Cookie Settings
Customize Consent Preferences

Customize your consent preferences for Cookie Categories and advertising tracking preferences for Purposes & Features and Vendors below. You can give granular consent for each and . Most vendors require explicit consent for personal data processing, while some rely on legitimate interest. However, you have the right to object to their use of legitimate interest.

Cookie Categories

Always Active

Necessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.

Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.

No cookies to display.

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.

No cookies to display.

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

No cookies to display.

Advertisement cookies are used to provide visitors with customized advertisements based on the pages you visited previously and to analyze the effectiveness of the ad campaigns.

No cookies to display.

Other cookies are those that are being identified and have not been classified into any category as yet.

Purposes & Features

Cookies, device or similar online identifiers (e.g. login-based identifiers, randomly assigned identifiers, network based identifiers) together with other information (e.g. browser type and information, language, screen size, supported technologies etc.) can be stored or read on your device to recognise it each time it connects to an app or to a website, for one or several of the purposes presented here.

  • Most purposes explained in this notice rely on the storage or accessing of information from your device when you use an app or visit a website. For example, a vendor or publisher might need to store a cookie on your device during your first visit on a website, to be able to recognise your device during your next visits (by accessing this cookie each time).

Advertising presented to you on this service can be based on limited data, such as the website or app you are using, your non-precise location, your device type or which content you are (or have been) interacting with (for example, to limit the number of times an ad is presented to you).

  • A car manufacturer wants to promote its electric vehicles to environmentally conscious users living in the city after office hours. The advertising is presented on a page with related content (such as an article on climate change actions) after 6:30 p.m. to users whose non-precise location suggests that they are in an urban zone.
  • A large producer of watercolour paints wants to carry out an online advertising campaign for its latest watercolour range, diversifying its audience to reach as many amateur and professional artists as possible and avoiding showing the ad next to mismatched content (for instance, articles about how to paint your house). The number of times that the ad has been presented to you is detected and limited, to avoid presenting it too often.

Information about your activity on this service (such as forms you submit, content you look at) can be stored and combined with other information about you (for example, information from your previous activity on this service and other websites or apps) or similar users. This is then used to build or improve a profile about you (that might include possible interests and personal aspects). Your profile can be used (also later) to present advertising that appears more relevant based on your possible interests by this and other entities.

  • If you read several articles about the best bike accessories to buy, this information could be used to create a profile about your interest in bike accessories. Such a profile may be used or improved later on, on the same or a different website or app to present you with advertising for a particular bike accessory brand. If you also look at a configurator for a vehicle on a luxury car manufacturer website, this information could be combined with your interest in bikes to refine your profile and make an assumption that you are interested in luxury cycling gear.
  • An apparel company wishes to promote its new line of high-end baby clothes. It gets in touch with an agency that has a network of clients with high income customers (such as high-end supermarkets) and asks the agency to create profiles of young parents or couples who can be assumed to be wealthy and to have a new child, so that these can later be used to present advertising within partner apps based on those profiles.

Advertising presented to you on this service can be based on your advertising profiles, which can reflect your activity on this service or other websites or apps (like the forms you submit, content you look at), possible interests and personal aspects.

  • An online retailer wants to advertise a limited sale on running shoes. It wants to target advertising to users who previously looked at running shoes on its mobile app. Tracking technologies might be used to recognise that you have previously used the mobile app to consult running shoes, in order to present you with the corresponding advertisement on the app.
  • A profile created for personalised advertising in relation to a person having searched for bike accessories on a website can be used to present the relevant advertisement for bike accessories on a mobile app of another organisation.

Information about your activity on this service (for instance, forms you submit, non-advertising content you look at) can be stored and combined with other information about you (such as your previous activity on this service or other websites or apps) or similar users. This is then used to build or improve a profile about you (which might for example include possible interests and personal aspects). Your profile can be used (also later) to present content that appears more relevant based on your possible interests, such as by adapting the order in which content is shown to you, so that it is even easier for you to find content that matches your interests.

  • You read several articles on how to build a treehouse on a social media platform. This information might be added to a profile to mark your interest in content related to outdoors as well as do-it-yourself guides (with the objective of allowing the personalisation of content, so that for example you are presented with more blog posts and articles on treehouses and wood cabins in the future).
  • You have viewed three videos on space exploration across different TV apps. An unrelated news platform with which you have had no contact builds a profile based on that viewing behaviour, marking space exploration as a topic of possible interest for other videos.

Content presented to you on this service can be based on your content personalisation profiles, which can reflect your activity on this or other services (for instance, the forms you submit, content you look at), possible interests and personal aspects. This can for example be used to adapt the order in which content is shown to you, so that it is even easier for you to find (non-advertising) content that matches your interests.

  • You read articles on vegetarian food on a social media platform and then use the cooking app of an unrelated company. The profile built about you on the social media platform will be used to present you vegetarian recipes on the welcome screen of the cooking app.
  • You have viewed three videos about rowing across different websites. An unrelated video sharing platform will recommend five other videos on rowing that may be of interest to you when you use your TV app, based on a profile built about you when you visited those different websites to watch online videos.

Information regarding which advertising is presented to you and how you interact with it can be used to determine how well an advert has worked for you or other users and whether the goals of the advertising were reached. For instance, whether you saw an ad, whether you clicked on it, whether it led you to buy a product or visit a website, etc. This is very helpful to understand the relevance of advertising campaigns.

  • You have clicked on an advertisement about a “black Friday” discount by an online shop on the website of a publisher and purchased a product. Your click will be linked to this purchase. Your interaction and that of other users will be measured to know how many clicks on the ad led to a purchase.
  • You are one of very few to have clicked on an advertisement about an “international appreciation day” discount by an online gift shop within the app of a publisher. The publisher wants to have reports to understand how often a specific ad placement within the app, and notably the “international appreciation day” ad, has been viewed or clicked by you and other users, in order to help the publisher and its partners (such as agencies) optimise ad placements.

Information regarding which content is presented to you and how you interact with it can be used to determine whether the (non-advertising) content e.g. reached its intended audience and matched your interests. For instance, whether you read an article, watch a video, listen to a podcast or look at a product description, how long you spent on this service and the web pages you visit etc. This is very helpful to understand the relevance of (non-advertising) content that is shown to you.

  • You have read a blog post about hiking on a mobile app of a publisher and followed a link to a recommended and related post. Your interactions will be recorded as showing that the initial hiking post was useful to you and that it was successful in interesting you in the related post. This will be measured to know whether to produce more posts on hiking in the future and where to place them on the home screen of the mobile app.
  • You were presented a video on fashion trends, but you and several other users stopped watching after 30 seconds. This information is then used to evaluate the right length of future videos on fashion trends.

Reports can be generated based on the combination of data sets (like user profiles, statistics, market research, analytics data) regarding your interactions and those of other users with advertising or (non-advertising) content to identify common characteristics (for instance, to determine which target audiences are more receptive to an ad campaign or to certain contents).

  • The owner of an online bookstore wants commercial reporting showing the proportion of visitors who consulted and left its site without buying, or consulted and bought the last celebrity autobiography of the month, as well as the average age and the male/female distribution of each category. Data relating to your navigation on its site and to your personal characteristics is then used and combined with other such data to produce these statistics.
  • An advertiser wants to better understand the type of audience interacting with its adverts. It calls upon a research institute to compare the characteristics of users who interacted with the ad with typical attributes of users of similar platforms, across different devices. This comparison reveals to the advertiser that its ad audience is mainly accessing the adverts through mobile devices and is likely in the 45-60 age range.

Information about your activity on this service, such as your interaction with ads or content, can be very helpful to improve products and services and to build new products and services based on user interactions, the type of audience, etc. This specific purpose does not include the development or improvement of user profiles and identifiers.

  • A technology platform working with a social media provider notices a growth in mobile app users, and sees based on their profiles that many of them are connecting through mobile connections. It uses a new technology to deliver ads that are formatted for mobile devices and that are low-bandwidth, to improve their performance.
  • An advertiser is looking for a way to display ads on a new type of consumer device. It collects information regarding the way users interact with this new kind of device to determine whether it can build a new mechanism for displaying advertising on this type of device.

Content presented to you on this service can be based on limited data, such as the website or app you are using, your non-precise location, your device type, or which content you are (or have been) interacting with (for example, to limit the number of times a video or an article is presented to you).

  • A travel magazine has published an article on its website about the new online courses proposed by a language school, to improve travelling experiences abroad. The school’s blog posts are inserted directly at the bottom of the page, and selected on the basis of your non-precise location (for instance, blog posts explaining the course curriculum for different languages than the language of the country you are situated in).
  • A sports news mobile app has started a new section of articles covering the most recent football games. Each article includes videos hosted by a separate streaming platform showcasing the highlights of each match. If you fast-forward a video, this information may be used to select a shorter video to play next.

Your data can be used to monitor for and prevent unusual and possibly fraudulent activity (for example, regarding advertising, ad clicks by bots), and ensure systems and processes work properly and securely. It can also be used to correct any problems you, the publisher or the advertiser may encounter in the delivery of content and ads and in your interaction with them.

  • An advertising intermediary delivers ads from various advertisers to its network of partnering websites. It notices a large increase in clicks on ads relating to one advertiser, and uses data regarding the source of the clicks to determine that 80% of the clicks come from bots rather than humans.

Certain information (like an IP address or device capabilities) is used to ensure the technical compatibility of the content or advertising, and to facilitate the transmission of the content or ad to your device.

  • Clicking on a link in an article might normally send you to another page or part of the article. To achieve this, 1°) your browser sends a request to a server linked to the website, 2°) the server answers back (“here is the article you asked for”), using technical information automatically included in the request sent by your device, to properly display the information / images that are part of the article you asked for. Technically, such exchange of information is necessary to deliver the content that appears on your screen.

The choices you make regarding the purposes and entities listed in this notice are saved and made available to those entities in the form of digital signals (such as a string of characters). This is necessary in order to enable both this service and those entities to respect such choices.

  • When you visit a website and are offered a choice between consenting to the use of profiles for personalised advertising or not consenting, the choice you make is saved and made available to advertising providers, so that advertising presented to you respects that choice.

Information about your activity on this service may be matched and combined with other information relating to you and originating from various sources (for instance your activity on a separate online service, your use of a loyalty card in-store, or your answers to a survey), in support of the purposes explained in this notice.

In support of the purposes explained in this notice, your device might be considered as likely linked to other devices that belong to you or your household (for instance because you are logged in to the same service on both your phone and your computer, or because you may use the same Internet connection on both devices).

Your device might be distinguished from other devices based on information it automatically sends when accessing the Internet (for instance, the IP address of your Internet connection or the type of browser you are using) in support of the purposes exposed in this notice.

With your acceptance, your precise location (within a radius of less than 500 metres) may be used in support of the purposes explained in this notice.

With your acceptance, certain characteristics specific to your device might be requested and used to distinguish it from other devices (such as the installed fonts or plugins, the resolution of your screen) in support of the purposes explained in this notice.

Vendors

 (1)
 (1)

#AGM Final Schedule

Typing errors and inaccuracies excepted.
The abstracts can be accessed via the Abstract ID (Link to #IMC).

Typing errors and inaccuracies excepted.
The abstracts can be accessed via the Abstract ID (Link to #IMC).

To view the full title, simply press and hold on the truncated text—after a moment, a tooltip will display the complete title

08:00 – 09:00 Registration
# Scheduled Presenter Title
0 09:00 – 09:15 Organizing Committee
(Rainer Prinz, Lindsey Nicholson, Emily Collier)
Opening, welcoming remarks, logistics, details
Swipe the table with your finger/mouse
Session 1: Global Glacier Changes
# Scheduled Abstract ID Presenter Title
1 09:15 – 09:30 Magnus Mar Magnusson Greetings from the IGS
2 09:30 – 09:45 28.7305 Michael Zemp Into the International Year of Glaciers’ Preservation 2025 – Perspectives from the World Glacier Monitoring Service
3 09:45 – 10:00 28.7217 Samuel Cook Global ice thickness inversions using deep learning
4 10:00 – 10:15 28.7480 Lilian Schuster Impact of global warming on glaciers until 2300: Figures for the State of the Cryosphere reports 2023 and 2024
Swipe the table with your finger/mouse
Coffee Break
Session 2: Regional Changes in Snow or Ice
# Scheduled Abstract ID Presenter Title
5 10:45 – 11:00 28.7272 Roberto Sergio Azzoni Belvedere, 1951–2023: A Glacier Odyssey
6 11:00 – 11:15 28.7308 Andrea Securo The Glaciers of the Dolomites: last 40 years of melting
7 11:15 – 11:30 28.7372 Tiziana Lazzarina Zendrini A century of late-summer snowline fluctuations in the Ortles-Cevedale Group: a reconstruction from historical photos
8 11:30 – 11:45 28.7297 Cecilia Delia Almagioni Snow cover variability and trends over Karakoram, Western Himalaya and Kunlun Mountains: Insights from MODIS (2001–2024) and Reanalysis Data
9 11:45 – 12:00 28.7312 Valerie Reppert Climate Signals from Neumayer, Coastal Dronning Maud Land, Antarctica: A 33-Year Statistical Analysis of Snow Accumulation in a Stake Farm
Swipe the table with your finger/mouse
Lunch Break
Feb 27, 2025: Poster Session 1
Time Slot: 13:00 – 14:30
# Abstract ID Poster Presenter Title
A1 28.7270 Blanka Barbagallo HLSL30 vs. Landsat 8: A Cross-Comparison of Albedo Products in the Karakoram Range
A2 28.7271 Andreas Henz Integration of high-resolution glacier modelling with geomorphological data for the reconstruction of past glacier fluctuations in the European Alps
A3 28.7273 Patrick Schmitt Goodbye Glaciers!? – A hiking signpost project to raise glacier loss awareness
A4 28.7279 Lea Hartl Decadal overview of mass balance at five Austrian glaciers and 2023/24 results
A5 28.7280 Michele Di Biase First inventory of the paraglacial activity in the Venosta Valley (Italy) in relation to the recent glacial recession
A6 28.7281 Giorgia Dassie On the release of microplastics from UHMWPE ski bases to snow. A spectroscopic analysis.
A7 28.7284 Davide Fugazza What influences Algal blooms on the Greenland Ice Sheet? Insights from field work and satellite data at Qaanaaq glacier.
A8 28.7285 Anees Ahmad Fusion of Sentinel-1 interferometric coherence and Sentinel-2 MSI for debris-covered glacier boundary delineation
A9 28.7288 Celine Walker Global catalogue of future glacier lakes using novel bed topography
A10 28.7289 Alessia Spezza Intercomparison of gauge based, reanalysis and satellite gridded precipitation datasets in High Mountain Asia: insights from observations and runoff data.
A11 28.7291 Marcus Gastaldello Spatio-temporal Degradation of Alpine Cold Firn in the 21st Century
A12 28.7293 Jan Niklas Richter A Remote-Only Approach to SEB Model Calibration: First Insights from Hintereisferner Glacier
A13 28.7294 Leonora Seiler Reconstruction of rockfall activity from supraglacial deposits on Witenwasserengletscher, Switzerland and its relation to climatic factors
A14 28.7296 João Gomes Ilha Unveiling Amazon proxies in high-mountain environments, the Quelccaya Ice Core, Peru
A15 28.7300 Thorsten Seehaus Gaussian Process Regression for ICESat-2 Point-Cloud Interpolation
A16 28.7306 Akash Patil Investigating firn density and accumulation history in the Aletsch glacier’s accumulation area using Ground Penetrating Radar
A17 28.7309 Giovanni Kappenberger From a glacier to a lake: the icebergs of the Geren Pass.
A18 28.7342 Vijaya Kumar Thota Historical glacier elevation changes in southwest Antarctic Peninsula
A19 28.7353 Sonia Morgese Sensitivity analysis of energy balance equation on a debris covered glacier. The case of Belvedere Glacier, Italy.
A20 28.7840 Bernhard Hynek Accumulation by avalanches as a significant contributor to the mass balance of a peripheral glacier of Greenland

 

Session 3: Monitoring
# Scheduled Abstract ID Presenter Title
10 14:30 – 14:45 28.7414 Matthias Huss Swiss glacier monitoring: New approaches from the local to the regional scale
11 14:45 – 15:00 28.7311 Joel Harper In Situ Measurement of Meltwater Infiltration Mechanisms in Snow and Firn
12 15:00 – 15:15 28.7292 Anna Siebenbrunner Glacier Monitoring on the Fly: Quantifying Ice Volume and Analyzing Subglacial Topography with UAV-borne GPR
13 15:15 – 15:30 28.7436 Fanny Brun Glacier mass balance monitoring, research questions and capacity building in Nepal
14 15:30 – 15:45 28.7404 Stefania Federici Advancing Microplastic Research: European Networking Opportunities
Swipe the table with your finger/mouse
Coffee Break
Session 4: Remote Sensing
# Scheduled Abstract ID Presenter Title
15 16:15 – 16:30 28.7295 Amaury Dehecq Impact of DEMs spatial resolution on glacier geodetic mass balance
16 16:30 – 16:45 28.7301 Luc Beraud An improved processing of ASTER elevation time series in High Mountain Asia to study glacier surge dynamics
17 16:45 – 17:00 28.7290 Jonathan Fipper Drivers and impacts of the vertical structure of the troposphere at Villum Research Station, Northeast Greenland
18 17:00 – 17:15 28.7490 Dagmar Brombierstäudl Quantifying aufeis volumes in Central Ladakh, India: Insights from satellite and terrestrial imagery
19 17:15 – 17:30 28.7396 Moritz Koch The state and fate of Glaciar Perito Moreno, Patagonia
Swipe the table with your finger/mouse
19:00 Come2Gether & Socializing Event
Session 5: Processes
# Scheduled Abstract ID Presenter Title
1 09:00 – 09:15 28.7839 Lindsey Nicholson The second Hintereisferner Experiment HEFEX II
2 09:15 – 09:30 28.7449 Leo Schlagbauer Foehn winds on McCall Glacier, Alaska: Identification and impacts
3 09:30 – 09:45 28.7398 Robert Peal The influence of westerly moisture transport events on Kilimanjaro’s glaciers
4 09:45 – 10:00 28.7345 Marie Schroeder Turbulent Fluxes in a Land Terminating Vertical Ice Cliff
5 10:00 – 10:15 28.7676 Christoph Mayer Snow cover, glacier size and melt; runoff variability from a medium size alpine glacier
Swipe the table with your finger/mouse
Coffee Break
Session 6: Subsurface Processes
# Scheduled Abstract ID Presenter Title
6 10:45 – 11:00 28.7351 Christophe Ogier Water pockets in Alpine glaciers: what are they, why do they form, and how do they burst?
7 11:00 – 11:15 28.7275 Leonardo Stucchi Evaluation of non-conductive heat transfer in supraglacial debris of Belvedere Glacier, Italy.
8 11:15 – 11:30 28.7433 Ian Arburua Delaney Variations in subglacial sediment transport capacity with respect to water discharge
9 11:30 – 11:45 28.7304 Guillaume Jouvet Revisiting Mercanton’s Visionary Experiment on the Rhône Glacier with a Numerical Model
11:45 – 11:50 TBA Outlook to the #AGM29 in Milano
Swipe the table with your finger/mouse
Lunch Break
Feb 28, 2025: Poster Session 2
Time Slot: 12:45 – 14:15
# Abstract ID Poster Presenter Title
B1 28.7267 Maria Heinrich Multi-sensor satellite observations of snow area extent in mountain regions
B2 28.7276 Michael Zemp The second Glacier Mass Balance Intercomparison Exercise 2025–26
B3 28.7373 Martina Lodigiani Remote Sensing Applications for Monitoring Periglacial Environments: Insights from the “Agile Arvier” Project
B4 28.7385 Mamta K C Comparing neural operator based surrogate models on glacier dynamics prediction.
B5 28.7392 Theresa Dobler Detailed velocity map and long-term glacier surface velocities of the slow-flowing Vernagtferner in the Austrian Alps
B6 28.7399 Diego Pacheco Ferrada Glacier mapping using Deep Neural Networks in the Tropical Andes
B7 28.7413 Annelies Voordendag Seasonal variations in the three-dimensional velocities of the ice lollipop at Hintereisferner (Austria)
B8 28.7417 Francesco Ioli Potentials and challenges of free SPOT 5 stereo imagery archive to derive glacier elevation changes in the Alpine region
B9 28.7420 José Manuel Muñoz Hermosilla Initializing a glacier model for simulations of debris transport: A case study of the Oberaletsch Glacier
B10 28.7421 Daniel Farinotti Traces of an englacial reservoir? First results from a helicopter-borne GPR survey at Glacier de la Bonne Pierre, France
B11 28.7431 Paolo Perret Integrating radar and multi-sensor approaches for debris-covered glacier studies: insights from the Forca glacier (Italy)
B12 28.7432 Florian Hardmeier A novel particle tracking approach to model debris-covered glaciers in the Instructed Glacier Model (IGM)
B13 28.7438 Evan Miles Kon Chukurbashi: prospective for a high elevation ice core in the Pamir mountains
B14 28.7444 Jorge Andres Berkhoff Leal Mapping glacier ice thickness in Chile
B15 28.7453 Gabriele Schwaizer Changing ablation patterns on glacier in the Alps during the melting seasons 2015 – 2023 observed by means of Sentinel-2 data
B16 28.7458 Andrea Scolamiero SAR Wet Snow in High Mountains
B17 28.7477 Anne Hartig Comparison of glacier surface classes in the Ötztal Alps from the openAMUNDSEN model and from remote sensing data
B18 28.7491 Alexander Raphael Groos A high-resolution debris thickness map for the Kanderfirn in the Swiss Alps derived from UAV-based infrared thermography
B19 28.7494 Jules Bredon Glacial response to modern climate change through a transect of several sediment cores, the case of Qalerallit Imaa fjord, southwest GrIS
B20 28.7688 Astrid Lambrecht Combining glaciological field surveys, REmote sensing and regional Climate modelling to Analyse the variability of accumulation in the Pamir mountains (RECAP)
B21 28.7287 Nadine Salzmann Initiating permafrost research in Bhutan: strategy and first results from the CRYO-SPIRIT project

 

Session 7: Modelling
# Scheduled Abstract ID Presenter Title
10 14:15 – 14:30 28.7277 Christoph Posch Comparative analysis of mass balance estimates at Greenland’s most studied peripheral glacier
11 14:30 – 14:45 28.7299 Franziska Temme The climatic imprint on recent glacier evolution in the Cordillera Darwin Icefield, Tierra del Fuego
12 14:45 – 15:00 28.7302 Audrey Goutard Impact of surface liquid water retention on glacier mass balance: application to Mera Glacier (Nepal) using SURFEX-ISBA-Crocus
13 15:00 – 15:15 28.7282 Marin Kneib Combined field measurements for quantifying the dynamics of an on-glacier avalanche deposit and its underlying processes
14 15:15 – 15:30 28.7268 Oskar Hermann Translating Observation Uncertainty into Model Calibration unsing the Ensemble Kalman Filter
Swipe the table with your finger/mouse
Coffee Break
Session 8: Modelling, Paleoclimate and Ecology
# Scheduled Abstract ID Presenter Title
15 16:00 – 16:15 28.7338 Martin Rückamp Future retreat of Great Aletsch Glacier and Hintereisferner – an East-West comparison
16 16:15 – 16:30 28.7274 Patrick Schmitt Deglaciation in western Austria: Perspectives from observations and modeling
17 16:30 – 16:45 28.7298 Azzurra Spagnesi Weißseespitze glacier (Eastern Rhaetian Alps): a 6 kyr paleoclimatic and paleoenvironmental reconstruction
18 16:45 – 17:00 28.7286 Gilda Varliero Viral Dynamics in Glacier Microbiomes: Insights from the Rhone Glacier
Swipe the table with your finger/mouse
END OF #AGM28