School of Earth Sciences and Engineering, Hohai University, Nanjing, China.
Key Laboratory of Surficial Geochemistry, Ministry of Education; Department of Hydrosciences, School of Earth Sciences and Engineering, Nanjing University, Nanjing, China.
J Environ Manage. 2021 Mar 15;282:111964. doi: 10.1016/j.jenvman.2021.111964. Epub 2021 Jan 20.
Coastal aquifer management (CAM) considering conjunctive optimization of pumping and injection system for seawater intrusion (SI) mitigation poses significant decision-making challenges. CAM needs to pose multiple objectives and massive decision variables to explore tradeoff strategies between the conflicting resources, economic, and environmental requirements. Here, we investigate a joint artificial injection scheme for ameliorating SI by establishing an evolutionary multi-objective decision-making framework that combines simulation-optimization (S-O) modelling with a cost-benefit analysis, and demonstrate the framework on a large-scale CAM case in Baldwin County, Alabama. First, a SI numerical model, using SEAWAT, was configured to predict the vulnerable region as an SI encroachment area with the scenarios of minimum and maximum pumping capacity. As a result, a smaller number of candidate sites were selected in the SI encroachment area for implementing groundwater injection to avoid the computationally infeasible SI optimization with an inordinate number of injection related decision variables. Second, the effective S-O methodology of niched Pareto tabu search combined with a genetic algorithm (NPTSGA), which considers the moving-well option, was applied to discover optimal pumping/injection (P/I) strategies (including P/I rates and injection well locations) between three conflicting management objectives under complicated SI constraints. Third, for practical operation of the P/I schemes, a cost-benefit analysis provides judgment criteria to allow decision-makers to implement more sustainable P/I strategies to capture the different realistic preferences. The implementation of three extreme optimization solutions for the case study indicates that, compared to the initial unoptimized scheme, a maximum increase of a factor of 3 in groundwater extraction rates, a maximum reduction of 17% in extent of SI, and a maximum 82.3 million US dollars in comprehensive benefits are specifically achieved by conjunctive P/I optimization. The robustness in the decision alternatives attributed to the uncertainty in physical parameters of hydraulic conductivity was discovered through global sensitivity analysis. The proposed framework provides a decision support system for multi-objective CAM with combined pumping control and engineering measures for SI mitigation.
沿海含水层管理（CAM）考虑联合优化海水入侵（SI）缓解的抽注系统，这对决策制定提出了重大挑战。CAM 需要提出多个目标和大量决策变量，以探索相互冲突的资源、经济和环境要求之间的权衡策略。在这里，我们通过建立一个将模拟-优化（S-O）建模与成本效益分析相结合的进化多目标决策框架，研究了联合人工注水方案来改善 SI，并用阿拉巴马州鲍德温县的大规模 CAM 案例来验证该框架。首先，使用 SEAWAT 配置了一个 SI 数值模型，以预测脆弱区域作为 SI 入侵区域，并考虑了最小和最大抽水量的场景。结果，在 SI 入侵区域中选择了较少的候选地点进行地下水注入，以避免由于与注水井相关的决策变量数量过多而导致计算上不可行的 SI 优化。其次，应用了有效 S-O 方法，即带有遗传算法的小生境帕累托禁忌搜索（NPTSGA），考虑了移动井选项，以在复杂 SI 约束下，在三个相互冲突的管理目标之间发现最优的抽注（P/I）策略（包括 P/I 率和注水井位置）。最后，为了实际运行 P/I 方案，成本效益分析提供了判断标准，使决策者能够实施更可持续的 P/I 策略，以满足不同的实际偏好。对案例研究的三种极端优化方案的实施表明，与初始未经优化的方案相比，地下水提取率最大可提高 3 倍，SI 最大可减少 17%，综合效益最大可提高 8230 万美元。通过全局敏感性分析发现，物理参数（水力传导率）不确定性导致决策替代方案具有稳健性。该框架为多目标 CAM 提供了决策支持系统，包括联合抽水控制和工程措施来缓解 SI。
