Dr. Christoph Marty

The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. Therefore, we assess the reliability of two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. Our findings reveal that all methods exhibit similar performance, with relative biases of less than ∼3 % in replicating SWE observations commonly used for model evaluations. However, the ΔSNOW model tends to underestimate daily HNW by ∼17 %, whereas HS2SWE and FSM2oshd combined with a particle filter data assimilation scheme provide nearly unbiased estimates, with relative biases below ∼5 %. In contrast to the parsimonious parametric methods, we show that the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Overall, our results underscore the potential of utilizing commonly available daily HS data in conjunction with appropriate modeling techniques to provide valuable insights into snow accumulation processes. Our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of simulations and should ideally already be incorporated during the calibration and development of models.

Im vorliegenden Bericht werden die Schnee- und Lawinensituation und Lawinen mit Personen- und Sachschaden beschrieben.

Wetter, Schneedecke und Lawinengefahr
Der erste Teil (Kapitel 2, ab Seite 8) umfasst einen Ruckblick auf die Schnee- und Lawinensituation in den Schweizer Alpen und im Schweizer Jura. Dabei wird auf den Schneehohenverlauf und die wichtigsten Niederschlagsereignisse sowie die bedeutendsten Lawinenperioden mit der Entwicklung des Schneedeckenaufbaus und der Schneedeckenstabilitat eingegangen. Weitere Informationen zu einzelnen Perioden, Gefahren- und Schneehohenkarten konnen im AvaBlog, die vollstandigen Bulletins im Archiv im Internet unter www.slf.ch abgerufen werden.

Lawinen mit Personen- und Sachschaden
Der zweite Teil des Berichtes beinhaltet die Lawinen mit Personen- und Sachschaden (Kapitel 3, ab Seite 38). Nebst einer Zusammenfassung aller uns bekannten Lawinen mit Personen- und Sachschaden des Winters 2023/24 werden auch einige ausgewahlte Unfalle detailliert beschrieben (ab Seite 57).

Snow plays a crucial role in regional climate systems worldwide. It is a key variable in the context of climate change because of its direct feedback to the climate system, while at the same time being very sensitive to climate change. Long-term spatial data on snow cover and snow water equivalent are scarce, due to the lack of satellite data or forcing data to run land surface models back in time. This study presents an R package, SnowQM, designed to correct for the bias in long-term spatial snow water equivalent data compared to a shorter-term and more accurate dataset, using the more accurate data to calibrate the correction. The bias-correction is based on the widely applied quantile mapping approach. A new method of spatial and temporal grouping of the data points is used to calculate the quantile distributions for each pixel. The main functions of the package are written in C⁺⁺ to achieve high performance. Parallel computing is implemented in the C⁺⁺ part of the code. In a case study over Switzerland, where a 60-year snow water equivalent climatology is produced at a resolution of 1 d and 1 km, SnowQM reduces the bias in snow water equivalent from -9 to -2 mm in winter and from -41 to -2 mm in spring. We show that the C⁺⁺ implementation notably outperforms simple R implementation. The limitations of the quantile mapping approach for snow, such as snow creation, are discussed. The proposed spatial data grouping improves the correction in homogeneous terrain, which opens the way for further use with other variables.

Le rapport comprend une rétrospective de la situation nivologique et avalancheuse dans les Alpes suisses et le Jura suisse. L’évolution de la hauteur de neige et les principaux épisodes de précipitations ainsi que les périodes avalancheuses les plus importantes avec l’évolution de la structure et de la stabilité du manteau neigeux y sont abordés. De plus amples informations sur les différentes pé-riodes, les cartes de danger et de hauteur de neige peuvent être consultées sur l’AvaBlog (anciennement rapport hebdomadaire), les bulletins complets dans les archives sur Internet à l’adresse www.slf.ch.

Der Winter 2022/23 war ausserordentlich schneearm und warm. Während im Vorjahr der Winter bereits als «extrem schneearm im Süden» beschrieben wurde, war der Winter 2022/23 durch rekordtiefe Schneehöhen in der ganzen Schweiz charakterisiert. Vor allem zwischen Mitte Februar und Mitte März waren die Schneehöhen im Schweizer Alpenraum so tief wie noch nie seit Messbeginn. Die Schneearmut ergab sich aus der Kombination von unterdurchschnittlichen Niederschlagsmengen und zu warmen Temperaturen.

Snow depth plays an important role in the seasonal climatic and hydrological cycles of alpine regions. Previous studies have shown predominantly decreasing trends in average seasonal snow depth across the European Alps. Additionally, prior work has shown bivariate statistical relationships between average seasonal snow depth and mean air temperature or precipitation. Building upon existing research, our study uses observational records of in situ station data across Austria and Switzerland to better quantify the sensitivity of historical changes in seasonal snow depth through a multivariate framework that depends on elevation, mean temperature, and precipitation. These historical sensitivities, which are obtained over the 1901-1902 to 1970-1971 period, are then used to estimate snow depths over the more recent period of 1971-1972 to 2020-2021. We find that the year-to-year estimates of snow depths, which are derived from an empirical-statistical model (SnowSens), that rely solely on the historical sensitivities are nearly as skillful as the operational SNOWGRID-CL model used by the weather service at GeoSphere Austria. Furthermore, observed long-term changes over the last 50 years are in better agreement with SnowSens than with SNOWGRID-CL. These results indicate that historical sensitivities between snow depth, temperature, and precipitation are quite robust over decadal-length scales of time, and they can be used effectively to translate expected long-term changes in temperature and precipitation into changes in seasonal snow depth.

L’hiver 2023/24 a été exceptionnellement arrosé et très doux. Contrairement à l’année précédente, qui avait été relativement sèche, avec donc peu de neige, les précipitations ont donc été importantes. En raison des températures supérieures à la moyenne, les hauteurs de neige en dessous de 1400 m d’altitude ont été toutefois nettement inférieures à la normale. L’hiver 2023/24 n’a donc pas été peu enneigé en raison de faibles précipitations, mais parce qu’il a plu fréquemment aux basses et moyennes altitudes. Audessus de 2200 m, en revanche, les hauteurs de neige ont été clairement supérieures aux moyennes. Il y avait donc de grandes différences en fonction de l’altitude : peu de neige en bas, beaucoup de neige en haut. Audessus de 2200 mètres, l’hiver (de novembre à avril) a en effet été l’un des plus enneigés, par exemple, 5e rang au Weissfluhjoch dans la série de mesures effectuées depuis plus de 80 ans. Le semestre d’hiver 2023/24 pourrait donc constituer une référence pour les hivers futurs. En effet, comme chacun sait, les modèles climatiques prévoient des hivers légèrement plus arrosés avec des températures nettement trop chaudes. [...]

Many methods exist to model snow densification in order to calculate the depth of a single snow layer or the depth of the total snow cover from its mass. Most of these densification models need to be tightly integrated with an accumulation and melt model and need many forcing variables at high temporal resolution. However, when trying to model snow depth (HS) on climatological timescales, which is often needed for winter tourism-related applications, these preconditions can cause barriers. Often, for these types of applications, empirical snow models are used. These can estimate snow accumulation and snowmelt based on daily precipitation and temperature data only. To convert the resultant snow water equivalent (SWE) time series into snow depth, we developed the empirical model SWE2HS. SWE2HS is a multilayer densification model which uses daily snow water equivalent as sole input. A constant new snow density is assumed and densification is calculated via exponential settling functions. The maximum snow density of a single layer changes over time due to overburden and SWE losses. SWE2HS has been calibrated on a data set derived from a network of manual snow stations in Switzerland. It has been validated against independent data derived from automatic weather stations (AWSs) in the European Alps (Austria, France, Germany, Switzerland) and against withheld data from the Swiss manual observer station data set which was not used for calibration. The model fits the calibration data with root mean squared error (RMSE) of 8.4 cm, coefficient of determination (R²) of 0.97, and bias of -0.3 cm; it is able to achieve RMSE of 20.5 cm, R² of 0.92, and bias of 2.5 cm on the validation data set from automatic weather stations and RMSE of 7.9 cm, R² of 0.97, and bias of -0.3 cm on the validation data set from manual stations. The temporal evolution of the bulk density can be reproduced reasonably well on all three data sets. Due to its simplicity, the model can be used as post-processing tool for output of any other snow model that provides daily snow water equivalent output. Owing to its empirical nature, SWE2HS should only be used in regions with a similar snow climatology as the European Alps or has to be recalibrated for other snow climatological conditions. The SWE2HS model is available as a Python package which can be easily installed and used.

Our current knowledge of spatial and temporal snow depth trends is based almost exclusively on time series of non-homogenised observational data. However, like other long-term series from observations, they are prone to inhomogeneities that can influence and even change trends if not taken into account. In order to assess the relevance of homogenisation for time-series analysis of daily snow depths, we investigated the effects of adjusting inhomogeneities in the extensive network of Swiss snow depth observations for trends and changes in extreme values of commonly used snow indices, such as snow days, seasonal averages or maximum snow depths in the period 1961-2021. Three homogenisation methods were compared for this task: Climatol and HOMER, which apply median-based adjustments, and the quantile-based interpQM. All three were run using the same input data with identical break points. We found that they agree well on trends of seasonal average snow depth, while differences are detectable for seasonal maxima and the corresponding extreme values. Differences between homogenised and non-homogenised series result mainly from the approach for generating reference series. The comparison of homogenised and original values for the 50-year return level of seasonal maximum snow depth showed that the quantile-based method had the smallest number of stations outside the 95 % confidence interval. Using a multiple-criteria approach, e.g. thresholds for series correlation (>0.7) as well as for vertical (<300 m) and horizontal (<100 km) distances, proved to be better suited than using correlation or distances alone. Overall, the homogenisation of snow depth series changed all positive trends for derived series of snow days to either no trend or negative trends and amplifying the negative mean trend, especially for stations >1500 m. The number of stations with a significant negative trend increased between 7 % and 21 % depending on the method, with the strongest changes occurring at high snow depths. The reduction in the 95 % confidence intervals of the absolute maximum snow depth of each station indicates a decrease in variation and an increase in confidence in the results.

Snowpack is an important temporal water storage for downstream areas, a potential source of natural hazards (avalanches or floods) and a prerequisite for winter tourism. Here, we use thousands of manual measurements of the water equivalent of the snow cover (SWE) from almost 30 stations between 1,200 and 2,900 m a.s.l. from four long-term monitoring programs (earliest start in 1937) in the center of the European Alps to derive daily SWE based on snow depth data for each station. The inferred long-term daily SWE time series were analyzed regarding spatial differences, as well as potential temporal changes in variability and seasonal averages during the last 7 decades (1957-2022). The investigation based on important hydro-climatological SWE indicators demonstrates significant decreasing trends for mean SWE (Nov-Apr) and for maximum SWE, as well as a significantly earlier occurrence of the maximum SWE and earlier disappearance of the continuous snow cover. The anomalies of mean SWE revealed that the series of low-snow winters since the 1990s is unprecedented since the beginning of measurements. Increased melting during the accumulation period below 2000 m a.s.l is also observed-especially in the most recent years–as well as slower melt rates in spring, and higher day-to-day variability. For these trends no regional differences were found despite the climatological variability of the investigated stations. This indicates that the results are transferable to other regions of the Alps.

Rising anthropogenic greenhouse gases, such as CO₂, CH₄, N₂O and others, are known to absorb and emit terrestrial thermal radiation back to the Earth's surface, leading to radiative forcing and rising surface temperatures. Nonetheless, radiation measurements now show that the rapid increase in temperature over continents since the end of the last century, which is more than twice as large as the average global warming, is also related to a clearing of the sky over land surfaces, which leads to an increase in sunshine hours, and hence, increasing solar radiation absorbed at the Earth's surface. By contrasting rising temperatures with annual sunshine hours and solar and thermal radiation in Central Europe, the measurements show that thermal radiation steadily increases owing to the rising greenhouse effect. However, the rapidly increasing warming since the end of the last century has been reinforced by a strong increase in solar radiation at the surface, resulting from rising annual sunshine hours as a positive greenhouse warming feedback, which is larger than the increase in thermal greenhouse radiation, and hence, the strongest driver of the rapidly increasing warming over continents. The rapid temperature increase in Central Europe, of more than one degree over the last decade, is larger in lowlands than in the Alps.

Austrian observations of snow depth date back to 1895 and are thus among the longest available quantitative snow information from hydrometeorological networks worldwide. It is well known that such long-term observations are prone to inhomogeneities, which may not only affect climatologies and trends, but derived products used in research or practice. While the reliability of available methods for detecting breaks in snow time series has been shown before and could also be confirmed by our work, we focused on improving the adjustment method. Conventional methods often refer to the median of difference or quotient series (INTERP), whereas our proposed method also uses a quantile-wise adjustment (InterpQM), which is useful to minimize a bias on the tails of the frequency distribution. We demonstrated the success of the new method by using Swiss parallel snow depth observations. Errors of the analyzed indicators could be reduced in 68% of the cases when compared to INTERP. The results were best for large snow depths, being up to 19% better. Overall, InterpQM was better in 75% of validation cases for the daily large, 72% of all observations and 56% of mean seasonal snow depth cases. We describe the performed homogenization procedure in detail, including quality control, gap filling, homogeneity testing, break detection, calculation of and improvements to the adjustment method. Our results show that snow depth time series generally have a lower number of breaks compared to station data of other climate variables. This underlines their high quality, even if measuring snow presents challenges. Using Austrian snow depth series as an example, the effects of the new adjustment method on trends were analyzed using the Mann-Kendall and Sen's Slope. Homogenization may have a significant effect on derived trends: Two of the six adjusted series were changed from nonsignificant to significant and one vice versa.

Knowledge concerning possible inhomogeneities in a data set is of key importance for any subsequent climatological analyses. Well-established relative homogenization methods developed for temperature and precipitation exist but have rarely been applied to snow-cover-related time series. We undertook a homogeneity assessment of Swiss monthly snow depth series by running and comparing the results from three well-established semi-automatic break point detection methods (ACMANT – Adapted Caussinus-Mestre Algorithm for Networks of Temperature series, Climatol – Climate Tools, and HOMER – HOMogenizaton softwarE in R). The multi-method approach allowed us to compare the different methods and to establish more robust results using a consensus of at least two change points in close proximity to each other. We investigated 184 series of various lengths between 1930 and 2021 and ranging from 200 to 2500 m a.s.l. and found 45 valid break points in 41 of the 184 series investigated, of which 71 % could be attributed to relocations or observer changes. Metadata are helpful but not sufficient for break point verification as more than 90 % of recorded events (relocation or observer change) did not lead to valid break points. Using a combined approach (two out of three methods) is highly beneficial as it increases the confidence in identified break points in contrast to any single method, with or without metadata.

Snow avalanche hazard mapping has a long tradition in the European Alps. Hazard maps delineate areas of potential avalanche danger and are only available for selected areas where people and significant infrastructure are endangered. They have been created over generations, at specific sites, mainly based on avalanche activity in the past. For a large part of the area (90 % in the case of the canton of Grisons) only strongly generalized hazard indication maps are available (SilvaProtect), not showing impact information such as pressure. This is a problem when new territory with no or an incomplete historical record is to be developed. It is an even larger problem when trying to predict the effects of climate change at the state scale, where the historical record may no longer be valid. To close this gap, we develop an automated approach to generate spatially coherent hazard indication mapping based on a digital elevation model for the canton of Grisons (7105 km²) in the Swiss Alps. We calculate eight different scenarios with return periods ranging from frequent to very rare as well as with and without taking the protective effects of the forest into account, resulting in a total of approximately 2 million individual avalanche simulations. This approach combines the automated delineation of potential release areas, the calculation of release depths and the numerical simulation of the avalanche dynamics. We find that between 47 % (most frequent scenario) and 67 % (most extreme scenario) of the cantonal area can be affected by avalanches. Without forest, approximately 20 % more area would be endangered. This procedure can be applied worldwide, where high-spatial-resolution digital elevation models, detailed information on the forest and data on the snow climate are available, enabling reproducible hazard indication mapping also in regions where no avalanche hazard maps yet exist. This is invaluable for climate change studies. The simulation results are validated with official hazard maps, by assessments of avalanche experts, and by existing avalanche cadastres derived from manual mapping and mapping based on satellite datasets. The results for the canton of Grisons are now operationally applied in the daily hazard assessment work of the authorities. Based on these experiences, the proposed approach can be applied for further mountain regions.

Gefahrenkarten bilden eine wichtige Grundlage für den Umgang mit der Lawinengefahr im Alpenraum. Allerdings existieren diese Karten nur im Siedlungsbereich, für einen Grossteil der Fläche sind keine oder nur sehr begrenzte Informationen zur Lawinengefährdung verfügbar. Aus diesem Grund hat das WSL Institut für Schnee- und Lawinenforschung SLF zusammen mit dem Amt für Wald und Naturgefahren (AWN) des Kantons Graubünden eine Methode entwickelt, um flächendeckende Gefahrenhinweiskarten automatisiert zu erstellen. Die Kombination von Anrissgebiets-Ausscheidung mit der numerischen Simulation der Auslaufstrecken mit der Software RAMMS macht dies möglich. Dabei halten wir uns so weit wie möglich an die Vorgehensweise, wie sie in der Schweiz zur Erstellung der Gefahrenkarten verwendet wird.

Hazard maps are a crucial base for tackling avalanche hazard in the Alps. But such maps are only elaborated within specific, mostly settled areas, for the large part none or only very limited information on avalanche danger is available. Therefore, the WSL Institute for Snow and Avalanche Research SLF and the office for forest and natural hazards (AWN) of the canton Grisons developed a methodology to calculate spatial continuous hazard indication maps for large Regions. The combination of release area delineation with the numerical simulation of avalanche runout with the software RAMMS makes this possible. We stick as much as possible with the procedure applied for the hazard mapping generation in Switzerland.

Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and additionally derives liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events, and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018-2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient, and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS sensors under a broad range of climatic conditions, and LWC and HS are valuable add-ons.

To make sound decisions in the face of climate change, government agencies, policymakers and private stakeholders require suitable climate information on local to regional scales. In Switzerland, the development of climate change scenarios is strongly linked to the climate adaptation strategy of the Confederation. The current climate scenarios for Switzerland CH2018 - released in form of six user-oriented products - were the result of an intensive collaboration between academia and administration under the umbrella of the National Centre for Climate Services (NCCS), accounting for user needs and stakeholder dialogues from the beginning. A rigorous scientific concept ensured consistency throughout the various analysis steps of the EURO-CORDEX projections and a common procedure on how to extract robust results and deal with associated uncertainties. The main results show that Switzerland’s climate will face dry summers, heavy precipitation, more hot days and snow-scarce winters. Approximately half of these changes could be alleviated by mid-century through strong global mitigation efforts. A comprehensive communication concept ensured that the results were rolled out and distilled in specific user-oriented communication measures to increase their uptake and to make them actionable. A narrative approach with four fictitious persons was used to communicate the key messages to the general public. Three years after the release, the climate scenarios have proven to be an indispensable information basis for users in climate adaptation and for downstream applications. Potential for extensions and updates has been identified since then and will shape the concept and planning of the next scenario generation in Switzerland.

Global navigation satellite system (GNSS) refractometry enables automated and continuous in situ snow water equivalent (SWE) observations. Such accurate and reliable in situ data are needed for calibration and validation of remote sensing data and could enhance snow hydrological monitoring and modeling. In contrast to previous studies which relied on post-processing with the highly sophisticated Bernese GNSS processing software, the feasibility of in situ SWE determination in post-processing and (near) real time using the open-source GNSS processing software RTKLIB and GNSS refractometry based on the biased coordinate Up component is investigated here. Available GNSS observations from a fixed, high-end GNSS refractometry snow monitoring setup in the Swiss Alps are reprocessed for the season 2016/17 to investigate the applicability of RTKLIB in post-processing. A fixed, low-cost setup provides continuous SWE estimates in near real time at a low cost for the complete 2021/22 season. Additionally, a mobile, (near) real-time and low-cost setup was designed and evaluated in March 2020. The fixed and mobile multi-frequency GNSS setups demonstrate the feasibility of (near) real-time SWE estimation using GNSS refractometry. Compared to state-of-the-art manual SWE observations, a mean relative bias below 5% is achieved for (near) real-time and post-processed SWE estimation using RTKLIB.