Mountain glaciers are known to be strongly affected by global climate change. Here we compute temporally consistent changes in glacier area, surface elevation and ice mass over the entire European Alps between 2000 and 2014. We apply remote sensing techniques on an extensive database of optical and radar imagery covering 93% of the total Alpine glacier volume. Our results reveal rapid glacier retreat across the Alps (−39 km² a⁻¹) with regionally variable ice thickness changes (−0.5 to −0.9 m a⁻¹). The strongest downwasting is observed in the Swiss Glarus and Lepontine Alps with specific mass change rates up to −1.03 m.w.e. a⁻¹. For the entire Alps a mass loss of 1.3 ± 0.2 Gt a⁻¹ (2000–2014) is estimated. Compared to previous studies, our estimated mass changes are similar for the central Alps, but less negative for the lower mountain ranges. These observations provide important information for future research on various socio-economic impacts like water resource management, risk assessments and tourism.

Supraglacial debris affects glacier mass balance as a thin layer enhances surface melting, while a thick layer reduces it. While many glaciers are debris‐covered, global glacier models do not account for debris because its thickness is unknown. We provide the first globally distributed debris thickness estimates using a novel approach combining sub‐debris melt and surface temperature inversion methods. Results are evaluated against observations from 22 glaciers. We find the median global debris thickness is ∼0.15 ± 0.06 m. In all regions, the net effect of accounting for debris is a reduction in sub‐debris melt, on average, by 37%, which can impact regional mass balance by up to 0.40 m water equivalent (w.e.) yr^‐1. We also find recent observations of similar thinning rates over debris‐covered and clean ice glacier tongues is primarily due to differences in ice dynamics. Our results demonstrate the importance of accounting for debris in glacier modeling efforts.

Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modeling and glacier mass flux computations. Since 2014, repeat-pass synthetic aperture radar (SAR) data have been acquired by the Sentinel-1 satellite constellation as part of the Copernicus program of the EU (European Union) and ESA (European Space Agency). It enables global, near-real-time-like and fully automatic processing of glacier surface velocity fields at up to 6 d temporal resolution, independent of weather conditions, season and daylight. We present a new global data set of glacier surface velocities that comprises continuously updated scene-pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution. The velocity information is derived from archived and new Sentinel-1 SAR acquisitions by applying a well-established intensity offset tracking technique. The data set covers 12 major glacierized regions outside the polar ice sheets and is generated in an HPC (high-performance computing) environment at the University of Erlangen-Nuremberg. The velocity products are freely accessible via an interactive web portal that provides capabilities for download and simple online analyses: http://retreat.geographie.uni-erlangen.de (last access: 6 October 2021). In this paper, we give information on the data processing and how to access the data. For the example region of Svalbard, we demonstrate the potential of our products for velocity time series analyses at very high temporal resolution and assess the quality of our velocity products by comparing them to those generated from very high-resolution TerraSAR-X SAR and Landsat-8 optical (ITS_LIVE, GoLIVE) data. The subset of Sentinel-1 velocities for Svalbard analyzed in this paper is accessible via the GFZ Potsdam Data Services under the DOI https://doi.org/10.5880/fidgeo.2021.016 (Friedl et al., 2021). We find that Landsat-8 and Sentinel-1 annual velocity mosaics are in an overall good agreement, but speckle tracking on Sentinel-1 6 d repeat acquisitions derives more reliable velocity measurements over featureless and slow-moving areas than the optical data. Additionally, uncertainties of 12 d repeat Sentinel-1 mid-glacier scene-pair velocities have less than half (< 0.08 m d⁻¹) of the uncertainties derived for 16 d repeat Landsat-8 data (0.17–0.18 m d⁻¹).

The basal topography is largely unknown beneath most glaciers and ice caps, and many attempts have been made to estimate a thickness field from other more accessible information at the surface. Here, we present a two-step reconstruction approach for ice thickness that solves mass conservation over single or several connected drainage basins. The approach is applied to a variety of test geometries with abundant thickness measurements including marine- and land-terminating glaciers as well as a 2400 km² ice cap on Svalbard. The input requirements are kept to a minimum for the first step. In this step, a geometrically controlled, non-local flux solution is converted into thickness values relying on the shallow ice approximation (SIA). In a second step, the thickness field is updated along fast-flowing glacier trunks on the basis of velocity observations. Both steps account for available thickness measurements. Each thickness field is presented together with an error-estimate map based on a formal propagation of input uncertainties. These error estimates point out that the thickness field is least constrained near ice divides or in other stagnant areas. Withholding a share of the thickness measurements, error estimates tend to overestimate mismatch values in a median sense. We also have to accept an aggregate uncertainty of at least 25 % in the reconstructed thickness field for glaciers with very sparse or no observations. For Vestfonna ice cap (VIC), a previous ice volume estimate based on the same measurement record as used here has to be corrected upward by 22 %. We also find that a 13 % area fraction of the ice cap is in fact grounded below sea level. The former 5 % estimate from a direct measurement interpolation exceeds an aggregate maximum range of 6–23 % as inferred from the error estimates here.

Glaciers in tropical regions are very sensitive to climatic variations and thus strongly affected by climate change. The majority of the tropical glaciers worldwide are located in the Peruvian Andes, which have shown significant ice loss in the last century. Here, we present the first multi-temporal, region-wide survey of geodetic mass balances and glacier area fluctuations throughout Peru covering the period 2000–2016. Glacier extents are derived from Landsat imagery by performing automatic glacier delineation based on a combination of the NDSI and band ratio method and final manual inspection and correction. The mapping of debris-covered glacier extents is supported by synthetic aperture radar (SAR) coherence information. A total glacier area loss of $-\mathrm{548.5}\pm \mathrm{65.7}$ km² (−29 %, −34.3 km² a⁻¹) is obtained for the study period. Using interferometric satellite SAR acquisitions, bi-temporal geodetic mass balances are derived. An average specific mass balance of $-\mathrm{296}\pm \mathrm{41}$ kg m⁻² a⁻¹ is found throughout Peru for the period 2000–2016. However, there are strong regional and temporal differences in the mass budgets ranging from 45±97 to $-\mathrm{752}\pm \mathrm{452}$ kg m⁻² a⁻¹. The ice loss increased towards the end of the observation period. Between 2013 and 2016, a retreat of the glacierized area of $-\mathrm{203.8}\pm \mathrm{65.7}$ km² (−16 %, −101.9 km² a⁻¹) is mapped and the average mass budget amounts to $-\mathrm{660}\pm \mathrm{178}$ kg m⁻² a⁻¹. The glacier changes revealed can be attributed to changes in the climatic settings in the study region, derived from ERA-Interim reanalysis data and the Oceanic Nino Index. The intense El Niño activities in 2015/16 are most likely the trigger for the increased change rates in the time interval 2013–2016. Our observations provide fundamental information on the current dramatic glacier changes for local authorities and for the calibration and validation of glacier change projections.