Abstract. 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).
Abstract. Tourists and hikers visiting glaciers all year round face hazards such as sudden terminus collapses, typical of such a dynamically evolving environment. In this study, we analyzed the potential of different survey techniques to analyze hazards of the Forni Glacier, an important geosite located in Stelvio Park (Italian Alps). We carried out surveys in the 2016 ablation season and compared point clouds generated from an unmanned aerial vehicle (UAV) survey, closerange photogrammetry and terrestrial laser scanning (TLS). To investigate the evolution of glacier hazards and evaluate the glacier thinning rate, we also used UAV data collected in 2014 and a digital elevation model (DEM) created from an aerial photogrammetric survey of 2007. We found that the integration between terrestrial and UAV photogrammetry is ideal for mapping hazards related to the glacier collapse, while TLS is affected by occlusions and is logistically complex in glacial terrain. Photogrammetric techniques can therefore replace TLS for glacier studies and UAV-based DEMs hold potential for becoming a standard tool in the investigation of glacier thickness changes. Based on our data sets, an increase in the size of collapses was found over the study period, and the glacier thinning rates went from 4.55 ± 0.24 m a −1 between 2007 and 2014 to 5.20 ± 1.11 m a −1 between 2014 and 2016.
Abstract. In spite of the quite abundant literature focusing on fine debris deposition over glacier accumulation areas, less attention has been paid to the glacier melting surface. Accordingly, we proposed a novel method based on semiautomatic image analysis to estimate ice albedo from fine debris coverage (d). Our procedure was tested on the surface of a wide Alpine valley glacier (the Forni Glacier, Italy), in summer 2011, 2012 and 2013, acquiring parallel data sets of in situ measurements of ice albedo and high-resolution surface images. Analysis of 51 images yielded d values ranging from 0.01 to 0.63 and albedo was found to vary from 0.06 to 0.32. The estimated d values are in a linear relation with the natural logarithm of measured ice albedo (R = −0.84). The robustness of our approach in evaluating d was analyzed through five sensitivity tests, and we found that it is largely replicable. On the Forni Glacier, we also quantified a mean debris coverage rate (C r ) equal to 6 g m −2 per day during the ablation season of 2013, thus supporting previous studies that describe ongoing darkening phenomena at Alpine debris-free glaciers surface. In addition to debris coverage, we also considered the impact of water (both from melt and rainfall) as a factor that tunes albedo: meltwater occurs during the central hours of the day, decreasing the albedo due to its lower reflectivity; instead, rainfall causes a subsequent mean daily albedo increase slightly higher than 20 %, although it is short-lasting (from 1 to 4 days).
Biological processes on glacier surfaces affect glacier reflectance, influence surface energy budget and glacier response to climate warming, and determine glacier carbon exchange with the atmosphere. Currently, carbon balance of supraglacial environment is assessed as the balance between the activity of oxygenic phototrophs and the respiration rate of heterotrophic organisms. Here we present a metagenomic analysis of tiny wind-blown supraglacial sediment (cryoconite) from Baltoro (Pakistani Karakoram) and Forni (Italian Alps) glaciers, providing evidence for the occurrence in these environments of different and previously neglected metabolic pathways. Indeed, we observed high abundance of heterotrophic anoxygenic phototrophs, suggesting that light might directly supplement the energy demand of some bacterial strains allowing them to use as carbon source organic molecules, which otherwise would be respired. Furthermore, data suggest that CO 2 could be produced also by microbiologically mediated oxidation of CO, which may be produced by photodegradation of organic matter. The ISME Journal Climate change is determining a global cryosphere shrinkage and mountain glacier environments are declining (IPPC, 2014). The consequent loss of biodiversity is yet to be fully assessed, particularly the loss of functional biodiversity in extreme environments (Stibal et al., 2012;Boetius et al., 2015). Cryoconite holes, that is, small depressions on glacier surfaces whose formation is because of windborne debris (cryoconite), are the most biologically active environments on glaciers (Boetius et al., 2015).We used whole-metagenomic sequencing to investigate the main functions of six cryoconite holes from Forni (Italian Alps) and six from Baltoro (Pakistani Karakoram) glaciers. We focused on carbon and energy metabolisms by comparing the total coverage of marker genes for photosynthesis, use of inorganic and organic compounds as energy source and autotrophy/heterotrophy. We also used metagenomic sequences for the taxonomic attribution of microorganisms carrying specific metabolic genes Table S4 reports the marker genes whose coverage (mean number per base of reads mapping the genes) was used to infer the abundance of each metabolism. Supplementary Table S5 and Figure 1 report their normalized coverages. Finally, we measured chemical/physical parameters of cryoconite holes and oxygen consumption rates on the days of sampling, both under light and dark conditions (Supplementary Table S3 and Supplementary Figure S2). The main hypothesis tested was whether oxygenic phototrophy and organotrophic respiration represent the only significant metabolisms affecting carbon balance on glacier surface, as currently conceived, or other microbial processes could contribute to it.On the basis of 16S rRNA gene sequencing, Cyanobacteria represented 22 and 3% of the microbial community on Forni and Baltoro, respectively (Supplementary Figure S3). High abundance of cyanobacteria has been already observed in polar and alpine cryoconite (Segawa et al....
Abstract. The on-going glacier shrinkage in the Alps requires frequent updates of glacier outlines to provide an accurate database for monitoring or modeling 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. Fortunately, already the first S2 images acquired in August 2015 provided excellent mapping conditions for most of the glacierised regions in the Alps. We have used this opportunity to compile 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 a consistent update. However, cloud cover over many glaciers in Italy required including also S2 scenes from 2016. Whereas the automated mapping of clean glacier ice was straightforward using the band ratio method, the numerous debris-covered glaciers required in-tense manual editing. The uncertainty in the outlines was determined with a multiple digitising experiment of 14 glaciers by all participants. Topographic information for all glaciers was derived from the ALOS AW3D30 DEM. Overall, we derived a total glacier area of 1806 ± 60 km2 when considering 4394 glaciers > 0.01 km2. This is 14 % (−1.2 %/a) less than the 2100 km2 derived from Landsat scenes acquired in 2003 and indicating an unabated continuation of glacier shrinkage in the Alps since the mid-1980s. Due to the higher spatial resolution of S2 many small glaciers were additionally mapped in the new inventory or increased in size compared to 2003. An artificial reduction to the former extents would thus result in an even higher overall area loss. Still, the uncertainty assessment revealed locally considerable differences in interpretation of debris-covered glaciers, resulting in limitations for change assessment when using glacier extents digitised by different analysts. The inventory is available at: https://doi.pangaea.de/10.1594/PANGAEA.909133 (Paul et al., 2019).
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