Spatiotemporal variation of snowline altitude at the end of melting season across High Mountain Asia, using MODIS snow cover product

Tang, Zilong; Wang, Xiaoru; Deng, Gang; Wang, Xin; Jiang, Zongli; Sang, Guoqing

doi:10.1016/j.asr.2020.09.035

Cited by 45 publications

(26 citation statements)

References 57 publications

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“…average annual mass balance is already negative) [28,85]. These results are consistent with the conclusions drawn from previous studies on the relationship between the glacier mass balance and SLA at the end of melting season [23,25,88]. It would be more beneficial to reconstruct the annual mass balance time series if higher resolution data could be used for estimating spatial and temporal continuous SLA over a large-scale area.…”

Section: Discussionsupporting

confidence: 90%

“…Besides, SLA estimates can be applied to remove clouds from satellite snow cover products [18,19]. The SLA at the end of the melting season can also serve as a good proxy for the equilibrium line altitude (ELA) on glaciers, where much of the remaining end of summer snow cover is located, and therefore for glacier mass balance [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001–2019

Deng

Tang

et al. 2021

Sustainability

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Snow cover is an important water resource in arid and semi-arid regions of Central Asia, and is related to agricultural and livestock production, ecosystems, and socio-economic development. The snowline altitude (SLA) is a significant indicator for monitoring the changes in snow cover in mountainous regions under the changing climate. Here, we investigate the spatiotemporal variation of SLA in the Tienshan Mountains (TS) during 2001–2019 using Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products on a grid-by-grid basis. The potential influence of topographic factors (slope gradient and aspect) on SLA and the correlation between SLA, temperature, precipitation, and solar radiation are also investigated. The results are as follows: (1) The annual cycle of SLA shows strong seasonal fluctuations (from about 2000 m in late December to 4100 m in early August). The SLA over the TS exhibits a large spatiotemporal heterogeneity. (2) SLA increases with a steeper slope gradient. The SLA of the northerly aspect is generally less than the southerly. (3) The SLA over the TS generally shows an increasing trend in the recent years (2001–2019). The change trend of SLA varies in different months. Except for a slight decrease in June, the SLA increased in almost all months, especially at the start of the melt season (March and April) and the end of melting season (July and August). (4) The SLA increases with increased temperature/radiation in the TS, and decreases with increased precipitation. Solar radiation is the dominant climatic factor affecting the changes of SLA in the TS. Compared with precipitation, temperature is more correlated to SLA dynamics.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001–2019

Deng

Tang

et al. 2021

Sustainability

Self Cite

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“…Regional SLA estimation using satellite imagery, when measured at the end of the ablation period, can be approximated by the lower boundary of the continuous snow-covered area (SCA). [21][22][23] Remotely sensed snow products have been applied for snowline observations; for example, Tang et al 24,25 monitored the spatiotemporal patterns of the SLAs over High Mountain Asia using cloudremoved Moderate Resolution Imaging Spectroradiometer (MODIS) fractional snow cover data with 500 m resolution. Various methods [26][27][28] have also been developed for determining SLAs by applying various spectral band ratios to higher-resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Variability of the snowline altitude in the eastern Tibetan Plateau from 1995 to 2016 using Google Earth Engine

Liu

Zhang

et al. 2021

J. Appl. Rem. Sens.

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The glacier snowline can be used as an indicator of a glacier's equilibrium line, which is a pivotal parameter for studying the effect of climate change on glaciers. However, the relationship between snowline altitudes (SLAs) and climatic regime, as well as the comparison between different glacier types, has received less attention. Using Google Earth Engine, we first developed an automated algorithm that employs the Otsu thresholding method to distinguish snow-covered areas from clean ice on a near-infrared band of Landsat imagery available from 1995 to 2016 and further to delineate glacier SLAs in the three regions of the eastern Tibetan Plateau (TP). The three study regions, Sepu Kangri (maritime), Bu'Gyai Kangri (continental), and western Qiajajima (continental), in the eastern TP have different climate regimes and are on a latitudinal transect from south to north. We then investigated the impacts of climatic factors on the SLA and its variability over the period studied. The results over the eastern TP indicate that (1) the SLA increased by 94, 55, and 49 m from south to north during the 22-year period, with the SLA variations of maritime glaciers being the most pronounced; (2) the southern maritime glaciers were mainly affected by precipitation, whereas the northern continental glaciers were influenced by temperature. Owing to the difference in primary climatic factors affecting snowlines, continental glaciers were found to have higher SLAs on the south slope, whereas maritime glaciers had higher SLAs on the north slope.

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“…The snow line at the end of the melting season in a given year is close to the definition of snow line in glaciology, that is, the boundary between the snow-covered area all year round and the maximum melting area in summer. Above this limit, the surface is covered with snow all year round [28].…”

Section: Snow Line Extractionmentioning

confidence: 99%

Monitoring Rock Desert Formation Caused by Ice–Snow Melting in the Qinghai-Tibet Plateau Using an Optimized Remote Sensing Technique: A Case Study of Yushu Prefecture

Jia

Shi

et al. 2022

Remote Sensing

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The rapid and effective identification of the spatial distribution of rock deserts in ice–snow melting areas can provide useful information for the prevention of natural disasters. In this study, Landsat TM/OLI were used to track the evolution of snow lines in the snow melting areas of Yushu Tibetan Autonomous Prefecture of the Qinghai-Tibet Plateau (hereinafter referred to as Yushu Prefecture) during the last 30 years. A total of seven extraction schemes were used to classify the rock desert in the ice–snow melting area by remote sensing. Our results show that: (1) The accuracy of the multi-index factor compound analysis and object-oriented classification compound method was the highest. This method can provide a rapid and efficient reference scheme for rock desert extraction in the plateau ice–snow melting area. (2) The combinations of two single methods with higher accuracy can further improve the total accuracy. If a single method with lower accuracy is involved in the multi-method fusion, the accuracy of the method with lower accuracy can be improved. (3) In the past 30 years, there has been a large amount of ice–snow melting in Yushu Prefecture. The ice–snow melting area accounts for 53.78% (1451.04 km2) of the ice–snow area, and the bare rock and bare stone in the rock desert account for 63.77% of the total area of the ice–snow melting area. Bare sand and bare soil area account for 30.27% of the total area of ice and snow melting area.

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Spatiotemporal variation of snowline altitude at the end of melting season across High Mountain Asia, using MODIS snow cover product

Cited by 45 publications

References 57 publications

Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001–2019

Spatiotemporal Dynamics of Snowline Altitude and Their Responses to Climate Change in the Tienshan Mountains, Central Asia, during 2001–2019

Variability of the snowline altitude in the eastern Tibetan Plateau from 1995 to 2016 using Google Earth Engine

Monitoring Rock Desert Formation Caused by Ice–Snow Melting in the Qinghai-Tibet Plateau Using an Optimized Remote Sensing Technique: A Case Study of Yushu Prefecture

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