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For many safety-related applications such as hazard mapping or road management, well-documented avalanche events are crucial. Nowadays, despite the variety of research directions, the available data are mostly restricted to isolated locations where they are collected by observers in the field. Webcams are becoming more frequent in the Alps and beyond, capturing numerous avalanche-prone slopes. To complement the knowledge about avalanche occurrences, we propose making use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose supporting the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep-learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model's ability to delineate avalanches. The human corrections to the segmentation in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. The images can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GISs). If a webcam is mounted in a stable position, the georeferencing can be re-used for all subsequent images. In this way, all avalanches mapped in images from a webcam can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current data and data archived from webcams that cover Dischma Valley near Davos, Switzerland, and that have captured an image every 30 min during the daytime since the winter of 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions. [ABSTRACT FROM AUTHOR]

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m. [ABSTRACT FROM AUTHOR]

For many safety-related applications such as hazard mapping or road management, well documented avalanche events are crucial. Nowadays, despite research into different directions, the available data is mostly restricted to isolated locations where it is collected by observers in the field. Webcams are getting more frequent in the Alps and beyond, capturing numerous avalanche prone slopes several times a day. To complement the knowledge about avalanche occurrences, we propose to make use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose to support the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model's ability to delineate avalanches. The human corrections to the prediction in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. If mounted in a stable position, the camera can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GIS). In this way all avalanches mapped in an image can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current and archive data from webcams that cover the Dischma valley near Davos, Switzerland and capture an image every 30 minutes during daytime since the winter 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions. [ABSTRACT FROM AUTHOR]

The spatial and temporal distribution of Alpine snow is a sensitive gauge of environmental change. While understanding past snow dynamics is essential for reconstructing past climate and forecasting future trends, reliable snowpack data prior to the instrumental record are scarce. We present a novel pairing of cosmogenic paleothermometry and luminescence photochronometry which constrain the temperature and insolation history of bedrock outcrops at the Gotthard Pass, Switzerland, over the last ∼15,000 years. By coupling these results with cosmogenic 14C‐10Be chronology and modern in situ rock thermometry, we infer a ∼70‐day reduction of snowpack at the topographic mid‐slope. Our data indicate stable environmental conditions throughout the Holocene, followed by a 6.6 ± 2.9°C increase of ground surface temperature, coeval with an order‐of‐magnitude or more increase in ground surface insolation. Bracketing the onset of these changes between 1504 and 1807 CE, our findings tie the snowpack decline with the onset of human industrialization. Plain Language Summary: The extent of snow cover is shrinking in high elevation mountain environments due to climate change. However, it is challenging to determine when snow cover began to change because humans have only been monitoring snow cover for several decades, and snow cover has appeared to shrink over this entire time. We used two new geologic records of ground temperature and light exposure—both of which are impacted by the duration and amount of snow cover—to assess when snow cover began to change in the Gotthard Pass in Switzerland. We found that the middle slopes of the pass began experiencing snow cover loss three centuries ago, well before historical monitoring of snow cover in this region. This record of prolonged snow cover loss matters for the informed management of mountain water sources, rock and snow avalanche risk, and ecosystem change. Key Points: We present a novel data set of cosmogenic paleothermometry and luminescence photochronometry at the Gotthard Pass, SwitzerlandStable Holocene environmental conditions are followed by increases in ground temperature and insolation caused by snow cover declineThese environmental changes began between 1504 and 1807 CE, tying the observed snowpack decline with the onset of human industrialization [ABSTRACT FROM AUTHOR]

Consistent estimates of avalanche size are crucial for communicating not only among avalanche practitioners but also between avalanche forecasters and the public, for instance in public avalanche forecasts. Moreover, applications such as risk management and numerical avalanche simulations rely on accurately mapped outlines of past avalanche events. Since there is not a widely applicable and objective way to measure avalanche size or to determine the outlines of an avalanche, we need to rely on human estimations. Therefore, knowing about the reliability of avalanche size estimates and avalanche outlines is essential as errors will impact applications relying on this kind of data. In the first of three user studies, we investigate the reliability in avalanche size estimates by comparing estimates for 10 avalanches made by 170 avalanche professionals working in Europe or North America. In the other two studies, both completed as pilot studies, we explore reliability in the mappings of six avalanches from oblique photographs from 10 participants and the mappings of avalanches visible on 2.9 km 2 of remotely sensed imagery in four different spatial resolutions from 5 participants. We observed an average agreement of 66 % in the most frequently given avalanche size, while agreement with the avalanche size considered "correct" was 74 %. Moreover, European avalanche practitioners rated avalanches significantly larger for 8 out of 10 avalanches, compared to North Americans. Assuming that participants are equally competent in the estimation of avalanche size, we calculated a score describing the factor required to obtain the observed agreement rate between any two size estimates. This factor was 0.72 in our dataset. It can be regarded as the certainty related to a size estimate by an individual and thus provides an indication of the reliability of a label. For the outlines mapped from oblique photographs, we noted a mean overlapping proportion of 52 % for any two avalanche mappings and 60 % compared to a reference mapping. The outlines mapped from remotely sensed imagery had a mean overlapping proportion of 46 % (image resolution of 2 m) to 68 % (25 cm) between any two mappings and 64 % (2 m) to 80 % (25 cm) when compared to the reference. The presented findings demonstrate that the reliability of size estimates and of mapped avalanche outlines is limited. As these data are often used as reference data or even ground truth to validate further applications, the identified limitations and uncertainties may influence results and should be considered. [ABSTRACT FROM AUTHOR]