Private

FS 3.199

Glacier change observations for hydrological and sea-level rise assessments

Details

  • Full Title

    FS 3.199: Towards a second Glacier Mass Balance Intercomparison Exercise (GlaMBIE-II) – Glacier change observations for hydrological and sea-level rise assessments
  • Scheduled

    TBA
  • Location

    TBA
  • Co-Conveners

  • Assigned to Synthesis Workshop

    ---
  • Thematic Focus

    Cryo- & Hydrosphere, Monitoring, Remote Sensing, Water Resources
  • Keywords

    Glaciers, Mass balance, Glacier observations, Intercomparison exercise, Uncertainty assessment

Description

Glaciers, as distinct from the Greenland and Antarctic ice sheets, are found all over the world, covering mountain tops from the tropics to the mid-latitudes and the polar regions. Glaciers are not only sentinels of the climate crisis, but their changes have direct impacts on regional run-off and global sea-level rise. To quantify the associated impacts at different scales, the Glacier Mass Balance Intercomparison Exercise (GlaMBIE; www.glambie.org) presented a first community-based estimate based on all available observational methods for measuring glacier mass changes. Given this key role of glaciers, there is both an urgent need and great potential to continue and improve GlaMBIE towards the 7th IPCC Assessment Report (AR7), extending its time frame and increasing its temporal and spatial resolution. The aim of this session is

  1. to discuss the strengths, challenges, and limitations of the different observational methods for measuring glacier mass balance (gravimetry, altimetry, glaciological/in-situ measurements, and DEM differencing),
  2. to investigate potential systematic differences between methods and their causes, and
  3. to identify where improved methods and uncertainty assessments are needed.

The session brings together experts on observing glacier changes from in-situ and remote sensing technologies, as well as from the glacier modelling community, in order to discuss our observational capacities to understand the governing processes, monitor the present development, and forecast possible future glacier changes and their impact on regional water availability and global sea-level change.

Submitted Abstracts

ID: 3.8090

Towards a second Glacier Mass Balance Intercomparison Exercise 2025–26 – a Call for Data & Participation

Michael Zemp
Gourmelen, Noel; Jakob, Livia; Nussbaumer, Samuel U.; Welty, Ethan; Piermattei, Livia; Berthier, Etienne

Abstract/Description

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) 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 is planned to run 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.

ID: 3.9696

Regional glacier elevation changes assessment from optical DEM time series

Livia Piermattei
Ioli, Francesco; Webster, Clare; Kugler, Lucas; Treichler, Désirée; Mattea, Enrico; McNabb, Robert

Abstract/Description

This study is part of the Glacier Mass Balance Intercomparison Exercise (GlaMBIE). Here, we present our assessment of glacier elevation change using the geodetic method (DEM differencing) based on spaceborne optical data. We exploited the potential of the SPOT-5 satellite, operational from 2002 to 2015, which provided global coverage. Since 2021, the SPOT 1-5 image archive has been freely available as part of the SPOT World Heritage program run by CNES. However, observation periods vary across regions, limiting temporal coverage to less than five years in some areas. Iceland is selected as a pilot study due to its extensive SPOT-5 temporal coverage, further complemented by ArcticDEM data. The workflow starts with generating DEM time series at a regional scale and homogenising the data, including DEM co-registration, selection, noise filtering and void filling. To address challenges posed by sparse DEM time series, we developed a method to extrapolate elevation changes over 10-year intervals using the combined DEM time series. This method relies on the assumption that a relationship exists between elevation change and elevation; therefore, an elevation trend can be derived for elevation bands. We extract median elevations for fixed elevation bands (i.e., 100 m bins) from the DEMs time series and interpolate these values over time using linear regression. Elevation data are then extrapolated for each band and pre-defined periods, and area-weighted mean elevation changes are calculated for each glacier using RGI7.0. For comparability, we also applied our approach to derive elevation changes from time series of ASTER DEMs and compared our results with the pixel-based multi-temporal approach of Hugonnet et al. (2021) over a common observation period. Regional and individual glacier estimates from both methods are evaluated. This work discusses key challenges in using spaceborne optical data for regional glacier elevation change assessments, including limitations in the temporal coverage of SPOT-5, issues with DEM generation, co-registration, noise filtering, void filling, and methods for estimating mean elevation changes. Our findings contribute to improving regional assessments of glacier mass balance and advancing geodetic approaches using optical DEM time series.

ID: 3.10199

Integrating ASTER Multi-Decadal Geodetic Data with MODIS Annual Glacier-wide Albedo Measurements for Annual Glacier Mass Balance Estimation

Mattia Callegari
Schellenberger, Thomas; Marin, Carlo

Abstract/Description

Estimating annual glacier mass balance is crucial for assessing climate change impacts and understanding glacier water resource availability. On a regional level, the satellite-based geodetic method provides relevant results but is generally limited to multi-decadal estimates and lacks accuracy on an annual scale.
In this study, we propose a novel method that combines annual glacier-wide albedo anomalies derived from MODIS data with ASTER-based geodetic glacier mass balance estimates to produce annual glacier mass balance estimates at regional scales. This method leverages 20 years of ASTER-derived geodetic mass balance data to calibrate the relationship between annual albedo anomalies and mass balance anomalies, enabling the conversion of relative anomalies into absolute annual mass balance values.
Validation against in-situ measurements from the World Glacier Monitoring Service (WGMS) from 2000 to 2020 was conducted on glaciers in the Alps, Scandinavia, and Svalbard. Results showed a root mean square error (RMSE) of 0.23 m and R² of 0.55 for the Alps, an RMSE of 0.67 m and R² of 0.39 for Scandinavia, and an RMSE of 0.19 m and R² of 0.30 for Svalbard. These results indicate a reasonable agreement with in-situ observations, suggesting the method’s robustness across different glacial environments.
Building on this validated methodology, we have generated time series of annual glacier mass balance from 2000 to 2024 for all glaciers larger than 0.5 km² in the Alps, Scandinavia, and Svalbard. These newly derived datasets offer valuable insights into the temporal variability of glacier mass balance and provide a comprehensive assessment of regional glacier responses to climate change.
This study highlights the potential of integrating remote sensing products to improve large-scale annual glacier mass balance estimations. By leveraging multi-sensor satellite data, we propose a scalable approach to glacier monitoring, which could be extended to other glacierized regions worldwide. The results can contribute to a better understanding of glacier-climate interactions and have implications for water resource management and sea-level rise projections in a warming climate.

ID: 3.10947

Using repeat airborne LIDAR to assess surface elevation to assess spatial variation in changes of mainland Norway from ~2010 to ~2020

Liss Marie Andreassen
Elvehøy, Hallgeir; Kjøllmoen, Bjarne; Sjursen, Kamilla Haukland

Abstract/Description

In this study we use repeat high resolution airborne LiDAR and orthophotos to study changes in glacier area and surface elevation for the period ~2010 to ~2020. The earliest survey year is 2007 and the latest survey year is 2022. The mean (median) number of years surveyed is 9.5 (10) years. Our study area covers over a third of the glacier area in Norway and three of the GTN-G glacier sub-regions 08-01, -02 and -03. We derive glacier outlines from the time of survey using high resolution orthophotos. In cases where orthophotos are lacking or snow conditions are severe, we use satellite data or LiDAR data to derive glacier outlines. We demonstrate the importance of using updated glacier outlines due to retreat of glaciers compared to the RGI inventory. Elevation change results are in most cases more negative when using updated glacier masks corresponding to the period of comparison. We compare our surface elevation changes using repeat LiDAR with global studies using repeat ASTER.

ID: 3.13028

Patterns of glacier elevation change and appropriate models to assess observed and future ice loss

Whyjay Zheng
Willis, Mike

Abstract/Description

Digital Elevation Model (DEM) differencing involving more than two DEMs is used to estimate glacier elevation change. A good regression model fit to the time series of glacier elevations is crucial for both prediction and error assessment. Weighted or unweighted linear models are traditionally used for this purpose and typically have a good fit over area of a glacier where surface mass balance is the major factor affecting glacier elevation. These models are less credibile in regions experiencing dynamic changes in ice flow. A non-linear model may be a good alternative, and recent advances have shown how powerful models, such as the Gaussian progress regression, can be. However, a non-linear model suffers from over-fitting and poor extrapolation, and the model itself does not consider the underlying physical mechanism. Motivated by this background, we focus on the ArctiDEM strip data set over glacier areas with dynamic signals of elevation change, including calving, surging, subglacial draining, and ice collapse. We qualitatively classify them into several patterns. Each pattern can be modeled by a distinct linear or non-linear model for better performance. For example, an elevation time series classified as calving can be modeled with a step function for a more accurate future extrapolation. We envision these classifications as a good foundation for creating a training data set for an automatic elevation change analysis using AI.

ID: 3.13127

Downscaling Remote Sensing Data for Mountain Glacier Mass Balance

Mariia Usoltseva
Pail, Roland

Abstract/Description

Glaciers are crucial components of the Earth’s climate system and serve as indicators of climate change. Their substantial mass loss due to global warming significantly contributes to sea-level rise and impacts regional hydrology, downstream ecosystems and settlements. Despite considerable advancements in observational and modelling techniques, accurately quantifying glacier responses to climate change and predicting their future behaviour remain complex challenges, particularly in regions characterized by rapidly changing glaciers and complex topography. One of the key limitations in this field remains the availability of high-resolution regional datasets. In this study, we investigate the application of remote sensing data downscaling techniques to improve spatial and temporal resolution of glacial mass balance. We focus on the integration of relatively high-resolution surface elevation changes derived from satellite altimetry with coarse-resolution mass changes inferred from satellite gravimetry data to localize mass changes. This study mainly focuses on the glaciers of the Patagonia region. This region, characterized by rapid glacier retreat and complex climatic influences, serves as an ideal case study for integrating multiple satellite datasets and regional models. This approach aims to improve local assessments and provide a transferable framework for applying remote sensing downscaling in other regions where observational data is sparse. The findings contribute to advancing the use of satellite remote sensing for cryospheric studies and underscore the importance of high-resolution datasets in tracking and predicting glacial responses to climate change. Preliminary results highlight the potential of enhanced data integration techniques to resolve sub-regional mass changes, offering insights into glacier-climate interactions in Patagonia. The potential outcomes of this work aim to benefit the field of glacial modelling. The development of a downscaled glacial mass balance dataset, tailored for regional glacial systems or even individual glaciers, holds significant promise for model forcing and data assimilation to improve the estimates of future glacial melt and hydrological processes.