干旱区生态环境知识资源中心(Arid): Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning

Arid

DOI	10.1016/j.jhydrol.2022.128095
	Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning
	Jiang, Xingan; Fan, Chenyu; Liu, Kai; Chen, Tan; Cao, Zhigang; Song, Chunqiao
通讯作者	Song, CQ
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2022
卷号	612
英文摘要	In the context of accelerating climate changes and economic boom in the past decades, lakes have undergone drastic changes worldwide. Particularly in arid and semi-arid regions, those lakes were severely impacted and threatened due to their hydrologic sensitivity and ecological vulnerability. As the largest lake in Northwest China with arid climate, Bosten Lake provides precious water resources and ecosystem services to local communities. Although a large quantity of earlier efforts have been paid on Bosten Lake, there is still absence of tracking its changing trajectory at a long timescale (e.g., one century), which restricts the holistic understanding of the decadal periodic lake desiccations and their driving forces. This study employs a machine learning method to reconstruct the centenary covariations of water storage and salinity of Bosten Lake by integrating multi-source data. The results showed that, compared with the high stage of 6.76 x 10(9) m(3) in 1961, the lake water storage substantially dropped twice to 3.96 x 10(9) m(3) in 1987 and 4.67 x 10(9) m(3) in 2013. In recent years, the lake level rose rapidly and recovered back to the comparable stage of the 1960 s by 2020. Four metrics of accuracy evaluation employed in this study indicate the reliability of the XGBoost model, with the mean absolute error of 0.31 m, mean squared error of 0.37 m, r-square of 0.85, and adjusted r-square of 0.84. The centenary recon-struction results reveal that the lake salinity underwent six-phase fluctuations with the water level and storage changes during 1920-2020, with the highest value of 1.87 g/L in 1987 and the lowest value of 1.19 g/L in 2002. During the past century, the water salinity and storage of Bosten Lake were influenced chiefly by vapor pressure and precipitation, followed by wet day frequency, daily mean temperature, and potential evapotranspiration. Moreover, the uncertainty of the machine learning model was also explored and discussed. It could be mainly associated with the data accuracy of input climate variables and the ignorance of environmental impacts from the intense agricultural activities after the 1960 s. This study is expected to advance the scientific understanding of long-term change characteristics of Bosten Lake and to provide a technical reference of reconstructing cen-tenary hydrologic and environmental trajectory for dryland lakes.
英文关键词	Lake Water storage Salinity Bosten Lake Machine learning Climate change
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000838047300005
WOS关键词	CLIMATE-CHANGE ; BOSTEN LAKE ; SALINIZATION ; IMPACTS ; REGION
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393498
推荐引用方式 GB/T 7714	Jiang, Xingan,Fan, Chenyu,Liu, Kai,et al. Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning[J],2022,612.
APA	Jiang, Xingan,Fan, Chenyu,Liu, Kai,Chen, Tan,Cao, Zhigang,&Song, Chunqiao.(2022).Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning.JOURNAL OF HYDROLOGY,612.
MLA	Jiang, Xingan,et al."Centenary covariations of water salinity and storage of the largest lake of Northwest China reconstructed by machine learning".JOURNAL OF HYDROLOGY 612(2022).