Strong acceleration of glacier area loss in the Greater Caucasus over the past two decades

An updated glacier inventory is important for understanding glacier behavior given the accelerating glacier retreat observed around the world. Here, we present data from new glacier inventory at two time periods (2000, 2020) covering the entire Greater Caucasus (Georgia, Russia, and Azerbaijan). Satellite imagery (Landsat, Sentinel, SPOT) was used to conduct a remote-sensing survey of glacier change. The 30 m resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM; 17 November 2011) was used to determine aspect, slope and elevations, for all glaciers. Glacier margins were mapped manually and reveal that in 2000 the mountain range contained 2186 glaciers with a total glacier surface area of 1381.5 ± 58.2 km2. By 2020, glacier surface area had decreased to 1060.9 ± 33.6 km2. Of the 2223 glaciers, fourteen have an area > 10 km2 resulting the 221.9 km2 or 20.9 % of total glacier area in 2020. The Bezingi Glacier with an area of 39.4 ± 0.9 km2 was the largest glacier mapped in 2020 database. Our result represents a 23.2 ± 3.8 % (320.6 ± 45.9 km2) or −1.16 % yr−1 reduction in total glacier surface area over the last twenty years in the Greater Caucasus. Glaciers between 1.0 km2 and 5.0 km2 account for 478.1 km2 or 34.6 % in total area in 2000, while it account for 354.0 km2 or 33.4 % in total area in 2020. The rates of area shrinkage and mean elevation vary between the northern and southern and between the western, central, and eastern Greater Caucasus. Area shrinkage is significantly stronger in the eastern Greater Caucasus (−1.82 % yr−1), where most glaciers are very small. The observed increased summer temperatures and decreased winter precipitation along with increased Saharan dust deposition might be responsible for the predominantly negative mass balances of two glaciers with long-term measurements. Both glacier inventories are available from the Global Land Ice Measurements from Space (GLIMS) database and can be used for future studies.

High-resolution inventory to capture glacier disintegration in the Austrian Silvretta

A new high-resolution glacier inventory captures the rapid decay of the glaciers in the Austrian Silvretta for the years 2017 and 2018. Identifying the glacier outlines offers a wide range of possible interpretations of glaciers that have evolved into small and now totally debris-covered cryogenic structures. In previous inventories, a high proportion of active bare ice allowed a clear delineation of the glacier margins even by optical imagery. In contrast, in the current state of the glacier only the patterns and amounts of volume change allow us to estimate the area of the buried glacier remnants. We mapped the glacier outlines manually based on lidar elevation models and patterns of volume change at 1 to 0.5 m spatial resolution. The vertical accuracy of the digital elevation models (DEMs) generated from six to eight lidar points per square metre is of the order of centimetres. Between 2004/2006 and 2017/2018, the 46 glaciers of the Austrian Silvretta lost −29 ± 4 % of their area and now cover 13.1 ± 0.4 km2. This is only 32 ± 2 % of their Little Ice Age (LIA) extent of 40.9 ± 4.1 km2. The area change rate increased from 0.6 %/yr (1969–2002) to −2.4 %/yr (2004/2006–2017/2018). The Sentinel-2-based glacier inventory of 2018 deviates by just 1 % of the area. The annual geodetic mass balance referring to the area at the beginning of the period showed a loss increasing from −0.2 ± 0.1 m w.e./yr (1969–2002) to −0.8 ± 0.1 m w.e./yr (2004/2006–2017/2018) with an interim peak in 2002–2004/2006 of −1.5 ± 0.7 m w.e./yr. To keep track of the buried ice and its fate and to distinguish increasing debris cover from ice loss, we recommend inventory repeat frequencies of 3 to 5 years and surface elevation data with a spatial resolution of 1 m.

A new global gridded glacier dataset based on the Randolph Glacier Inventory version 6.0

Gridded glacier datasets are essential for various glaciological and climatological research because they link glacier cover with the corresponding gridded meteorological variables. However, there are significant differences between the gridded data and the shapefile data in the total area calculations in the Randolph Glacier Inventory (RGI) 6.0 at global and regional scales. Here, we present a new global gridded glacier dataset based on the RGI 6.0 that eliminates the differences. The dataset is made by dividing the glacier polygons using cell boundaries and then recalculating the area of each polygon in the cell. Our dataset (1) exhibits a good agreement with the RGI area values for those regions in which gridded areas showed a generally good consistency with those in the shapefile data, and (2) reduces the errors existing in the current RGI gridded dataset. All data and code used in this study are freely available and we provide two examples to demonstrate the application of this new gridded dataset.

A new automatic approach for extracting glacier centerlines based on Euclidean allocation

Glacier centerlines are crucial input for many glaciological applications. From the morphological perspective, we proposed a new automatic method to derive glacier centerlines, which is based on the Euclidean allocation and the terrain characteristics of glacier surface. In the algorithm, all glaciers are logically classified as three types including simple glacier, simple compound glacier, and complex glacier, with corresponding process ranges from simple to complex. The process for extracting centerlines of glaciers introduces auxiliary reference lines and follows the setting of not passing through bare rock. The program of automatic extraction of glacier centerlines was implemented in Python and only required the glacier boundary and digital elevation model (DEM) as input. Application of this method to 48 571 glaciers in the second Chinese glacier inventory automatically yielded the corresponding glacier centerlines with an average computing time of 20.96 s, a success rate of 100 % and a comprehensive accuracy of 94.34 %. A comparison of the longest length of glaciers to the corresponding glaciers in the Randolph Glacier Inventory v6.0 revealed that our results were superior. Meanwhile, our final product provides more information about glacier length, such as the average length, and the longest length, the lengths in the accumulation and ablation regions of each glacier.

Evaluation of the global glacier inventories and assessment of glacier elevation changes over north-western Himalaya

The study evaluates the global glacier inventories available for the study area viz., RGI, GAMDAM and ICIMOD, with the newly generated Kashmir University Glacier Inventory (KUGI) for three Himalaya basins; Jhelum, Suru and Chenab in the north-western Himalaya, comprising of 2096 glaciers spread over an area of 3300 km2. The KUGI was prepared from the Landsat data supplemented by Digital Elevation Model, Google Earth images and limited field surveys. The KUGI comprises of 154 glaciers in the Jhelum, 328 in the Suru and 1614 in the Chenab basin, corresponding to the glacier area of 85.9 ± 11.4 km2, 487 ± 16.2 km2 and 2727 ± 90.2 km2 respectively. The investigation revealed that most of the glaciers in the study area are

The new Swiss Glacier Inventory SGI2016: a detailed cartographic representation of Swiss glacier extent and supraglacial debris-cover

<p>With increasing anthropogenic greenhouse gas emissions and corresponding global warming, glaciers in Switzerland are shrinking rapidly as in many mountain ranges on Earth. Repeated glacier inventories are a key task to monitor such glacier changes and provide detailed information on the extent of glaciation, and important parameters such as area, elevation range, slope, aspect etc. for a given point or a period in time. Here we present the new Swiss Glacier Inventory (SGI2016) that has been acquired based on high-resolution aerial imagery and digital elevation models in cooperation with the Federal Office of Topography (swisstopo) and Glacier Monitoring in Switzerland (GLAMOS), bringing together topological and glaciological knowhow. We define the process, workflow and required glaciological adaptations to compile a highly accurate glacier inventory based on the digital Swiss topographic landscape model (swissTLM<sup>3D</sup>).</p><p>The SGI2016 provides glacier outlines (areas), supraglacial debris cover, ice divides and location points of all glaciers in Switzerland referring to the years 2013-2018, whereas most of the glacier outlines have been mapped based on aerial images acquired between 2015-2017 (75% in number and 87% in area), with the centre year 2016. The SGI2016 maps 1400 individual glacier entities with a total glacier surface area of 961 km<sup>2</sup> (whereof 11% / 104 km<sup>2</sup> are debris-covered) and constitutes the so far most detailed cartographic representation of glacier extent in Switzerland. Analysing the dependencies between topographic parameters and debris-cover fraction on the basis of individual glaciers reveals that short glaciers with a moderate mean slope and glaciers with a low median elevation tend to have high debris fractions. A change assessment between the SGI1973 and SGI2016 based on individual glacier entities affirms the largest relative area changes for small glaciers and for low-elevation glaciers, whereas the largest glaciers show small relative area changes, though large absolute changes. The analysis further indicates a tendency for glaciers with a high share of supraglacial debris to show larger relative area changes.</p><p>Despite of an observed strong glacier volume loss between 2010 and 2016, the total glacier surface area of the SGI2016 is somewhat larger than reported in the last Swiss glacier inventory SGI2010. Even though both inventories were created based on swisstopo aerial photographs, the additional data, tools, resources and methodologies used by the professional cartographers digitizing glacier outlines in 3D for the SGI2016, are able to explain the counter-intuitive difference between SGI2010 and SGI2016. A direct comparison of these two datasets is thus not meaningful, but an experiment where a representative glacier sample of the SGI2010 was re-assessed based on the approaches of the SGI2016 led to an upscaled total glacier surface area of 1010 km<sup>2</sup> for the Swiss Alps around 2010. This indicates an area loss of 49 km<sup>2</sup> between the two last Swiss glacier inventories. As swisstopo data products are and will be regularly updated, the SGI2016 is the first step towards a consistent and accurate data product of repeated glacier inventories in six-year time intervals that promises a high comparability for individual glaciers and glacier samples.</p>

Current state and recent changes in glacial systems in Russia

<p>The new glacier inventory created recently at the Institute of Geography of the Russian Academy of Sciences made it possible to study the current state and recent changes of glacial systems in Russia, where now there are 22 glacial systems. The total area of ​​glaciation on this territory is 54,531 km2 based on Sentinel 2 images obtained mainly in 2016-2019. This area is occupied by 7478 glaciers. The largest glacial system in area is located on the Novaya Zemlya archipelago (22,241.37 km2). It is followed by Severnaya Zemlya (16491.81 km2) and Franz Josef Land (12530.03 km2). The next largest glacial systems are locate on the Caucasus Mountains (1067.13 km2), Kamchatka (682.8 km2) and Altai (523.14 km2). The area of ​​glaciers on the Arctic island of Ushakov (283, 09 km2), in the Suntar Khayata mountains (132, 97 km2) and the Koryak Upland (254.1 km2) occupies a range from 100 to 300 km2.</p><p>The largest group is small glacial systems, the area of ​​which does not exceed 100 km2. They are located in different glaciological zones: the De Long Islands (65, 2 km2),  the Urals (10.45 km2), the Putorana Plateau (11.36 km2), the Byranga Mountains (29.94 km2), the Chersky Ridge (86.37 km2), the Chukotka Upland (15.98 km2). Northeast of the Koryak highlands (42.19 km2), Kodar Ridge (16.22 km2), Eastern Sayan (12.88 km2).</p><p>The remaining four regions are characterized by the smallest glacial systems. These are the Orulgan ridge (9.82km2) and the Kolyma Upland (6.62 km2), the Kuznetsk Alatau (3.42km2), the Barguzinsky (0.09) and Baikalsky ( 0.65km2) ridges. Despite their small size, these glacial systems are important from indicative point of view, fixing the zone of spatial distribution of glaciation. They indicate the growth points in the event of a change in climatic conditions according to a scenario favorable for glaciers.</p><p>The glacier area has decreased since the compilation of the USSR glacier Inventory (1965-1982) by 5603.9 km2 or 9.3%. The area of ​​polar glaciers has decreased less than glaciers in mountainous regions. Values ​​range from 5.44% (Novaya Zemlya) to 19.11% (De Longa Islands). Small glaciers were not found in the Khibiny. Glaciers in the Urals have reduced their area by 63%. The subpolar glacier systems of the Orulgan (46.6%), Chersky (44.4%), and Suntar-Khayata (34%) ridges reduced the area a little less. Reduction in the area of ​​glacial systems in the temperate belt ranges from 57% (Eastern Sayan) to 13% (Kodar). The largest glacial systems in the Caucasus, Kamchatka and Altai have reduced their areas by 25, 22 and 39 percent, respectively.</p><p>The results of our studies confirm the tendencies for the reduction of the glacier area throughout Russia. The exception is the glaciers of the volcanic regions of Kamchatka, which increased their size or remained stationary. The magnitude and rate of changes depend on the local climatic and orographic features.</p><p>The presentation includes the results obtained in the framework of the following research projects: № 0148-2019-0004 of the Research Plan of the Institute of Geography of RAS, № 18-05-60067 supported by RFBR. </p>