Linkage of Climatic Factors and Human Activities with Water Level Fluctuations in Qinghai Lake in the Northeastern Tibetan Plateau, China

Chang, Bo Yoon; He, Kuifeng; Li, Runjie; Sheng, Zhuping; Wang, Hui

doi:10.3390/w9070552

Cited by 31 publications

(18 citation statements)

References 32 publications

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“…The lake water budget is mainly contributed by precipitation and runoff from the five largest rivers: the Buha River, the Shaliu River, the Haergai River, the Ganzi River, and the Daotang River. The largest 286‐km‐long Buha river contributes up to 46.9% of surface runoff to the lake, while the second‐largest Shaliu River contributes around 14.5% (Chang et al, 2017). About 90% of the annual rainfall in the lake basin takes place between May and September (G. Q. Zhang et al, 2011).…”

Section: Study Area and Datamentioning

confidence: 99%

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Wen

Gao

et al. 2020

JGR Atmospheres

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Qinghai Lake is the largest lake in China. However, its influence on the local climate remains poorly understood. By using an atmosphere-lake coupled model, we investigated the impact of the lake on the local climate. After the adjustment of four key parameters, the model reasonably reproduced the lake-air interaction. Superimposed by the orographic effects on lake-land breeze circulation, the presence of the lake enhanced precipitation over the southern part of the lake and its adjacent land, while slightly reduced precipitation along the northern shore of the lake. The lake effect on local precipitation revealed a distinct seasonal and diurnal variability, reducing precipitation in May (−6.6%) and June (−4.5%) and increasing it from July (5.7%) to November (125.6%). During the open water season, the lake's daytime cooling effect weakened and the nighttime warming effect strengthened, affecting spatial distribution and intensity of lake-induced precipitation. In early summer, precipitation slightly decreased over the north part of the lake due to the lake's daytime cooling. In turn, lake-induced nighttime warming increased precipitation over the southern section of the lake and its adjacent land. With the start of the autumn cooling in September, heat and moisture fluxes from the lake resulted in precipitation increase in both daytime and nighttime over the entire lake. In October, the background atmospheric circulation coupled with the strong lake effects lead to a small amount but high proportion of lake-induced precipitation spreading evenly over the lake.

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Section: Study Area and Datamentioning

confidence: 99%

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Wen

Gao

et al. 2020

JGR Atmospheres

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“…For example, Han et al [23] found that the variation of precipitation and the operation of the Three Gorge Reservoir mainly affected the variation of water level in the Dongting Lake region. However, Chang et al [24] revealed that the water level of Qinghai lake depended more on climatic factors than on anthropogenic factors. Using the Cryosat-2 SARIn model data from 2010 to 2015, Jiang et al [21] revealed that the driving factors for water level change in the Qinghai-Tibet Plateau were variable and the autumn/winter temperature played an important role in the lake water level change.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variation and climatic response of water level of major lakes in China, Mongolia, and Russia

Tan

Liu

2020

Open Geosciences

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Lakes are important indicators of climate change. The change in lake water level objectively reflects the availability of regional water resources. Analyzing the changes in water level and climate response of major lakes in countries along the “Belt and Road” is essential for sustainable water use and ecological protection. Based on the water level datasets of 39 large lakes (>400 km2) in China, Mongolia, and Russia (CMR) from 2002 to 2016, this study analyzed the spatiotemporal characteristics of water levels in major lakes of CMR, and their responses to climatic factors containing temperature, precipitation, and evapotranspiration. The results showed that (1) the water level of main lakes in CMR slightly increased with change rates ranged from −0.36 to 0.48 m/a, and the trends varied in lakes, (2) the water level of most lakes was sensitive to temperature with sensitivity value ranged from −2.14 m/°C to 5.59 m/°C, (3) changes of annual cumulative precipitation and evapotranspiration contributed most to the change of lake water level, but key factors affecting water level varied in lakes. Human activity is an important driving factor for the change in water levels and its impacts need further study.

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“…Models play a significant role in assessment of water yield, because they can estimate and simulate the spatial distribution of water yield [10]. However, more and more attention has been paid to the prevent the west from desert spreading from the east, and is an important node to maintain ecological security in the northeast of Qinghai-Tibet Plateau [34,35]. In general, the temperature increases from northwest to southeast in the watershed.…”

Section: Introductionmentioning

confidence: 99%

“…Qinghai lake is the largest inland saline lake in China. Qinghai lake watershed is a natural barrier to Water 2020, 12, 11 3 of 18 prevent the west from desert spreading from the east, and is an important node to maintain ecological security in the northeast of Qinghai-Tibet Plateau [34,35]. In general, the temperature increases from northwest to southeast in the watershed.…”

mentioning

confidence: 99%

Assessing Changes of Water Yield in Qinghai Lake Watershed of China

Lian

Yuan

Wang

et al. 2019

Water

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Water yield is an important ecosystem service, which is directly related to human welfare and affects the sustainable development. Using the integrated valuation of environmental services and tradeoffs model (InVEST model), we simulated the dynamic change of water yield in Qinghai lake watershed, Qinghai, China, and verified the simulation results. This paper emphatically explored how precipitation change and land use/land cover change (LUCC) affected the change of water yield on the spatial and temporal scales. Before 2004, the areas of cultivated land and unused land showed a dramatic increasing tendency, while forestland and water area presented a decreasing trend. After 2004 cultivated land changed slowly, unused land decreased. Grassland revealed a general trend of decline during 1977-2018, while built-up land basically presented a linear increase. The results show that water yield fluctuated and increased during 1977 . From 1977 to 2000, the mean water yield in each sub-watershed showed an increasing trend and afterward a decreasing one. After 2000, the sub-watersheds basically showed an increasing tendency. There was a strong correlation, with a correlation coefficient of 0.954 ** (** correlation is significant at the 0.01 level), between precipitation and water yield. Land use/land cover change can change the hydrological state of infiltration, evapotranspiration, and water retention. Meanwhile, the correlation between built-up land and water yield was the highest, with a correlation coefficient of 0.932, followed by forestland, with a correlation coefficient of 0.897. Through the analysis of different scenarios, we found that compared with land use/land cover change, precipitation played a more dominant role in affecting water yield.Water 2020, 12, 11 2 of 18 accuracy of the model output and the validation and evaluation of the performance of models in different circumstances and locations [11,12]. The input parameters have a great influence on the output results of the model. Many studies use sensitivity analysis methods to analyze the dependence between model output and variables [13,14], and in turn adjust model input parameters to find a set of optimal parameters. In fact, the input parameters of water yield module need to be calculated by physical equation or empirical equation [15,16]. So, we should make the input parameters more reasonable and accurate by standardizing the input data and taking full account of the regional characteristics and differences. In addition, the output results of the model are compared and verified through the observation data to evaluate the performance of the model without sensitive analysis methods and parameter adjustment, so as to provide a supporting evidence for us to use the model in the similar areas without measured data or insufficient data.Since the 1970s, many distributed hydrological models, including soil and water assessment tool (SWAT) [17], artificial intelligence for ecosystem services (ARIES) [18], integrated valuation of ecosystem services and trade...

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Linkage of Climatic Factors and Human Activities with Water Level Fluctuations in Qinghai Lake in the Northeastern Tibetan Plateau, China

Cited by 31 publications

References 32 publications

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Effects of the Largest Lake of the Tibetan Plateau on the Regional Climate

Spatiotemporal variation and climatic response of water level of major lakes in China, Mongolia, and Russia

Assessing Changes of Water Yield in Qinghai Lake Watershed of China

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