|Water Science and Engineering 2018, 11(1) 1-7 DOI: https://doi.org/10.1016/j.wse.2018.03.003 ISSN: 1674-2370 CN: 32-1785/TV|
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Understanding groundwater table using a statistical model
Shao-feng Yan a,b, Shuang-en Yu a,*, Yu-bai Wu a,c, De-feng Pan d, Jia-gen Dong e
Shao-feng Yan, Shuang-en Yu, Yu-bai Wu, De-feng Pan, Jia-gen Dong
In this study, a statistical model was established to estimate the groundwater table using precipitation, evaporation, the river stage of the Liangduo River, and the tide level of the Yellow Sea, as well as to predict the groundwater table with easily measurable climate data in a coastal plain in eastern China. To achieve these objectives, groundwater table data from twelve wells in a farmland covering an area of 50 m 150 m were measured over a 12-month period in 2013 in Dongtai City, Jiangsu Province. Trend analysis and correlation analysis were conducted to study the patterns of changes in the groundwater table. In addition, a linear regression model was established and regression analysis was conducted to understand the relationships between precipitation, evaporation, river stage, tide level, and groundwater table. The results are as follows: (1) The groundwater table was strongly affected by climate factors (e.g., precipitation and evaporation), and river stage was also a significant factor affecting the groundwater table in the study area ( p < 0.01, where p is the probability value). (2) The groundwater table was especially sensitive to precipitation. The significance of the factors of the groundwater table were ranked in the following descending order: precipitation, evaporation, and river stage. (3) A triple linear regression model of the groundwater table, precipitation, evaporation, and river stage was established. The linear relationship between the groundwater table and the main factors was satisfied by the actual values versus the simulated values of the groundwater table (R2 ? 0.841, where R2 is the coefficient of determination).
|Keywords： Groundwater table Precipitation Evaporation Climate factors Regression statistics Coastal plain|
|Received 2017-03-26 Revised 2017-11-27 Online: 2018-01-31|
This work was supported by the Sate Key Program of the National Natural Science Foundation of China (Grant No. 51479063), the Public Welfare Industry Special Funds for Scientific Research Projects of the Ministry of Water Resources (Grant No. 200801025), and the Innovative Projects of Scientific Research for Postgraduates in Ordinary Universities in Jiangsu Province (Grant No. CXZZ13_0267).
|Corresponding Authors: firstname.lastname@example.org (Shuang-en Yu)|
|About author: email@example.com (Shuang-en Yu)|
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