CBET-EPSRC: Efficient Surrogate Modeling for Sustainable Management of Complex Seawater Intrusion-Impacted Aquifers

拟议研究的总体目标是在具有不同地质、水文技术和治理环境的沿海地区对水资源进行可持续管理。沿海地区的水资源压力已经很大，而且由于人口增加、生活水平提高以及气候变化和海平面上升的影响，预计还会加剧。我们将把重点放在沿海地区，这些地区的含水层过度开采导致海水入侵（SWI），从而导致地下水质量恶化，而SLR预计将进一步减少新鲜地下水的可用性。这些问题的解决方案将涉及更有效的抽水方案、减少需求以及脱盐等技术干预的组合。然而，确定这些问题的最优解决方案会带来极大的计算需求。该项目将通过开发计算效率高、鲁棒性强、精度高的沿海地下水系统替代模型，极大地推动模拟优化（SO）的发展和应用。在SWI问题中，关于SO和代理模型的有限文献主要集中在简化的水文地质环境和管理策略的数学表示上。然而，现实的SWI问题涉及水文地质的复杂性，包括离散的岩性相、断层和裂缝、咸水-淡水混合带动力学、地表水-地下水相互作用，以及非线性目标函数和连续和离散决策变量，以代表广泛的工程成分。我们假设这些水文地质和管理特征决定了准确和高效的代用物的建立；对于SWI问题，精确的替代SO模型至少可以比全尺寸模型快一个数量级。计算成本的降低使研究范围更广的单反和气候变化影响以及对这些影响的更广泛的管理响应成为可能。本研究的创新之处在于：(a)通过对简单到复杂问题的全尺寸SO模型进行测试，开发了一种构建稳健代理的系统方法；(b)在一系列水文地质和管理环境中评估替代模型精度和计算效率之间的权衡；(c)确定在三个“末端成员”案例研究含水层中管理沿海地下水资源的强有力的管理方案；(d)与水资源管理机构合作，开发有用的情景、优化框架和模型输出。三个测试含水层（加利福尼亚州的圣巴巴拉、佛罗里达州的比斯坎和巴哈马群岛的圣萨尔瓦多岛）具有不同的水文地质和管理特征以及校准良好的地下水流动模型。该项目的目标是：(a)开发SO-SWI-SLR测试问题，以提供对模型替代品的可靠评估；(b)根据测试用例含水层的管理，制订管理目标和限制，并确定与测试用例有关的情况；(c)规划、培训和评估“数据驱动”和“模型驱动”替代方案的性能，以确定测试案例含水层、一系列SLR率、气候学和地下水需求情景的最佳管理方案。这项工作将建立在我们的英美小组在模拟SLR和气候对SWI的影响以及为其他地下水问题开发SO模型方面的互补经验的基础上。

The overarching goal of the proposed research is the sustainable management of water resources in coastal regions with diverse geological, hydro-technical and governance settings. Pressures on water resources in coastal regions are already great and are expected to intensify due to increasing populations, standards of living and impacts from climate change and sea level rise (SLR). We will focus on coastal areas where aquifer over-drafting has caused seawater intrusion (SWI), thus deteriorating groundwater quality, and where SLR is expected to further reduce availability of fresh groundwater. Solutions to these problems will involve combinations of more efficient pumping schemes, demand reduction, and technological interventions such as desalination. However, determining optimal solutions for these problems poses extreme computational demands. This project will greatly advance the development and application of simulation-optimization (SO) by developing computationally efficient, robust, and accurate surrogate models for coastal groundwater systems.The limited literature on SO and surrogate modeling in SWI problems has focused on simplified hydrogeological settings and mathematical representations of management strategies. However, realistic SWI problems involve hydrogeological complexities, including discrete lithological facies, faults and fractures, saltwater-freshwater mixing zone dynamics, and surface-water groundwater interactions, as well as nonlinear objective functions and continuous and discrete decision variables to represent a wide range of engineering components. We hypothesize that these hydrogeologic and management features determine the building of accurate and efficient surrogates; and accurate surrogate SO models for SWI problems can be at least an order of magnitude faster than full-scale models. The reduced computational cost allows to investigate a broader range of SLR and climate change impacts and a wider range of management responses to these impacts. The innovative aspects of this research are: (a) development of a systematic approach for building robust surrogates by testing against full-scale SO models on simple to complex problems; (b) assessment of tradeoffs between surrogate model accuracy and computational efficiency across a range of hydrogeologic and management settings; (c) identification of robust management schemes for managing coastal groundwater resources in three "end-member" case study aquifers; and (d) collaboration with water management agencies to develop useful scenarios, optimization frameworks, and model output. The three test aquifers (Santa Barbara, California; Biscayne, Florida; and San Salvador Island, The Bahamas) have diverse hydrogeologic and management characteristics and well-calibrated groundwater flow models. The project objectives are: (a) develop SO-SWI-SLR test problems to provide robust evaluation of model surrogates; (b) formulate management objectives and constraints based on management of the test case aquifers, and identify scenarios relevant to the test cases; (c) program, train, and evaluate the performance of "data-driven" and "model-driven" surrogates to identify optimal management schemes for the test case aquifers, a range of SLR rates, climatology, and groundwater demand scenarios. This work will build on our US-UK group's complementary experience simulating SLR and climate impacts on SWI and in developing SO models for other groundwater problems.