Even though surge-type glaciers make up only a small percentage of all glaciers, related research contributes considerably to the general understanding of glacier flow mechanisms. Recent studies based on remote sensing techniques aimed to disentangle underlying processes related to glacier surges. They have proven the possibilities yielded by combining high performance computing and earth observation. In addition, modelling approaches to surges have seen increasing popularity, yet large spatial and temporal data about timing of surge incites are missing. We aimed to develop an algorithm that not only detects surge type glaciers but also determines the timing of a surge onset, while being computationally inexpensive, transferable, and expandable in time and space. The algorithm is based on time series analyses of glacier surface velocity derived from Sentinel-1 data. After seasonal and trend decomposition, outlier detection is performed by the General Studentized Extreme Deviate Test, an iterative algorithm well suited for outlier detection in univariate time series. To determine surges, cluster analysis is performed to identify outlier clusters, which are linked to glacier surges. We demonstrate the viability on the Svalbard archipelago for the period 2015 to 2021 where we have identified 18 glacier surges and the timing of their active phase.

Glaciar Perito Moreno, located in the Southern Patagonian Icefields, has long been considered stable despite widespread regional glacier retreat. Unlike neighboring glaciers, its frontal position and surface elevation remained relatively unchanged - until recently. For lake-terminating glaciers, retreat is strongly controlled by their basal topography, which remains poorly known for Glaciar Perito Moreno. Here, we present helicopter-borne ground-penetrating radar and bathymetric data, along with time series of surface elevation and velocity. We detect an acceleration in frontal surface lowering rates, from 0.34 m a⁻¹ (2000–2019) to 5.5 m a⁻¹ (2019–2024), accompanied by glacier acceleration and retreat. Using a simple numerical model projecting current thinning into the future, we demonstrate the potential for large scale buoyancy-driven retreat once the glacier recedes beyond a subglacial ridge. These findings reveal a high sensitivity to frontal dynamics and suggest that Glaciar Perito Moreno may now be following a similar pattern of other retreating lacustrine calving glaciers in Patagonia.

The measurement of ice thickness is of great importance for the accurate estimation of glacier volume and the delineation of bedrock topography. In particular, this is a crucial factor in forecasting the future evolution of glaciers in the context of a changing climate. In order to derive the ice thickness, the travel time of electromagnetic waves in radargrams acquired by radio-echo sounding (RES) systems is analyzed. This can only be achieved by identifying the ice surface and underlying ice bottom in corresponding radargrams. Manually identifying these two reflection horizons in RES data is a laborious and time-consuming process. Consequently, scientists are attempting to automate this task through the use of techniques such as deep learning. Such automation can significantly reduce the time between a field campaign and the calculation of the glacier's ice thickness distribution. In this paper, we present the first benchmark dataset for delineating the ice surface and bottom boundaries in RES data to facilitate standardized comparisons of deep learning models in the future. The “IceAnatomy” dataset comprises radargrams and the corresponding manual picks, amounting to a total of over 45 000 km of observations. The RES data originate from three sources: FAU (Friedrich-Alexander-Universität Erlangen-Nürnberg, Institute of Geography), CReSIS (Center for Remote Sensing and Integrated Systems), and AWI (Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research). The dataset comprises different RES systems as well as different pre-processing methods. In addition, the data were acquired over a large range of geographical and glaciological settings, featuring different thermal regimes present in Antarctica and the Southern Patagonian Ice Field. This diversity ensures that the models' behaviors can be analyzed in different scenarios. We define a standardized train–test split for each source in the dataset. This allows us to introduce not only a baseline model trained on the entire training set (the “omni”-model), but also three source-specific baseline models. The source-specific models are trained exclusively on the subset of the training data acquired by the specified source. The baseline models provide an initial benchmark against which subsequent models can be compared. The source-specific models demonstrate more accurate results than the omni-model. For the FAU, CReSIS, and AWI test sets, the source-specific models achieve mean meter errors of 2.1, 23.1, and 4.9 m for the ice surface and 9.1, 78.2, and 29.3 m for the ice bottom. In relation to the mean measured ice thickness of the test set, these errors equate to 1.2 %, 3.1 %, and 0.3 % for the ice surface and 4.9 %, 10.4 %, and 1.5 % for the ice bottom. The dataset and implementation are available at https://doi.org/10.5281/zenodo.14036897 (Dreier et al., 2024) and https://doi.org/10.5281/zenodo.14038570 (Dreier, 2024).

We present the first helicopter-borne ground-penetrating radar dataset over Glaciar Viedma, Upsala, and Perito Moreno, three of the largest outlet glaciers of the Southern Patagonian Icefield in South America. The dataset comprises 116.021 individual ice-thickness measurements along 232 km of flight tracks. Data were acquired during two campaigns in March and April 2022 as well as in October 2024 using a 25 MHz shielded broadband antenna deployed as a helicopter sling load. For the first time, we reveal the complex subglacial topography of these glaciers in their lower regions and measured bed reflections at Glaciar Upsala in depths of up to 800 m. The newly obtained measurements were incorporated into an ice-thickness reconstruction method to derive glacier-wide ice-thickness distribution maps and the corresponding bedrock topography. The latter exerts primary control on the response of water-terminating glaciers to a changing climate. Our 100-m gridded ice-thickness maps indicate that the three glaciers had a combined ice volume of 831.14 km³ in the year 2000. The dataset and the well-constrained glacier-wide grids provide a valuable basis for future studies aiming to better understand the mechanisms driving glacier retreat and the susceptibility of these large outlet glaciers to climate change. All data are publicly available at Zenodo (https://doi.org/10.5281/zenodo.17464164; Koch et al., 2025a).

Continuous monitoring of glacier calving fronts is essential for sea level rise projections. This study benchmarks Deep Learning systems for front delineation in Synthetic Aperture Radar imagery. While Deep Learning systems exhibit errors up to 221 m, human annotators deviate by only 38 m, underscoring the need for further research.