| 湖南宁乡大成桥岩溶地下水对暴雨响应特征及多元回归预测模型 |
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| 引用本文： | 潘宗源,吴远斌,贾龙,殷仁朝,马骁,陈婷.湖南宁乡大成桥岩溶地下水对暴雨响应特征及多元回归预测模型[J].中国岩溶,2020,39(2):232-242. |
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| 作者姓名： | 潘宗源 吴远斌 贾龙 殷仁朝 马骁 陈婷 |
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| 作者单位： | 1.中国地质科学院岩溶地质研究所/自然资源部、广西岩溶动力学重点实验室，广西桂林 541004 |
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| 基金项目： | 广西自然科学基金资助项目（2018GXNSFAA294020）；国家自然科学基金项目（41877300；41402284）；中国地质调查局地质调查项目（DD20190266）；中国地质科学院岩溶地质研究所基本科研业务费项目（2015017） |
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| 摘 要： | 通过降雨、水位监测等方法,研究湖南宁乡大成桥岩溶地下水位动态及对暴雨的响应过程,探讨地下水内在联系特征并建立回归预测模型。结果表明:雨季地下水位对降雨响应程度高,水位变幅大,而枯季则相反;在暴雨条件下,接受快速管道流补给的地下水位动态响应为陡升—陡降,水位变幅在13.6~42.8 m;接受裂隙、孔隙水补给的地下水位动态表现为缓升—缓降,年变幅为1.1 m;雨季地下水位对暴雨响应的滞后时间为1.2~4 h,较相同条件下的水位响应滞后时间缩短了1/2~2/3,水位变幅增大1.7~4.7倍;枯季地下水位对暴雨响应的滞后时间为4.2~13.2 h,水位响应滞后时间延长了3~8 h,水位变幅缩减了5~11倍;包气带厚度和含水饱和度对水位动态起着决定作用,包气带厚度与水位响应滞后时间负相关,含水饱和度与水位变幅正相关;通过相关性分析、拟合优度检验、显著性检验和残差分析等,确定最优回归预测模型,模型检验结果与实际水位动态过程吻合度较高。
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| 关 键 词： | 暴雨 水位动态 包气带 多元回归模型 |
Response characteristics of karst groundwater to rainstorm and the multiple regression prediction model in Dachengqiao, Ningxiang county, Hunan Province |
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| PAN Zongyuan,WU Yuanbin,JIA Long,YIN Renchao,MA Xiao,CHEN Ting.Response characteristics of karst groundwater to rainstorm and the multiple regression prediction model in Dachengqiao, Ningxiang county, Hunan Province[J].Carsologica Sinica,2020,39(2):232-242. |
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| Authors: | PAN Zongyuan WU Yuanbin JIA Long YIN Renchao MA Xiao CHEN Ting |
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| Institution: | 1.Institute of Karst Geology, CAGS/ Key Laboratory of Karst Dynamic, MNR&GZAR, Guilin, Guangxi 541004, China2.Guilin Branch of China Mobile Communications Group Guangxi Co., Ltd., Guilin, Guangxi 541004, China |
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| Abstract: | The groundwater activity in the karst mining area can cause serious geological hazards. It is the key to solve the problem of frequent disastersto understand the response characteristics of groundwater to extreme climate conditions. The Dachengqiao area was selected as the study area, which is about 2 km away from the Meitanba coal mine.The stratigraphic lithology in Dachengqiao is mainly the calcareous conglomerate of Cretaceous Baihuating formation, followed by limestone of Permian Qixia formation and Maokou formation.Affecting by the mining drainage karst aquifer,the maximum water level of Dachengqiao is more than 90 m deep.The main runoff direction of groundwater flows from south to north, and is discharged to the mine. In this paper,through the on-site monitoring methods such as the high frequency monitoring of karst groundwater level and rainfall,the dynamics of karst groundwater level and itsresponse process to heavy rainfall are studied,and the characteristics of ground water internal relationship are discussed and a regression predition model is established. The results indicated that karst groundwater level responded quicker to rainfall in rainy season, and the amplitude of groundwater level is larger compared with that in the dry season. Under the rainstorm condition, the dynamic response of karst groundwater level to the rapid pipeline flow is a steep rise-steepfall,with the variation range of 13.6 to 42.8 m.Whereas the dynamic characteristics of groundwater level supplied by fracture and pore water are slow rise-slow fall,withan annual variation of 1.1 m. The lag time of groundwater level response to rainstrom in therainy season ranged from 1.2 to 4 h, which is 1/2 to 2/3 shorter than that under the same conditions,and groundwater level variation increased by 1.7 to 4.7 times. In dry season,the lagtime of groundwater level response to rainstorm ranged from 4.2 to 13.2 h, which is extended by 3 to 8 h, and groundwater level variation is reduced by 5 to 11 times. According to the research, groundwater level dynamic characteristic was controlled by thickness and water saturation of vadose zone, which indicated thickness of vadose zone presented a negative relationship with response lag of groundwater level, and water saturation of aeration zone presented a positive relationship with groundwater level variation. The optimum regression prediction model is established through correlation analysis, fitting optimization, variance and residual analysis, and regression coefficientt test, which indicates as Hi=14.149+0.308y1+0.379y2-0.023Pi+0.051Ti. It is proved that the model can effectively predict the water level change process of monitoring sites under the condition of rainstorm. |
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| Keywords: | rainstorm groundwater level dynamic aeration zone multiple regression model |
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