基于数值模拟的岩溶地下水源保护区划分技术研究
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摘要
以山东省邹城市某岩溶水源地为例,研究数值模拟方法在岩溶地下水水源地保护区划分中的应用,以及水文地质参数对于水源地保护区划分结果的影响。基于GMS软件构建了研究区三维非稳定流地下水数值模型,采用质点追踪技术,通过反向追踪示踪粒子在运移100 d、1 000 d以及25 a后的位置及迁移轨迹,对该岩溶水源地进行保护区划分;改变数值模型中的主要水文地质参数,以变化后的保护区面积作为衡量参数敏感度的指标,并计算各参数的敏感度系数,研究水文地质参数对于保护区划分范围的影响。结果表明:当参数改变幅度在20%范围内时,一层垂向渗透系数(VK_1)的敏感度系数最大可达到2.63×10~(-3),二层垂向渗透系数(VK_2)的敏感度系数最大可达到3.64×10~(-3);垂向渗透系数的敏感度明显高于其他参数,说明垂向渗透系数对保护区划分范围的影响最大。因此,在应用数值模拟法对岩溶地下水源地进行保护区划分时,应尤其注意模型中各含水层垂向渗透系数取值的准确性和合理性。
Zoucheng City is located in the southwest of Shandong Province with a total area of 1,616 km~2.Taking a karst water source in Zoucheng City as an example, the application of numerical simulation methods in the division of karst groundwater protection zoning and the effect of hydrogeological parameters on the classification results of water source protection zones were studied. The water source is located in the middle of the Guoliji monoclinic karst water system, with Wangyun river in the south. Groundwater is abstracted from the fractured karst aquifer comprising the Middle and the Lower Ordovician carbonate rocks, with the single well yield of more than 3,000 m~3·d~(-1). There are two abstraction well fields in the water source, of which the first well field consists of 6 production wells and 4 production wells in second one. The karst groundwater runs from the southwest to the northeast, and mainly receives the leakage recharge of the overlying porous aquifer and the lateral runoff recharge. Using GMS software, a three-dimensional unsteady groundwater numerical model was constructed. Using particle tracing technique, the protection area of the karst water source area were divided by tracing the position and the migration trajectory of tracer particles after 100 days, 1,000 days and 25 years. Taking the change of the protection area after changing the parameters as the index to measure the sensitivity of the parameters, the sensitivity coefficient of each parameter was calculated and the influence of the hydrogeological parameters on the division scope of the protection area was studied. The numerical simulation shows that the maximum migration distance of the tracer particles after 100 d is 44.91 m, the maximum migration distance after 1,000 d is 301.85 m, and the maximum migration distance after 25 a is 1,523.27 m. According to this result, the area of the primary protection zone is 1.27×10~4m~2, the area of the secondary protection zone is 0.42 km~2, and the area of the quasi-protection zone is 3.47 km~2. The sensitivity analysis result shows that when the parameter change range is within 20%, the sensitivity coefficient of vertical permeability coefficient VK_1 of the first layer can reach 2.63×10~(-3), and the sensitivity coefficient of vertical permeability coefficient VK_2 of the second floor can reach 3.64×10~(-3). The sensitivity of the vertical permeability coefficient is significantly higher than other parameters, indicating that the vertical permeability coefficient has the greatest impact on the division of the protection area. Therefore, when applying the numerical simulation method to divide the protection areas of karst groundwater sources, we should pay special attention to the accuracy and rationality of the vertical permeability coefficient of each aquifer in the model.
Zoucheng City is located in the southwest of Shandong Province with a total area of 1,616 km~2.Taking a karst water source in Zoucheng City as an example, the application of numerical simulation methods in the division of karst groundwater protection zoning and the effect of hydrogeological parameters on the classification results of water source protection zones were studied. The water source is located in the middle of the Guoliji monoclinic karst water system, with Wangyun river in the south. Groundwater is abstracted from the fractured karst aquifer comprising the Middle and the Lower Ordovician carbonate rocks, with the single well yield of more than 3,000 m~3·d~(-1). There are two abstraction well fields in the water source, of which the first well field consists of 6 production wells and 4 production wells in second one. The karst groundwater runs from the southwest to the northeast, and mainly receives the leakage recharge of the overlying porous aquifer and the lateral runoff recharge. Using GMS software, a three-dimensional unsteady groundwater numerical model was constructed. Using particle tracing technique, the protection area of the karst water source area were divided by tracing the position and the migration trajectory of tracer particles after 100 days, 1,000 days and 25 years. Taking the change of the protection area after changing the parameters as the index to measure the sensitivity of the parameters, the sensitivity coefficient of each parameter was calculated and the influence of the hydrogeological parameters on the division scope of the protection area was studied. The numerical simulation shows that the maximum migration distance of the tracer particles after 100 d is 44.91 m, the maximum migration distance after 1,000 d is 301.85 m, and the maximum migration distance after 25 a is 1,523.27 m. According to this result, the area of the primary protection zone is 1.27×10~4m~2, the area of the secondary protection zone is 0.42 km~2, and the area of the quasi-protection zone is 3.47 km~2. The sensitivity analysis result shows that when the parameter change range is within 20%, the sensitivity coefficient of vertical permeability coefficient VK_1 of the first layer can reach 2.63×10~(-3), and the sensitivity coefficient of vertical permeability coefficient VK_2 of the second floor can reach 3.64×10~(-3). The sensitivity of the vertical permeability coefficient is significantly higher than other parameters, indicating that the vertical permeability coefficient has the greatest impact on the division of the protection area. Therefore, when applying the numerical simulation method to divide the protection areas of karst groundwater sources, we should pay special attention to the accuracy and rationality of the vertical permeability coefficient of each aquifer in the model.
引文
[1] 郝永艳. 三给地垒岩溶地下水系统及水源地保护区划分研究[D].太原:太原理工大学,2011.
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[5] 代美龄. 关中盆地典型地下水水源地保护区划分研究[D].西安:长安大学,2016.
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[7] 邓媛媛, 胡立堂, 高童,等. 吴忠市金积地下水饮用水源地保护区划分[J]. 南水北调与水利科技, 2013, 11(1):127-131.
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[12] Mcdonald M G, Harbaugh A W. A modular three-dimensional finite-difference ground-water flow model[M]// Techniques of Water-Resources Investigations of the United States Geological Survey,1988:387-389.
[13] 吴吉春,陆乐.地下水模拟不确定性分析[J].南京大学学报(自然科学版),2011,47(3):227-234.
[14] 曾献奎. 地下水流数值模拟不确定性分析与评价[D].南京:南京大学,2012.
[15] 吴雯倩,靳孟贵.淮北市地下水流数值模拟及水文地质参数不确定性分析[J].水文地质工程地质,2014,41(3):21-28.
[16] 欧阳琦,卢文喜,侯泽宇,等.基于替代模型的地下水溶质运移不确定性分析[J].中国环境科学,2016,36(4):1119-1124.
[17] 苗添升, 卢文喜, 欧阳琦,等. 地下水数值模拟的不确定性分析在水质预测中的应用[J]. 水电能源科学, 2016,34(8):20-23,44.
[18] 南天. 大兴迭隆起地区地下水流数值模拟的不确定性分析[D].北京:中国地质大学(北京),2016.
[19] 陆乐, 吴吉春. 地下水数值模拟不确定性的贝叶斯分析[J]. 水利学报, 2010, 41(3):264-271.
[20] 翟远征,王金生,苏小四,等.地下水数值模拟中的参数敏感性分析[J].人民黄河,2010,32(12):99-101.
[21] 陆燕,何江涛,王俊杰,等.北京市平原区地下水污染防控区划不确定性分析[J].环境科学,2012,33(9):3117-3123.
[2] 国家环境保护总局. 饮用水水源地保护区划分技术规范(HJ/T338-2007)[S].2007.
[3] 江广长,马腾. 地下水水源地保护区划分方法研究[J].安全与环境工程,2016,23(3):36-39,57.
[4] 黄丽丽. 磐石市地下水水源地保护区划分研究[D].长春:吉林大学, 2007.
[5] 代美龄. 关中盆地典型地下水水源地保护区划分研究[D].西安:长安大学,2016.
[6] 赵红梅,肖杰. 公式法与数值模拟法在地下水饮用水源保护区划分中的应用:成都平原某水源地为例[J].四川环境,2013,32(S1):60-64.
[7] 邓媛媛, 胡立堂, 高童,等. 吴忠市金积地下水饮用水源地保护区划分[J]. 南水北调与水利科技, 2013, 11(1):127-131.
[8] 翟立娟.岩溶水饮用水水源保护区划分技术方法:以邯郸市羊角铺水源地为例[J].中国岩溶,2011,30(1):47-52,71.
[9] 卜华,陈占成,张良鹏.山东羊庄岩溶水系统饮用水水源地保护区划分探讨[J].科技创新导报,2008(20):110-112,114.
[10] 王晓玮,王延岭,赵志伟,等.基于数值模型的肥城市地下水源地保护区划分[J].山东国土资源,2017,33(4):29-33.
[11] 李星宇,南天,王新娟,等.数值模拟方法在隐伏岩溶水源地保护区划分及污染治理中的应用[J].中国岩溶,2014,33(3):280-287.
[12] Mcdonald M G, Harbaugh A W. A modular three-dimensional finite-difference ground-water flow model[M]// Techniques of Water-Resources Investigations of the United States Geological Survey,1988:387-389.
[13] 吴吉春,陆乐.地下水模拟不确定性分析[J].南京大学学报(自然科学版),2011,47(3):227-234.
[14] 曾献奎. 地下水流数值模拟不确定性分析与评价[D].南京:南京大学,2012.
[15] 吴雯倩,靳孟贵.淮北市地下水流数值模拟及水文地质参数不确定性分析[J].水文地质工程地质,2014,41(3):21-28.
[16] 欧阳琦,卢文喜,侯泽宇,等.基于替代模型的地下水溶质运移不确定性分析[J].中国环境科学,2016,36(4):1119-1124.
[17] 苗添升, 卢文喜, 欧阳琦,等. 地下水数值模拟的不确定性分析在水质预测中的应用[J]. 水电能源科学, 2016,34(8):20-23,44.
[18] 南天. 大兴迭隆起地区地下水流数值模拟的不确定性分析[D].北京:中国地质大学(北京),2016.
[19] 陆乐, 吴吉春. 地下水数值模拟不确定性的贝叶斯分析[J]. 水利学报, 2010, 41(3):264-271.
[20] 翟远征,王金生,苏小四,等.地下水数值模拟中的参数敏感性分析[J].人民黄河,2010,32(12):99-101.
[21] 陆燕,何江涛,王俊杰,等.北京市平原区地下水污染防控区划不确定性分析[J].环境科学,2012,33(9):3117-3123.