Interrelation among climate factors, snow cover, grassland vegetation, and lake in the Nam Co basin of the Tibetan Plateau

Chen, Siyu; Liang, Tiangang; Xie, Hongjie; Feng, Qisheng; Huang, Xiaodong; Yu, Hongbin

doi:10.1117/1.jrs.8.084694

Cited by 15 publications

(14 citation statements)

References 49 publications

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“…There is an inverted U-shaped relationship between the interannual NDVI trend and the shortest distance to lakes, that is, a positive correlation within twenty kilometers and negative correlation when exceeding twenty kilometers, which has also been found in other studies [70]. Li et al [71] and Chen et al [72] believed that there were two possible reasons for explaining the relationships. On the one hand, in summer, the lake effect causes a decrease in temperature in the vicinity of the lakes, which leads to an unsuitable temperature for the vegetation growth.…”

Section: The Relationships Between Interannual Ndvi Trends and Envirosupporting

confidence: 75%

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

Cheng

Guo

et al. 2019

Remote Sensing

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The ecosystem of extra-high mountain areas is very fragile. Understanding local vegetation changes is crucial for projecting ecosystem dynamics. In this paper, we make a case for Himalayan mountain areas to explore vegetation dynamics and their influencing factors. Firstly, the interannual trends of the normalized difference vegetation index (NDVI) were extracted by the Ensemble Empirical Mode Decomposition (EEMD) algorithm and linear regression method. Moreover, the influence of environmental factors on interannual NDVI trends was assessed using the Random Forests algorithm and partial dependence plots. Subsequently, the time-lag effects of seasonal NDVI on different climatic factors were discussed and the effects of these factors on seasonal NDVI changes were determined by partial correlation analysis. The results show that (1) an overall weak upward trend was observed in NDVI variations from 1982 to 2015, and 1989 is considered to be the breakpoint of the NDVI time series; (2) interannual temperature trends and the shortest distance to large lakes were the most important factors in explaining interannual NDVI trends. Temperature trends were positively correlated with NDVI trends. The relationship between the shortest distance to large lakes and the NDVI trend is an inverted U-shaped; (3) the time-lags of NDVI responses to four climatic factors were shorter in Autumn than that in Summer. The NDVI responds quickly to precipitation and downward long-wave radiation; (4) downward long-wave radiation was the main climate factor that influenced NDVI changes in Autumn and the growing season because of the warming effect at night. This study is important to improve the understanding of vegetation changes in mountainous regions.

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Section: The Relationships Between Interannual Ndvi Trends and Envirosupporting

confidence: 75%

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

Cheng

Guo

et al. 2019

Remote Sensing

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“…The dense distribution of lakes is a major feature of the QTP, and the total lake area accounts for about half of the China's total lake area (Zhu et al, 2010). Lakes are an important component of terrestrial hydrosphere, exchanging heat and the climate driving force to changes in lake, as well as the evaluation of regional ecological environment and sustainable development (Chen et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

The response of lake area and vegetation cover variations to climate change over the Qinghai-Tibetan Plateau during the past 30 years

Zhang

Chang

et al. 2018

Science of The Total Environment

137

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Lakes and vegetation are important factors of the Earth's hydrological cycle and can be called an "indicator" of climate change. In this study, long-term changes of lakes' area and vegetation coverage in the Qinghai-Tibetan Plateau (QTP) and their relations to the climate change were analyzed by using Mann-Kendall method during the past 30years. Results showed that: 1) the lakes' area of the QTP increased significantly during the past 30years as a whole, and the increasing rates have been dramatically sped up since the year of 2000. Among them, the area of Ayakekumu Lake has the fastest growing rate of 51.35%, which increased from 618km in the 1980s to 983km in the 2010s; 2) overall, the Normalized Difference Vegetation Index (NDVI) increased in the QTP during the past 30years. Above 79% of the area in the QTP showed increasing trend of NDVI before the year of 2000; 3) the air temperature increased significantly, the precipitation increased slightly, and the pan evaporation decreased significantly during the past 30years. The lake area and vegetation coverage changes might be related to the climate change. The shifts in the temporal climate trend occurred around the year 2000 had led the lake area and vegetation coverage increasing. This study is of importance in further understanding the environmental changes under global warming over the QTP.

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“…The Tibetan Plateau (TP), known as "the roof of the world", "the third pole" and "the water tower of Asia", is the largest plateau in China and the highest plateau in the world [5,15], and it is considered the perfect location to identify the effects of global climate change [16][17][18]. Qinghai Lake is the largest inland saline lake on TP, and it has attracted extensive attention due to its special geographical location and its wide area characterized by fragile ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Long-Term Water Level Variations in Qinghai Lake in China

Fang

Rubinato

et al. 2019

Water

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Qinghai Lake is the largest inland saline lake on the Tibetan Plateau. Climate change and catchment modifications induced by human activities are the main drivers playing a significant role in the dramatic variation of water levels in the lake (Δh); hence, it is crucial to provide a better understanding of the impacts caused by these phenomena. However, their respective contribution to and influence on water level variations in Qinghai Lake are still unclear and without characterizing them, targeted measures for a more efficient conservation and management of the lake cannot be implemented. In this paper, data monitored during the period 1960–2016 (e.g., meteorological and land use data) have been analyzed by applying multiple techniques to fill this gap and estimate the contribution of each parameter recorded to water level variations (Δh). Results obtained have demonstrated that the water level of Qinghai Lake declined between 1960 and 2004, and since then has risen continuously and gradually, due to the changes in evaporation rates, precipitation and consequently surface runoff associated with climate change effects and catchment modifications. The authors have also pinpointed that climate change is the main leading cause impacting the water level in Qinghai Lake because results demonstrated that 93.13% of water level variations can be attributable to it, while the catchment modifications are responsible for 6.87%. This is a very important outcome in the view of the fact that global warming clearly had a profound impact in this sensitive and responsive region, affecting hydrological processes in the largest inland lake of the Tibetan Plateau.

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Interrelation among climate factors, snow cover, grassland vegetation, and lake in the Nam Co basin of the Tibetan Plateau

Cited by 15 publications

References 49 publications

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

Interannual and Seasonal Vegetation Changes and Influencing Factors in the Extra-High Mountainous Areas of Southern Tibet

The response of lake area and vegetation cover variations to climate change over the Qinghai-Tibetan Plateau during the past 30 years

Analysis of Long-Term Water Level Variations in Qinghai Lake in China

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