scholarly journals Predicting threat of climate change to the Chinese grouse on the Qinghai—Tibet plateau

2014 ◽  
Vol 20 (2) ◽  
pp. 73-82 ◽  
Author(s):  
Nan Lyu ◽  
Yue-Hua Sun
Keyword(s):  
Climate Change ◽  
Tibet Plateau ◽  
Qinghai Tibet Plateau ◽  
Chinese Grouse

scholarly journals Spatial and Temporal Differences in Alpine Meadow, Alpine Steppe and All Vegetation of the Qinghai-Tibetan Plateau and Their Responses to Climate Change

2021 ◽  
Vol 13 (4) ◽  
pp. 669
Author(s):  
Hanchen Duan ◽  
Xian Xue ◽  
Tao Wang ◽  
Wenping Kang ◽  
Jie Liao ◽  
...  
Keyword(s):  
Climate Change ◽  
Alpine Meadow ◽  
Global Climate ◽  
Growing Season ◽  
Tibet Plateau ◽  
Vegetation Types ◽  
Analysis Method ◽  
Alpine Steppe ◽  
Qinghai Tibet Plateau ◽  
Variation Trends

Alpine meadow and alpine steppe are the two most widely distributed nonzonal vegetation types in the Qinghai-Tibet Plateau. In the context of global climate change, the differences in spatial-temporal variation trends and their responses to climate change are discussed. It is of great significance to reveal the response of the Qinghai-Tibet Plateau to global climate change and the construction of ecological security barriers. This study takes alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau as the research objects. The normalized difference vegetation index (NDVI) data and meteorological data were used as the data sources between 2000 and 2018. By using the mean value method, threshold method, trend analysis method and correlation analysis method, the spatial and temporal variation trends in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau were compared and analyzed, and their differences in the responses to climate change were discussed. The results showed the following: (1) The growing season length of alpine meadow was 145~289 d, while that of alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau was 161~273 d, and their growing season lengths were significantly shorter than that of alpine meadow. (2) The annual variation trends of the growing season NDVI for the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau increased obviously, but their fluctuation range and change rate were significantly different. (3) The overall vegetation improvement in the Qinghai-Tibet Plateau was primarily dominated by alpine steppe and alpine meadow, while the degradation was primarily dominated by alpine meadow. (4) The responses between the growing season NDVI and climatic factors in the alpine meadow, alpine steppe and the overall vegetation of the Qinghai-Tibet Plateau had great spatial heterogeneity in the Qinghai-Tibet Plateau. These findings provide evidence towards understanding the characteristics of the different vegetation types in the Qinghai-Tibet Plateau and their spatial differences in response to climate change.

Spatial variability of topsoil δ13C across Qinghai-Tibet Plateau

2021 ◽  
Author(s):  
Yunsen Lai ◽  
Shaoda Li ◽  
Xiaolu Tang ◽  
Xinrui Luo ◽  
Liang Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Spatial Variability ◽  
Soil Carbon ◽  
Tibet Plateau ◽  
Learning Approach ◽  
Carbon Turnover ◽  
Machine Learning Approach ◽  
Qinghai Tibet Plateau ◽  
Soil Carbon Turnover

<p>Soil carbon isotopes (δ<sup>13</sup>C) provide reliable insights at the long-term scale for the study of soil carbon turnover and topsoil δ<sup>13</sup>C could well reflect organic matter input from the current vegetation. Qinghai-Tibet Plateau (QTP) is called “the third pole of the earth” because of its high elevation, and it is one of the most sensitive and critical regions to global climate change worldwide. Previous studies focused on variability of soil δ<sup>13</sup>C at in-site scale. However, a knowledge gap still exists in the spatial pattern of topsoil δ<sup>13</sup>C in QTP. In this study, we first established a database of topsoil δ<sup>13</sup>C with 396 observations from published literature and applied a Random Forest (RF) algorithm (a machine learning approach) to predict the spatial pattern of topsoil δ<sup>13</sup>C using environmental variables. Results showed that topsoil δ<sup>13</sup>C significantly varied across different ecosystem types (p < 0.05).  Topsoil δ<sup>13</sup>C was -26.3 ± 1.60 ‰ for forest, 24.3 ± 2.00 ‰ for shrubland, -23.9 ± 1.84 ‰ for grassland, -18.9 ± 2.37 ‰ for desert, respectively. RF could well predict the spatial variability of topsoil δ<sup>13</sup>C with a model efficiency (pseudo R<sup>2</sup>) of 0.65 and root mean square error of 1.42. The gridded product of topsoil δ<sup>13</sup>C and topsoil β (indicating the decomposition rate of soil organic carbon, calculated by δ<sup>13</sup>C divided by logarithmically converted SOC) with a spatial resolution of 1000 m were developed. Strong spatial variability of topsoil δ<sup>13</sup>C was observed, which increased gradually from the southeast to the northwest in QTP. Furthermore, a large variation was found in β, ranging from -7.87 to -81.8, with a decreasing trend from southeast to northwest, indicating that carbon turnover rate was faster in northwest QTP compared to that of southeast. This study was the first attempt to develop a fine resolution product of topsoil δ<sup>13</sup>C for QTP using a machine learning approach, which could provide an independent benchmark for biogeochemical models to study soil carbon turnover and terrestrial carbon-climate feedbacks under ongoing climate change.</p>

scholarly journals The Impacts of Human Activities on Ecosystems within China’s Nature Reserves

2019 ◽  
Vol 11 (23) ◽  
pp. 6629
Author(s):  
Ping Zhu ◽  
Wei Cao ◽  
Lin Huang ◽  
Tong Xiao ◽  
Jun Zhai
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Arid Zone ◽  
Ecosystem Dynamics ◽  
Tibet Plateau ◽  
Warm Temperate Zone ◽  
Nighttime Light ◽  
Qinghai Tibet Plateau ◽  
Warm Temperate ◽  
Semi Arid

Protected areas (PAs) provide refuges for threatened species and are considered to be the most important approach to biodiversity conservation. Besides climate change, increasing human population is the biggest threat to biodiversity and habitats in PAs. In this paper, the temporal and spatial variations of land cover changes (LCC), vegetation fraction (VFC), and net primary productivity (NPP) were studied to present the ecosystem dynamics of habitats in 6 different types of national nature reserves (NNRs) in 8 climate zones in China. Furthermore, we used Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light datasets and the human disturbance (HD) index estimated from LCC to quantify the living and developing human pressures within the NNRs in the period 2000–2013. The results showed that (1) the living human activities of NNRs increased apparently in the humid warm-temperate zone, Qinghai-Tibet Plateau, mid-temperate semi-arid zone, and mid-temperate humid zone, with the highest increase of nighttime light observed in inland wetlands; (2) the developing human activities in NNRs indicated by the HD index were higher in the humid warm-temperate zone and mid-temperate semi-arid zone as a result of increasing areas of agricultural and built activities, and lower in the sub-tropics due to improved conservation of forest ecosystems; (3) the relationship between HD and VFC suggests that ecosystems in most NNRs of south-subtropics, mid-temperate arid zone and Qinghai-Tibet Plateau were predominantly impacted by climate change. However, HDs were the prevalent factor of ecosystem dynamics in most NNRs of north-subtropics, mid-temperate semi-arid and humid zones.

Potential CO2 emissions from defrosting permafrost soils of the Qinghai-Tibet Plateau under different scenarios of climate change in 2050 and 2070

CATENA ◽  
2017 ◽  
Vol 149 ◽  
pp. 221-231 ◽  
Author(s):  
Anna Bosch ◽  
Karsten Schmidt ◽  
Jin-Sheng He ◽  
Corina Doerfer ◽  
Thomas Scholten
Keyword(s):  
Climate Change ◽  
Co2 Emissions ◽  
Tibet Plateau ◽  
Permafrost Soils ◽  
Qinghai Tibet Plateau
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