Mapping Frozen Ground in the Qilian Mountains in 2004–2019 Using Google Earth Engine Cloud Computing

Abstract

The permafrost in the Qilian Mountains (QLMs), the northeastern margin of the Qinghai–Tibet Plateau, changed dramatically in the context of climate warming and increasing anthropogenic activities, which poses significant influences on the stability of the ecosystem, water resources, and greenhouse gas cycles. Yet, the characteristics of the frozen ground in the QLMs are largely unclear regarding the spatial distribution of active layer thickness (ALT), the maximum frozen soil depth (MFSD), and the temperature at the top of the permafrost or the bottom of the MFSD (TTOP). In this study, we simulated the dynamics of the ALT, TTOP, and MFSD in the QLMs in 2004–2019 in the Google Earth Engine (GEE) platform. The widely-adopted Stefan Equation and TTOP model were modified to integrate with the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) in GEE. The N-factors, the ratio of near-surface air to ground surface freezing and thawing indices, were assigned to the freezing and thawing indices derived with MODIS LST in considerations of the fractional vegetation cover derived from MODIS normalized difference vegetation index (NDVI). The results showed that the GEE platform and remote sensing imagery stored in Google cloud could be quickly and effectively applied to obtain the spatial and temporal variation of permafrost distribution. The area with TTOP < 0 °C is 8.4 × 104 km2 (excluding glaciers and lakes) and accounts for 46.6% of the whole QLMs, the regional mean ALT is 2.43 ± 0.44 m, while the regional mean MFSD is 2.54 ± 0.45 m. The TTOP and ALT increase with the decrease of elevation from the sources of the sub-watersheds to middle and lower reaches. There is a strong correlation between TTOP and elevation (slope = −1.76 °C km−1, p < 0.001). During 2004–2019, the area of permafrost decreased by 20% at an average rate of 0.074 × 104 km2·yr−1. The regional mean MFSD decreased by 0.1 m at a rate of 0.63 cm·yr−1, while the regional mean ALT showed an exception of a decreasing trend from 2.61 ± 0.45 m during 2004–2005 to 2.49 ± 0.4 m during 2011–2015. Permafrost loss in the QLMs in 2004–2019 was accelerated in comparison with that in the past several decades. Compared with published permafrost maps, this study shows better calculation results of frozen ground in the QLMs.

Biochemical composition and function of subalpine shrubland and meadow soil microbiomes in the Qilian Mountains, Qinghai–Tibetan plateau, China

“…The Qilian Mountains, on the northeastern edge of the Qinghai–Tibetan Plateau, play a significant role in retaining water, mitigating climate change, and maintaining biodiversity ( Qian et al, 2019 ). Shrublands, comprising evergreen shrubs and alpine deciduous shrubs, and meadows are the primary vegetation type in high elevation regions ( Qi et al, 2021 ). A combination of environmental factors and anthropogenic activities have substantially reduced shrubland area since the 1950s ( Sun et al, 2016 ).…”

Influence of Open-Pit Coal Mining on Ground Surface Deformation of Permafrost in the Muli Region in the Qinghai-Tibet Plateau, China

“…The Qinghai-Tibet Plateau (QTP) is the largest area of permafrost in the mid-to low latitude and with the highest elevation across the world [3]. With increasing global warming, the permafrost in the QTP has been degrading at a large scale [4][5][6][7][8][9][10]. The landscape structure and hydrothermal regimes of underlying permafrost have changed due to extensive human activities such as mining development, engineering construction, and urban expansion, which affect the soil water content and heat exchange process and intensify the degradation of permafrost [11][12][13][14].…”

A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province

“…In recent years, under the influence of global warming, the climate of Qinghai has been warming and humidifying, glaciers and snowfields are shrinking year by year, rivers, lakes, and wetlands are shrinking, soil erosion is expanding, and the water-conserving function is deteriorating seriously. Soil moisture is an important surface characteristic parameter and has an irreplaceable role in related land degradation, drought monitoring, and water conservation monitoring [21]. Therefore, an urgent need exists to systematically monitor the soil moisture information in Qinghai Province, which is an area that seriously lacks ground truth data, making the use of remote sensing data to retrieve SM in Qinghai Province of significant potential value.…”