Dr. Mathias Bavay

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.

From the start, the SNOWPACK model was created to address the needs of the avalanche warning in Switzerland based on a large network of Automatic Weather Stations (AWS). An energy balance approach was chosen that incorporated the modeling of the snow metamorphism in order to reproduce the stratigraphy, an arbitrary number of layers and the ability to simulate very thin layers. But a number of annoying issues remained for the daily use in an operational toolchain, linked to the handling of raw data coming from the AWS as well as the limitations of the AWS themselves. After the data preprocessing got separated from the SNOWPACK model itself as the MeteoIO library (in 2008), it experienced a sustained development effort over more than 15 years. Many typical data issues could be identified and addressed, often with timeseries filters in MeteoIO while a few had to be addressed in the SNOWPACK model itself. More flexibility was also allowed by new developments aimed at supporting operational constraints such as the inability to physically access some of the AWS in the winter, even to perform emergency maintenance.
This paper shows some of the limitations of the measurement network from the SNOWPACK point of view and some typical data problems that have been encountered in the last 15 years while forcing SNOWPACK with raw AWS data. Some of the solutions that have been implemented and the compromises that had to be made are presented as well as their remaining limitations.

Snow in mountain catchments serves as a constant water release during the melting season, supporting many human activities downstream. However, it also poses threats to human life and infrastructures, e.g. with wet- or glide-snow avalanches. These avalanches are likely to occur more frequently with a warming climate but are hard to predict due to the spatio-temporal complexity of the involved processes. The snow liquid water content is a key parameter to monitor to address both potentialities and challenges of a melting snowpack. Synthetic Aperture Radar products like Sentinel-1 are valid tools for detecting wet snow, as liquid water increases dielectric losses absorption coefficients, resulting in low backscattering values. Although energy-balance snow models can simulate liquid water distribution as well as other scattering properties, continuous ground truth measurements are needed for validation. However, such datasets are rare, because they are very demanding as of resources and expertise. We present part of a comprehensive dataset of full snow profiles covering one snow season at the Weissfluhjoch field site (Switzerland). This dataset includes detailed manual measurements of important snow microwave properties: temperature, density, specific surface area, liquid water content, and surface roughness. The high temporal and vertical resolution of the dataset allowed us to track the evolution of the wetting front in detail. Moreover, making use of the Snow Microwave Radiative Transfer model (SMRT), we were able to use ground data to successfully reproduce the Sentinel-1 backscattering. Finally, we could give a detailed explanation of the physical processes driving Sentinel-1 backscattering trends and explore the potential of interpreting these trends to track the melting snow process.

Understanding the dynamics of treeline ecotones under global change requires long-term ecological and environmental data. The Stillberg ecological treeline research site in the Swiss Alps was established in 1975 by planting 92,000 seedlings of Larix decidua, Pinus cembra and Pinus mugo ssp. uncinata, and has been continuously monitored since then. Here, we present curated long-term data acquired over almost 50 years at the Stillberg site, and we synthesise the major research findings. The long-term datasets comprise 6.5 million ecological and environmental records from the 40-year afforestation experiment, as well as from a 9-year free-air CO₂ enrichment experiment crossed with a 6-year soil warming experiment, a 12-year nutrient addition experiment and an 8-year multifactorial tree seedling recruitment experiment. Our datasets further include 38 million records of 25 meteorological parameters measured at an hourly resolution from 1975 to 1996, and at a 10 min resolution since 1997. We provide all datasets and the corresponding metadata as open research data. Almost five decades of research in this treeline ecotone showed high mortality after tree establishment that was closely related to microclimatic variability. The two Pinus species survived at a much lower rate than L. decidua, due to indirect pathogen interactions. Furthermore, CO₂ enrichment only increased growth of L. decidua, while warming increased growth of P. mugo ssp. uncinata and two Vaccinium shrub species. Enhanced nutrient availability stimulated growth in tree and understorey shrub species. In addition, soil warming and CO₂ enrichment stimulated microbial activity and decreased soil carbon stocks. These findings improve our understanding of ecological processes in the treeline ecotone under global change and confirm the importance of tree growth and establishment limitations. The enhanced availability and quality of these long-term data are expected to foster whole-system approaches and transdisciplinary research syntheses, supporting the development of effective global change adaptation strategies.

Algae populate multiple habitats, including snow and ice, where they can form red blooms. These decrease snow albedo, accelerating snowmelt and potentially feeding back on snow and glacier decline caused by climate change. Quantifying this feedback requires the understanding of bloom evolution with climate change. Little, however, is known about the drivers of red snow blooms. Here, we develop an algorithm to analyze 5 y of satellite data from the European Alps and separate bloom occurrences from similarly colored Saharan dust depositions. In a second step, we combine the occurrences of blooms with meteorological data and snow simulations to identify the drivers of blooms. Results show that the upward migration of algae from the ground and blooming requires the presence of liquid water throughout the whole snow column for at least 46 d. Our limited data suggest that moderate dust amounts provide nutrients favorable to bloom, whereas large dust amounts hasten snowmelt and reduce its duration below the threshold required for blooming. Over the period studied, blooms cover 1.3% of the area above 1,800 m elevation, advancing the snow melt-out date by 4 to 21 d in these areas. Under warmer climates, maximum snow mass will decrease whereas snowmelt duration, that controls algal blooms' occurrences, is less sensitive to global temperature increase. In this respect, the impact of bloom on snowmelt will either remain stable (RCP4.5) or decrease (RCP8.5). Algal blooms in the Alps therefore do not constitute a positive climate feedback.

As numerical model developers, we have experienced first hand how most users struggle with the configuration of the models, leading to numerous support requests. Such issues are usually mitigated by offering a graphical user interface (GUI) that flattens the learning curve. Developing a GUI, however, requires a significant investment for the model developers, as well as a specific skill set. Moreover, this does not fit with the daily duties of model developers. As a consequence, when a GUI has been created – usually within a specific project and often relying on an intern - the maintenance either constitutes a major burden or is not performed. This also tends to limit the evolution of the numerical models themselves, since the model developers try to avoid having to change the GUI.
In this paper we describe an approach based on an XML description of the required numerical model configuration elements (i.e., the data model of the configuration data) and a C++/Qt tool (Inishell) that populates a GUI based on this description on the fly. This makes the maintenance of the GUI very simple and enables users to easily get an up-to-date GUI for configuring the numerical model. The first version of this tool was written almost 10 years ago and showed that the concept works very well for our own surface process models. A full rewrite offering a more modern interface and extended capabilities is presented in this paper.

This study compares the ability of two degree-day models (Poli-Hydro and a hybrid degree-day implementation of Alpine3D) and one full energy-balance melt model (Alpine3D) to predict the discharge on two partly glacierized Alpine catchments of different size and intensity of exploitation, under present conditions and climate change as projected at the end of the century. For the present climate, the magnitude of snowmelt predicted by Poli-Hydro is sensibly lower than the one predicted by the other melt schemes, and the melting season is delayed by 1 month. This difference can be explained by the combined effect of the reduced complexity of the melting scheme and the reduced computational temporal resolution. The degree-day implementation of Alpine3D reproduces a melt season closer to the one obtained with its full solver; in fact, the onset of the degree-day mode still depends upon the full energy-balance solver, thus not bringing any particular benefit in terms of inputs and computational load, unlike with Poli-Hydro. Under climate change conditions, Alpine3D is more sensitive than Poli-Hydro, reproducing discharge curves and volumes shifted by 1 month earlier as a consequence of the earlier onset of snowmelt. Despite their benefits, the coarser temporal computational resolution and the fixed monthly degree days of simpler melt models like Poli-Hydro make them controversial to use for climate change applications with respect to energy-balance ones. Nevertheless, under strong river regulation, the influence of calibration might even overshadow the benefits of a full energy-balance scheme.

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.

Increasing temperatures and snow scarcity pose a serious threat to ski tourism. While the impacts of climate change on ski tourism have been elaborated extensively, little is known so far on the vulnerability of winter tourism towards both internal climate variability and climate change. We use a 50-member single model large ensemble from a regional climate model to drive the physically-based snowpack model SNOWPACK for eight stations across the Swiss Alps to model daily snow depth, incorporating both natural snow conditions and including technical snow production. We make a probabilistic assessment of the vulnerability of ski tourism towards internal climate variability in a future climate by analyzing selected tourism-related snow indicators and find significant overall decrease in snow reliability in the future. Further, we show how the sensitivity towards internal climate variability differs among different tourism-related snow indicators and find that certain indicators are more vulnerable to internal climate variability than others. We show that technical snow production is an appropriate adaptation strategy to tackle risks from climate change and internal climate variability. While technical snow production can drastically reduce uncertainties related to internal climate variability, in low elevations, the technique reaches its limits to counteract global warming by the mid of the century.

The Global Cryosphere Watch (GCW) was initiated by the World Meteorological Organization (WMO) as a mechanism to support the delivery of Earth System monitoring, modelling and prediction services focused on the cryosphere. GCW fosters international coordination and partnerships with the goal of providing authoritative, clear and usable data, information and analyses on the past, current and future state of the cryosphere. It fosters sustained and mutually beneficial partnerships between research and operational institutions, by linking research and operations as well as scientists and practitioners. This is important as most available cryospheric data come from the scientific community. It is generally managed by research institutes which often do not have the infrastructure, the resources, nor the mandate to enable FAIR data management, which is necessary for interoperability and discovery at data level. This implies that data do not fit into standardized systems or dataflows for broader data access and exchange (as exists at the WMO) and thus have been unavailable for operational meteorological and climate applications. This lack of standardization also impairs the reuse of data within the scientific community. GCW is bridging this gap through a data portal and software stack enabling the transformation of sparsely documented and highly variable data into standardized and well documented data suitable for downstream applications with data level interoperability. A processing engine converts raw data provided by the data producers into NetCDF-CF standard files with NetCDF Attribute Convention for Dataset Discovery (ACDD) metadata. The data portal web front end harvests the metadata necessary for its search engine through an OPeNDAP server so no manual editing of the medatadata is necessary. When a user downloads some data from the web portal, it gets the requested data through the OPeNDAP server.

Snow management, i. e., snowmaking and grooming, is an integral part of modern ski resort management. While the current snow cover distribution on the slopes is often well known thanks to the usage of advanced monitoring techniques, information about its future evolution is usually lacking. Management-enabled numerical snowpack models driven by meteorological forecasts can help to fill this gap.
In the frame of the H2020 project PROSNOW, the snowpack models AMUNDSEN, Crocus, and SNOWPACK/Alpine3D are applied in nine pilot ski resorts across the European Alps for forecasting snow conditions in time scales from days to several months ahead. We present the integration of detailed snowmaking and grooming practices implemented in the three models and show how they can be adapted to individual ski resorts. An ensemble of snow management configurations accounting for a comprehensive set of possible tactical and strategic operational choices is introduced, along with an approach to homogeneously spatialize the results of the three snow models over different areas of the ski resorts. First simulation results are presented for the nine pilot ski resorts in the form of distributed snow water equivalent (SWE) maps along with SWE and snow depth time series for two selected seasons in the past.

An important feature of snow cover is the fact that its volume and duration is subject to large year-to-year fluctuations. As frozen precipitation, snow cover is a natural water reservoir that delays precipitation to runoff and is thus of outstanding importance for the seasonal water balance in Switzerland. Over a whole year, approximately 40% (22 km3) of the annual runoff currently comes from snow melting and only 1-2 % from glacier melting. Typically, the snow cover in the Alpine region builds up over the autumn and winter months, reaches its maximum between February and May, depending on the altitude, and dominates the runoff processes during melting in the following spring and summer months. [...]

Ein wichtiges Merkmal der Schneebedeckung ist die Tatsache, dass deren Volumen und Dauer grossen Jahr-zu-Jahr Schwankungen unterliegt. Als gefrorener Niederschlag ist die Schneedecke ein natürlicher Wasserspeicher, der den Niederschlag verzögert zum Abfluss bringt und damit eine herausragende Bedeutung für den saisonalen Wasserhaushalt in der Schweiz hat. Über ein ganzes Jahr betrachtet, stammen aktuell ungefähr 40% (ca. 22 km3) des jährlichen Abflusses aus der Schneeschmelze und nur gerade 1-2 % aus der Gletscherschmelze. Typicherweise baut sich die Schneedecke im Alpenraum über die Herbst- und Wintermonate hinweg auf, erreicht ihr Maximum je nach Höhenlage zw. Februar und Mai und dominiert während des Abschmelzens in den folgenden Frühlings- und Sommermonaten die Abflussprozesse. [...]

The application of numerical modelling of the snowpack in support of avalanche hazard prediction is increasing. Modelling, in complement to direct observations and weather forecasting, provides information otherwise unavailable on the present and future state of the snowpack and its mechanical stability. However, there is often a perceived mismatch between the capabilities of modelling tools developed by research organizations and implemented by some operational services, and the actual operational use of those by avalanche forecasters. This causes frustration on both sides. By summarizing currently implemented modelling tools specifically designed for avalanche forecasting, we intend to diminish and contribute to bridging this gap. We highlight specific features and potential added value, as well as challenges preventing a more widespread use of these modelling tools. Lessons learned from currently used methods are explored and provided, as well as prospects for the future, including a list of the most critical issues to be addressed.

Originally developed to fulfill the needs of applications consuming meteorological data, the Open Source MeteoIO library offers many valuable features for operational systems. It has been from the very start developed with a focus on robustness and efficiency for applications that must run unattended. Its use has been expanded to cover a wide variety of operational tasks from monitoring to data standardization to data hub and from time series at single locations to time series of gridded data. It is now used by several operational services as well as several companies.

Forcing snow cover models with high resolution numerical weather prediction models has provided new insight into the spatial distribution and temporal evolution of, for instance, snow depth and snow water equivalent in mountainous terrain. Tough, of particular interest to avalanche forecasting services are spatial patterns of snow instability. However, it is presently unclear what level of model complexity is required to obtain snow instability information such that the locations where the danger is most pronounced can be identified. Alpine3D is a spatially distributed (surface), threedimensional (atmospheric) model for analyzing and predicting dynamics of snow-dominated surface processes in mountainous topography. We forced Alpine3D with gridded meteorological data for the region of Davos, Switzerland (~ 20 km x 20 km; 100 m grid spacing) with the aim to identify spatial patterns of potential instability with regard to slope elevation and aspects. We simulated the snow cover using SNOWPACK - implemented in Alpine 3D - to identify spatial patterns across the model domain for two winters between 2016 and 2018. The winter season 2016-2017 was a below-average winter in terms of snow depth and hence persistent weak layers existed throughout the entire study region. In contrast, the winter season 2017-2018 showed above-average snow depth with few critical weak layers. General patterns of the two winters were reproduced by the simulations. Despite the high computational costs spatially distributed model chains show promising potential for supporting operational avalanche forecasting.

Large avalanches usually encounter different snow conditions along their track. When they release as slab avalanches comprising cold snow, they can subsequently develop into powder snow avalanches entraining snow as they move down the mountain. Typically, this entrained snow will be cold (T < −1 ∘C) at high elevations near the surface, but warm (T > −1 ∘C) at lower elevations or deeper in the snowpack. The intake of warm snow is believed to be of major importance to increase the temperature of the snow composition in the avalanche and eventually cause a flow regime transition. Measurements of flow regime transitions are performed at the Vallée de la Sionne avalanche test site in Switzerland using two different radar systems. The data are then combined with snow temperatures calculated with the snow cover model SNOWPACK. We define transitions as complete when the deposit at runout is characterized only by warm snow or as partial if there is a warm flow regime, but the farthest deposit is characterized by cold snow. We introduce a transition index F_t, based on the runout of cold and warm flow regimes, as a measure to quantify the transition type. Finally, we parameterize the snow cover temperature along the avalanche track by the altitude H_s, which represents the point where the average temperature of the uppermost 0.5 m changes from cold to warm. We find that F_t is related to the snow cover properties, i.e. approximately proportional to H_s. Thus, the flow regime in the runout area and the type of transition can be predicted by knowing the snow cover temperature distribution. We find that, if H_s is more than 500 m above the valley floor for the path geometry of Vallée de la Sionne, entrainment of warm surface snow leads to a complete flow regime transition and the runout area is reached by only warm flow regimes. Such knowledge is of great importance since the impact pressure and the effectiveness of protection measures are greatly dependent on the flow regime.

The application of physically-based numerical modeling of the snowpack in support of avalanche hazard prediction is increasing. Modeling, in complement to direct observations and weather forecasting, provides information otherwise unavailable on the present and future state of the snowpack and its mechanical stability. However, there is a significant mismatch between the capabilities of modeling tools developed by research organizations and implemented by some operational services, and the actual operational use of those by avalanche forecasters, thereby causing frustration on both sides. By summarizing currently implemented modeling tools specifically designed for avalanche forecasting, we intend to diminish and contribute to bridge this gap. We highlight specific features and potential added value, as well as challenges preventing a more widespread use of these modeling tools. Lessons learned from currently used methods are explored and provided, as well as prospects for the future, including a list of the most critical issues to be addressed.

Comparison of energy Balance (EB) and temperature Index (TI) snow/ice melt methods for glacio-hydrological modeling in the Alps has been the subject of numerous studies; however, the debate on which method is preferable under which conditions and for which application is still ongoing. In this study, both melting routines were tested within the complex numerical framework of the fully distributed model (Alpine3D) to analyze the differences. We included two high-altitude Swiss alpine glaciered catchments, which represent two distinct types of glaciers, namely cirque and valley glaciers, with different weather conditions, topography, and data type. In this experiment, hydrological discharge in the Damma catchment was overestimated by the model with EB approach, which demonstrated Nash-Sutcliffe efficiency (NSE) of 0.61 mainly due to the overestimation of wind speed and longwave radiation. On the other hand, model with TI approach obtained a high NSE of 0.93, as this simple conceptual model does not rely on the abovementioned variables. For the Arolla catchment, better results were observed for both EB and TI melt schemes, with slight underestimation in modeled runoff as shown by NSEs of 0.89 and 0.77, respectively. The very satisfactory performance of EB model at the Arolla catchment is due to the use of input data sets from local weather stations that are more representative of the real catchment conditions. It was observed that the EB melt model performs better if driven with correct meteorological data sets from catchment-representative stations and that the calibration procedure of TI-melt model significantly affects its accuracy.