Private

FS 3.150

Methodological advances in mountain research

Details

  • Full Title

    FS 3.150: Methodological advances in mountain research
  • Scheduled

    TBA
  • Location

    TBA
  • Co-Conveners

  • Assigned to Synthesis Workshop

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  • Thematic Focus

    Atmosphere, Cryo- & Hydrosphere, Low-to-no-snow, Monitoring, Remote Sensing
  • Keywords

    Methods, Elevation dependent warming, Remote sensing, Modeling

Description

The content was (partly) adapted by AI

Mountain environments present unique challenges for researchers due to their steep environmental gradients, complex topography, variable weather conditions, distinctive vegetation, and rapid hydrological responses that often invalidate assumptions applied in other landscapes. This session will showcase methodological advances, innovations, limitations, and adaptations in GIS, remote sensing and machine learning techniques applied in this unique landscape. We welcome contributions from all fields, encouraging an interdisciplinary perspective to deepen the understanding of these challenges and potential solutions. Topics may include methodological advances in remote sensing, GIS, geostatistics, and machine learning applied to understand vegetation dynamics, snow and ice changes, hydrological applications, and more.

Submitted Abstracts

ID: 3.8112

Characterizing tree line vegetation using terrestrial laser scanning and non-linear models in Nepalese mountain

Kishor Prasad Bhatta
Basnet, Prakash; Seidel, Dominik; Hölscher, Dirk

Abstract/Description

The tree line ecotone in the Himalayas is shaped by diverse topographic and climatic conditions. Variation in forest structure may influence ecosystem functions but so far have remained unexplored. We assessed the forest structure and its complexity across the tree line ecotone at the wind- and at the leeward side of the Annapurna range. Plots (n = 90) were situated between 3500 m and 4100 m asl. The windward side receives more than 3000 mm yr-1 rainfall while at the leeward side rainfall is only slightly higher than 100 mm yr-1, with higher rainfall at lower elevation. At the windward side broad-leaved Rhododendron species dominated the forest while at the leeward side needle-leaved species including Pinus wallichiana were dominant. Forest structure as obtained by terrestrial laser scanning suggests that the windward side had a lower vertical structural complexity than the leeward side. Forest structure changed with elevation across the ecotone as indicated by general additive models. At the windward side, tree density, diameter, height as well as forest canopy cover and stand structural complexity declined continuously with elevation. At the leeward side, tree and stand structural attributes did not decrease until close to the tree line but then sharply declined within a short altitudinal range. Thus, our study highlights the differences between rainfall regimes and underscores the importance of elevation for stand structural complexity across the tree line ecotone.

ID: 3.9212

Global geodatabase of mountain glacier extents at the Last Glacial Maximum

Augusto Lima
Margold, Martin; Hughes, Anna L. C.; Dulfer, Helen E.; Barr, Iestyn; Rentier, Eline S.; Laabs, Benjamin; Flantua, Suzette G. A.

Abstract/Description

Mountain regions are essential for understanding Earth’s climatic history, as their cycles of glacial advance and retreat have shaped landscapes, ecosystems, and regional climates during the Quaternary, leaving behind palaeoglacier records that reveal past climate dynamics. These glacier records are particularly important for understanding regional and local climate variations, as mountain glaciers respond sensitively to climatic changes, highlighting the importance of studying their past glaciation. This higher sensitivity is evident in the timing of maximum glacial extent (i.e. local Last Glacial Maximum, LLGM) in mountains, which often occurred outside the global LGM (26–19 kyr BP). However, existing global palaeoglacier databases (e.g., Ehlers and Gibbard, 2004; Ehlers et al., 2011) have not been updated to incorporate glacier extensions reconstructed in the last decade.

To address this gap, we present a new open-access global geodatabase of mountain glacier extents for the LGM. This synthesis integrates ice-extent reconstructions from 213 studies across 271 mountain ranges globally, standardising over 16,300 individual glacier reconstructions into a digital geodatabase covering the period 57-14 kyr BP. We implemented a hierarchical mountain range classification system, compiled metadata from each publication, and linked each reconstruction to its original sources. This effort has updated the state of knowledge in 157 mountain ranges, added over 9,450 new glacier reconstructions, and identified a gap in research in 114 mountain ranges where no updated reconstructions appear to have been produced in at least 13 years.

Our geodatabase is a powerful resource for investigating regional past climate variability, mountain landscape evolution, and ecological impacts of glaciations. It provides glacier masks for validating and refining climate-glacier modelling and offers spatial boundaries for paleoecological reconstructions of mountain ecosystems. Furthermore, it identifies research gaps and understudied regions, guiding future work in Quaternary science. We anticipate releasing the database soon with the corresponding publication and website, along with detailed methodology and guidelines for further use.

ID: 3.10140

Challenges of Linear Assumptions in Remote Sensing Indices

Mathieu Gravey
Harsh, Beria; Rumpf, Sabine

Abstract/Description

Remote sensing indices like NDVI are widely used to analyze vegetation, snow (NDSI), water dynamics (NDWI), … However, their non-linearity and bounded nature create challenges when applying common analytical methods such as averaging, resolution scaling, and trend estimation. These issues are particularly evident in mountainous and low-vegetation environments, where complex terrain, vegetation gradients, and seasonal variability further complicate interpretation.

This study examines how spatial and temporal averaging can misrepresent environmental patterns, how resolution choices influence index behavior, and why linear regression can produce misleading trends. We review existing methodologies, discuss their limitations, and explore how researchers have attempted to address these issues. By assessing these challenges, we aim to provide a clearer understanding of the implications of linear assumptions in remote sensing analyses.

ID: 3.10268

Spatiotemporal forecasting of snow water equivalent: the potential of hybrid machine learning models

Oriol Pomarol Moya
Nussbaum, Madlene; Mehrkanoon, Siamak; Kraaijenbrink, Philip; Gouttevin, Isabelle; Karssenberg, Derek; Immerzeel, Walter W.

Abstract/Description

Snow water equivalent (SWE) is a crucial component of mountain hydrology but still faces large uncertainties in its quantification due to its high temporal and spatial variability. While machine learning (ML) has been applied to similar domains with notable results, its use for SWE prediction has been hindered by the quality, quantity and extent of the measured data. Hybrid models that integrate simulated data from physics-based models with ML have shown promising results in data-scarce scenarios. A comparison of hybrid models was performed, targeting temporal and spatial extrapolation of SWE. Crocus snow model simulations were used together with data from ten meteorological and snow observation stations throughout the northern hemisphere containing 7-20 years of data. Two main setups were tested; a common post-processor approach, where the outputs and state variables from Crocus were fed as additional predictors to the ML model at each time step, and a data augmentation approach, where Crocus simulations for stations at which no measurements are available were used as additional training points. The results show that the post-processor approach is best suited for predicting SWE in years excluded during training. However, when predicting in left-out stations, the data augmentation setup achieved the largest increase in performance, reducing the root mean squared error by 22% compared to Crocus and by 42% compared to a measurement-based ML model. A feature importance analysis reveals that the hybrid model predictions are most influenced by physically-sound variables, such as incoming radiation, current SWE status, air temperature and snowfall. After proving the ability of hybrid models for predicting SWE under data-scarce conditions, ongoing work will further assess their robustness and applicability on an extensive dataset covering the northern hemisphere.

ID: 3.10790

Estimating complex topographic changes in mountain regions from 3D point clouds

Shoujun Jia
de Vugt, Lotte; Mayr, Andreas; Rutzinger, Martin

Abstract/Description

Topographic change estimation is an essential requirement to investigate where, when, how, and why Earth surface processes occur, especially regarding rigid spatial movements and non-rigid shape changes of local surfaces. For area-wide geomorphologic process detection and quantification, 3D laser scanning is widely utilized to survey topographic surfaces. The resulting 3D point clouds have advantages over those derived from other remote sensing methods in observation accuracy, dimension, resolution, and the ability to penetrate high vegetation canopy. However, there are several challenges involved in estimating topographic changes in mountainous terrain, such as point cloud heterogeneities, rough topographic surfaces, and complex topographic changes. Unlike point-to-point comparison in existing studies, this research works on these challenges by considering topographic dynamics as a deformation of Riemann manifold surfaces. This new perspective makes it possible to comprehensively understand complex topographic dynamics by separating rigid and non-rigid surface changes. More specifically, to capture the topographic changes for all points in point clouds and avoid the difficulty caused by global topographic roughness, we adaptively represent Riemann manifold surfaces within the local scope of each observation based on polynomial functions. Then, both rigid (i.e., translation & rotation) and non-rigid (i.e., stretch & distortion) topographic changes are estimated from pairwise point clouds, by computing the transformation parameters and the deformation tensors of the Riemann manifold surfaces. Moreover, to detect significant changes, the errors occurring in point cloud acquisition and processing (e.g., registration errors, surface fitting) are considered to quantify the uncertainties by error propagation. The proposed method is tested in a mountain region (Sellrain, Tyrol, Austria), where an extreme rainfall event triggered a large number of landslides in 2015. The area has been surveyed by airborne laser scanning in 2013 and 2017. By applying various topographic change features (i.e., translation, rotation, stretch, and distortion), we identify different topographic processes (e.g., identified 84 out of a total of 88 reference landslides) and analyse their spatial change patterns via clustering analysis. The result demonstrates the effectiveness and applicability of the proposed method for estimation and interpretation of topographic changes in mountains.

ID: 3.10866

Discussion on the Influence of Intermediate Principal Stress in Rock Deformation Analysis and State-of-the-Art Knowledge

Kamran Panaghi
Takemura, Takato

Abstract/Description

One of the prominent concerns in rock slope stability analysis and design of earth structures is how crack propagation tendency in rock would impact the efficacy of precautionary measures. For this to be scrutinized, engineers need to consider influential factors such as loading magnitude and direction, crack density in the domain, as well as other contributing elements such as pore water pressure. While there is a broad literature on the aforementioned, the role of intermediate principal stress on rock behavior and its variations with time is known to a lesser extent due to the existing experimental limitations. Since the occurrence of earthquakes alter the distribution of stresses in active tectonic regions, newly-established domains with different mechanical and hydraulic properties would emerge. This necessitates more rigorous rock stability evaluations in domains wherein potentially new water pathways with different stress distribution mechanisms from before exist. In this work, the impetus for research on the relevant phenomena and recent advances in related studies as well as some theoretical backgrounds are elaborated upon. Furthermore, the results based on experimental observations are discussed and conclusions are drawn accordingly.

ID: 3.10992

Gravimetry: a powerful monitoring method for alpine cryological and hydrogeological processes

Landon J.S. Halloran
Amschwand, Dominik; Mohammadi, Nazanin; Carron, Antoine

Abstract/Description

Time-lapse gravimetry (TLG) is a non-invasive, integrative, geophysical/geodetic method in which changes in g can be measured to parts-per-billion precision. Measured gravity changes are, in part, the result of water/ice storage changes. Thus, TLG can be a powerful tool for the monitoring of the two hydrological components of alpine systems that are the most difficult to quantify: ground ice and groundwater.

We recently carried out the first periglacial application of TLG. At the well-known Murtèl rock glacier (Graubünden, Switzerland), we measured seasonal gravity changes in the July to September period and used UAV photogrammetry to correct for the effect of snow mass. Our results reveal spatial variations in active layer thaw (11 to 64 cm water equivalent) that would be challenging to observe directly. Comparison of our high precision data with a historic gravimetry survey also suggests the occurrence of permafrost degradation over the past three decades.

At other Alpine sites, we have employed TLG to monitor groundwater storage recession during the summer/autumn snow-free period. The gravimetry data provide quantitative insights into groundwater storage that would typically require extensive borehole infrastructure to obtain. TLG is a unique and effective method for quantifying subsurface storage variations and is particularly well-suited for alpine regions due to their pronounced annual hydrological variability and insufficient subsurface monitoring.

ID: 3.11221

Integrating Drone-based Ground Penetrating Radar and Thermal Infrared Imaging to Study Debris-Covered Glaciers

Adam Tjoelker
Baraër, Michel; Valence, Eole; Charonnat, Bastien; Mougeot, Emma; Masse-Dufresne, Janie; McKenzie, Jeffrey; Mark, Bryan

Abstract/Description

Debris-covered glaciers present acute challenges for fieldwork due to the presence of rocky debris and steep slopes. Consequently, the spatial distribution of surficial debris layers and the processes governing debris-covered glacier evolution remain understudied. Recent advances in drone-based remote sensing have improved our observation abilities for debris-covered glaciers, including ground-penetrating radar (GPR) and thermal infrared imagery. We present research that integrates thermal infrared imagery with airborne GPR to improve investigations of supraglacial debris. Data for this study was collected in the summer of 2024 on a debris-covered glacier in the Shár Shaw Tagà (also known as Grizzly Creek) watershed in Kluane National Park and Reserve, Yukon, Canada.

While airborne GPR provides unprecedented spatial coverage, it is limited in the minimum debris thickness that can be measured (approximately 1/4 the wavelength, or 0.37 m for the 200 MHz GPR radar). As a result of this limitation, we present a novel technique to measure a range of debris thicknesses by combining airborne GPR and thermal infrared photogrammetry surveys. In locations where debris is thin (< 0.5 m), thermal imaging enables debris thickness estimation where the radar would be unable to distinguish the surface and the ice/debris interface. Conversely, where debris thicknesses is greater, the GPR can more effectively evaluate debris thickness when empirical relationships of thickness and temperature become less precise. Combining these two techniques allows the debris thickness to be accurately assessed across varied debris thicknesses over the 0.5 ha study site. This new technique for debris mapping has applications for larger areas, where varied supraglacial debris thicknesses prevent estimations using a single technique.

ID: 3.11507

Integrating Geophysical Methods to Map Buried Ice and Groundwater Pathways in Deglaciating Valley

Eole Valence
Charonnat, Bastien; Tjoelker, Adam; Baraer, Michel; Dimech, Adrien; Masse-Dufresne, Janie; Richard, Jessy; Duvillard, Pierre-Allain; McKenzie, Jeffrey

Abstract/Description

Mountainous environments pose significant challenges for subsurface hydrological investigations due to their complex topography, unstable terrain, and variable hydrological conditions. In glacierized catchments, buried ice and permafrost can act as impermeable barriers, influencing groundwater pathways and meltwater storage. However, mapping these features remains challenging, particularly in deglaciating valleys where debris cover complicates remote sensing interpretations. We present a multi-method geophysical approach combining drone-based ground-penetrating radar (GPR), electrical resistivity tomography (ERT) with induced polarization (IP), and surface nuclear magnetic resonance (sMNR) to improve subsurface hydrological mapping in a rapidly changing mountain environment. ERT and IP provide high confidence in distinguishing buried ice from sediments and rock debris, though they are limited by logistical constraints and survey coverage. Drone-based GPR enhances measurement density and accessibility in unstable terrain, while sMNR, the only geophysical method solely sensitive to liquid water, can detect groundwater presence constrained by buried ice. This methodological integration allows for improved characterization of subsurface heterogeneity in a deglaciating valley in the Yukon, Canada. Our findings highlight the necessity of combining multiple geophysical techniques to advance hydrological mapping in glacierized catchments, offering new insights into water storage and transfer in mountain environments.

ID: 3.12031

Forest canopy height modelling using space-borne LiDAR systems in the Himalayan region

Akshay Paygude
Pande, Hina; Tiwari, Poonam

Abstract/Description

GEDI is a modern full-waveform LiDAR mission, operating from the International Space Station, designed to capture vertical vegetation structure at global scale. Similarly, the ICESat-2 launched by NASA in 2018 has received extensive attention for forest canopy height mapping. However, the measurements from space-borne LiDAR sensors are affected by undulating mountainous terrain, making them less accurate. This study attempts to develop a canopy height model in the Western Himalayan Region by integrating observations from GEDI and ICESat-2. A combination of multispectral, hyperspectral and backscatter datasets were used as predictive variables. Variable selection results suggest higher significance of optical vegetation indices as compared to backscatter variables. The accuracy of canopy height models in the Himalayan region can be improved by selecting high-quality LiDAR observations and identifying appropriate predictive variables. The integration of hyperspectral data for modeling forest variables is constrained by the limited spatial and temporal coverage of hyperspectral datasets.

ID: 3.12345

The role of abundance in community modelling: Predicting plant communities in the Andean super-páramo

Lisa Danzey
Leigh, Andrea; Nicotra, Adrienne; Peyre, Gwendolyn

Abstract/Description

Understanding the distribution of biodiversity has been a longstanding focus of ecologists; yet explorations beyond individual trajectories to communities remain challenging. The community assembly process is complex with biotic and abiotic factors interacting at varying levels of significance depending on the system. In the northern Andes, the super-páramo sits at the highest elevation reaches (> 4200 m) of the broader páramo ecoregion – a montane ecosystem within a tropical biodiversity hotspot. The geographically isolated super-páramo hosts specific and ecologically unique plant communities that are sensitive to environmental change. Emerging techniques in community modelling are promising for predicting future shifts, yet we still can improve current methods of incorporating biotic interactions in a way that more closely reflects reality. Current modelling frameworks infer biotic interactions through co-occurrence patterns derived from presence-absence data. Yet, abundance data remains underutilised in informing the strength and consistency of these patterns. Here, we use predictive species distribution models (SDMs) and community assembly rules to refine co-occurrence patterns by incorporating an abundance criterion. We adopt two techniques that (i) model first and assemble later; or (ii) assemble first and model later. We fitted SDMs with 556 vegetation plot data obtained from VegAndes and abiotic variables extracted from the CHELSA database. The first technique followed the Spatially Explicit Species Assemblage Modelling framework (SESAM); we built individual SDMs and assembled models by applying richness constraints and probability ranking rules. The second technique employed joint-SDMs that consider biotic interactions by modelling correlated residuals among species after accounting for environmental factors, and from these models build entire communities. For both techniques, the abundance filter was incorporated during the assembly step and weighted relevant to other assembly processes. Overall, the models predicted super-páramo communities well with mismatched areas being quite local, most likely resulting from oversampling in heterogenous landscapes. We found community predictions differed when considering abundance compared to the core techniques, offering the opportunity to improve the ecological relevance of models. These novel improvements could be used to predict future redistribution of super-páramo biodiversity under climate change.

ID: 3.12528

Evaluating IoT-Based Monitoring System in Alpine Environments: A Practical Assessment

Sophia Brockschmidt
Keßler, Bernarda; Mandl, Bernhard; Schafferer, Martin; Schmiedinger, Thomas

Abstract/Description

BACKGROUND
Alpine regions are dynamic landscapes where monitoring is essential for tracking environmental changes and natural hazards. Traditional methods face challenges due to inaccessibility and harsh conditions. IoT-based sensor networks offer continuous, real-time data collection, but their effective deployment requires structured guidance.
OBJECTIVES
This study assesses the practical applicability of a newly developed guideline for deploying IoT-based monitoring systems in alpine environments. By testing the guideline with students, researchers, and local stakeholders, the study evaluates its effectiveness in facilitating environmental monitoring.
METHODS
A mixed-method approach is applied, including case studies, experiments, and participatory trials. The guideline is tested through IoT deployment exercises, focusing on sensor durability, network reliability, and data accuracy under extreme conditions. RESULTS
Preliminary results suggest that the guideline improves accessibility and reliability in alpine monitoring. Furthermore, the guideline allows significantly shorter development times and rapid adaptation of existing systems to specific conditions regarding the area of application and the parameters to be monitored.
CONCLUSIONS
IoT solutions significantly advance alpine monitoring by enabling high-resolution and long-term data collection. Validating and refining the guideline with diverse user groups ensures its practical relevance. Future research will integrate automated data processing and adaptive network configurations for enhanced resilience. Short Summary: This study evaluates a guideline for deploying IoT-based monitoring systems in alpine environments, testing its usability with different user groups to enhance environmental data collection and resilience under extreme conditions

ID: 3.13153

Mapping Glacier Foreland Features and their Evolution Using Earth Observation Data

Vanessa Streifeneder
Hölbling, Daniel; Dabiri, Zahra; Nafieva, Elena; Albrecht, Florian; Abad, Lorena; Laher, Matthias

Abstract/Description

Climate change is accelerating glacier retreat in high-mountain areas, together with related geomorphological and periglacial processes, leading to major changes in glacier forelands. This includes changes in glacial lakes and river courses, landslides, deformation of debris deposits, and revegetation. Earth observation (EO) data offer excellent opportunities to map and monitor features and their evolution in glacier forelands. We use both optical and synthetic aperture radar (SAR) data to map glacial forefield features and associated landform changes in selected study areas in the Austrian Alps. Using object-based image analysis (OBIA) and optical satellite imagery, complemented by digital elevation model (DEM) data, we semi-automatically map specific features and landforms such as landslides, screes, and moraines. Time series of synthetic aperture radar (SAR) data are used to track surface deformation changes using advanced interferometric SAR (InSAR) techniques. The delineated features from the optical data will be enriched with InSAR-derived surface deformation rates to gain deeper insights into how landforms in glacier forelands evolve over time. Our findings can contribute to a better understanding of landscape evolution in recently deglaciated areas and provide valuable information for alpine associations to support the maintenance of hiking infrastructure in these changing landscapes. Additionally, the results may aid alpine infrastructure managers, cartographic divisions, and mountaineers in making better informed decisions to improve the safety and sustainability of high-altitude hiking.

ID: 3.13388

A Multidisciplinary perspective to study Climate Change and Mountain Ecosystems: the working group on mountain research of the National Research Council of Italy (CNR)

Angela Marinoni
Marinoni, Angela; Bonasoni, Paolo; Mazari VIllanova, Luigi; Mazzini, Martina; Aldighieri, Barbara; Cerasa, Marina; Chiarle, Marta; Crisci, Alfonso; D'Andrea, Ettore; Di Lonardo, Sara; Gabrieli, Jacopo; Gilardoni, Stefania; Giordan, Daniele Giordan; Guyennon, Nicolas; Ianniello, Antonietta; Losavio, Clelia; Morabito, Marco; Mosca, Pietro; Nigrelli, Guido; Pratali, Lorenza; Provenzale, Antonello; Putero, Davide; Rogora, Michela; Sanna, Laura; Terzago, Silvia; Girolami, Michele; Petracchini, Francesco

Abstract/Description

Mountains are considered the first sentinels of climate change, experiencing intensified and amplified effects compared to other regions of the Earth. As dynamic platforms for studying climate and environmental changes, mountains play a vital role in understanding atmosphere, hydrosphere, biosphere, lithosphere and cryosphere processes. As vital water sources and biodiversity hotspots, mountain ecosystems provide key services that are increasingly threatened by rising temperatures, extreme weather events, and other climate-driven disruptions. To address these challenges, a dedicated working group within the Department of Earth System Sciences and Environmental Technologies (DSSTTA) of the National Research Council of Italy (CNR) has been established. This team focuses on four core activities: long-term environmental monitoring, assessing past, present and future climate impacts, developing future scenarios, and fostering education, awareness, and communication. Continuous running observation programs at CNR research stations provide critical data to assess environmental changes and offer insights into global climate change. These efforts aim to create knowledge for a correct information for climate policies and decision-making to mitigate adverse impacts and support sustainable mountain conservation, adaptation and management. Research spans multiple disciplines, including atmospheric pollution, ecosystem dynamics, water resource management, glacial and slope stability also in snowy conditions. Additionally, high resolution climate projections are being developed to improve predictions of mountain-specific impacts of climate change. The group also investigates the health implications of pollution, thermal comfort and climate change on mountain populations. One of the group’s primary tasks is to consolidate existing knowledge and resources, identify gaps in understanding and create transdisciplinary research networks. This includes fostering collaboration with stakeholders and mountain associations, such as the Italian Alpine Club (CAI). Furthermore, outreach initiatives will support education and raise awareness, especially among local communities, tourists, and policymakers, about the importance of protecting mountain environments. By emphasizing interdisciplinary research and outreach, the working group aims to drive proactive climate adaptation and mitigation strategies in mountain regions, ensuring the resilience of mountain ecosystems and communities.

ID: 3.13550

Radar-based Detection of Water films for Gliding Snow – A laboratory Approach

Martin Schafferer
Brockschmidt, Sophia; Keßler, Bernarda; Mandl, Bernhard; Schmiedinger, Thomas

Abstract/Description

Climate change poses significant challenges in alpine areas, particularly with gliding snow hazards affecting natural habitats and ski resorts. The key mechanism involves a water film forming between the ground and snowpack, which reduces friction and can lead to avalanches. Currently, management involves artificial triggering by deliberately adding water to decrease ground-snow friction. Understanding this water film’s behavior is crucial – its presence and interaction with ground structure enables early detection of potential gliding snow, allowing intervention before visible glide cracks appear and supporting decision-making when cracks are already present.
To address this challenge, a technological approach to improve the detection and monitoring of these water films. A downward-looking radar system is used to detect water films and derive the connection structure within the snowpack.
The development and validation of the system take place in a controlled laboratory setting to precisely determine the key measurement parameters. Materials that match the density of snow and have the ability to retain water are used to simulate realistic snow conditions. In the laboratory, extensive tests are conducted to assess penetration depth and measurement accuracy, with a particular focus on detecting water films of varying thickness. Frequency tests enable deeper measurements, allowing for a more detailed analysis of the structure and stability of the simulated snow layer. The experimental setup includes different material layers with varying water content to capture diverse snow metamorphosis scenarios. The recorded parameters—such as reflection properties, radar signal penetration depth, and variations in water content—are then integrated into a data-driven model. Using machine learning approaches, the development of the snowpack and its stability are analyzed and predicted. The entire approach focuses on the reliable detection of liquid water within the snowpack and the integration of these measurements into predictive algorithms to improve gliding snow forecasting. The development of the measurement system remains centered on the laboratory setup to ensure optimal calibration and validation of measurement methods under controlled conditions.