A new global dataset of mountain glacier centerlines and lengths

Zhang, Dahong; Zhou, Gang; Li, Wen; Zhang, Shiqiang; Yao, Xiaojun; Wei, Shimei

doi:10.5194/essd-14-3889-2022

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…The WGMS employs the Fluctuations of Glaciers methodology for this purpose. In addition, the study used version 6.0 of the Randolph Glacier Inventory (RGI 6.0), which contains digital outlines of glaciers worldwide, sourced from the Global Land Ice Measurements from Space initiative [55]. To analyze the influence of the El Niño/Southern Oscillation on glacier fluctuations, the study employed the bi-monthly multivariate El Niño/Southern Oscillation (MEI.v2, https://psl.noaa.gov/enso/mei/, accessed on 10 February 2023) index [56,57].…”

Section: Auxiliary Datamentioning

confidence: 99%

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

et al. 2023

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Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a−1 to 9.4 m·a−1. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation.

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Section: Auxiliary Datamentioning

confidence: 99%

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

et al. 2023

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“…The ASTER GDEM V3 data with the resolution of 30 meters was used as topography reference for image coregistration, geocoding and topographical correction. The GGCLDS [10] (global glacier centerline dataset) data is currently a great available global glaciers centerline dataset, and in this paper, region "R01" covering Alaska in this dataset was used. The Glacier outline data used in this study were obtained from Randolph Glacier Inventory version 6.0 [11] (RGI 6.0).…”

Section: Datasetsmentioning

confidence: 99%

Variability of glacier velocity and the influencing factors in the Saint Elias Mountains, Southeastern Alaska

Zhang,

Lin

2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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The Saint Elias Mountains is an important glacierized area in southeastern Alaska. Glacier velocity is a key parameter for understanding glacier variation. In the context of global warming, glacier velocity variation has been widely studied, but the details of their inter-annual, intra-annual and seasonal variation are unclear. Using SAR images with offset tracking and SBAS method, the three-dimensional velocity across the study area were obtained. The vertical velocity was further divided into surface parallel flow (SPF) and non-surface parallel flow (nSPF), where SPF is produced by the downhill glacier movement under its own gravity, while nSPF is caused by the deformation inside the glacier. The inter-annual, intra-annual and seasonal variations of glacier velocity in the study area during 2017-2020 and their influencing factors were analyzed. The results showed that: (1) The velocity of different glaciers varied significantly. In the vertical direction, the nSPF component was significantly greater than SPF regardless of the glacier velocity itself. (2) There was no striking inter-annual variation of velocity distribution and magnitude between 2017, 2018, 2019 and 2020. We also found different variation patterns in different seasons. (3) We used the flowline of Valerie glacier and analyzed the intra-annual velocity variation throughout 2018. The velocity was significantly higher during winter and spring. (4) The seasonal variation of velocity was not completely consistent with that of temperature and precipitation, and was mainly affected by subglacial hydrological conditions.

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“…The Randolph Glacier Inventory (RGI) 6.0 includes information on the worldwide outlines, areas, and various attributes of glaciers such as elevation, length, and volume. The RGI serves as a valuable resource for studying glacier dynamics [49,50]. The RGI dataset is freely available and can be accessed through the Global Land Ice Measurements from Space (GLIMS) website at http://www.glims.org/RGI/ (accessed on 6 November 2022).…”

Section: Auxiliary Datamentioning

confidence: 99%

Spatiotemporal Variations of Glacier Mass Balance in the Tomur Peak Region Based on Multi-Source Altimetry Remote Sensing Data

Cheng,

Du,

et al. 2023

Remote Sensing

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Alpine glaciers are sensitive indicators of regional climate change, which can affect regional ecological stability and social development. Variations in glacier mass balance (GMB) are an important parameter in studying glacier change. In this study, data from the Ice, Cloud, and Land Elevation Satellite-1 (ICESat-1), the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), and CryoSat-2 (Ku-band) were combined, and high-resolution ALOS DEM was employed to denoise. After that, the polynomial fitting method was used to analyze the characteristics of glacier surface elevation (GSE) variations from 2003–2020 in the Tomur Peak Region of the Central Asian Tianshan Mountains and the regional GMB was calculated. Research results showed that: (1) From 2003–2020, the GSE of the Tomur Peak Region had an overall −8.95 ± 4.48 m variation, the average rate of which was −0.53 ± 0.26 m/yr (/yr is /year). Overall, elevations of most glaciers in the Tomur Peak Region had downward trends, with a rate of change of −0.5 to 0 m/yr. The fastest rate of elevation decline in the Koxkar Glacier Tongue was −1.5 m/yr. The elevation of some altimetric points in the Eastern Tomur Peak Region showed a rising state, with a maximum rate of variation of 1.0 m/yr. (2) From 2003–2020, the average GMB in the Tomur Peak Region was −1.51 ± 0.04 Gt/yr. In the region of elevation below 4000 m, small glaciers dominated, with a GMB of −0.61 ± 0.04 Gt/yr. With increasing elevation, the melting rate of glaciers gradually slowed down, but overall, the mass balance remained in a state of decline. (3) Climate was the main driving factor of GMB change in the study area. From 2003–2020, in the Tomur Peak Region, the average annual temperature continued to increase at a rate of 0.04 ± 0.02 °C/yr, and this was the main influencing factor for the negative GMB in the Tomur Peak Region. In the same period, the annual precipitation showed a rising trend with a linear variation rate of 0.12 ± 0.06 mm/yr, and the rising precipitation was the influencing factor for the gradually slowing change in the GMB in the study area.

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A new global dataset of mountain glacier centerlines and lengths

Cited by 8 publications

References 59 publications

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Landsat Satellites Observed Dynamics of Snowline Altitude at the End of the Melting Season, Himalayas, 1991–2022

Variability of glacier velocity and the influencing factors in the Saint Elias Mountains, Southeastern Alaska

Spatiotemporal Variations of Glacier Mass Balance in the Tomur Peak Region Based on Multi-Source Altimetry Remote Sensing Data

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