Glacier Area and Snow Cover Changes in the Range System Surrounding Tarim from 2000 to 2020 Using Google Earth Engine

Glacier and snow are sensitive indicators of regional climate variability. In the early 21st century, glaciers in the West Kunlun and Pamir regions showed stable or even slightly positive mass budgets, and this is anomalous in a worldwide context of glacier recession. We studied the evolution of snow cover to understand whether it could explain the evolution of glacier area. In this study, we used the thresholding of the NDSI (Normalized Difference Snow Index) retrieved with MODIS data to extract annual glacier area and snow cover. We evaluated how the glacier trends related to snow cover area in five subregions in the Tarim Basin. The uncertainty in our retrievals was assessed by comparing MODIS results with the Landsat-5 TM in 2000 and Landsat-8 OLI in 2020 glacier delineation in five subregions. The glacier area in the Tarim Basin decreased by 1.32%/a during 2000–2020. The fastest reductions were in the East Tien Shan region, while the slowest relative reduction rate was observed in the West Tien Shan and Pamir, i.e., 0.69%/a and 1.08%/a, respectively, during 2000–2020. The relative glacier stability in Pamir may be related to the westerlies weather system, which dominates climate in this region. We studied the temporal variability of snow cover on different temporal scales. The analysis of the monthly snow cover showed that permanent snow can be reliably delineated in the months from July to September. During the summer months, the sequence of multiple snowfall and snowmelt events leads to intermittent snow cover, which was the key feature applied to discriminate snow and glacier.

Variation of Runoff and Runoff Components of the Upper Shule River in the Northeastern Qinghai–Tibet Plateau under Climate Change

Quantifying the impact of climate change on hydrologic features is essential for the scientific planning, management and sustainable use of water resources in Northwest China. Based on hydrometeorological data and glacier inventory data, the Spatial Processes in Hydrology (SPHY) model was used to simulate the changes of hydrologic processes in the Upper Shule River (USR) from 1971 to 2020, and variations of runoff and runoff components were quantitatively analyzed using the simulations and observations. The results showed that the glacier area has decreased by 21.8% with a reduction rate of 2.06 km2/a. Significant increasing trends in rainfall runoff, glacier runoff (GR) and baseflow indicate there has been a consistent increase in total runoff due to increasing rainfall and glacier melting. The baseflow has made the largest contribution to total runoff, followed by GR, rainfall runoff and snow runoff, with mean annual contributions of 38%, 28%, 18% and 16%, respectively. The annual contribution of glacier and snow runoff to the total runoff shows a decreasing trend with decreasing glacier area and increasing temperature. Any increase of total runoff in the future will depend on an increase of rainfall, which will exacerbate the impact of drought and flood disasters.

Cambio de área glaciar en el volcán Maipo (Andes Centrales), una aproximación morfométrica: 4 décadas de registros satelitales

Current climatic conditions in Central Andes (CA) (31-36 °S) have triggered the reduction of glacier area. Although CA are geographically circumscribed to an area under the same macroclimatic domain, their rugged topography creates several topoclimates as response to the effects of elevation, slope and aspect (morphometric factors). This study explores the impact of morphometric factors on the evolution of the glacial surface located above of Maipo volcano (34°09'50''S; 69°49'53''W). Through the use of 11 LANDSAT images (MSS, TM and OLI), the spatio-temporal evolution (period 1976-2020) of the glacier area was reconstructed. On this period, glacier area was reduced by 6 ± 0.5 km2 (-0.14 ± 0.01 km2a-1), equal to 63 % of 1976 glacial area (9.6 ± 0.5 km2). Fifty percent of the reduction occurred between 3,900 and 4,000 m elevation, with absolute losses towards lower elevations. In addition, it was detected that for every 100 m of ascent the relative area loss rate decreased 0.1 %a-1 (R2 = 0.81; p-value

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.

Estimating the Changes in Glaciers and Glacial Lakes in the Xixabangma Massif, Central Himalayas, between 1974 and 2018 from Multisource Remote Sensing Data

The continuous melting of valley glaciers can impact the water levels of glacial lakes and create glacial lake outburst floods (GLOFs). The Xixabangma massif is one of the most populated areas in the Himalayas and has suffered from multiple GLOFs. To estimate the glacier melting rate in the past four decades and analyze the outburst risk of glacial lakes in the Xixabangma massif, we determined changes in glacier mass balance, glacier area and glacial lake area based on KH-9 images, TanDEM-X images, Landsat images, SRTM DEM and ICESat-2 elevations. Our results show that, from 1974 to 2018, the total glacier area shrank from 954.01 km2 to 752.46 km2, whereas the total glacial lake area grew from 20.90 km2 to 38.71 km2. From 1974 to 2000, 2000 to 2013 and 2013 to 2018, the region-wide glacier mass balance values were −0.16 m w.e./a, −0.31 m w.e./a and −0.29 m w.e./a, respectively. Three glacial lakes, named Gangxico, Galongco and Jialongco, respectively, expanded by 127.14%, 373.45% and 436.36% from 1974 to 2018, and the mass loss rates of their parent glaciers from 2000 to 2013 increased by 81.72%, 122.22% and 160.00% relative to those during 1974 to 2000. The dams of these three lakes are unstable, and their drainage valleys directly connect to a major town and its infrastructure. Due to current high-water levels, possible external events such as ice collapse, landslide, heavy rainfall and earthquakes can easily trigger GLOFs. Hence, we deemed that the Gangxico, Galongco and Jialongco glacial lakes are dangerous and require special attention.

Active rock glaciers of the contiguous United States: geographic information system inventory and spatial distribution patterns

In this study we present the Portland State University Active Rock Glacier Inventory (n=10 332) for the contiguous United States, derived from the manual classification of remote sensing imagery (Johnson, 2020; https://doi.org/10.1594/PANGAEA.918585). Individually, these active rock glaciers are found across widely disparate montane environments, but their overall distribution unambiguously favors relatively high, arid mountain ranges with sparse vegetation. While at least one active rock glacier is identified in each of the 11 westernmost states, nearly 88 % are found in just five states: Colorado (n=3889), Montana (n=1813), Idaho (n=1689), Wyoming (n=839), and Utah (n=834). Mean active rock glacier area is estimated at 0.10 km2, with cumulative active rock glacier area totaling 1004.05 km2. Active rock glaciers are assigned to a three-tier classification system based on area thresholds and surface characteristics known to correlate with downslope movement. Class 1 features (n=7042, average area = 0.12 km2) appear to be highly active, Class 2 features (n=2415, average area = 0.05 km2) appear to be intermediately active, and Class 3 features (n=875, average area = 0.04 km2) appear to be minimally active. This geospatial inventory will allow past active rock glacier research findings to be spatially extrapolated, help facilitate further active rock glacier research by identifying field study sites, and serve as a valuable training set for the development of automated rock glacier identification and classification methods applicable to other large regional studies.

Glacier changes in the Chhombo Chhu Watershed of the Tista basin between 1975 and 2018, the Sikkim Himalaya, India

Glaciers of the Tista basin represent an important water source for mountain communities and a large population downstream. The article presents observable changes in the Chhombo Chhu Watershed (CCW) glacier area of the Tista basin, the Sikkim Himalaya. The CCW contains 74 glaciers (> 0.02 km2) with a mean glacier size of 0.61 km2. We determined changes in glaciers from the declassified Hexagon Keyhole-9 (KH-9) (1975), Landsat 5 Thematic Mapper (TM) (1989), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2000), Landsat 5 TM (2010), and Sentinel-2A (2018) images. The total glacier area in 1975 was 62.6 ± 0.7 km2; and by 2018, the area had decreased to 44.8 ± 1.5 km2, an area loss of 17.9 ± 1.7 km2 (0.42 ± 0.04 km2 a−1). Clean glaciers exhibited more area loss of 11.8 ± 1.2 km2 (0.27 ± 0.03 km2 a−1) than partially debris-covered and maximally debris-covered glaciers. The area loss is 5.0 ± 0.4 km2 (0.12 ± 0.01 km2 a−1) for partially covered glaciers and 1.0 ± 0.1 km2 (−0.02 ± 0.002 km2 a−1) for maximally covered glaciers. The glacier area loss in the CCW of the Sikkim Himalaya is 0.62 ± 0.5 km2 a−1 during 2000–2010, and it is 0.77 ± 0.6 km2 a−1 during 2010–2018. Field investigations of selected glaciers and climatic records also support the glacier recession in the CCW due to a significant increase in temperature (0.25 ∘C a−1) and more or less static precipitation since 1995. The dataset is now available from the Zenodo web portal: https://doi.org/10.5281/zenodo.4457183 (Chowdhury et al., 2021).

Simulation of the Potential Distribution of the Glacier Based on Maximum Entropy Model in the Tianshan Mountains, China

Under the background of global climate change, the variation in the spatial distribution and ice volume of mountain glaciers have a profound influence on regional economic development and ecological security. The development of glaciers is like biological succession; when climate change approaches or exceeds the threshold of suitable conditions for glacier development, it will lead to changes in potential distribution pattern. Therefore, from the perspective of the "biological" characteristics of glaciers, it is a beneficial exploration and attempt in the field of glaciology to explore its potential distribution law with the help of the niche model. The maximum entropy model (MaxEnt) can explain the environmental conditions suitable for the survival of things by analyzing the mathematical characteristics and distribution laws of samples in space. According to glacier samples and the geographical environment data screened by correlation analysis and iterative calculation, the potential distribution pattern of Tianshan glaciers in China in reference years (1970–2000) was simulated by MaxEnt. This paper describes the contribution of geographical environmental factors to distribution of glaciers in Tianshan Mountains, quantifies the threshold range of factors affecting the suitable habitat of glaciers, and predicts the area variation and distribution pattern of glaciers under different climate scenarios (SSP1-2.6, SSP5-8.5) in the future (2040–2060, 2080–2100). The results show that the MaxEnt model has good adaptability to simulate the distribution of glaciers. The spatial heterogeneity of potential distribution of glaciers is caused by the spatio-temporal differences of hydrothermal combination and topographic conditions. Among the environmental variables, precipitation during the wettest month, altitude, annual mean temperature, and temperature seasonality have more significant effects on the potential distribution of glaciers. There is significant spatial heterogeneity in the potential distribution of glaciers in different watersheds, altitudes, and aspects. From the forecast results of glacier in various climatic scenarios in the future, about 18.16–27.62% of the total reference year glacier area are in an alternating change of melting and accumulation, among which few glaciers are increasing, but this has not changed the overall retreat trend of glaciers in the study area. Under the low emission scenario, the glacier area of the Tianshan Mountains in China decreased by 18.18% and 23.73% respectively in the middle and end of the 21st century compared with the reference years and decreased by 20.04% and 27.63%, respectively, under the high emission scenario, which showed that the extent of glacier retreat is more intense under the high emission scenario. Our study offers momentous theoretical value and practical significance for enriching and expanding the theories and analytical methods of the glacier change.