Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China.
Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China.
Environ Res. 2018 Aug;165:425-430. doi: 10.1016/j.envres.2017.10.039. Epub 2017 Oct 26.
Seawater intrusion is a complex groundwater - seawater interaction process, and it is influenced by many factors from ground surface to underground, from groundwater to seawater. Generally, for seawater intrusion model, some model parameters and boundary conditions are always specified by model users' personal experiences or literature's reference value. The defective model would damage the groundwater management for controlling and preventing seawater intrusion when making decisions are based on this model. In order to improve the reliability of seawater intrusion model, the influences of model inputs on output should be identified prior at optimizing model inputs. Dagu river basin, Jiaozhou Bay is one of the most serious areas of seawater intrusion in China, and it is chosen as the study area in this study. The seawater intrusion model of Dagu river basin is built based on a general program SEAWAT4. The key influence factors of model output are analyzed by two sensitivity analysis methods, i.e., stepwise regression and mutual entropy. The results demonstrated that the most important influence factors which have largest sensitivities to groundwater Cl concentration are the precipitation rate and groundwater pumping in agriculture area. In addition, the hydraulic conductivity of zone 1 has a non-negligible influence on seawater intrusion process. Stepwise regression analysis is capable of identifying most important influence factor, and it can't handle complicated nonlinear input-output relationship. Mutual entropy analysis is reliable for identifying the influence factors for complex seawater intrusion model.
海水入侵是一个复杂的地下水-海水相互作用过程，受到从地表到地下、从地下水到海水的多种因素的影响。一般来说，对于海水入侵模型，一些模型参数和边界条件总是由模型用户的个人经验或文献参考值指定的。在基于该模型做出决策时，有缺陷的模型会破坏地下水管理，以控制和预防海水入侵。为了提高海水入侵模型的可靠性，应该在优化模型输入之前，先识别模型输入对输出的影响。大沽河流域，胶州湾是中国海水入侵最严重的地区之一，因此选择该地区作为本研究的研究区域。基于通用程序 SEAWAT4 构建了大沽河流域海水入侵模型。通过两种敏感性分析方法，即逐步回归和互信息，分析了模型输出的关键影响因素。结果表明，对地下水中 Cl 浓度影响最大的最重要影响因素是农业区的降水量和地下水开采量。此外，第 1 区的水力传导率对海水入侵过程有不可忽视的影响。逐步回归分析能够识别最重要的影响因素，但无法处理复杂的非线性输入-输出关系。互信息分析对于识别复杂的海水入侵模型的影响因素是可靠的。
