Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau

Wang, Taihua; Yang, Dawen; Fang, Beijing; Yang, Wencong; Qin, Yue; Wang, Yuhan

doi:10.1016/j.scitotenv.2018.08.369

Cited by 62 publications

(44 citation statements)

References 49 publications

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“…ac.uk/cru/data/hrg/cru_ts_4.01/) with 0.5° resolution for regions outside China. The original coarse resolution air temperature was resampled to a 1-km spatial resolution with an elevation adjustment (33) T adjusted = T orginal + ( ELEV 1km − ELEV coarse ) × TLR (1) where ELEV 1km is the elevation from the Shuttle Radar Topography Mission (SRTM) version 4 global 90-m product (http://srtm.csi. cgiar.org/srtmdata/) resampled to 1-km resolution, ELEV coarse is the elevation at the coarse resolution of 0.1° (CMFD) or 0.5° (CRU), and TLR is the air temperature lapse rate of a certain month (°C km −1 ).…”

Section: Geospatial Distributed Datamentioning

confidence: 99%

“…cgiar.org/srtmdata/) resampled to 1-km resolution, ELEV coarse is the elevation at the coarse resolution of 0.1° (CMFD) or 0.5° (CRU), and TLR is the air temperature lapse rate of a certain month (°C km −1 ). The values of TLR for different months were calculated by a linear regression method based on the observations at the meteorological stations across TP (33) with the mean annual TLR of −4.2°C km −1 . Freezing index (FI) and thawing index (TI) (°C days) were then calculated as the cumulative sum of the monthly air temperature below and above 0°C multiplied by the number of days in the corresponding month.…”

Section: Geospatial Distributed Datamentioning

confidence: 99%

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Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau

et al. 2020

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Soil organic carbon (SOC) stored in permafrost across the high-latitude/altitude Northern Hemisphere represents an important potential carbon source under future warming. Here, we provide a comprehensive investigation on the spatiotemporal dynamics of SOC over the high-altitude Tibetan Plateau (TP), which has received less attention compared with the circum-Arctic region. The permafrost region covers ~42% of the entire TP and contains ~37.21 Pg perennially frozen SOC at the baseline period (2006–2015). With continuous warming, the active layer is projected to further deepen, resulting in ~1.86 ± 0.49 Pg and ~3.80 ± 0.76 Pg permafrost carbon thawing by 2100 under moderate and high representative concentration pathways (RCP4.5 and RCP8.5), respectively. This could largely offset the regional carbon sink and even potentially turn the region into a net carbon source. Our findings also highlight the importance of deep permafrost thawing that is generally ignored in current Earth system models.

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Section: Geospatial Distributed Datamentioning

confidence: 99%

Section: Geospatial Distributed Datamentioning

confidence: 99%

Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau

et al. 2020

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“…Statistical models, e.g., decision tree or logistic regression models, have been applied to Tibetan Plateau permafrost mapping through building relationship between permafrost indices and multiple environmental factors including satellitebased land surface temperature and snow cover extent products. More recent studies are also developed using deep learning methods to create Tibetan Plateau permafrost maps (Wang et al, 2019a;Huang et al, 2020). Empirical models such as the TTOP (temperature at the top of permafrost) model and Kudryavtsev model that are derived from simplified heat transfer equations and require fewer inputs have been also widely used in Tibetan Plateau permafrost mapping (Zhao et al, 2017a;Zou et al, 2017).…”

Section: Permafrost Monitoring Using Optical and Infrared Remote Sensingmentioning

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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“…CC BY 4.0 License. the permafrost extent on the entire TP is 45.9% (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) and they predict future permafrost degradation of 25.9% by the 2040s and 43.9% by the 2090s (Wang et al, 2019). Cheng and Wu (2007) also conclude that more than "half of the permafrost may become relict and/or even disappear by 2100".…”

Section: Introductionmentioning

confidence: 97%

Insights in a remote cryosphere: A multi method approach to assess permafrost occurrence at the Qugaqie basin, western Nyainqêntanglha Range, Tibetan Plateau

Buckel¹,

Reinosch²,

Hördt³

et al. 2020

Preprint

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Abstract. Permafrost as a climate-sensitive parameter, its occurrence and distribution plays an important role in the observation of global warming. However, field-based permafrost distribution data and information on the subsurface ice content at the large area of the southern mountainous Tibetan Plateau (TP) is very sparse. Existing models based on boreholes and remote sensing approaches suggest permafrost probabilities for most of the Tibetan mountain ranges. Field data to validate permafrost models are generally lacking because access of the mountain regions in extreme altitudes is limited. The study provides geomorphological and geophysical field data from a north-orientated high-altitude catchment in the western Nyainqêntanglha Range. Our multi-method-approach combines (A) geomorphological mapping data, (B) subsurface ice-occurrence derived from electrical resistivity tomography (ERT) data, and (C) multi-annual displacement rates from Interferometric Synthetic Aperture Radar (InSAR) analysis to assess the lower occurrence of the probable permafrost around 5400 m a.s.l. in the Qugaqie basin. Periglacial landforms such as rockglaciers and protalus ramparts are located in the periglacial zone from 5300–5600 a.s.l. The altitudinal periglacial landform distribution is supported by ERT data detecting ice-rich permafrost in a rockglacier at 5500 m a.s.l. and ice lenses around the rockglacier (5450 m a.s.l.). The highest, multiannual displacement rates up to 150 mm/y are observed typically on these rockglaciers. However, seasonality of rockglacier surface displacement like in other high mountain areas is missing. This study closes the gap of unknown state of periglacial features and potential permafrost occurrence in a high-elevated basin at the western Nyainqêntanglha Range (Tibetan Plateau) and suppose – compared to other high mountain regions – a higher-elevated permafrost occurrence.

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Data-driven mapping of the spatial distribution and potential changes of frozen ground over the Tibetan Plateau

Cited by 62 publications

References 49 publications

Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau

Permafrost thawing puts the frozen carbon at risk over the Tibetan Plateau

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

Insights in a remote cryosphere: A multi method approach to assess permafrost occurrence at the Qugaqie basin, western Nyainqêntanglha Range, Tibetan Plateau

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