scholarly journals Spatio-Temporal Characteristics of Global Warming in the Tibetan Plateau during the Last 50 Years Based on a Generalised Temperature Zone - Elevation Model

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e60044 ◽  
Author(s):  
Yanqiang Wei ◽  
Yiping Fang
Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.


Author(s):  
Cheryl Colopy

From a remote outpost of global warming, a summons crackles over a two-way radio several times a week: . . . Kathmandu, Tsho Rolpa! Babar Mahal, Tsho Rolpa! Kathmandu, Tsho Rolpa! Babar Mahal, Tsho Rolpa! . . . In a little brick building on the lip of a frigid gray lake fifteen thousand feet above sea level, Ram Bahadur Khadka tries to rouse someone at Nepal’s Department of Hydrology and Meteorology in the Babar Mahal district of Kathmandu far below. When he finally succeeds and a voice crackles back to him, he reads off a series of measurements: lake levels, amounts of precipitation. A father and a farmer, Ram Bahadur is up here at this frigid outpost because the world is getting warmer. He and two colleagues rotate duty; usually two of them live here at any given time, in unkempt bachelor quarters near the roof of the world. Mount Everest is three valleys to the east, only about twenty miles as the crow flies. The Tibetan plateau is just over the mountains to the north. The men stay for four months at a stretch before walking down several days to reach a road and board a bus to go home and visit their families. For the past six years each has received five thousand rupees per month from the government—about $70—for his labors. The cold, murky lake some fifty yards away from the post used to be solid ice. Called Tsho Rolpa, it’s at the bottom of the Trakarding Glacier on the border between Tibet and Nepal. The Trakarding has been receding since at least 1960, leaving the lake at its foot. It’s retreating about 200 feet each year. Tsho Rolpa was once just a pond atop the glacier. Now it’s half a kilometer wide and three and a half kilometers long; upward of a hundred million cubic meters of icy water are trapped behind a heap of rock the glacier deposited as it flowed down and then retreated. The Netherlands helped Nepal carve out a trench through that heap of rock to allow some of the lake’s water to drain into the Rolwaling River.


2020 ◽  
Author(s):  
Zuonan Cao ◽  
Peter Kühn ◽  
Thomas Scholten

<p>The Tibetan Plateau is the third-largest glaciated area of the world and is one of the most sensitive regions due to climate warming, such as fast-melting permafrost, dust blow and overgrazing in recent decades. In the past 50 years, the warming rate on the Tibetan Plateau is higher than the global average warming rate with 0.40 ± 0.05 °C per decade. The climate warming is most distinct in the northeastern Tibetan Plateau, implying increasing air and surface temperatures as well as duration and depth of thawing. The main ecological consequences are a disturbed vegetation cover of the surface and a depletion of nutrient-rich topsoils (Baumann et al., 2009, 2014) coupled with an increase of greenhouse gas emissions, mainly CO<sub>2</sub> (Bosch et al., 2017). Due to the extreme environmental conditions resulting from the intense and rapid tectonic uplift, highly adaptive and sensitive ecosystem have developed, and the Plateau is considered to be a key area for the environmental evolution of Earth on regional and global scales, which is particularly sensitive to global warming (Jin et al., 2007; Qiu, 2008). Climate warming and land-use change can reduce soil organic carbon (SOC) stocks as well as soil nitrogen (N) and phosphorus (P) contents and soil quality. Many species showed their distributions by climate-driven shifts towards higher elevation. In Tibetan Plateau, however, the elevational variations of the alpine grassland are rare (Huang et al., 2018) and it is largely unknown how the grass line will respond to global warming and whether soils play a major role. With this research, the hypothesis is tested that soil quality, given by SOC, N and P stocks and content, is a driving factor for the position and structure of the grass line and that soil quality is one of the major controls of biodiversity and biomass production in high-mountain grassland ecosystems.</p><p>A Fourier transformation near and mid-infrared spectroscopy (FT-NMIRS) should be used to measure soil P fractions rapid and for large numbers of soil samples, and analyze environmental factors, including temperature, precipitation, soil development, soil fertility, and the ability of plants to adapt to the environmental impact of climate using FT-NMIRS.</p><p>We explored first near-infrared spectroscopy (NIRS) in soils from grassland on the Tibetan Plateau, northwestern China and extracted P fractions of 196 samples from Haibei Alpine Meadow Ecosystem Research Station, Chinese Academy of Sciences, at four depths increments (0-10 cm 10-20 cm 20-40 cm and 40-70 cm) with different pre-nutrient additions of nitrogen (N) an P. The fractionation data were correlated with the corresponding NIRS soil spectra and showed significant differences for depth increments and fertilizer amendments. The R<sup>2</sup> of NIRS calibrations to predict P in traditional Hedley fractions ranged between 0.12 and 0.90. The model prediction quality was higher for organic than for inorganic P fractions and changed with depth and fertilizer amendment. The results indicate that using NIRS to predict the P fractions can be a promising approach compared with traditional Hedley fractionation for soils in alpine grasslands on the Tibetan Plateau.</p>


2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen

<p><strong>Abstract</strong>:</p><p>Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002–2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25° resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R<sup>2</sup> larger than 0.65, and RMSE less than 0.08 cm<sup>3</sup> cm<sup>-3</sup> and Bias less than 0.07 cm<sup>3</sup> cm<sup>-3 </sup>at 0.25° and 1° spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial and temporal characteristics and trend of SM over TP were analyzed based on this dataset.</p><p><strong>Keywords: </strong>Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau</p>


2014 ◽  
Vol 7 (1) ◽  
pp. 169-194 ◽  
Author(s):  
Wei Wang ◽  
Xiaodong Huang ◽  
Jie Deng ◽  
Hongjie Xie ◽  
Tiangang Liang

2017 ◽  
Vol 12 (1) ◽  
pp. 014011 ◽  
Author(s):  
Lulu Song ◽  
Qianlai Zhuang ◽  
Yunhe Yin ◽  
Xudong Zhu ◽  
Shaohong Wu

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