up to 0 °C indicating a temperature and snow structure dependency of the dielectric property of snow; (2) wet snow heating at stagnating temperature of 0 °C and the presence of uniformed distributed liquid water changes the dielectric properties. The presence of liquid water decreases the impedance of the snow sample until water starts to percolate; and (3) the start of water percolation is between 5–12 water volume fraction depending on the snow density and confirms the literature findings. The onset of water percolation initiated an inhomogeneity in snow and water distribution, strongly affecting the dielectric properties of the snow. These findings are pertinent to the interpretation of the snow melt run-off of spring snow. These laboratory measurements allow to find the narrow range of the starting point of water percolation in coarse-grained snow and to extract the corresponding dielectric properties which is important for remote sensing.