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

Abstract

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.

Contrasting changes of snow cover between different regions of the Tibetan Plateau during the latest 21 years

“…The areas with snow decreasing are mainly in the Yangtze, Lancang River, Nujiang River, Yarlungzangbo, and Ganges, located in the southeastern and south part of the plateau. The gradual rise of the snowline height in these areas from 1995 to 2016 (Liu et al, 2021) also confirms the decreasing trend of snow cover. Areas of increasing and decreasing snow coexist in the Amu Darya, Indus, Tarim in the northwestern part of the plateau, and in the Qaidam and Yellow Rivers in the northeast.…”

El Niño Enhances Snowline Rise and Ice Loss on the World's Largest Tropical Ice Cap

“…Typically, a suite of images, often from Landsat satellites are paired with one or more Digital Elevation Models (DEMs) and a glacier outline from the beginning of the temporal scale of 75 interest (Li et al, 2022). From there, a variety of thresholds are evaluated and set for the area of interest with the SLA extracted from automated calculations to be used as the ELA (Racoviteanu et al, 2019) or manual tracing of outputs (Liu et al, 2021).…”

“…Since routine ground-based measurements in a remote location such as south-central Peru are difficult to maintain, using satellite imagery to estimate the Equilibrium Line Altitude (ELA) has become a viable solution. Previous studies have shown 60 that the end of dry season (September) location of the snowline altitude (SLA) can act as a proxy for the ELA and ultimately be used to infer the mass balance of a glacier or ice cap (Fang et al, 2011;Hu et al, 2020;Liu et al, 2021;Racoviteanu et al, 2019). Here, we use a suite of imagery spanning 1985 to 2023 to assess the QIC at the end of the dry season through a cloudbased analysis of satellite imagery.…”

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

“…The OTSU thresholding method [60] is an adaptive technique used for image segmentation. In this study, it is used for distinguishing between ice and snow [61,62]. Moreover, this technique is well-suited for delimiting snowlines for a vast number of glaciers, in comparison to manual interpretation and semi-automated methods [63].…”