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Although worldwide inventories of glacier area have been coordinated internationally for several decades, a similar effort for glacier ice thicknesses was only initiated in 2013. Here, we present the third version of the Glacier Thickness Database (GlaThiDa v3), which includes 3 854 279 thickness measurements distributed over roughly 3000 glaciers worldwide. Overall, 14 % of global glacier area is now within 1 km of a thickness measurement (located on the same glacier) – a significant improvement over GlaThiDa v2, which covered only 6 % of global glacier area and only 1100 glaciers. Improvements in measurement coverage increase the robustness of numerical interpolations and model extrapolations, resulting in better estimates of regional to global glacier volumes and their potential contributions to sea-level rise. In this paper, we summarize the sources and compilation of glacier thickness data and the spatial and temporal coverage of the resulting database. In addition, we detail our use of open-source metadata formats and software tools to describe the data, validate the data format and content against this metadata description, and track changes to the data following modern data management best practices. Archived versions of GlaThiDa are available from the World Glacier Monitoring Service (e.g., v3.1.0, from which this paper was generated: https://doi.org/10.5904/wgms-glathida-2020-10; GlaThiDa Consortium, 2020), while the development version is available on GitLab (https://gitlab.com/wgms/glathida, last access: 9 November 2020).

Advanced knowledge of the ice thickness distribution within glaciers is of fundamental importance for several purposes, such as water resource management and the study of the impact of climate change. Ice thicknesses can be modeled using ice surface features, but the resulting models can be prone to considerable uncertainties. Alternatively, it is possible to measure ice thicknesses, for example, with ground-penetrating radar (GPR). Such measurements are typically restricted to a few profiles, with which it is not possible to obtain spatially unaliased subsurface images. We developed the Glacier Thickness Estimation algorithm (GlaTE), which optimally combines modeling results and measured ice thicknesses in an inversion procedure to obtain overall thickness distributions. GlaTE offers the flexibility of being able to add any existing modeling algorithm, and any further constraints can be added in a straightforward manner. Furthermore, it accounts for the uncertainties associated with the individual constraints. Properties and benefits of GlaTE are demonstrated with three case studies performed on different types of alpine glaciers. In all three cases, subsurface models could be found that are consistent with glaciological modeling and GPR data constraints. Since acquiring GPR data on glaciers can be an expensive endeavor, we additionally employed elements of sequential optimized experimental design (SOED) for determining cost-optimized GPR survey layouts. The calculated cost–benefit curves indicate that a relatively large amount of data can be acquired before redundant information is collected with any additional profiles, and it becomes increasingly expensive to obtain further information.

Ice thickness is a key parameter for glacier mass estimations and glacier dynamics simulations. Multiple physical models have been developed by glaciologists to estimate glacier ice thickness. However, obtaining internal and basal glacier parameters required by physical models is challenging, often leading to simplified models that struggle to capture the nonlinear characteristics of ice flow and resulting in significant uncertainties. To address this, this study proposes a convolutional neural network (CNN)-based deep learning model for glacier ice thickness estimation, named the Coordinate-Attentive Dense Glacier Ice Thickness Estimate Model (CADGITE). Based on in situ ice thickness measurements in the Swiss Alps, a CNN is designed to estimate glacier ice thickness by incorporating a new architecture that includes a Residual Coordinate Attention Block together with a Dense Connected Block, using the distance to glacier boundaries as a complement to inputs that include surface velocity, slope, and hypsometry. Taking ground-penetrating radar (GPR) measurements as a reference, the proposed model achieves a mean absolute deviation (MAD) of 24.28 m and a root mean square error (RMSE) of 37.95 m in Switzerland, outperforming mainstream physical models. When applied to 14 glaciers in High Mountain Asia, the model achieves an MAD of 20.91 m and an RMSE of 27.26 m compared to reference measurements, also exhibiting better performance than mainstream physical models. These comparisons demonstrate the good accuracy and cross-regional transferability of our approach, highlighting the potential of using deep learning-based methods for larger-scale glacier ice thickness estimation.

The effect of climate change on water resources and sea-level rise is largely determined by the size of the ice reservoirs around the world and the ice thickness distribution, which remains uncertain. Here, we present a comprehensive high-resolution mapping of ice motion for 98% of the world’s total glacier area during the period 2017–2018. We use this mapping of glacier flow to generate an estimate of global ice volume that reconciles ice thickness distribution with glacier dynamics and surface topography. After reallocating volume connected to the Antarctic ice sheet, the results suggest that the world’s glaciers have a potential contribution to sea-level rise of 257 ± 85 mm, which is 20% less than previously estimated. At low latitudes, our findings highlight notable changes in freshwater resources, with 34% more ice in the Himalayas and 27% less ice in the tropical Andes of South America, affecting water availability for local populations. This mapping of glacier flow and thickness redefines our understanding of global ice-volume distribution and has implications for the prediction of glacier evolution around the world, since accurate representations of glacier geometry and dynamics are of prime importance to glacier modelling. Potential sea-level rise from the world’s glaciers is 20% less than previously thought, according to an estimate based on high-resolution maps of glacier ice velocity and thickness.

Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

Knowledge of the ice thickness distribution of the world’s glaciers is a fundamental prerequisite for a range of studies. Projections of future glacier change, estimates of the available freshwater resources or assessments of potential sea-level rise all need glacier ice thickness to be accurately constrained. Previous estimates of global glacier volumes are mostly based on scaling relations between glacier area and volume, and only one study provides global-scale information on the ice thickness distribution of individual glaciers. Here we use an ensemble of up to five models to provide a consensus estimate for the ice thickness distribution of all the about 215,000 glaciers outside the Greenland and Antarctic ice sheets. The models use principles of ice flow dynamics to invert for ice thickness from surface characteristics. We find a total volume of 158 ± 41 × 103 km3, which is equivalent to 0.32 ± 0.08 m of sea-level change when the fraction of ice located below present-day sea level (roughly 15%) is subtracted. Our results indicate that High Mountain Asia hosts about 27% less glacier ice than previously suggested, and imply that the timing by which the region is expected to lose half of its present-day glacier area has to be moved forward by about one decade.The ice volume of glaciers outside the Greenland and Antarctic ice sheets totals about 158,000 km3, with about 27% less ice in High Mountain Asia than thought, according to multiple models that estimate ice thickness from surface characteristics.

Knowledge of the ice thickness distribution of glaciers and ice caps is an important prerequisite for many glaciological and hydrological investigations. A wealth of approaches has recently been presented for inferring ice thickness from characteristics of the surface. With the Ice Thickness Models Intercomparison eXperiment (ITMIX) we performed the first coordinated assessment quantifying individual model performance. A set of 17 different models showed that individual ice thickness estimates can differ considerably – locally by a spread comparable to the observed thickness. Averaging the results of multiple models, however, significantly improved the results: on average over the 21 considered test cases, comparison against direct ice thickness measurements revealed deviations on the order of 10 ± 24 % of the mean ice thickness (1σ estimate). Models relying on multiple data sets – such as surface ice velocity fields, surface mass balance, or rates of ice thickness change – showed high sensitivity to input data quality. Together with the requirement of being able to handle large regions in an automated fashion, the capacity of better accounting for uncertainties in the input data will be a key for an improved next generation of ice thickness estimation approaches.

Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit field-based ice thickness observations in the Himalaya. Remote-sensing applications, together with mathematical models, provide alternative techniques for glacier ice thickness and volume estimation. The objective of the present research is to assess the application of artificial neural network (ANN) modelling coupled with remote-sensing techniques to estimate ice thickness on individual glaciers with direct field measurements. We have developed two ANN models and estimated the ice thickness of Chhota Shigri Glacier (western Himalaya) on ten transverse cross sections and two longitudinal sections. The ANN model estimates agree well with ice thickness measurements from a ground-penetrating radar, available for five transverse cross sections on Chhota Shigri Glacier. The overall root mean square errors of the two ANN models are 24 and 13 m and the mean bias errors are ±13 and ±6 m, respectively, which are significantly lower than for other available models. The estimated mean ice thickness and volume for Chhota Shigri Glacier are 109 ± 17 m and 1.69 ± 0.26 km3, respectively.

This study describes the morphological and dynamic changes of Parkachik Glacier, Suru River valley, Ladakh Himalaya, India. We used medium-resolution satellite images; CORONA KH-4, Landsat and Sentinel-2A from 1971–2021, and field surveys between 2015 and 2021. In addition, we used the laminar flow-based Himalayan Glacier Thickness Mapper and provide results for recent margin fluctuations, surface ice velocity, ice thickness, and identified glacier-bed overdeepenings. The results revealed that overall the glacier retreated by −210.5 ± 80 m with an average rate of 4 ± 1 m a−1 between 1971 and 2021. Whereas a field study suggested that the glacier retreat increased to −123 ± 72 m at an average rate of −20 ± 12 m a−1 between 2015 and 2021. Surface ice velocity was estimated using COSI-Corr on the Landsat data. Surface ice velocity in the lower ablation zone was 45 ± 2 m a−1 in 1999–2000 and 32 ± 1 m a−1 in 2020–2021, thus reduced by 28%. Further, the maximum thickness of the glacier is estimated to be ~441 m in the accumulation zone, while for glacier tongue it is ~44 m. The simulation results suggest that if the glacier continues to retreat at a similar rate, three lakes of different dimensions may form in subglacial overdeepenings.

The purpose of this study was to determine the glacier displacement, velocity, and thickness of seven major glaciers of Bhutan and predicted potential glacier lake formation site with its depth. We named the glacier identification (ID) number 1–7 for seven glaciers. From the study, the glacier velocity between the central trunk and snout saw rapid fluctuations in 1976–1978 with an average uncertainty velocity of ± 27.10 m/year and a decreasing velocity trend. The year 2013–2014 has the lowest uncertainty in glacier velocity, with a value of ±1.24 m/year. The glacier velocity progressively increases from the snout to the main central trunk for all the glaciers with a value of 0 to 98.63 m/year. The glacier with the highest average velocity is glacier ID 5, which has a velocity of 25.58 m/year. From 2000 and 2022, all of the glaciers’ thicknesses significantly decreased from 0 to 468.2 m. The thickness of glacier ID 6 was lowered by −192.3 ± 1.99 m, making it the highest among the seven glaciers. In the future, a glacier lake is predicted to form at the base of each glacier. Glacier ID 6 is predicted to form the largest lake with a surface area of 2.572 km2 and a depth of 208.5 m.