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Study On The Dynamic Variation Of Groundwater Level And Its Influence Factors In Guangping County

Posted on:2018-06-25Degree:MasterType:Thesis
Country:ChinaCandidate:C Q XuFull Text:PDF
GTID:2310330515468129Subject:Engineering
Abstract/Summary:PDF Full Text Request
With the rapid development of economy,the exploitation of groundwater is increasing,resulting in the decline of groundwater level and water quality deterioration.Taking Guangping County as an example,the dynamic changes of groundwater depth and its influencing factors were analyzed in this paper.The time series variation of groundwater level and the impact of artificial adjustment of exploitation on groundwater level were also discussed in this paper.The research content of this paper is of great significance to groundwater protection,groundwater level statistics and solving the problem of water shortage.The conclusions are as follows:1.The monthly average depth data of groundwater level in three wells in 2006 to2015 were studied and the following conclusions are obtained: On the whole,the groundwater depth of three wells is increasing.The water depth of the monitoring wells in Chengguan and Nanyanchi begins to decline in 2013 while the water depth of Pinggudian is still steadily increasing.2.The analysis of the number of precipitation days from 1956-2015 in Guangping county showed that: The number of multi-year average precipitation days in Guangping County is 94.78 days.The number of spring,summer,autumn and winter were 19.3 days,42.52 days,22.93 days and 10.02 days,respectively.The rainy days are mainly concentrated in summer that the precipitation is less in spring and autumn while is least in winter.Through the frequency spectrum analysis,the annual precipitation days are shown to be periodic at different time scales: 11 years(IMF1),(IMF2),and 5 years(IMF3).The spectrum analysis of annual precipitation cycle in Guangping County shows corresponding periodicity on different time scales: there are4 years(IMF1),9 years(IMF2),3(IMF3)years of cyclical fluctuations.In addition,the trend of precipitation series is tested that the significance is not strong,so there is no significant trend.3.The average monthly groundwater depth data,the data of average monthly rainfall and the exploitation amount of 7 exploited well Guangping county from 98 to 13 areused to get the conclusion: The groundwater level and precipitation show no obvious correlation while the change of groundwater level is small and the change of groundwater level is large in low water year,which indicates precipitation has also a certain influence on water level.The optimal equation of water level and exploitation quantity of the seven monitoring wells in Guangping County is established by MATLAB,and the correlation coefficients of the seven functions are 0.66,0.91,0.88,0.86,0.85,0.77 and 0.92 respectively,indicating the groundwater level and exploitation exists a strong correlation.The synergy of production and rainfall on underground water level are analyzed by using the SPSS and the regression equation is obtained.The composite coefficient is 0.900 and using F and D-W test show that the model fitting degree is very good.The result shows that the main factor affecting groundwater level is the amount of exploitation.4.The monthly data of groundwater level in the 96 months from 2006 to 2013 analyzed in time series analysis.With level data,the dynamic variation of groundwater level is analyzed by time series modeling.The joint simulation model has high accuracy and can be used to calculate the variation of groundwater level.In order to control the mounts of groundwater exploitation,two categories of the agriculture and water conservancy projects are implemented in 2014,which make the water recovery rate of the monitoring wells in Chengguan and Nanyanchiare 1.18%and 6.13%respectively.Besides,the downward trend of the water depth in the project area declines more significantly than that in the non-project area.
Keywords/Search Tags:groundwater level dynamics, rainfall, exploitation amount, periodicity, time series
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