Conference Agenda

The cryosphere responds to variations in the climate. Monitoring glaciers requires research into their dynamics. The surface velocity of the Gangotri glacier was obtained in this study using the Sentinel-1 dataset. Modifying the laminar flow model improved estimates of ice thickness. Moreover, the glacier mass balance has been calculated using changes in ice thickness between 2017 and 2022. An average velocity of 0.09 m/day was observed with stretches from 0.12 to 0.23 m/day in the central trunk. A mean thickness of 189 ± 17.01 m was determined for the glacial ice. The thickest areas, with the least drag, were measured to be 587 ± 52.83 m in the middle part. Negative mass rates of -1.3 to -0.5 m.w.e./year were observed for the glacier system (with thickness changes of -3 to -0.6 m/year) due to the glacier's decreased thickness throughout time.

The Himalayan region hosts the largest reservoir of snow and ice outside the polar regions. However, ongoing climate change has resulted in widespread glacier retreat, heightening the frequency and magnitude of extreme events, including flashfloods, landslides, and Glacier Lake Outburst Floods. The Beas Basin in the northwestern Himalaya exemplifies this vulnerability, where cryospheric transformations directly threaten downstream communities, hydropower systems, and infrastructure. Despite its critical importance, long-term basin-scale records remain limited. Therefore, this study investigates the long-term cryospheric evolution of the Beas Basin and identifies emerging glacial lakes using an integrated remote-sensing and modelling-approach. Glacier mass balance from 1951 to 2024 was estimated using an Improved Accumulation-Area-Ratio method, incorporating equilibrium-line-altitudes derived from ASTER-DEM and meteorological data, alongside glacier extents from Landsat and Sentinel imagery. Current glacier ice reserves were quantified using laminar-flow and volume–area scaling methods, with surface velocities derived from sub-pixel Landsat image correlation, and slope from DEMs. Future glacial lake formation was assessed using the HIGTHIM tool, which integrates ice thickness, bed topography, and moraines. Results indicate a mean area-weighted mass balance of –0.46±0.26m.w.e.a⁻¹, corresponding to 17.75Gt cumulative ice loss (~48% of glacier-stored mass) since 1951 and a current ice reserve of 19.60±3.5 Gt. Sixty-three potential glacial lake sites were identified, with four existing lakes projected to expand, totalling 122±22 million-m³of water. These findings reveal extensive cryospheric reorganisation, with significant implications for hydrology, water security, and hazard management. The study demonstrates the value of combining satellite observations with process-based modelling for monitoring Himalayan glacier dynamics in data-sparse regions.

The intrusion of relatively warm ocean waters beneath Antarctic ice shelves is a key driver of basal melting and strongly influences ice-shelf stability. However, previous studies investigating warm-water pathways have largely relied on single-source datasets, such as ship-based Conductivity–Temperature–Depth (CTD) measurements, which are spatially sparse and limited to a few well-surveyed regions. Recent advances in multi-source remote sensing datasets provide new opportunities to address these limitations. In this study, a multi-source remote sensing–based framework is developed to identify potential pathways of relatively warm water intrusion beneath Antarctic ice shelves and to quantify the associated basal melting. The Moscow University Ice Shelf (MUIS) is used as a case study. Across the continental shelf, CTD observations, sub-ice-shelf bathymetry, and modeled ocean circulation are integrated to infer potential intrusion routes. At the ice-shelf front and base, EN4 reanalysis data are used to characterize seawater properties, while satellite-derived basal melt products are applied to analyze spatial and vertical patterns of basal melting. Results indicate that relatively warm water is mainly concentrated at depths of 300–500 m, coinciding with bathymetric depressions that facilitate its intrusion beneath MUIS. Enhanced basal melting occurs near the ice front and grounding line, primarily within the upper 0–500 m of the ice-shelf draft, with an average melt rate of ~6 m yr⁻¹. The proposed framework provides a transferable approach for investigating ocean-driven melting beneath Antarctic ice shelves.

The Antarctic Ice Sheet (AIS), the largest terrestrial ice mass on Earth, contains approximately 90% of the planet's total ice volume. This study quantifies ice discharge and associated uncertainties in AIS estimates through Input-Output method, evaluating the impact of flux gate locations on discharge magnitude and measurement uncertainty. Through analysis of key factors contributing to discharge uncertainty, we propose a gate positioning strategy that optimizes the balance between proximity to the grounding line and uncertainty minimization.

NASA’s ICESat-2, a single-photon lidar satellite launched in 2018, has for six years delivered pole-wide elevation data with <0.4 cm/yr precision. To verify and exploit these data over Antarctica, we built a “space-air-ground” calibration chain. (1) A cross-track array of corner-cube retro-reflectors (CCRs) was installed at Kunlun, Taishan and Zhongshan stations; one deployment captures both ascending and descending passes, doubling efficiency. GNSS-PPP/RTK solutions overcome the absence of fixed reference points and position CCRs to within 1 cm; comparison with ICESat-2 tracks shows sub-4 cm vertical accuracy, confirming stable on-orbit performance. (2) UAV photogrammetry during the 36th CHINARE expedition produced 5 cm-resolution DEMs of crevassed ice margins at Zhongshan/Prydz Bay. Fused with RTK ground control, these reveal ICESat-2 planimetric offsets of 2–5 m and serve as “truth” for a new Photon-Cloud algorithm that corrects slope-induced positioning errors and extends the mission’s utility in rugged terrain. (3) Whole-continent cross-over analysis of repeat tracks shows millimetre-level consistency between ascending and descending orbits; an improved cross-track model extracts robust elevation-change time series for stable ice interiors. The integrated framework provides ICESat-2 Antarctic accuracy metrics, refined processing tools and a transferable protocol for future polar photon-counting altimetry missions.

Accurate mapping of exposed rock is fundamental for cryospheric and geospatial analyses in Antarctica, yet existing products are of limited resolution and tend to underestimate true rock exposure. We present a weakly supervised deep-learning framework that refines existing rock masks by combining Sentinel-2 multispectral imagery with elevation and slope data from the Reference Elevation Model of Antarctica (REMA). A U-Net with eight input channels (six spectral bands, elevation, slope) is trained using imperfect Landsat- and GeoMap based labels. Trained on data from the Antarctic Peninsula, the model produces a 10~m rock mask that delineates small and shaded outcrops more effectively than existing datasets. While quantitative evaluation is constrained by imperfect reference data, qualitative inspection indicates improved rock–snow separation. The workflow is fully automated, requires no manual annotation, and scales efficiently to all rock-hosting regions of the continent reachable by Sentinel-2 multispectral coverage. Beyond rock mapping, the framework is transferable to other scenarios with incomplete or uncertain reference data, such as vegetation, snow, or water mapping. The resulting rock mask for complete Antarctica, together with the trained model and preprocessing scripts, will be released to support reproducible large-scale mapping and future cryospheric research.