Characteristic, changes and impacts of permafrost on Qinghai-Tibet Plateau

Guodong, Cheng; Zhao, Lin; Ren, Li; Wu, Xiaodong; Sheng, Yu; Hu, Guojie; Zou, Defu; Jin, Huijun; Li, Xin; Wu, Qingbai

doi:10.1360/tb-2019-0191

Cited by 178 publications

(48 citation statements)

References 40 publications

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“…Due to the widespread distribution of frozen ground across the QTP and its important role in the ecological environment of this region, the permafrost degradation has attracted much attention (Cheng et al, 2019;Cheng & Wu, 2007;Hu et al, 2019;Wu et al, 2010). The impacts of permafrost and seasonally frozen ground degradation on hydrological runoff are gradual processes, but the influence of seasonally frozen ground has been more pronounced than that of permafrost (Cuo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Improving Permafrost Physics in a Distributed Cryosphere‐Hydrology Model and Its Evaluations at the Upper Yellow River Basin

Song

Wang

et al. 2020

JGR Atmospheres

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Frozen soil undergoing freeze-thaw cycles has effects on local hydrology, ecosystems, and engineering infrastructure by global warming. It is important to clarify the hydrological processes of frozen soil, especially permafrost. In this study, the performance of a distributed cryosphere-hydrology model (WEB-DHM, Water and Energy Budget-based Distributed Hydrological Model) was significantly improved by the addition of enthalpy-based permafrost physics. First, we formulated the water phase change in the unconfined aquifer and its exchanges of water and heat with the upper soil layers, with enthalpy adopted as a prognostic variable instead of soil temperature in the energy balance equation to avoid instability when calculating water phase changes. Second, more reasonable initial conditions for the bottom soil layer (overlying the unconfined aquifer) were considered. The improved model (hereinafter WEB-DHM-pf) was carefully evaluated at three sites with seasonally frozen ground and one permafrost site over the Qinghai-Tibetan Plateau ("the Third Pole"), to demonstrate the capability of predicting the internal processes of frozen soil at the point scale, particularly the "zero-curtain" phenomenon in permafrost. Four different experiments were conducted to assess the impacts of augmentation of single model improvement on simulating soil water/ice and temperature dynamics in frozen soil. Finally, the WEB-DHM-pf was demonstrated to be capable of accurately reproducing the zero curtain, detecting long-term changes in frozen soil at the point scale, and discriminating basin-wide permafrost from seasonally frozen ground in a basin at the headwaters of the Yellow River.

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Section: Introductionmentioning

confidence: 99%

Improving Permafrost Physics in a Distributed Cryosphere‐Hydrology Model and Its Evaluations at the Upper Yellow River Basin

Song

Wang

et al. 2020

JGR Atmospheres

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“…ALT also continued to increase at sites located in permafrost regions of the hinterland of the QTP by about 0.2 m decade −1 since the 1980s (Fig. 2.11; Cheng et al 2019;Zhao et al 2019). In 2019, ALT was, on average, slightly smaller in the QTP than in 2018 (0.02 m).…”

Section: ) Tropospheric Temperature-jr Christy C a Mears S Pomentioning

confidence: 94%

“…At 20-m depth, ground temperatures have risen to 0°C. Borehole temperatures measured in the hinterland of the QTP showed remarkable warming tendencies with variable rates that are highest in lowertemperature permafrost (Cheng et al 2019;Sun et al 2019).…”

Section: ) Tropospheric Temperature-jr Christy C a Mears S Pomentioning

confidence: 99%

Global Climate

Barichivich¹,

Osborn²,

Harris³

et al. 2020

Bulletin of the American Meteorological Society

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“…The thermal state of frozen ground was usually represented by two key indicators: the mean annual temperature at the top of the permafrost or the bottom of maximum frozen soil depth (MFSD) (TTOP) (usually at depth of 1-3 m), and the temperature at the depth of zero annual amplitude (TZAA) (with temperature amplitude within 0.1 • C and usually at the depth of 10-25 m) [1,[4][5][6][7]. Over the past three or four decades, permafrost has experienced wide degradation in the context of climate warming and increasing anthropogenic activities, which is characterized by rising of ground temperatures, increasing of active layer thickness (ALT), decreasing of maximum frozen soil depth (MFSD), as well as the disappearance of isolated permafrost [3,[8][9][10][11][12][13]. The variation in permafrost exerts significant influences on the alterations of hydrological cycles [14][15][16], thermokarst-inducing processes [17], exacerbation of biogeochemical cycles [18,19], changes in landscapes and geomorphologies [17,20], as well as losses of stabilization of surface hydrothermal dynamics [21,22].…”

Section: Introductionmentioning

confidence: 99%

“…The traditional permafrost research was based mainly on long-term field observations [4,10,36], indirect obtaining of the thermal state of permafrost with freezing and thawing indices on the ground surface [37,38], and sometimes numerical simulations and machine learning [39,40]. The traditional technical processes of remote sensing analysis are to first acquire and store data, and then perform preprocessing, extract information, analyze geoscientific issues, and extract thematic information using relevant algorithm and methods.…”

Section: Introductionmentioning

confidence: 99%

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

Yuan

Li²,

Ran

et al. 2021

Remote Sensing

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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.

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Characteristic, changes and impacts of permafrost on Qinghai-Tibet Plateau

Cited by 178 publications

References 40 publications

Improving Permafrost Physics in a Distributed Cryosphere‐Hydrology Model and Its Evaluations at the Upper Yellow River Basin

Improving Permafrost Physics in a Distributed Cryosphere‐Hydrology Model and Its Evaluations at the Upper Yellow River Basin

Global Climate

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

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