Characteristics of ground surface temperature at Chalaping in the Source Area of the Yellow River, northeastern Tibetan Plateau

Luo, Dejun; Liu, Lei; Jin, Huijun; Wang, X. F.; Chen, Fangfang

doi:10.1016/j.agrformet.2019.107819

Cited by 69 publications

(36 citation statements)

References 56 publications

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“…In addition, although using ground surface temperature as upper boundary conditions can reduce the need of calculating surface energy balance budget, those data may not be widely available at the regional scale. The remotely sensed land surface temperature can be used as a substitute, but the difference between ground surface temperature and land surface temperature can be large due to the buffering effects of surface vegetation or snow cover (Luo et al, 2018;Luo et al, 2020).…”

Section: Current Progressmentioning

confidence: 99%

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

Jiang

Zheng

et al. 2020

Front. Earth Sci.

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Recent climate change has induced widespread soil thawing and permafrost degradation in the Tibetan Plateau. Significant advances have been made in better characterizing Tibetan Plateau soil freeze/thaw dynamics, and their interaction with local-scale ecohydrological processes. However, factors such as sparse networks of in-situ sites and short observational period still limit our understanding of the Tibetan Plateau permafrost. Satellite-based optical and infrared remote sensing can provide information on land surface conditions at high spatial resolution, allowing for better representation of spatial heterogeneity in the Tibetan Plateau and further infer the related permafrost states. Being able to operate at “all-weather” conditions, microwave remote sensing has been widely used to retrieve surface soil moisture, freeze/thaw state, and surface deformation, that are critical to understand the Tibetan Plateau permafrost state and changes. However, coarse resolution (>10 km) of current passive microwave sensors can add large uncertainties to the above retrievals in the Tibetan Plateau area with high topographic relief. In addition, current microwave remote sensing methods are limited to detections in the upper soil layer within a few centimetres. On the other hand, algorithms that can link surface properties and soil freeze/thaw indices to permafrost properties at regional scale still need improvements. For example, most methods using InSAR (interferometric synthetic aperture radar) derived surface deformation to estimate active layer thickness either ignore the effects of vertical variability of soil water content and soil properties, or use site-specific soil moisture profiles. This can introduce non-negligible errors when upscaled to the broader Tibetan Plateau area. Integrating satellite remote sensing retrievals with process models will allow for more accurate representation of Tibetan Plateau permafrost conditions. However, such applications are still limiting due to a number of factors, including large uncertainties in current satellite products in the Tibetan Plateau area, and mismatch between model input data needs and information provided by current satellite sensors. Novel approaches to combine diverse datasets with models through model initialization, parameterization and data assimilation are needed to address the above challenges. Finally, we call for expansion of local-scale observational network, to obtain more information on deep soil temperature and moisture, soil organic carbon content, and ground ice content.

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Section: Current Progressmentioning

confidence: 99%

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

Jiang

Zheng

et al. 2020

Front. Earth Sci.

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“…The study area is located in the transitional zone of seasonally frozen ground and sporadic, discontinuous permafrost at the northeast boundary of the permafrost region of the QTP (Jin et al, 2009; Luo, Jin, Wu, et al, 2018). The vegetation is dwarfed and herbaceous and dominated by paludal and typical alpine meadows and steppes (Jin et al, 2009; Luo et al, 2014; Luo et al, 2020). The distribution of frozen ground is more complex, and its vertical distribution is diverse.…”

Section: Study Area and Data Setsmentioning

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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“…Therefore, we calculated the fractional vegetation cover (FVC) with the MODIS normalized difference vegetation index (NDVI) and then assigned the N-factors to each grid point in consideration of the FVC. In this study, we first reclassified the NDVI into five classes (0-0.2, 0.2-0.4, 0.4-0.6, 0.6-0.8, and 0.8-1.0), and then assigned the thawing N-factors of 0.5, 0.6, 0.7, 0.8, and 0.9, as well as freezing N-factors of 1.5, 1.2, 1.0, 0.9, and 0.8, to each grid point in consideration of vegetation differences, as the freezing and thawing N-factors for the surface characteristics on the TP are generally within 0.5-0.8 and 0.9-1.5, respectively [72,75,77]. The FVC is computed as follows:…”

Section: Surface Vegetation and The N-factorsmentioning

confidence: 95%

“…Here, FI L and TI L are the freezing and thawing indices derived from LST products, respectively; T s is the nominal daily LST; T f is the freezing point (usually defined as 0 • C); and T is the average daily LST. As a result of the complex influence of surface characteristics, the mean annual ground surface temperature varies greatly even in the same geographical unit with consistent elevation and similar vegetation [72]. The insulating effects of snow cover in the winter and vegetation cover in the summer cause a large temperature deviation between ground surface temperature and the near-surface or land surface temperatures.…”

Section: Data 231 Modis Lstmentioning

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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Characteristics of ground surface temperature at Chalaping in the Source Area of the Yellow River, northeastern Tibetan Plateau

Cited by 69 publications

References 56 publications

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

Progress and Challenges in Studying Regional Permafrost in the Tibetan Plateau Using Satellite Remote Sensing and Models

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

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

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