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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.

Glaciers and ice caps are experiencing strong mass losses worldwide, challenging water availability, hydropower generation, and ecosystems. Here, we perform the first-ever glacier evolution projections based on deep learning by modelling the 21st century glacier evolution in the French Alps. By the end of the century, we predict a glacier volume loss between 75 and 88%. Deep learning captures a nonlinear response of glaciers to air temperature and precipitation, improving the representation of extreme mass balance rates compared to linear statistical and temperature-index models. Our results confirm an over-sensitivity of temperature-index models, often used by large-scale studies, to future warming. We argue that such models can be suitable for steep mountain glaciers. However, glacier projections under low-emission scenarios and the behaviour of flatter glaciers and ice caps are likely to be biased by mass balance models with linear sensitivities, introducing long-term biases in sea-level rise and water resources projections. Deep learning unveils a nonlinear sensitivity of glacier mass changes to future climate warming, with important implications for water resources and sea-level rise coming from glaciers and particularly ice caps.

Assessing glacier surface mass balance (SMB) is essential for evaluating glacier response to climate change. However, traditional in situ measurement methods are labour intensive and often lack the temporal and spatial resolutions required to fully constrain SMB models. Here, we explore the potential of the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique which exploits reflected satellite signals to track surface height changes for continuous SMB estimation. Using data from 13 GNSS stations operating between 2019 and 2021 on Glacier d’Argentière (French Alps), we compare GNSS-IR-derived SMB with estimates from snow pits, wooden stakes, continuous ice-melt measurements using a SmartStake device, and a degree-day model. We demonstrate that the GNSS-IR technique can reliably estimate SMB values that closely match independent in situ measurements, while also offering the advantages of spatial integration and long-term time series that capture both snowfall events and snow/ice melt. We show that glacier surface roughness and antenna height, when the glacier is snow-free, strongly influence uncertainties, which can be reduced to as little as 2 cm d −1 using a smoothing filter. Finally, we demonstrate that the GNSS-IR technique can further constrain the degree-day factor, particularly its temporal evolution throughout the ablation season.

We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Ven μ s/CNES-ISA, Pléiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers (<250 m wide) remain challenging. Given the estimated precision for Sentinel-2, we also conclude that velocity fluctuations of the order of 10 m/yr can only be captured with repeat cycles longer than 60 days. Comparing Sentinel-2 with Pléiades, Planet and Ven μ s imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.

Amazonian deforestation has accelerated during the last decade, threatening an ecosystem where almost one third of the regional rainfall is transpired by the local rainforest. Due to precipitation recycling, the southwestern Amazon, including the Amazon-Andes transition region, is particularly sensitive to forest loss. This study evaluates the impacts of Amazonian deforestation on the hydro-climatic connectivity between the Amazon and the eastern tropical Andes during the austral summer (December–January–February) in terms of hydrological and energetic balances. Using 10-years high-resolution simulations (2001–2011) with the Weather Research and Forecasting Model, we analyze control and deforestation scenario simulations. Regionally, deforestation leads to a reduction in the surface net radiation, evaporation, moisture convergence and precipitation (~ 20%) over the entire Amazon basin. In addition, during this season, deforestation increases the atmospheric subsidence over the southern Amazon and weakens the regional Hadley cell. Atmospheric stability increases over the western Amazon and the tropical Andes inhibiting convection in these areas. Consequently, major deforestation impacts are observed over the hydro-climate of the Amazon-Andes transition region. At local scale, nighttime precipitation decreases in Bolivian valleys (~ 20–30%) due to a strong reduction in the humidity transport from the Amazon plains towards the Andes linked to the South American low-level jet. Over these valleys, a weakening of the daytime upslope winds is caused by local deforestation, which reduces the turbulent fluxes at lowlands. These alterations in rainfall and atmospheric circulation could impact the rich Andean ecosystems and its tropical glaciers.

The increased availability of high-resolution and high-quality digital elevation models (DEMs) allows for the investigation of small-scale glaciological changes and improved precision in geodetic mass-balance estimates. However, high precision and careful methodological choices are required to retrieve glacier-wide mass changes at annual to seasonal time scales. Here, we used a 7-year time series of 12 DEMs of the Glacier d'Argentière, in the French Alps, derived from the Pléiades optical satellites to assess the ability of sub-metre stereoscopic satellite images to retrieve annual-to-seasonal mass balances. We found good agreement between the five annual and the four winter mass-balance values estimated using a geodetic method and those of in situ glaciological measurements: mean values via the geodetic method are −0.66 m w.e. and 1.47 m w.e. for annual and winter balances, respectively; mean absolute discrepancies are 0.25 m w.e. (annual) and 0.36 m w.e. (winter). Our study identified three main limitations of this methodology: (i) the intrinsic DEM precision; (ii) the lack of control over the satellite acquisition dates; and (iii) the density assumption. The consistency between the methods demonstrates the potential of short time-scale glacier mass-balance monitoring using very high-resolution satellite images.

The ongoing glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring, modelling purposes (e.g. determination of run-off, mass balance, or future glacier extent), and other applications. With the launch of the first Sentinel-2 (S2) satellite in 2015, it became possible to create a consistent, Alpine-wide glacier inventory with an unprecedented spatial resolution of 10 m. The first S2 images from August 2015 already provided excellent mapping conditions for most glacierized regions in the Alps and were used as a base for the compilation of a new Alpine-wide glacier inventory in a collaborative team effort. In all countries, glacier outlines from the latest national inventories have been used as a guide to compile an update consistent with the respective previous interpretation. The automated mapping of clean glacier ice was straightforward using the band ratio method, but the numerous debris-covered glaciers required intense manual editing. Cloud cover over many glaciers in Italy required also including S2 scenes from 2016. The outline uncertainty was determined with digitizing of 14 glaciers several times by all participants. Topographic information for all glaciers was obtained from the ALOS AW3D30 digital elevation model (DEM). Overall, we derived a total glacier area of 1806±60 km2 when considering 4395 glaciers >0.01 km2. This is 14 % (−1.2 % a−1) less than the 2100 km2 derived from Landsat in 2003 and indicates an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. It is a lower-bound estimate, as due to the higher spatial resolution of S2 many small glaciers were additionally mapped or increased in size compared to 2003. Median elevations peak around 3000 m a.s.l., with a high variability that depends on location and aspect. The uncertainty assessment revealed locally strong differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitized by different analysts. The inventory is available at https://doi.org/10.1594/PANGAEA.909133 (Paul et al., 2019).

The Tropical Andes stand out as one of the mountain regions most affected by global warming. Here, we present a synthesis of recent glacier evolution in the Cordillera Blanca, based on the glaciers that have been monitored with in situ glaciological measurements over the past decades. In particular, we describe the evolution of the Glacier Yanamarey, a reference glacier in the Peruvian Andes in the World Glacier Monitoring Service database. This glacier is currently on the verge of vanishing—similar to several other glaciers in the Cordillera Blanca. Finally, we address the current state of glacier monitoring in the region and the main challenges to ensure the continuity and sustainability of these monitoring programs.

Abstract Modeling the short‐term (<50 years) evolution of glaciers is difficult because of issues related to model initialization and data assimilation. However, this timescale is critical, particularly for water resources, natural hazards, and ecology. Using a unique record of satellite remote‐sensing data, combined with a novel optimisation and surface‐forcing‐calculation method within the framework of the deep‐learning‐based Instructed Glacier Model, we are able to ameliorate initialization issues. We thus model the committed evolution of all glaciers in the European Alps up to 2050 using present‐day climate conditions, assuming no future climate change. We find that the resulting committed ice loss exceeds a third of the present‐day ice volume by 2050, with multi‐kilometer frontal retreats for even the largest glaciers. Our results show the importance of modeling ice dynamics to accurately retrieve the ice‐thickness distribution and to predict future mass changes. Thanks to high‐performance GPU processing, we also demonstrate our method's global potential.

Global Land Ice Measurements from Space (GLIMS), an initiative to build and distribute a database of global glacier data, has recently begun to track glaciers that have recently disappeared. GLIMS provides a definition of “extinct” glaciers for our community, and the final determination of extinction is left to local experts. There are currently 181 glaciers in the GLIMS Glacier Database that are marked as “extinct”, though we recognize that there have been many more reported in the literature. GLIMS welcomes more submissions to make the list more complete.