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Article

Post-Little Ice Age Equilibrium-Line Altitude and Temperature Changes in the Greater Caucasus Based on Small Glaciers

by
Levan G. Tielidze
1,2,*,
Andrew N. Mackintosh
1,
Alexander Gavashelishvili
2,
Lela Gadrani
2,3,
Akaki Nadaraia
2 and
Mikheil Elashvili
2,4
1
Securing Antarctica’s Environmental Future, School of Earth, Atmosphere and Environment, Monash University, Clayton, VIC 3800, Australia
2
School of Natural Sciences and Medicine, Ilia State University, 0179 Tbilisi, Georgia
3
Climate Change Institute, University of Maine, Orono, ME 04469, USA
4
Department of Mathematics, Bridgewater State University, Bridgewater, MA 02324, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(9), 1486; https://doi.org/10.3390/rs17091486
Submission received: 17 March 2025 / Revised: 18 April 2025 / Accepted: 19 April 2025 / Published: 22 April 2025
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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Abstract

:
Understanding glacier and climate variations since pre-Industrial times is crucial for evaluating the present-day glacier response to climate change. Here, we focus on twelve small glaciers (≤2.0 km2) on both the northern and southern slopes of the Greater Caucasus to assess post-Little Ice Age glacier–climate fluctuations in this region. We reconstructed the Little Ice Age glacier extent using a manual detection method based on moraines. More recent glacier fluctuations were reconstructed using historical topographical maps and satellite imagery. Digital elevation models were used to estimate the topographic characteristics of glaciers. We also used the accumulation area ratio (AAR) method and a regional temperature lapse rate to reconstruct glacier snowlines and corresponding temperatures since the 1820s. The results show that all selected glaciers have experienced area loss, terminus retreat, and equilibrium line altitude (ELA) uplift over the last 200 years. The total area of the glaciers has decreased from 19.1 ± 0.9 km2 in the 1820s to 9.7 ± 0.2 km2 in 2020, representing a −49.2% loss, with an average annual reduction of −0.25%. The most dramatic reduction occurred between the 1960s and 2020, when the glacier area shrank by −35.5% or −0.59% yr−1. The average terminus retreat for all selected glaciers was −1278 m (−6.4 m/yr−1) during the last 200 years, while the average retreat over the past 60 years was −576 m (−9.6 m/yr−1). AAR-based (0.6 ± 0.05) ELA reconstructions from all twelve glaciers suggest that the average ELA in the 1820s was about 180 m lower (3245 ± 50 m a.s.l.) than today (3425 ± 50 m a.s.l.), corresponding to surface air temperatures <1.1 ± 0.3 °C than today (2001–2020). The largest warming occurred between the 1960s and today, when snowlines rose by 105 m and air temperatures increased by <0.6 ± 0.3 °C. This study represents a first attempt at using glacier evidence to estimate climate changes in the Caucasus region since the Little Ice Age, and it can be used as a baseline for future studies.

1. Introduction

The latest phase of the Neoglacial is known as the Little Ice Age (LIA)—a period of lower temperatures and glacier expansion. This time interval spanned from the mid-13th century to the early-mid 19th century [1,2,3]. The timing of the most recent LIA maximum is not always synchronous and varies from region to region because of different climate factors and topography [4,5,6]. Following the end of the LIA, mountain glaciers began to retreat globally with only minor re-advances [7,8,9]. This retreat has been linked with anthropogenic warming, specifically during the late 20th and early 21st centuries [10,11]. Under current climate projections, it is anticipated that warming will be substantially amplified in mountain regions [12], including the Greater Caucasus [13], a region containing more than 2000 glaciers [14]. These glaciers act as vital reservoirs of freshwater, feeding numerous rivers and streams that sustain communities in the region [15]. Glacial snow meltwater also supports unique alpine ecosystems, providing habitats for various plant and animal species [16,17,18]. Like many other mountain glaciers worldwide [19], those in the Caucasus are shrinking at an alarming rate, leading to a reduction in glacier size and mass [20,21,22], making this region very distinctive globally for having the largest relative ice loss (−35%) along with European Alps (−39%) from 2000 to 2023 [23]. This decrease in ice has significant implications for natural hazards, such as rock ice avalanches [24,25] and glacial lake outburst floods (GLOFs) [26,27]. The Greater Caucasus can thus be considered one of the key areas for investigating the past and present responses of glaciers to climate change.
Remote sensing offers valuable insights into the present state of glaciation and recent changes [28,29,30]. However, this approach is limited by the availability of historical data. To reconstruct glacial changes before the late 19th/mid-20th century (the era of earliest topographical maps and satellite imagery), proxy archives are required [31,32,33]. Geomorphological records, including glacial landforms such as moraines, provide such a record of past glacier extents and dynamics. Moraines are also often used to reconstruct the geometries of former glaciers, which are then used as proxies for the palaeoclimate [5,34,35]. Well-preserved dated moraine features in the Greater Caucasus indicate that the last largest glacier advances of the LIA occurred between the 1810s [36] and 1840s [37]. We therefore use the 1820s as a reference point of the approximate regional LIA termination in the Greater Caucasus, i.e., 1820 was assumed as the date from which rates of glacier changes were calculated.
There are various estimates of the temperature changes between the 19th century and the present, including gridded datasets [38,39,40,41]. But these data are not usually from the mountainous regions, and hence reconstructing climate from glaciers offers a chance to check whether temperature changes in poorly observed locations and often at high elevation are consistent with other records. Understanding the relationship between glacier change and climate parameters often involves estimating past equilibrium line altitudes (ELAs) and comparing their depression from present-day values [42]. The ELA, defined as the elevation where annual snow accumulation equals melt, effectively separates the accumulation and ablation zones of a glacier [43,44]. Thus, the ELA variability provides a useful metric for assessing glacier response to climatic forcings, thereby facilitating palaeoclimatic reconstructions. In addition, snowline altitudes (SLAs), which mark the minimum elevation of continuous snow cover for present-day glaciers at the end of the melt season, are commonly used as approximations of ELAs and therefore as proxies for climate reconstruction [45].
The aims of this study are the following: (1) to assess glacier geometry and subsequent changes for twelve valley glaciers in the Greater Caucasus since the last phase of LIA and (2) to reconstruct the past ELA and modern snowlines of these glaciers and corresponding temperatures based on the accumulation area ratio (AAR) and regional lapse rate to gain insights into the post-LIA glacier–climate change in the Caucasus region. For this purpose, we selected small valley glaciers from the northern and southern slopes (six glaciers from each slope) of the Central Greater Caucasus. The size of the individual glaciers ranges between 0.5 and 2.5 km2. These glaciers were mainly selected because of the simple geometry (single valleys) and well-preserved moraine features. These types of glaciers are also ideal for past snowline reconstructions, avoiding potential difficulties associated with tributary glaciers [35,46].

2. Study Area

The Greater Caucasus Mountain range stretches for about 1200 km from west to east between the Black Sea and the Caspian Sea, serving as a natural border between Russia to the north and Georgia and Azerbaijan to the south. The Main Caucasus Watershed Ridge is the highest and most continuous part of the range, with peaks exceeding 5000 m above sea level (a.s.l.). Active uplift and thrust–fold processes create steeper southern slopes, while the north-dipping layers of Mesozoic sediments form the northern slopes and have a more gentle transition into the Russian foreland basin [47]. Based on geological and geomorphological characteristics, as well as topography and climate, the Greater Caucasus can be divided into three main sections: Western—from the Black Sea to mount Elbrus (5642 m a.s.l.), Central—between the mounts Elbrus and Kazbegi (5047 m a.s.l.), and Eastern—from Mount Kazbegi to the Caspian Sea [48] (Figure 1). The Central Caucasus is the highest part of the region, containing eight peaks over 5000 m.
The abundance of glacial landforms in the Greater Caucasus, including cirques, U-shaped valleys, and moraines, provides evidence of both past and ongoing glacial activity [49]. The most recent inventory [14] indicates the presence of 2200 glaciers within the Greater Caucasus, encompassing a total surface area of 1060.9 ± 33.6 km2 in 2020.
The Greater Caucasus Mountains exhibit a complex geological composition shaped by various tectonic and sedimentary processes over geological time. The western part of the Greater Caucasus features a core of Proterozoic and Paleozoic metamorphic rocks, including schists, gneisses, and granites. These ancient rocks, predating the Jurassic Period (older than 200 million years), have been exposed due to tectonic uplift. Upper layers are formed by Mesozoic sedimentary rocks, primarily composed of sandstones, shales, and limestones [50]. The Central region is also characterized by a core of Proterozoic and Paleozoic crystalline and metamorphic rocks, including slate, phyllite, quartzite, meta-conglomerate, marble, chert, and volcaniclastic rocks, with Extensive Jurassic and Cretaceous limestones, sandstones, and shales dominating the sedimentary cover [51]. The Eastern part of the Greater Caucasus is predominantly composed of Lower to Middle Jurassic shales and sandstones. These sedimentary layers have undergone intense deformation, resulting in complex structural features, with notable Cretaceous volcanic formations present. Unlike the Western segment, the Eastern Greater Caucasus lacks an exposed crystalline core [52].
The climate of the Caucasus is shaped by its complex topography, including elevation, slope orientation, and atmospheric circulation patterns. The northern slopes, mainly situated in Russia, exhibit a continental climate with cold winters and relatively mild summers. Average annual temperatures vary significantly based on altitude, ranging from approximately −10 °C at elevations above 3000 m to around 8 °C in the foothills. Winter temperatures at higher altitudes can drop below −20 °C, while temperatures seldom exceed 15 °C above 2000 m. Conversely, the southern slopes, particularly in Georgia, experience a more temperate climate due to the moderating effect of the Black Sea. This results in milder winters and warmer summers, with mean annual temperatures fluctuating between −5 °C in the highlands and 14 °C in the lowlands. Winter temperatures in this region rarely fall below −10 °C [53].
Precipitation levels differ considerably across the Caucasus, particularly between the northern and southern slopes. The Georgian side, especially in the Western region, receives substantial precipitation due to orographic lifting caused by moist air masses from the Black Sea. Some regions receive more than 2500 mm of precipitation annually. In contrast, the Russian side experiences lower precipitation levels, typically ranging from 600 mm in the foothills to 1200 mm at higher elevations. The Northeastern regions, influenced by dry air masses originating from the Caspian Sea, receive significantly less precipitation—sometimes as low as 400 mm—leading to semi-arid conditions [53,54].
Snow accumulation is a critical component of the Caucasus climate system. The northern slopes experience extensive seasonal snow cover, often exceeding 3 m in depth at altitudes above 2500 m. Snow at this elevation typically remains on the ground from October through June, sustaining numerous glaciers, particularly in the Central Caucasus. On the southern slopes, snow cover is less persistent, usually lasting from December to April at elevations above 2000 m [55,56].

3. Previous Studies

Georgian scholar Vakhushti Bagrationi was among the first to document glaciers in the Caucasus, describing their characteristics as early as the 18th century [57]. Regular glaciological research in the region began between the mid-19th and early 20th centuries, with scientists, including Abich [58], Mushketov [59], Rossikov [60], Freshfield [61], Dinik [62], Bush [63,64], Déchy [65], Podozerskiy [66], and Reinhardt [67], recording glacier extents through detailed field observations and photography. Glaciological research in the Greater Caucasus became more structured during major international scientific efforts, such as the International Polar Year (IPY, 1932–1933) and the International Geophysical Year (IGY, 1957–1958), which helped advance glacier monitoring through coordinated field observations, aerial photography, and geomorphic studies [68,69,70,71]. The groundwork established by these initiatives enabled the expansion of research during the mid-20th century, facilitated by Soviet scientific programs. Political instability in the 1990s severely disrupted glaciological research in the Caucasus. It was only in the following decade that research slowly began again with a primary focus on remote sensing to determine changes in glacier area and length [20,72,73], as well as geodetic mass balance [21,22] and debris cover assessment [74,75,76], for the late 20th and early 21st centuries. Other recent studies have also focused on Holocene glacial fluctuations. E.g., Solomina et al. [7] analyzed glacier variations in the Northern Caucasus over the past millennium, identifying three distinct phases of LIA glacier advances. The earliest advance (1250–1400 CE) coincided with declining temperatures, allowing glaciers to expand. A more pronounced expansion (1500–1630 CE) marked the coldest interval of the LIA, driving glaciers to their most extensive positions. The maximum advances of two glaciers (Bolshoy Azau and Kashkatash) were determined to have occurred in the late 1830s–1840s CE. Tielidze et al. [36] reconstructed Chalaati Glacier fluctuations in the Georgian Caucasus since the Little Ice Age (LIA) using a combination of cosmogenic exposure dating, dendrochronological data, historical maps, and remote sensing. Dating of the oldest lateral moraine indicates the Little Ice Age began around CE 1250–1330. Dendrochronology and lichenometry reveal a secondary maximum extent of the Chalaati Glacier CE 1810. From that point until 2018, the glacier area decreased by about 34%, and its length retreated by 2.3 km.
Research conducted on glacier snowlines and equilibrium line altitudes (ELAs) in the Caucasus region began in the late 19th century. E.g., Déchy [77] (c.f. Drygalski and Fritz [78]) reported that in the 1890s, the snowline was at 2900 m a.s.l. on the northern slope and 2700 m a.s.l. on the southern slope of the Western Greater Caucasus. In the Central Greater Caucasus, the snowline ranged from 3200 m a.s.l. on the northern slope to 3100 m a.s.l. on the southern slope. The snowline was highest in the Eastern Greater Caucasus, varying from 3450 m a.s.l. on the northern slopes to 3800 m a.s.l. on the southern slopes. On the Elbrus Massif, the snowline was 3700 m a.s.l. on the southern slopes and 3900 m a.s.l. on the northern slopes. Reinhardt [67] determined the snowline in the Georgian Caucasus using 1880–1910 topographical maps and field research, establishing a mean snowline of 3090 m a.s.l. Based on 1950–1960 aerial imagery and large-scale topographical maps, Gobejishvili [79] (c.f Tielidze [80]) calculated a mean snowline elevation of 3260 m a.s.l. across several river basins. The highest snowline values (3500 m a.s.l.) were found in the Eastern Georgian Caucasus, correlating with lower annual snow precipitation. Based on Kotlyakov and Krenke [81], there was an approximate 100 m rise in the snowline from the first half of the nineteenth century to the late twentieth century in the Greater Caucasus. Panov [82] determined that the snowline of Caucasus glaciers rose by 50 to 150 m between the late 19th century and the 1970s. Notably, the magnitude of this rise differed across the Caucasus. In the Western and Central regions, the southern slopes exhibited a greater snowline uplift than the northern slopes. However, in the Eastern Caucasus, the analysis revealed a lesser snowline rise on the southern slopes (70 m) than on the northern slopes (100 m). Bushueva [83] documented ELA depressions ranging from 70 to 200 m in several Northern Caucasus valley glaciers when comparing the LIA maximum extent to the early 21st century. Her estimations further revealed that glaciers in the Northern Caucasus experienced a 12–34% retreat in length, a 3–33% decrease in area, and a 4–38% loss in volume.
Using 19 global glacier length records, Klok and Oerlemans [84] reconstructed ELAs for 1910–1959 and 1960–2000s. The results indicated an average ELA increase of 33 m between 1910 and 1959. Following this, a decrease in ELAs was observed from approximately 1960 to 1980. Between 1980 and 2000, the majority of reconstructed ELAs did not exceed the elevations recorded in 1980.

4. Data and Methods

Mapping of the LIA glacier extent requires geomorphological knowledge [25,85], high-resolution maps and photographs [86], satellite imagery, orthophotos [87], and digital elevation models (DEMs) [88]. Considering all of this, we provide a detailed description of our database and the methodology used in this study.

4.1. Data

4.1.1. Satellite Imagery and Digital Elevation Models

To identify the LIA moraines, this study utilized 3m-resolution cloud-free PlanetScope imagery provided by Planet Labs (https://www.planet.com/explorer/) (accessed on 22 January 2025) from late summer (August–September) of 2024, minimizing snow cover (Figure 2). PlanetScope images consist of four bands (Blue, Green, Red, and Near Infrared (NIR)) and are automatically georeferenced radiometrically, geometrically, and atmospherically [89]. Despite being a relatively recent satellite product, having launched in 2016, these images have already been utilized for various applications in glacier research [90,91,92,93]. Due to their high spatial and temporal resolution, PlanetScope images have proven especially valuable for studying small glaciers [94]. They enable precise mapping of small glaciers with minimal uncertainty, far surpassing medium-resolution satellite data (e.g., ASTER 15/30 m, Landsat 15/30 m, and Sentinel 10 m). The daily revisit cycle is particularly beneficial for capturing images at the end of the ablation season when snow cover is minimal, thus increasing the availability of data for glacier studies (Table 1).
High-resolution Google Earth satellite imagery from 2012 to 2022 was also utilized for identifying the Little Ice Age (LIA) moraines. Google Earth imagery is commonly used in glacier studies across various mountain regions [95,96] and is considered one of the best available sources for detecting small glaciers and surrounding landforms. Google Earth utilizes SPOT or DigitalGlobe products (e.g., Quickbird or IKONOS), which offer a spatial resolution comparable to that of aerial photographs [96]. These images are georectified using a DEM derived from Shuttle Radar Topography Mission (SRTM) data, with a horizontal and vertical accuracy of up to ±30 m and <16 m, respectively [97]. For our study, only snow-free and cloud-free imagery was selected for the LIA moraine survey and associated glacier area mapping (Table 1).
For the year 2020, Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) digital elevation model (DEM) was used. However, this DEM is not directly generated from the PALSAR data. Instead, it is a pre-existing DEM (such as SRTM) that has been modified (Radiometric Terrain Corrected from Alaska Satellite Facility) and used to perform the terrain correction on the radar imagery. This type of DEM has 12.5 m horizontal resolution and is widely used in glacier change assessments [98,99,100,101]. The DEM was obtained from the Distributed Active Archive Center (DAAC) of the Alaska Satellite Facility (ASF) (https://vertex.daac.asf.alaska.edu) (accessed on 25 January 2025) [102].

4.1.2. Old Maps

Early topographic surveys in the Caucasus produced valuable large-scale maps (1:42,000), enabling historical glacier change analysis [7,57,83,103,104]. While these maps hold significant potential for visualizing past glacier extents, they remain underutilized. This study leverages seven topographical maps to quantify geometric changes (length, area, terminus, and snowline elevations) of selected glaciers [31,105]. These maps were compiled with the plane-table method using the old Russian unit of length, such as the Verst (1 Verst = ~2.14 m), and drawn at a scale of 1:42,000 (Table 1). Some of these maps were previously used for the Georgian glacier inventory, where the detailed analysis and georeferencing revealed certain defects in the shape of large glaciers [57]. In particular, the inaccessible firn areas of various valley glaciers were depicted incorrectly, although the tongues and terminus elevation of majority of glaciers were accurately determined. Hence, maps of the 1890s were mainly used for mapping the tongues and controlling terminus elevation for the glaciers by the late 19th century (Figure 2).

4.2. Methods

4.2.1. Reconstruction of Glacier Geometry

Reconstruction of past glacier topography relied heavily on geomorphological evidence [106]. Mapping strategy was based on terminal and lateral moraines and visible cirque headwalls [107,108,109]. Manual mapping of the LIA glacier outlines across the tongues was accurately accomplished due to the well-preserved lateral and terminal moraines, although reconstruction of the accumulation zones was relatively challenging due to the scarcity of morphological evidence. This, however, does not create a large difference in the results because the largest changes in glacier extent in response to climate forcing occur around the terminus. In some places, the 1890s topographic maps were also unreliable due to inaccurately depicted firn areas. Therefore, LIA and 1890s accumulation zones were mapped using local knowledge and more detailed 1:50,000 topographical maps from the 1960s. These maps, known for their precision and accurate representation of glacier shapes, include firn areas. These maps have also been instrumental in recent Caucasus glacier inventories [73]. Glacier outlines from 1960 and 2020 were extracted from existing regional glacier inventories [14,73].
The reconstruction of LIA glacier surfaces was performed through the manual delineation of contour lines at 20 m vertical intervals. Large-scale topographical maps from the 1960s were used as the basis for altitude measurements (Table 1). These contours were designed to simulate the characteristic topography of glacier surfaces, featuring convexity near the terminus, horizontal alignment at mid-elevations, and concavity near the headwall. This procedure resulted in a digital elevation model (DEM) with a 12.5 m resolution (Figure 3). While the manual reconstruction of glacier surface contours constitutes the primary source of uncertainty, it is assumed that these errors are randomly distributed and unlikely to introduce significant systematic deviations [35,109,110,111].
LIA and post-LIA glacier area uncertainty was assessed using a buffer method, consistent with current practices in modern glacier mapping [73,112,113]. A buffer width of 10 m was applied to the glacier outlines from 1820 to 1890, and the uncertainty was derived by calculating the ratio of the buffered area to the original glacier area. This procedure yielded an average uncertainty of ±4.5% and ±4.4% for 1820 and 1890, respectively. A similar approach, but with buffer width of 5 m and 3 m, was used for glacier outlines from 1960 and 2020 (as the pixel size of topo maps and planet images), generating an uncertainty of ±3.3% and ±2.1%, respectively. For a more complete description of uncertainty assessment for maps from the 1890s and 1960s, see previous studies [57,73].

4.2.2. Reconstruction of Equilibrium Line Altitude

The equilibrium line altitude (ELA) of a glacier represents the mean elevation at which the annual rates of snow accumulation and ice ablation are in balance. Given the ELA’s strong dependence on local climatic conditions, particularly winter precipitation and summer air temperature, its temporal variations provide a direct reflection of changes in these environmental parameters. Consequently, the ELA is a valuable tool for understanding glacier response to climate change and for reconstructing past climatic conditions [114,115,116].
The accumulation area ratio (AAR) method [117] is likely the most widely used technique for approximating the ELA of former glaciers [46,118,119]. This method determines the AAR by dividing the glacier’s accumulation area (the area above the ELA) by its total area [120,121]. Studying AAR and ELA variations offers insight into glacier mass balance and climate fluctuations.
In their foundational research, Meier and Post [117] reported an accumulation area ratio (AAR) range of 0.5–0.8 for steady-state valley glaciers. However, these boundary values are infrequently used when reconstructing past ELAs [122]. Instead, an AAR of 0.6 ± 0.05, derived from glaciers in the Southern Alps, New Zealand [43], is commonly accepted as a global reference for non-tropical glaciers [123,124]. An AAR of 0.6 ± 0.05 is also considered indicative of steady-state conditions in small (≤4.0 km2) valley glaciers (Djankuat and Garabashi) in the Greater Caucasus [119].
In this study, the ELA for selected glaciers from 1820, 1890, and 1960 was estimated using an accumulation area ratio (AAR) of 0.6 ± 0.05 through a GIS-based automated toolbox [125] with a vertical uncertainty of ±50 m. This toolbox, which operates by extracting contour lines from reconstructed digital elevation models (DEMs), is capable of processing both individual glaciers [35] and large-scale datasets of multiple glaciers [126]. The methodology requires glacier hypsometry but does not necessitate knowledge of mass balance gradients (Figure 4).

4.2.3. Reconstruction of Late Summer Snowline

The late summer snowline may be broadly defined as the altitudinal limit below which all the previous winter’s snow cover has melted away on any type of terrain. The late summer snowline is clearly visible on glaciers and frequently aligns with elevation contours. This snowline typically occurs at a similar elevation across multiple glaciers in a localized region at any given time [127,128,129]. Due to the substantial contrast in reflectivity between the previous winter’s snow and the underlying glacier ice, the late summer snowline is typically readily identifiable on medium- and high-resolution satellite imagery. It is assumed that for midlatitude glaciers, the averaged elevation of the late summer snowline approximates the ELA [128,130]. Based on this assumption, the present-day ELA can be reconstructed using remote-sensing data, such as high- and medium-resolution satellite imagery, where the snowline is generally discernible, thereby enabling the investigation of climate–glacier relationships in remote areas for which direct in situ measurements are unavailable [131,132].
The estimation of present-day late summer snowline elevations was conducted using medium-resolution satellite imagery—Landsat 5 (11 August 2011) and Sentinel 2 (3 September 2015, 4 September 2020). This multi-temporal approach was adopted to minimize bias arising from annual climatic variability, which significantly affects snowline altitude in mountainous environments at the end of the ablation season. Following manual snowline delineation, altitudes were derived using a 2007 ALOS PALSAR digital elevation model with a vertical accuracy of 25 m. In the next stage, we used the average elevations computed from three different years (2011–2015–2020) for each glacier as the estimation of the present-day late summer snowline (or ELA) (Figure 4). Despite relatively high accuracy in the ALOS DEM and satellite images, we used an uncertainty of ±50 m in the present-day snowline elevation to ensure consistency with ELA uncertainty values from 1820, 1890, and 1960.

4.2.4. Temperature Reconstructions

To estimate past temperatures relative to the present-day (2001–2020), we converted the difference between paleo-ELAs and present-day ELAs into temperature changes (temperature anomalies) using a temperature lapse rate, assuming precipitation remained constant. For this, we rely on the present-day (2011–2020) ELA and the regional temperature lapse rate. The temperature lapse rate in the Greater Caucasus ranges from approximately 5.2 °C km−1 in summer [133] to 5.8 °C km−1 annually [57], and even up to 7.8 °C km−1 annually [134]. We average these lapse rates and adopt a value of 6.0 °C km−1, as also suggested by Stone and Carlson [135]. We also added uncertainty of ±0.3 °C/0.1 km−1 based on the ELA estimation uncertainty (±50 m). For a more complete discussion of temperature lapse rates and their controls, see Mackintosh et al. [34].
The trend in ELA-derived temperature anomalies was estimated using linear regression. To evaluate the validity of our glacier-ELA-derived temperature anomaly reconstruction, we conducted a comparative analysis with (1) temperature anomalies reconstructed from the Elbrus ice core [133]; (2) temperature anomalies reconstructed from Taxus baccata tree-ring records from the Batsara Nature Reserve, Eastern Georgia [136]; (3) instrumental temperature records from Mestia weather station, Western Georgia [57,137]; (4) instrumental temperature records from Elbrus [133]; and (5) the Hadley Centre Central England Temperature (HadCET) dataset, the world’s longest instrumental record adjusted for urban warming since 1974 [138,139]. These datasets were partitioned into seasonal subsets, defining wintertime as October through March and summertime as April through September. The trend analyses and plotting were performed using R version 4.4.2 [140].

4.2.5. Terminus Measurement

Accurate measurement of glacier terminus change is crucial for tracking glacier dynamics over time [141]. In this study, we use the topographic center line, defined as the midpoints between the lateral margins of the glacier. This centerline was established by tracing the line of maximum Euclidean distance between the delineated glacier margins down to the glacier terminus. Terminus changes were assessed by comparing glacier outlines from different years along the ice front, oriented perpendicular to the flow. Large-scale topographic maps from the 1960s served as the basis for glacier terminus elevation for 1820 and 1960, while archival maps from the 1890s were used for terminus elevations from that period. For 2020, terminus elevations were extracted from the ALOPS PALSAR DEM from 2007.

5. Results

The LIA glacier area was delineated for a subset of twelve glaciers situated within the Central Greater Caucasus (Figure 1). Our results indicate a decrease in total glacier area from 19.8 ± 0.9 km2 in the 1820s to 9.7 ± 0.2 km2 in 2020, which is equivalent to −51% (or −0.26% yr−1) of ice loss (Table 2, Figure 5). The highest area loss (−56.9% or −0.28% yr−1) was found on the southern slopes, in the Georgian side of the Greater Caucasus, while the northern slopes on the Russian side experienced relatively low area loss (−50.7% or −0.25% yr−1). Rates of decrease were different for individual glaciers. The largest percentage change between LIA maxima and 2020 was observed at glacier GEO_1, which exhibited a −73.7% areal reduction, and the smallest percentage change was observed at glacier RUS_4, which only decreased by −33.9% over the same period (Table 2, Figure 5). The highest reduction rates were observed within the last 60 years, when the glaciers lost −35.2% of their total area, equivalent to −0.59% yr−1. The lowest decrease rates were observed between the 1820s and 1890s, when glaciers lost only −8.5%, or −0.12% yr−1 area.
The mean area for all selected glaciers in 1820 was 1.65 km2, while it reduced to 0.81 km2 in 2020. During this time, the mean elevation for the glaciers studied increased by 130 m. The maximum elevation of glaciers was stable until the 1960s, but it decreased by 51 m by 2020 (Figure 6).
We observed a significant upward shift of terminus (or minimum) elevation for all glaciers over the study period (Figure 6 and Figure 7). The average upward shift during the 200 years was +425 m (or +2.1 m yr−1), although individual glaciers show different upward values, e.g., glaciers RUS_2 and RUS_5 experienced the highest terminus upwards of +630 m (or +3.2 m yr−1) and +602 m (or +3.0 m yr−1), respectively. The difference between the southern and northern slopes of the Greater Caucasus was also significant, with +380 m (or +1.9 m yr−1) and +470 m (or +2.4 m yr−1), respectively. The highest terminus elevation upward for all selected glaciers (except RUS_5) occurred after the 1960s.
By measuring the distance between LIA terminal moraines (outlines) and current (2020) glacier snout positions, we obtained a direct measure of glacier retreat. Our findings confirm that all selected glaciers have retreated since the LIA. Specifically notable are glaciers GEO_1, GEO_2, and RUS_2, with an absolute retreat of about −1500 m (each glacier) during the last 200 years (Figure 8). The average retreat of all selected glaciers since the LIA was about −1280 m, while almost 45% of this retreat (−580 m) occurred after the 1960s. Glacier terminus retreat on the southern slopes was relatively smaller (−1220 m) during the study period compared to the northern slopes (−1340 m).
The ELA across all twelve glaciers demonstrated an upward trend throughout the investigated period (Figure 9 and Figure 10; Table 2). However, the ELA increase was not uniform across the glaciers and varied between individual glaciers. The highest ELA increase was observed at the GEO_5 glacier, with an absolute value of +297 over the last 200 years. The lowest ELA increase, only +95 m, was recorded for the GEO_2 glacier during the same time. To reduce the uncertainty caused by estimating ELA from an individual (single) glacier and to increase the reliability of our findings, in terms of the regional scale, we used an average estimate from all selected glaciers. The estimated average ELA in the 1820s was 3245 ± 50 m a.s.l., which is approximately 180 m lower than the present-day ELA (2011–2020), corresponding to air temperatures <1.1 ± 0.3 °C cooler than today, assuming no changes in precipitation. Between the 1820s–1890s and 1890s–1960s, the ELA increased by about +35 m and +40 m, respectively. The most significant rise in ELA occurred over the past six decades, with an increase of approximately 105 m, equivalent to a warming of about 0.6 ± 0.3 °C since the 1960s.
The linear trend analysis of the temperature change from 1820 to 2020 revealed significant variations across different proxy and instrumental datasets (Figure 11). The glacier ELA-derived reconstruction exhibited a statistically highly significant warming trend of 0.518 °C per century (p < 0.001). Similarly, the Elbrus ice core suggested a significant warming of 0.581 °C per century (p < 0.005), which was comparable in magnitude to our outcome. The tree-ring width (TRW)-derived winter reconstruction showed a statistically significant warming of 0.170 °C per century (p < 0.05). Instrumental records from the Mestia weather station indicated a near-significant warming trend of 0.581 °C per century during winter (p = 0.084), while Elbrus summertime observations demonstrated a statistically significant warming of 1.315 °C per century (p < 0.05). Conversely, no statistically significant trends were observed in Mestia summertime (p = 0.407) or Elbrus wintertime (p = 0.744) observations. The Hadley Centre Central England Temperature (HadCET) dataset, both winter and summer, exhibited statistically highly significant warming trends of 0.703 °C per century (p < 0.001) and 0.462 °C per century (p < 0.001), respectively.
The glacier ELA-derived reconstruction revealed a substantial warming trend, falling within the range observed in the instrumental records, particularly those from Mestia and the HadCET dataset. However, the magnitude of warming indicated by the Elbrus summertime observations was notably higher. The tree-ring width (TRW)-derived winter reconstruction, while statistically significant, exhibited a considerably lower warming rate, potentially reflecting localized environmental factors or differences in proxy sensitivity. The absence of significant trends in Mestia summertime and Elbrus wintertime observations suggests seasonal variability and the influence of regional climate drivers. The HadCET dataset, representing a large-scale temperature record, provides a broader context, indicating significant warming trends in both winter and summer.

6. Discussion

6.1. Comparison of Glacier Changes Within the Greater Caucasus

The delineation of 12 LIA glacier outlines in the Greater Caucasus was achieved through moraine mapping from satellite images and digital elevation products. The LIA and modern ELAs were subsequently calculated using manual mapping and the AAR method (see Supplement) [142]. A comparison between LIA and modern ELAs allowed the estimation of the post-LIA rise in air temperature in the Greater Caucasus region.
Previous studies [14,57] have indicated that the regional glacier decrease in the Greater Caucasus between 1890 and 1960 was −14.87 ± 1.9% (or −0.21% yr−1), which is slightly lower than our estimate of −16.9 ± 4.4% (or −0.24% yr−1) during the same time for all selected glaciers (although this coincides with our uncertainty). However, over the last 60 years, the same regional studies showed a loss of −36.7 ± 2.2% (or −0.61% yr−1) in the total glacier area, while our findings show a relatively lower decline rate of −35.3 ± 2.7% (or −0.59% yr−1); again, these values match the uncertainty.
The glacier ELA-derived reconstruction of the temperature anomaly revealed a significant warming trend, falling within the range observed in the instrumental records, particularly those from Mestia [57,137] and the HadCET dataset [138,139]. Our estimates of the post-LIA temperature rise of <1.1 ± 0.3 °C between the 1820s and today are also consistent with reconstructed long-term (1870–2015) annual temperature records from the Elbrus ice core [133]. However, dendrochronological reconstructions of winter temperatures over the last 200 years from Eastern Georgia, Caucasus, reported a significantly lower warming rate of 0.174 °C per century [136]. Conversely, instrumental summertime observations from Elbrus demonstrated a considerably higher warming rate of 1.315 °C per century (Figure 11). These variations likely stem from localized environmental factors, including topographic and landscape heterogeneity, as well as inherent differences in the sensitivity and response of the respective temperature proxies. Our results are also similar to reconstructions of past temperatures at many different glaciers distributed globally, which show a cumulative warming of 0.94 ± 0.31 Kelvin over the period 1830–2000 [143] (noting that our reconstruction extends to 2020, not 2000).
The equilibrium line altitudes from the Little Ice Age to the present exhibit considerable inter-glacier variability within the Greater Caucasus. This can be a result of the complex interplay between microclimate change (including wind, cloud cover, and exposure to prevailing winds) and the unique local topographic and physical characteristics of individual glaciers., e.g., the varying elevations, slopes, and orientations of individual glaciers significantly influence how each glacier responds to climate changes [144,145]. Glaciers located at different altitudes or on different slopes may experience different temperature and precipitation (snow accumulation) patterns. Different aspects of a glacier also significantly affect solar radiation input and thus ablation rates. Steeper glaciers might experience different mass balance dynamics compared to gentler ones, leading to varying ELA changes [146,147]. Differences in ice flow, glacier thickness, and the presence of meltwater can also contribute to these variations. The altitude and the gradient of the surrounding terrain can cause differences in how glaciers accumulate and lose ice, affecting their ELA [44]. Understanding this variability requires detailed, glacier-specific studies that consider these multiple interacting factors. Consequently, when reconstructing regional temperatures based on the ELA data, the utilization of averaged ELAs from multiple glaciers is more robust than relying on a single glacier, which may not accurately reflect the regional mean [148].
We propose that the increased rates of glacier area reduction, terminus retreat, and upward shift of the ELA observed in recent decades, specifically since the 1960s, are predominantly attributable to increased air temperatures [22,149]. Apart from the observed increase in temperatures, glaciers of the Northern Greater Caucasus have experienced a longer ablation (melt) season. E.g., Rototaeva et al. [150] reported that melting on the Garabashi Glacier at the snowline altitude (3800–4000 m a.s.l.) has ended 2–3 weeks later (in early October) in recent years. Furthermore, instrumental measurements from Zopkhito and Chalaati glaciers in the Southern Greater Caucasus have confirmed the extension of the ablation season since the 1960s [134]. A recent study from the Greater Caucasus has also indicated that a reduction in surface albedo, along with increased temperatures during the ablation period over the last few decades, has resulted in the up-glacier migration of the snowline [22]. This phenomenon has had a particularly strong impact on small glaciers, which often experienced a substantial loss of their firn reserves and snow cover (and thus the late summer snowline) in the accumulation areas over the past two decades.

6.2. Comparison with Glacier Changes in Other Regions and Implications for Water Security

Areal changes (−49.2%) of the Caucasus glaciers (in spite of their small number) since the 1820s are smaller than those in the European Alps, where Reinthaler and Paul [151] reported about −57% regional area loss from LIA (1850s) to 2015. This value was even higher (−64%) for small glaciers (1–5 km2) in the European Alps. The results obtained in this study are consistent with observations from other mid-latitude regions of Eurasia, indicating a similar sensitivity to climatic changes. As an example, Ganyushkin et al. [152] reported comparable values of deglaciation in the Altai Mountains, where glaciers decreased by 47.9% from the LIA to the 2020s. Similarly, Li and Li [146] showed that glaciers in the Central Tien Shan region of China experienced a significant areal reduction of approximately 42.3% from the LIA maximum to the 2000s, concurrent with a mean ELA increase of 100 to 150 m.
The continued and accelerated area reduction in glaciers in the Caucasus Region will result in changes to river discharge over the coming decades, thereby significantly impacting water security for downstream communities, agriculture, and ecosystems that depend on a reliable water supply. Global estimates of glacier contributions to river flows [153] indicate that Caucasus glaciers may be nearing or have surpassed "peak water", the point at which increased meltwater from warming is outweighed by diminishing glacier volume. This result, which is consistent with our own finding of accelerating glacier area reduction, suggests that further ice loss will lead to declining water availability in the Caucasus.

7. Conclusions

The aim of this study was to improve our understanding of overall long-term glacier-climate fluctuations in the Greater Caucasus during the post-LIA period. For this purpose, we used well-preserved moraine features, archival maps, and satellite imagery with a combination of digital elevation models and climatic data. We also used the AAR method for ELA and corresponding temperature estimation over the last 200 years. Of the twelve selected glaciers with LIA reconstructions, all glaciers (except one—RUS_5) exhibited more than −40% area loss between the 1820s and 2020s. The lowest decrease (−8.5%) occurred between the 1820s and 1890s, which was doubled (−16.9%) in the 1890s–1960s. The highest glacier area loss (−35.5%) was recorded over the last 60 years.
Average terminus upward shifts along with the increased ELA were observed for all selected glaciers. Glaciers on the northern slopes experienced higher terminal uplift (+470) over the past 200 years than those on the southern slopes (+380). However, the difference in ELA increase between these two slopes was minimal. The ELA depressions of individual glaciers were inhomogeneous and ranged from about 297 to 95 m, confirming that using average ELAs from multiple glaciers is more robust when reconstructing regional temperatures.
The increased rate of ELA rise observed between the 1820s and today corresponds to a <1.1 ± 0.3 °C warming, assuming no changes in precipitation. The most significant rise in ELA occurred over the past six decades, with an increase of approximately 105 m, equivalent to a warming of about 0.6 ± 0.3 °C since the 1960s.
More research is warranted to enhance our comprehension of the glacier–climate mechanisms in this mountain region. Future research should include climate, glacier, and hydrological modeling to quantify the contributions of temperature and precipitation to glacier changes and past and future shifts in the ELA, along with glacier contributions to river flows. Additionally, expanding the study area (e.g., number of representative glaciers) will help assess the regional significance of the findings.

Supplementary Materials

The following supporting information can be downloaded at: https://doi.org/10.5281/zenodo.15236049 (accessed on 21 April 2025) [142].

Author Contributions

Conceptualization, L.G.T.; methodology, L.G.T.; writing—original draft preparation, L.G.T.; writing—review and editing, L.G.T., A.N.M., A.G., L.G., A.N., and M.E.; visualization, L.G.T. and A.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Shota Rustaveli National Science Foundation of Georgia (SRNSFG; grant no FR-23-4258). Levan Tielidze and Andrew Mackintosh were supported by the Australian Research Council (ARC) Special Research Initiative (SRI) Securing Antarctica’s Environmental Future (SR200100005).

Data Availability Statement

Reconstructed glacier surfaces in three dimensions (KMZ format), along with reconstructed ELAs and glacier polygons, are openly available in Zenodo: https://doi.org/10.5281/zenodo.15236049 (accessed on 21 April 2025).

Acknowledgments

We would like to thank three anonymous reviewers for their thoughtful and constructive comments, which clearly improved the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. (a)—Map of the Greater Caucasus, illustrating regional divisions and the extent of glaciers in 2020 (blue). Selected glaciers are shown with yellow dots. (b)—Context map showing the Caucasus region’s position within neighboring countries. The 2011 ASTER GDEM v3 serves as the background for both panels.
Figure 1. (a)—Map of the Greater Caucasus, illustrating regional divisions and the extent of glaciers in 2020 (blue). Selected glaciers are shown with yellow dots. (b)—Context map showing the Caucasus region’s position within neighboring countries. The 2011 ASTER GDEM v3 serves as the background for both panels.
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Figure 2. (a)—an example of LIA glacier mapping strategy (GEO_1 Glacier). The 3 m resolution Planet Lab image (17 August 2024) is used as a background. (b)—Large-scale (1:42,000) topographical map showing the same place in 1890s. Terminus elevation is given in Versts (Historical Russian units of measurement). (c)—Large-scale (1:50,000) topographical map showing the same glacier in the 1960s.
Figure 2. (a)—an example of LIA glacier mapping strategy (GEO_1 Glacier). The 3 m resolution Planet Lab image (17 August 2024) is used as a background. (b)—Large-scale (1:42,000) topographical map showing the same place in 1890s. Terminus elevation is given in Versts (Historical Russian units of measurement). (c)—Large-scale (1:50,000) topographical map showing the same glacier in the 1960s.
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Figure 3. (a)—Manually delineated glacier margins and contour lines at 20 m vertical intervals (GEO_3 Glacier). (b)—The reconstructed LIA glacier surfaces and generated 12.5 m resolution DEM. ALOS PALSAR DEM from 2007, with a resolution of 12.5 m, is used for both panels as a background.
Figure 3. (a)—Manually delineated glacier margins and contour lines at 20 m vertical intervals (GEO_3 Glacier). (b)—The reconstructed LIA glacier surfaces and generated 12.5 m resolution DEM. ALOS PALSAR DEM from 2007, with a resolution of 12.5 m, is used for both panels as a background.
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Figure 4. An example of reconstructed glacier geometry (RUS_4 Glacier), ELA, and corresponding temperatures relative to present (2001–2020). (a)—1820, (b)—1890, (c)—1960, and (d)—2020. Uncertainty of the ELA is ± 50 m.
Figure 4. An example of reconstructed glacier geometry (RUS_4 Glacier), ELA, and corresponding temperatures relative to present (2001–2020). (a)—1820, (b)—1890, (c)—1960, and (d)—2020. Uncertainty of the ELA is ± 50 m.
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Figure 5. (a)—Total glacier area decrease for an idividual glacier in 1820, 1890, 1960, and 2020. (b)—Averaged annual area decrease rates for an individual glacier in three time periods—1820–1890, 1890–1960, and 1960–2020.
Figure 5. (a)—Total glacier area decrease for an idividual glacier in 1820, 1890, 1960, and 2020. (b)—Averaged annual area decrease rates for an individual glacier in three time periods—1820–1890, 1890–1960, and 1960–2020.
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Figure 6. Mean area along with min., max., mean., elevation changes for all 12 glaciers in 1820, 1890, 1960, and 2020.
Figure 6. Mean area along with min., max., mean., elevation changes for all 12 glaciers in 1820, 1890, 1960, and 2020.
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Figure 7. Terminus elevation upward for all 12 glaciers in 1820, 1890, 1960, and 2020.
Figure 7. Terminus elevation upward for all 12 glaciers in 1820, 1890, 1960, and 2020.
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Remotesensing 17 01486 g008
Figure 8. Terminus retreat for all 12 glaciers in 1820, 1890, 1960, and 2020.
Figure 8. Terminus retreat for all 12 glaciers in 1820, 1890, 1960, and 2020.
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Remotesensing 17 01486 g009
Figure 9. Results of the ELA estimations for all 12 glaciers in 1820, 1890, 1960, and 2020.
Figure 9. Results of the ELA estimations for all 12 glaciers in 1820, 1890, 1960, and 2020.
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Remotesensing 17 01486 g010
Figure 10. Average cumulative curves (surface profiles) and reconstructed ELAs for all twelve glaciers in (a)—1820s, (b)—1890s, (c)—1960, and (d)—2020. The gray-shaded box indicates the uncertainty.
Figure 10. Average cumulative curves (surface profiles) and reconstructed ELAs for all twelve glaciers in (a)—1820s, (b)—1890s, (c)—1960, and (d)—2020. The gray-shaded box indicates the uncertainty.
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Remotesensing 17 01486 g011
Figure 11. a—Reconstructed glacier ELA-derived temperature anomaly. The red line corresponds to the ELA-based average temperature curve; b—instrumental temperature records from Elbrus [133]; c—the Hadley Centre Central England Temperature (HadCET) dataset, the world’s longest instrumental record adjusted for urban warming since 1974 [138,139]; d—instrumental temperature records from Mestia, Western Georgia [57,137]; e—temperature anomalies reconstructed from Taxus baccata tree-ring-width (TRW) records from the Batsara Nature Reserve, Eastern Georgia [136]; f—temperature anomalies reconstructed from the Elbrus ice core [133]. A temperature anomaly represents the difference between an observed temperature and the baseline average temperature, which, in this case, is calculated over the period of 2001–2020. Wintertime is defined as October through March, and summertime is defined as April through September. Curves represent temperature anomalies averaged within a rolling window of 10 years. The rolling window is right-aligned, where each point reflects the average temperature anomaly for the corresponding year and the preceding years within the window.
Figure 11. a—Reconstructed glacier ELA-derived temperature anomaly. The red line corresponds to the ELA-based average temperature curve; b—instrumental temperature records from Elbrus [133]; c—the Hadley Centre Central England Temperature (HadCET) dataset, the world’s longest instrumental record adjusted for urban warming since 1974 [138,139]; d—instrumental temperature records from Mestia, Western Georgia [57,137]; e—temperature anomalies reconstructed from Taxus baccata tree-ring-width (TRW) records from the Batsara Nature Reserve, Eastern Georgia [136]; f—temperature anomalies reconstructed from the Elbrus ice core [133]. A temperature anomaly represents the difference between an observed temperature and the baseline average temperature, which, in this case, is calculated over the period of 2001–2020. Wintertime is defined as October through March, and summertime is defined as April through September. Curves represent temperature anomalies averaged within a rolling window of 10 years. The rolling window is right-aligned, where each point reflects the average temperature anomaly for the corresponding year and the preceding years within the window.
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Table 1. Cartographic and satellite products utilized in this study.
Table 1. Cartographic and satellite products utilized in this study.
SourceIDDateScale/Resolution
Map
Verst mapsXIX251887–18901:42,000
XIX261887–1888
XX241897
XX251890–1896
XX261890
XXI301888–1889
XXII301891
Military topographical mapsK_38_25_b1960s1:50,000
K_38_25_g1960s
K_38_26_a1960s
K_38_26_b1960s
K_38_26_v1960s
K_38_27_g1960s
K_38_39_b1960s
K_38_40_g1960s
K_38_40_v1960s
K_38_52_b1960s
Satellite image
Landat 5 TMLT51710302011223MOR0011 August 201130 m
Sentinel L2 AS2A_MSIL1C_V20150903T0758263 September 201510 m
Sentinel L2 AS2B_MSIL1C_20200904T0806094 September 202010 m
Planet20240817_080904_86_24ee_3B17 August 20243 m
Planet20240817_080902_50_24ee_3B17 August 20243 m
Planet20240817_071803_33_24bf_3B17 August 20243 m
Planet20240818_072525_95_24c2_3B18August 20243 m
Planet20240818_072528_16_24c2_3B18 August 20243 m
Planet20240818_080439_91_24e6_3B18 August 20243 m
Planet20240818_080442_06_24e6_3B18 August 20243 m
Planet20240819_071632_28_24ca_3B19 August 20243 m
Planet20240819_071634_49_24ca_3B19 August 20243 m
Planet20240819_072157_50_24c0_3B19 August 20243 m
Planet20240819_072159_70_24c0_3B19 August 20243 m
Planet20240819_082530_60_2488_3B19 August 20243 m
Google Earth-2012–2022<1 m
Digital elevation model
ALOS PALSARAP_07844_FBD_F0840_RT115 July 200712.5 m
ALOS PALSARAP_08340_FBD_F0850_RT118 August 200712.5 m
ALOS PALSARAP_08763_FBD_F0850_RT116 September 200712.5 m
Table 2. Glacier area, terminal elevation, and ELA change over the last 200 years for twelve selected glaciers in the Central Greater Caucasus.
Table 2. Glacier area, terminal elevation, and ELA change over the last 200 years for twelve selected glaciers in the Central Greater Caucasus.
Name/GLIMS IDCoordinatesArea (km2)
(Uncertainty Range ± 4.5–2.1%)		Terminus Elevation (m a.s.l.)
(Uncertainty ± 25 m)		ELA (m a.s.l.)
(Uncertainty ± 50 m)
1820s1890s1960s20201820s1890s1960s20201820s1890s1960s2020
Southern Slope
GEO_1/G042320E43145N43°8′58″N 42°19′6″E1.861.671.150.4927552812293030303084312231833268
GEO_2/G042477E43195N43°11′49″N 42°28′36″E2.452.302.001.1824622500254629833265329032953360
GEO_3/G042452E43142N43°8′28″N 42°27′5″E1.371.240.980.6827502814299030653254328333323399
GEO_4/G042542E43110N43°6′34″N 42°32′21″E1.261.130.930.5425502675277129653024307031543292
GEO_5/G042673E43114N43°6′48″N 42°40′2″E1.571.471.130.6726262752282529253124314631733421
GEO_6/G043638E42796N42°47′51″N 43°38′14″E1.141.010.860.6026352725284530853272333333973426
Northern Slope
RUS_1/G042460E43239N43°14′16″N 42°27′35″E1.381.230.980.5727772880295431033201322732673333
RUS_2/G042752E43244N43°14′38″N 42°44′48″E1.991.831.561.1025562686277031853545356736103676
RUS_3/G042782E43211N43°12′40″N 42°46′34″E0.790.700.560.2328472889301831903173321032353334
RUS_4/G043403E43008N43° 0′52″N 43°24′34″E2.572.382.111.7024982585268030603270331033723626
RUS_5/G043772E42828N42°49′47″N 43°46′20″E2.212.111.921.4327862825310033883604361536343705
RUS_6/G043765E42614N42°36′51″N 43°45′39″E1.221.060.880.5527052835296030653124317632013248
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Tielidze, L.G.; Mackintosh, A.N.; Gavashelishvili, A.; Gadrani, L.; Nadaraia, A.; Elashvili, M. Post-Little Ice Age Equilibrium-Line Altitude and Temperature Changes in the Greater Caucasus Based on Small Glaciers. Remote Sens. 2025, 17, 1486. https://doi.org/10.3390/rs17091486

AMA Style

Tielidze LG, Mackintosh AN, Gavashelishvili A, Gadrani L, Nadaraia A, Elashvili M. Post-Little Ice Age Equilibrium-Line Altitude and Temperature Changes in the Greater Caucasus Based on Small Glaciers. Remote Sensing. 2025; 17(9):1486. https://doi.org/10.3390/rs17091486

Chicago/Turabian Style

Tielidze, Levan G., Andrew N. Mackintosh, Alexander Gavashelishvili, Lela Gadrani, Akaki Nadaraia, and Mikheil Elashvili. 2025. "Post-Little Ice Age Equilibrium-Line Altitude and Temperature Changes in the Greater Caucasus Based on Small Glaciers" Remote Sensing 17, no. 9: 1486. https://doi.org/10.3390/rs17091486

APA Style

Tielidze, L. G., Mackintosh, A. N., Gavashelishvili, A., Gadrani, L., Nadaraia, A., & Elashvili, M. (2025). Post-Little Ice Age Equilibrium-Line Altitude and Temperature Changes in the Greater Caucasus Based on Small Glaciers. Remote Sensing, 17(9), 1486. https://doi.org/10.3390/rs17091486

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