PICO
This session focuses on advances in observing and modelling mountain glaciers and ice caps at the regional to global scale. We invite both observation- and modelling-based contributions, which may include, but are not limited to the following topics:
• comparative studies of glacier evolution across single or multiple mountain ranges
• glacier-related impact studies on sea-level contribution, mountain hazards, mountain hydrology, etc.
• advances in large-scale monitoring
(e.g., AI-supported monitoring, multi-sensor homogenisation, meta-analysis of ground-based data, process inferences)
• advances in large-scale modelling
(e.g., reconciling AI with classical approaches, including physical processes, model coupling to others subsystems, improving strategies for data assimilation, refining climatic downscaling)
• regional to global-scale data products and scalable modelling frameworks
Rocky debris layers cover an increasing portion of glacier ablation areas as glaciers thin and retreat in response to climate change, progressively altering surface melt rates. However, determining the thickness and physical properties of supraglacial debris that are required for accurate representation of debris in glacier melt models is challenging, and measurements are scarce. Here, we provide an openly available dataset (DebDab, https://zenodo.org/records/14224835) that compiles physical properties and thickness of supraglacial debris over 83 glaciers in 13 regions of the Randolph Glacier Inventory. The majority of the database (90%) is compiled from 172 sources in the literature, while the remaining 10% has not been published before. DebDab contains 8,286 data entries for supraglacial debris thickness, of which 1,852 include sub-debris ablation rates too, 167 data entries of thermal conductivity of debris, 157 of aerodynamic surface roughness length, 77 of debris albedo, 56 of debris emissivity and 37 of debris porosity. We show regional differences in the distribution of debris thickness measurements, as well as an uneven spatial coverage with well-sampled regions like Central Europe and South Asia, but gaps in the Andes and Alaska. Additionally, debris thickness measurements are mostly concentrated at lower glacier elevations, leaving mid-glacier areas under-sampled, which may affect the dataset's representativeness. We also provide the most detailed scatter plot of debris thickness and ablation rates yet, with Østrem curves fitted for 19 glaciers, based entirely on observational data, and supporting the well-documented reduction in melt rates after the initial few centimetres of debris and the subsequent minimal reduction in melt rates for thicker debris. DebDab can be used in energy balance, melt and surface mass balance models by incorporating site-specific debris properties, or to evaluate remote sensing estimates of debris thickness and surface roughness. It can also help future field campaigns on debris-covered glaciers by identifying undersampled regions, glaciers and properties. DebDab is open to new data submissions from the community as more data of supraglacial debris properties become available.
How to cite: Fontrodona-Bach, A., Groeneveld, L., Miles, E., McCarthy, M., Shaw, T., Melo Velasco, V., and Pellicciotti, F.: DebDab: A database of physical properties of supraglacial debris, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10102, https://doi.org/10.5194/egusphere-egu25-10102, 2025.
Melting glaciers are icons of the climate crisis and severely impact local geohazards, regional freshwater availability, and global sea levels. Well-constrained observations of glacier mass change and associated uncertainties are required to assess these downstream impacts and provide the baseline for calibrating and validating models for future projections. Previous assessments of global glacier mass changes were hampered by spatial and temporal limitations and the heterogeneity of datasets from different observation methods. The Glacier Mass Balance Intercomparison Exercise (GlaMBIE; https://glambie.org) set out to tackle these challenges through a community effort to collect, homogenise, combine, and analyse glacier mass changes from in situ and remote-sensing observations.
This presentation summarises the results and lessons learned from the first GlaMBIE (2022−24) and introduces GlaMBIE-2, which runs from 2025 to 2026. In GlaMBIE-2, we aim to compile additional mass-change estimates to broaden observational coverage from different methods, extend the data series back to 1992 to align with available ice-sheet estimates, and update the time series to 2025 to cover the latest developments. In addition, we are running pilot studies to better understand the apparent bias between digital elevation model (DEM) differencing and altimetry and to increase the spatio-temporal resolution of our estimates to further hydrological applications. We invite the research community to participate in this collaborative effort by contributing their expertise and glacier mass change data, whether from in situ observations, repeat mapping from optical imaging and radar interferometry, laser and radar altimetry, and gravimetry.
How to cite: Zemp, M., Gourmelen, N., Jakob, L., Nussbaumer, S. U., Welty, E., and Berthier, E.: The second Glacier Mass Balance Intercomparison Exercise 2025–26 – a Call for Data & Participation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5164, https://doi.org/10.5194/egusphere-egu25-5164, 2025.
Quantifying the ongoing retreat of glaciers and ice sheets – and projecting their futures – are major societal concerns due to their contribution to sea-level rise and influence on water resources, natural hazards, and associated socioeconomic impacts. However, our ability to confidently project glacier and ice-sheet mass change is often limited by a severe lack of observations that reliably constrain both their input (snow) and output (flow) mass fluxes. To address these needs, in April 2024 NASA selected Snow4Flow as an Earth Venture Suborbital (EVS-4) mission. Snow4Flow will capture the spatial variability in snow accumulation and ice volume across 4 Arctic and near-Arctic regions that contain hundreds of rapidly changing glaciers to deliver more reliable, societally relevant projections of land-ice change. Our target areas are Alaska and far western Canada, southeastern Greenland, the Canadian High Arctic, and Svalbard. We will perform spatially extensive multi-frequency airborne radar-sounding surveys in March–May 2027–2029, in conjunction with ground-validation campaigns. Snow4Flow will drive foundational improvements to Northern Hemisphere land-ice boundary conditions and forcing data, including orographic precipitation patterns in alpine environments, ice thickness and subglacial topography, and will directly leverage them into state-of-the-art models and projections. All associated software, datasets and model outputs will be rapidly and openly distributed to enable both independent use and assessment, along with portability to other glacierized regions on Earth.
How to cite: Holt, J. W., MacGregor, J., and Andrews, L. C.: Snow4Flow: A new NASA airborne mission to measure and model the state and fate of Arctic glaciers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12435, https://doi.org/10.5194/egusphere-egu25-12435, 2025.
Monitoring glacier flow velocity on the Greenland Ice Sheet is crucial for understanding mass balance and assessing its impact on sea level rise. However, the present high temporal resolution velocity primarily derived from Sentinel-1 SAR data, often exhibit gaps during melt seasons due to daily freeze-thaw cycles on the ice surface.
Here, we constructed a 6-day velocity time series from 2017 to 2021 for six outlet glaciers to comprehensively capture their velocity variations by combining Sentinel-1 and -2 data, including Petermann Glacier, Jakobshavn Isbræ, Helheim Glacier, Kangerlussuaq Glacier, Nioghalvfjerdsfjorden Glacier, and Zachariæ Isstrøm Glacier. The offset-tracking technique was applied to derive initial velocity time series from SAR and optical data separately, pairing each image with its three subsequent acquisitions. A least squares method based on connected components then calculates the time series for Sentinel-1 and Sentinel-2 separately, which were then fused using a weighted least squares method, with weights determined by RMSEs.
Sentinel-2 data effectively filled the summer gaps of the glacier velocity time series that only generated with Sentinel-1 imagery (such as NSIDC-0766), improving the coverage rates by over 30% in summer. The filled gaps concentrated in the elevation range of 600-1400 meter for Petermann Glacier, Nioghalvfjerdsfjorden Glacier, and Zachariæ Isstrøm Glacier, while for Jakobshavn Isbræ, it was most prominent between 1000-1800 meter. The coverage increase for Helheim Glacier and Kangerlussuaq Glacier is most significant in the elevation of 1500-2000 meter. These improvements are primarily observed in the radar glacier zones of wet snow zone and the percolation zone, where daily freeze-thaw more frequently occurred, leading to decoherent of its surface backscattering. In contrast, improvements are less pronounced at the dry snow zone where no thawing occurs and ice crevasses distributed glacier terminus with abundant features for offset-tracking.
At the groundline, Petermann exhibited relatively stable flow, ranging from 1.17 to 1.20 km/yr. Jakobshavn Isbræ showed significant variability, peaking at 4.39 km/yr in 2019 before declining to 2.52 km/yr in 2021. Helheim displayed lower velocities, ranging from 0.15 to 0.26 km/yr, while Kangerlussuaq maintained consistently high flow rates of 4.16 to 4.57 km/yr. Zachariæ Isstrøm demonstrated a steady increase from 0.68 to 0.74 km/yr, and Nioghalvfjerdsfjorden showed minor variations, ranging from 1.14 to 1.17 km/yr. The velocity map gap filled by Sentinel-2 revealed quicker flow rates during the summer months, especially for Jakobshavn Isbræ, Kangerlussuaq, and Zachariæ Isstrøm , reaching up to 1.0 m/day, indicating a lower estimation of the glacier mass loss with the flux gate method. As for other outlet glaciers, Petermann Glacier, Nioghalvfjerdsfjorden Glacier and Helheim Glacier, the underestimation of velocity using only Sentinel-1 velocity time series was more pronounced further from the glacier terminus. Precision analysis shows the Sentinel-1 offset-tracking precision is approximately 10 times better than that of Sentinel-2, emphasizing the importance of weighted fusion when combining the datasets.
How to cite: Mao, Y., Li, G., and Chen, Z.: Integrating Sentinel-2 and Sentinel-1 Imagery to Analyze Glacier Velocity Variability of Six Outlet Glaciers in Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15276, https://doi.org/10.5194/egusphere-egu25-15276, 2025.
Globally, mountain glaciers are retreating under the effects of climate change. Many of these mountain glaciers are part of important water tower regions (Immerzeel et al., 2020), and their retreat threatens the water security of local communities and downstream catchments. In the Semi-Arid Chilean Andes, mountain glaciers are particularly important as water from precipitation is limited in this arid climate, which is currently experiencing a ‘mega drought’. However, estimating the changing ice volume is challenging due to two key reasons. Firstly, a large proportion of the glaciers are small in extent and have a high degree of debris-cover, meaning the ice extent is challenging to measure using satellite remote sensing data. This is most pronounced in the case of rock glaciers, which are numerous in this region. Secondly, there are few in situ observations of ice thickness and extent to validate the multispectral remote sensing observations. Given this context, we present new observations of glacier elevation changes using derived from recently acquired Synthetic Aperture Radar (SAR) observations. This work aims to better understand how glacier mass balance in the Semi-Arid Chilean Andes affects water resources for downstream catchments, and we evaluate the applicability of the SAR data including ESA’s Sentinel-1 in this context. We use DEM differencing and radar backscatter analyses to study glacier changes in order to track retreat and estimate changing ice content. We will present the findings of this work and comment on the possible opportunities and limitations this approach offers.
How to cite: Fox, E., Palmer, S., Rangecroft, S., Harrison, S., and Schwartz-Marin, E.: Glacier elevation changes in the Semi-Arid Chilean Andes from Synthetic Aperture Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11234, https://doi.org/10.5194/egusphere-egu25-11234, 2025.
Alpine glaciers are analogues to remote polar ice streams and serve as accessible natural
laboratories for understanding the key processes driving ice flow. Here, we capture temporal
variability of surface velocities as a proxy for processes at the glacier bed using a terrestrial radar
interferometer (GPRI, GAMMA Portable Radar Interferometer). We conducted two field
campaigns in October 2023 and June 2024 to measure velocity variability over several days at a
temporal resolution of three minutes. We focus on a steep icefall zone in which the onset of basal
sliding is hypothesized. A common challenge in processing terrestrial radar data is the
contribution of atmospheric turbulence to the measured interferometric phase. To reduce this
effect, we stack 2653 (1420) one-hour interferograms for each of the two field campaigns. After
stacking, displacement on fixed rock walls is minimal compared to the mean ice velocity. Across
both time series, we captured velocity variability on daily as well as seasonal time scales. On the
steep ice fall, mean velocity differences between the fall and spring campaigns show ~30% faster
flow in the spring season, when more surface meltwater may lubricate the glacier bed leading to
seasonally accelerated glacier flow. This research highlights the effectiveness and challenges of
terrestrial radar interferometry and provides valuable information for understanding glacier
dynamics in alpine environments.
How to cite: Konietzko, J., Wild, C. T., Muhle, L. S., Drews, R., and Mantelli, E.: Glacier Flow Dynamics from Terrestrial Radar Interferometry: Grenzgletscher, Switzerland , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-943, https://doi.org/10.5194/egusphere-egu25-943, 2025.
Long-term observations of glacier dynamics are crucial for understanding past climatic shifts and assessing current glacier conditions. The Ladakh region, known for its high-altitude arid landscape, amasses about 40% of Indian glacier wealth, which is vital for local communities who rely on meltwater for their water needs. However, human-induced warming has accelerated glacier depletion globally, including in the Trans-Himalayas. Despite its importance, Ladakh remains underrepresented in glaciological research, highlighting the need for continued studies on glacier dynamics in the region. Glacier velocity and mass balance is interlinked with each other and is a manifestation of mass change in the glacier system. This study aims to present inter-annual glacier velocity over the Rulung and Gyama massifs of Karzok range, Ladakh using the global dataset i.e. ITS_LIVE which is available from 1980s to 2018, calculated using multiple satellite missions. Furthermore, mass balance calculations are performed to quantify changes in glacier mass. This study also evaluates the influence of both climatic and topographic factors on glacier dynamics. The Rulung and Gyama massifs contain 52 and 100 glaciers, respectively, covering total areas of 22.2 ± 1.4 km² and 44.9 ± 2.7 km². Notably, the glaciers in both massifs are relatively small, with average sizes of 0.39 km² for Rulung and 0.45 km² for Gyama. Consequently, the magnitude of glacier velocity is also low. The surface velocity of Rulung Glacier ranges from 0.37 ± 0.21 m/y to 8.17 ± 2.80 m/y, with an average of 2.33 ± 1.63 m/y. In contrast, the velocity of Gyama Glacier varies from 0.40 ± 0.11 m/y to 6.53 ± 3.32 m/y, with an average of 1.73 ± 0.70 m/y. Over the study period, the velocity across both massifs exhibited a decreasing trend, with an average slowdown of 0.05 m/y (31%) in Rulung and 0.01 m/y (8%) in Gyama. Both glaciers show a negative mass balance, with rates of -0.17 ± 0.03 m w.e./y for Rulung and -0.07 ± 0.01 m w.e./y for Gyama. The observed slowdown in glacier velocity and the associated mass loss can be attributed to sustained warming in the region and an erratic precipitation pattern, both of which primarily govern glacier dynamics. The decreasing velocity and negative mass balance are interlinked, as reduced flow rates often correlate with a loss of glacier mass, further accelerating the retreat and thinning of glaciers. Additionally, regional heterogeneity in velocity patterns can be explained by topographic factors, which exert a significant influence on glacier dynamics The overall decline in both glacier velocity and mass balance highlights the ongoing depletion of glaciers in the region, posing a substantial threat to water security and increasing the risk of natural hazards to communities living in close proximity to the glaciers. The study recommends timely attention towards depleting glaciers to better manage the important water resources.
Keywords: Glacier changes, remote sensing, glacier mass balance, glacier velocity, climate change, Karzok Range, Ladakh Himalaya
How to cite: Prajapati, M., Garg, P. K., and Mukherjee, S.: Spatiotemporal glacier dynamics over the Rulung and Gyama massifs, Ladakh (2000–2018): Influences of topographic and climatic factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16727, https://doi.org/10.5194/egusphere-egu25-16727, 2025.
Glacier surges are spectacular events that lead to surface elevation changes of tens of meters in a period of a few months to a few years, with different patterns of mass transport. They can result in surface elevation changes of more than 100 m in a few months. Recent developments in remote sensing have enabled the estimation of glacier elevation change and surface velocity at monthly resolution. These two variables are crucial to constrain the physical mechanisms responsible for glacier surges.
In this work, we exploit a large archive of Digital Elevation Models (DEMs) over 2000-2019 from the ASTER optical satellite sensor. The time series is filtered and homogenized to monthly elevations, in order to study surging glaciers in the Karakoram (Beraud et al., under review). This workflow implements a LOWESS method – locally weighted polynomial regression for filtering and a B-spline method ALPS-REML as elevation temporal interpolation. Additionally, we use ITS_LIVE glacier surface velocities, regularized to monthly dates using the temporal closure of the displacement measurement network (Charrier et al., 2022).
On the modelling side, Thogersen et al. (2019; 2024) theorised a surge propagation mechanism based on the rate and state approach of basal friction. They found that, first, a surge is triggered when a shear stress is reached over a sufficiently large area and, second, it exists relationship between the velocity of the surge front propagation and the sliding velocity. We then explore over about five glaciers the ability of the two datasets to test Thogersen's theory of surge initiation and propagation.
References:
Beraud, L., Brun, F., Dehecq, A., Hugonnet, R., and Shekhar, P.: Glacier surge monitoring from temporally dense elevation time series: application to an ASTER dataset over the Karakoram region, https://doi.org/10.5194/egusphere-2024-3480, In review.
Charrier, L., Yan, Y., Koeniguer, E. C., Leinss, S., and Trouve, E.: Extraction of Velocity Time Series With an Optimal Temporal Sampling From Displacement Observation Networks, IEEE Transactions on Geoscience and Remote Sensing, 60, 1–10, https://doi.org/10.1109/TGRS.2021.3128289, 2022
Thøgersen, K., Gilbert, A., Schuler, T. V., and Malthe-Sørenssen, A.: Rate-and-state friction explains glacier surge propagation, Nature Communications, 10, 2823, https://doi.org/10.1038/s41467-019-10506-4, 2019.
Thøgersen, K., Gilbert, A., Bouchayer, C., and Schuler, T. V.: Glacier Surges Controlled by the Close Interplay Between Subglacial Friction and Drainage, Journal of Geophysical Research: Earth Surface, 129, e2023JF007 441, https://doi.org/10.1029/2023JF007441, 2024.
How to cite: Béraud, L., Dehecq, A., Brun, F., Gilbert, A., Charrier, L., Hugonnet, R., and Shekhar, P.: Regional observation of glacier surges from space: monthly time series and application to physical theories., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16010, https://doi.org/10.5194/egusphere-egu25-16010, 2025.
The surface mass balance (SMB) of glaciers represents the link between glaciers and their local climate. Quantifying the SMB is essential for calibrating glacier mass-balance models, improving our understanding of the glacier’s response to a changing climate, which affects freshwater availability, sea-level rise, and the risk of natural hazards, among others.
While the SMB cannot be measured directly from space, it can be derived from observations of elevation change, ice velocity, and ice thickness (gradients). Such approaches have been successfully applied in detailed studies of individual glaciers with high spatial and temporal data coverage. However, extending these efforts to regional or global scales present significant challenges due to inconsistent temporal data coverage, coarse spatial resolution, and large uncertainties linked to regional datasets. As a result, many regional glacier evolution models continue to rely on single glacier-wide average mass-balance estimates from long-term geodetic elevation change measurements for model calibration. However, this can lead to model overparameterization and equifinality problems, which are major sources of uncertainty in projections. With the advent of extensive remote sensing datasets and machine learning approaches, there is now an unprecedented opportunity to estimate spatial SMB patterns across glaciers, on regional to global scales.
In this study, we estimate spatial SMB patterns on glaciers in the Swiss Alps with a generalised approach that does not rely on high spatial coverage from in-situ measurements, but rather on datasets with a regional availability. More specifically, we use observational datasets of ice thickness and ice velocity fields derived from remote sensing to calculate the ice flux divergence and combine this with the continuity equation for ice thickness and observations of elevation change to estimate spatial SMB patterns. To optimize the calculation of the ice flux divergence, which relies on non-local ice flow behaviour, we employ a machine learning approach to determine the best filtering (smoothing) parameters for the spatial velocity and thickness gradients. The performance of the method is assessed by comparing SMB estimates with in-situ SMB values derived from stake measurements. This study aims at providing a scalable framework for estimating spatially resolved SMB patterns, with potential applications at the global scale.
How to cite: Izeboud, M., Van Tricht, L., and Zekollari, H.: Glacier Blueprints: Deriving Spatial Surface Mass Balance from Remote Sensing at a Regional Scale with Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-200, https://doi.org/10.5194/egusphere-egu25-200, 2025.
World-wide glaciers are losing mass which affects global sea-level, river runoff, freshwater influx to the oceans, glacier-related hazards, and landscape changes, with implications for human livelihoods and ecosystems.
Robust glacier mass balance estimates at a high temporal and spatial resolution are hence essential to effective adaptation strategies.
We outline a probabilistic formalism, based on a modified particle scheme, for using stake, glacier wide, and geodetic mass balance measurements to infer the parameters of a numerical glacier evolution model - the Python Glacier Evolution Model, PyGEM. The particle method iteratively estimates the posterior probability distribution of the dynamical glacier state vector while successfully accommodating data gaps as well as model nonlinearity and non-Gaussianity.
Our method is tested on different glaciers representing a broad range of climatic conditions and glacial contexts across Scandinavia.
The approach leverages the combined strengths of the numerical model’s glacier physics-based predictive capabilities with the observations’ direct representation of glacier conditions, providing a robust estimate of glacier mass balance and its associated uncertainties.
This study underscores the value of Bayesian data assimilation, offering a robust and computationally tractable tool for estimating past, current and future glacier changes with high spatiotemporal coverage.
How to cite: Guillet, G., Aalstad, K., Yilmaz, Y., and Hock, R.: Long-Term Glacier Mass Balance Reanalysis Using Data Assimilation: Case Studies from Scandinavia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17436, https://doi.org/10.5194/egusphere-egu25-17436, 2025.
Modelling the evolution of mountain glaciers in response to climate change is essential for accurate projections of global sea level rise and changes in the regional hydrological cycle. Glacier evolution and Earth system models applied at regional to global scales typically rely on simple temperature-index and snow accumulation models to describe spatio-temporal variations in glacier surface mass balance. The major advantage of temperature-index models over more complex energy balance models is their computational efficiency, the limited number of calibration parameters and the global availability of the required basic input data (i.e. air temperature and precipitation). However, temperature-index models based solely on an empirical relationship between melt and air temperature are not suitable for tropical and subtropical regions where incoming shortwave radiation and evaporation have a major control on the energy exchange at the glacier surface. In addition, several studies have shown that these models are oversensitive to air temperature variations and are not robust over time, so they need to be recalibrated for changing climatic conditions. This is problematic for forward modelling. Models of intermediate complexity, such as simplified energy balance models, are thought to be more robust over time and therefore more suitable for long-term modelling. The drawback of more complex models, however, is that they are more computationally expensive, require additional input data and have more degrees of freedom, making them prone to equifinality problems. Most glacier surface mass/energy balance models are now calibrated against geodetic observations available for basically any glacier worldwide. While these observations provide a consistent basis for model calibration, they do not allow the mass balance gradients to be constrained. This uncertainty can lead to large over- or underestimates of ablation and accumulation rates, with consequences for modelling glacier runoff and evolution. Here we present the results of a modelling experiment in which we compared the robustness and spatio-temporal transferability of two surface mass balance models of different complexity, constrained not only with geodetic but also with snowline observations automatically derived from Sentinel-2 data, for all monitored glaciers in the Alps.
How to cite: Groos, A. R., Sommer, C., Tabone, I., and Fürst, J. J.: Surface mass balance modelling of the Alps constrained by geodetic and snow line observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20803, https://doi.org/10.5194/egusphere-egu25-20803, 2025.
The retreat of glaciers has received considerable attention due to its implications for water availability and hydropower generation, thereby raising significant concerns for both the environment and society. Consequently, understanding the impact of climate on glacier evolution has become essential. In the present study, we investigate the application of various Machine Learning/Deep Learning models, specifically Linear Regression, Neural Networks, XG Boost, and Random Forest, to predict surface mass balance across two geographically distinct regions: the Swiss Alps and Svalbard. We also compared and analyzed different input datasets, such as ERA5, ERA5-Land (higher resolution), and a downscaled climate dataset to understand the impact of selecting different climate datasets and spatial resolutions. The performance of these models is evaluated based on different combinations of input variables to ascertain their impact on prediction accuracy.
How to cite: Moudgil, P. S., Bij de Vaate, I., Hock, R., and Guillet, G.: Predicting Surface Mass Balance of Valley Glacier using Machine Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3927, https://doi.org/10.5194/egusphere-egu25-3927, 2025.
On-glacier avalanches contribute to non-linear mass balance patterns and, by channeling snow from upper headwalls onto the glacier surface, can maintain glaciers at low elevations despite increasing temperatures. Here we combine a gravitational snow redistribution model estimating avalanching with the Open Global Glacier Model (OGGM) to quantify the current and future contribution of avalanches to glacier mass balance for all mountain glaciers in the world. The avalanche contribution is added as a multiplicative correction factor of solid precipitation per elevation band, and the resulting mass balance is calibrated against global-scale geodetic data based on DEM differencing. The avalanche model is evaluated against a set of remote sensing observations at various spatial scales, including flux inversions and avalanche deposit outlines from Sentinel-1, and the influence of avalanches is quantified using ensemble simulations with CMIP6 climate data until 2100.
The model results show that avalanches can contribute substantially to glacier mass balance, with a strong spatial variability between glaciers and regions. The region most affected is New Zealand, with 19% of the total snow accumulation originating from avalanches on average. At the glacier scale, this avalanche contribution shows a strong variability that depends on glacier area and slope. Some glaciers more than double their snow accumulation while others lose mass by avalanching, and accounting for this contribution leads to more local variability in the mass balance gradients. We find that, at the regional scale and for many individual glaciers, accounting for avalanching has little impact on the simulated future evolution of glacier volume. This is because the effect of avalanching is already implicitly taken into account in the calibration against glacier-specific geodetic mass balance. However, for individual glaciers, explicitly accounting for the effect of avalanches can substantially impact the projected evolution. This is especially relevant for small glaciers at low elevations that, in the model simulations, may survive several decades longer than they would otherwise. We also find indications that removal of snow by avalanching may lead to a higher sensitivity to warming, and therefore faster thinning of steep glaciers at high elevation.
How to cite: Kneib, M., Maussion, F., Carcanade, G., Brun, F., Farinotti, D., Huss, M., van Tiel, M., Jouberton, A., and Champollion, N.: Contributions of avalanches to glacier mass balance at the global scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6326, https://doi.org/10.5194/egusphere-egu25-6326, 2025.
Knowledge of ice thickness is essential for understanding past and predicting future changes of glaciers systems in response to climatic changes. Various methods exist on how to best estimate ice thickness from surface information in data sparse regions. These estimates are vital as they serve as starting point for future glacier evolution under different climatic scenarios.
These projections serve to determine future sea-level contribution or to inform adaptation or mitigation strategies required in response to glacier retreat.
Methods for mapping glacier ice thickness typically utilize surface information and combine it with the perfect plasticity assumption, mass-conservation or the stress balance to infer the unknown thickness distribution. In data sparse regions, estimates remain largely unconstrained and might deviate considerably not only on local scales.
Several maps of glacier ice thickness have been presented for Chile. Most of them however had global or at least a larger target region. So often site-specific measurements were not considered or at most for loose validation. This presents the first systematic effort to integrate local field measurements conduced by the Chilean Water Directorate (DGA) between 2012 and 2014 into an ice thickness reconstruction
These measurements of a constant basal shear stress (τy) at the ice-bedrock interface to infer ice thickness and subglacial topography. This approach avoids overly complex parametrization and is particularly well-suited for data-sparse regions. For this study, ice thickness was reconstructed using surface elevation, glacier outlines and extensive GPR measurements.
Validation results demonstrated achieving root mean square errir of 0.47 meters and a bias of 0.65 meter compared These findings underscore the importance of integrating local measurements with advanced modeling techniques to enhance the accuracy of ice-thickness maps in Chile.
How to cite: Berkhoff, J., Fürst, J., Sommer, C., Farias, D., Schaefer, M., Rodriguez, J. L., Uribe, J., and Ugalde, F.: Mapping glacier ice thickness in Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18678, https://doi.org/10.5194/egusphere-egu25-18678, 2025.
The Southern Patagonian Icefield has been observed to exhibit one of the highest mass loss rates globally. However, the individual glaciers within this icefield show significant variations in their contributions to these loss rates, both in terms of space and time. This phenomenon is particularly evident in the glaciers Perito Moreno, Viedma, and Upsala, the latter being the largest glaciers in Argentina with its adjacent basins. Since the climatic sensitivity of lake-terminating glaciers can be strongly influenced by the bedrock topography, we surveyed these three glaciers for the first time with a 25 MHz helicopter-borne radio-echo sounding system. The data was then incorporated into an state-of-the-art reconstruction approach to derive basin-wide ice thickness information and, subsequently, information regarding the subglacial bedrock topography. This enables the investigation of the potential of future glacier retreat due to the role of buoyancy-driven glacier calving. Furthermore, we analyzed the elevation changes from 2000 to 2024 based on SRTM and TanDEM-X microwave satellite data, the surface velocity evolution of these glaciers, and incorporated bathymetric measurements.
How to cite: Koch, M., Sommer, C., Blindow, N., Fürst, J. J., and Braun, M. H.: Unveiling the role of the bedrock topography on glacier evolution in Patagonia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21894, https://doi.org/10.5194/egusphere-egu25-21894, 2025.
