Catastrophic mass flows originating from the high mountain cryosphere often cause hazard cascades. With increasing human activities in the alpine region and the sensitivity of glaciers, snow and permafrost to climate warming, the cryospheric hazard risks tend to be more active in the Tibetan Plateau(TP). The remoteness and inaccessibility of high mountain regions make the remote sensing technology the most effective approach for monitoring and investigating the cryospheric hazards. Exploring the evolution process and impact of ice avalanches and subsequent hazards are crucial to understand drivers and nature of mass flow process chain in order to prevent and mitigate potential hazard consequences. In this study, the glacier and ice-rock avalanches occurred in Arutso and Sedongpu basins in Tibet were investigated on the basis of Sentinel-2 time-series images and in-situ investigation, the evolution process of these events were reconstructed, and impacts and driving factors were analyzed. The massive Chamoli rock-ice avalanche occurred in February 2021 in the western Himalayas was also reviewed using using same methods. The study shows that Arutso glacier No. 53 (Arutso-53) avalanche completely melted away in July 2018 after two years from occurrence to final disappearance, while Arutso glacier No. 50 (Arutso-50) avalanche completely melted by the end of August 2023 after lasting for seven years. Four large-scale ice-rock avalanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow, landscape and geomorphologic shape in the basin, but also caused serious disasters in the basin and downstream. In March 2021, a small-scale ice-rock avalanche and debris flow event occurred again in the Sedongpu basin. The glacier and ice-rock avalanches occurred in Tibet area were mainly induced by climate warming, heavy precipitation and seismic activity, which acting on specific topographic and geomorphic structure of glacier properties in the high mountain regions. Catastrophic hazard events originating from the high-elevation glacier and ice-rock collapses in the mountain cryosphere and subsequent hazard cascades in the TP and Himalayas highlight the importance of mountain hazard monitoring, early warning systems and sustainable mountain development.

Barley landraces accumulated variation in adapting to extreme highland environments during long-term domestication in Tibet, but little is known about their population structure and genomic selection traces. In this study, tGBS (tunable genotyping by sequencing) sequencing, molecular marker and phenotypic analyses were conducted on 1,308 highland and 58 inland barley landraces in China. The accessions were divided into six sub-populations and clearly distinguished most six-rowed, naked barley accessions (Qingke in Tibet) from inland barley. Genome-wide differentiation was observed in all five sub-populations of Qingke and inland barley accessions. High genetic differentiation in the pericentric regions of chromosomes 2H and 3H contributed to formation of five types of Qingke. Ten haplotypes of the pericentric regions of 2H, 3H, 6H and 7H were further identified as associated with ecological diversification of these sub-populations. There was genetic exchange between eastern and western Qingke but they shared the same progenitor. The identification of 20 inland barley types indicated multiple origins of Qingke in Tibet. The distribution of the five types of Qingke corresponded to specific environments. Two predominant highland-adaptative variations were identified for low temperature tolerance and grain color. Our results provide new insights into the origin, genome differentiation, population structure and highland adaptation in highland barley which will benefit both germplasm enhancement and breeding of naked barley.

AbstractHigh Mountain Asia (HMA) glaciers are critical water reserves for montane regions, which are readily influenced by climate change. The glacier mass balance during 2000–2021 over HMA was estimated by comparing the elevations from ICESat-2 and the NASADEM. Radar penetration depth could be one of the intrinsic error sources in estimating glacier mass balance by using NASADEM. Therefore, we doubled elevation differences between the X-band Shuttle Radar Topography Missions (SRTMs) and NASADEM to estimate the potential error. The spatial characteristics of the altitude-dependent penetration depth can be detected in most sub-regions of HMA. Relatively deep penetrations in the Himalaya (2.3–3.7 m) and Hissar Alay (4.3 m) regions and small penetrations in the south-eastern HMA (1.0 m) were observed. The HMA region experienced a significant mass loss at a rate of −0.18 ± 0.12 m w.e. a−1, in which the Hengduan Shan exhibited the highest mass loss of −0.62 ± 0.10 m w.e. a−1, the West Kun Lun experienced a substantial mass gain of 0.23 ± 0.13 m w.e. a−1, and the Karakoram showed a more or less balance. Our results are in agreement with previous studies that assessed the mass balance of HMA glaciers from different methods.

Accurate multitemporal glacier change investigations and analyses are lacking on the southeastern Tibetan Plateau (SETP). A combination of photogrammetry, optical remote sensing, and synthetic aperture radar datasets can accurately identify large-scale glaciers; In this article, glaciers in three periods on the SETP (1970s, 2000, 2020) were identified from multisource remote sensing data based on a deep learning method and manual visual interpretation, and multitemporal glacial inventory data from relatively high-frequency source imagery. Totals of 11648, 12993, and 11875 glaciers were identified in the 1970s, 2000, and 2020, with total areas of 13372.08 km2, 11692.31 km2, and 10612.94 km2, respectively. The general distribution of SETP glaciers was identified to be typical of alpine glaciers dominated by small-sized glaciers. The average elevation of glaciers was approximately 5000 m; the slopes were mostly lower than 40°, and the main aspect was southeast, followed by south and southwest. The glaciers retreated from the 1970s to 2020, and a total glacier area of approximately 2759.14 km2 was degraded during this time, with an average annual melting rate of 0.45% yr−1. Rising summer temperatures may be the driving force behind the continuous decline in the glacier area. Overall, the results obtained in this article showed relatively low uncertainty involved in the identification of glaciers compared to some previous studies. The results can provide accurate glacier information for glacier monitoring and modeling studies on the SETP.

Glaciers in the Himalayan mountain ranges contain huge amounts of water and are essential for the water security of their downstream basins. Due to the spatiotemporal heterogeneity and uncertainty of the glacier mass change (GMC) in the Himalayas, more refined and deeper investigations on the GMC and its driving forces are needed to complement the current research. We present the mass balance of Himalayan glaciers during 2000 to 2013 using the latest global digital elevation models; this method focuses on establishing a high-coverage result of the GMC and resolving the problems of timestamps and penetration uncertainties. The GMC of the entire Himalayas is -0.32 +/- 0.15 mw.e.yr(-1); most of the lost mass drains into the Indus and Ganges river basins. Glacier mass loss gradually decreases from east to west, with the East and Central Himalayas exhibiting the highest heterogeneity in the vertical and horizontal directions, respectively. We propose two multivariate regression models with 12 potential driving factors that explain 63% to 89% of the GMC across different regions of the Himalayas. Precipitation significantly influences changes in glacier mass throughout the Himalayas, whereas debris coverage and glacier size have insignificant effects on changing the glacier mass. Our analysis reveals that seasonal climate-driven changes in the glacier mass of the Himalayas may threaten the downstream water supply. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

Glacier mass balance is one of the most direct indicators reflecting corresponding climate change. In the context of global warming, most glaciers are melting and receding, which can have significant impacts on ecology, climate, and water resources. Thus, it is important to study glacier mass change, in order to assess and project its variations from past to future. Here, the Karakoram, one of the most concentrated glacierized areas in High-Mountain Asia (HMA), was selected as the study area. This study utilized SRTM-C DEM and ICESat-2 to investigate glacier mass change in the Karakoram, and its response to climatic and topographical factors during 2000–2021. The results of the data investigation showed that, overall, the “Karakoram Anomaly” still exists, with an annual averaged mass change rate of 0.02 ± 0.09 m w.e.yr-1. In different sub-regions, it was found that the western and central Karakoram glaciers gained ice mass, while the eastern Karakoram glaciers lost ice mass in the past two decades. In addition, it was discovered that the increasing precipitation trend is leading to mass gains in the western and central Karakoram glaciers, whereas increasing temperature is causing ice mass loss in the eastern Karakoram glacier. Generally, decreasing net shortwave radiation and increasing cloud cover in the Karakoram restricts ice mass loss, while topographical shading and debris cover also have dominant impacts on glacier mass change.

Microwave remote sensing is one of the main approaches to glacier monitoring. This paper provides a comparative analysis of how different types of radar information differ in identifying debris-covered alpine glaciers using machine learning algorithms. Based on Sentinel-1A data, three data suites were designed: A backscattering coefficient (BC)-based data suite, a polarization decomposition parameter (PDP)-based data suite, and an interference coherence coefficient (ICC)-based data suite. Four glaciers with very different orientations in different climatic zones of the Tibetan Plateau were selected and classified using an integrated machine learning classification approach. The results showed that: (1) The boosted trees and subspace k-nearest neighbor algorithms were optimal and robust; and (2) the PDP suite (63.41–99.57%) and BC suite (55.85–99.94%) both had good recognition accuracy for all glaciers; notably, the PDP suite exhibited better rock and debris recognition accuracy. We also analyzed the influence of the distribution of glacier surface aspect on the classification accuracy and found that the more asymmetric it was about the sensor orbital plane, the more difficult it was for the BC and PDP suites to recognize the glacier, and a large slope could further reduce the accuracy. Our results suggested that during the inventory or classification of large-scale debris-covered alpine glaciers, priority should be given to polarization decomposition features and elevation information, and it is best to divide the glaciers into multiple subregions based on the spatial relationship between glacier surface aspect and radar beams.

The glaciers on the Tibetan Plateau (TP) constitute critical sources of water for the proglacial lakes and many rivers found downstream. To better understand the evolution of glaciers and the impact of this on proglacial lakes, seven glaciers corresponding to continenṅtal, subcontinental, and marine climate types that are influenced by westerlies and the Indian summer monsoon were selected for study. The evolution of the edges of these glaciers and their associated proglacial lakes were identified based on the visual interpretation of Landsat TM/ETM+/OLI images. A dataset covering the period 1976–2020 that included the glacier and proglacial lake edge vectors was then created. The relative errors in the areas of the individual glaciers were less than 3%, and for the proglacial lakes these errors were in the range 0%–7%. The dataset was used to effectively compare the changes in glaciers and proglacial lakes that have occurred over the past four decades. The most striking changes that were found were the retreat of glaciers and the formation of small proglacial lakes. This dataset could also be used as a proxy to support research on changes in mountain glaciers, particularly their response to climate change and water resources. This response is of great scientific significance and is important in many applications, including assessments of the ecological problems caused by melting glaciers. The dataset can be downloaded from http://doi.org/10.57760/sciencedb.j00076.00131.

Abstract. Antarctic digital elevation models (DEMs) are essential for human fieldwork, ice topography monitoring and ice mass change estimation. In the past thirty decades, several Antarctic DEMs derived from satellite data have been published. However, these DEMs either have coarse spatial resolutions or vague time stamps, which limit their further scientific applications. In this study, the new-generation satellite laser altimeter Ice, Cloud, And Land Elevation Satellite-2 (ICESat-2) is used to generate a fine-scale and specific time-stamped Antarctic DEM for both the ice sheet and ice shelves. Approximately 4.69 × 109 ICESat-2 measurement points from November 2018 to November 2019 are used to estimate surface elevations at resolutions of 250 m, 500 m and 1 km based on a spatiotemporal fitting method, which results in a modal resolution of 250 m for this DEM. Approximately 74 % of Antarctica is observed, and the remaining observation gaps are interpolated using the ordinary kriging method. National Aeronautics and Space Administration Operation IceBridge (OIB) airborne data are used to evaluate the generated Antarctic DEM (hereafter called the ICESat-2 DEM) in individual Antarctic regions and surface types. Overall, a median bias of 0.11 m and a root-mean-square deviation of 8.27 m result from approximately 1.4 × 105 spatiotemporally matched grid cells. The accuracy and uncertainty of the ICESat-2 DEM vary in relation to the surface slope and roughness, and more reliable estimates are found in the flat ice sheet interior. The ICESat-2 DEM is superior to previous DEMs derived from satellite altimeters for both spatial resolution and elevation accuracy and comparable to those derived from stereo-photogrammetry and interferometry. The decimeter-scale accuracy and specific time stamp make the ICESat-2 DEM an essential addition to the existing Antarctic DEM groups, and it can be further used for other scientific applications.