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、水文技术和治理背景的沿海地区实现水资源的可持续管理。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，预计压力还会加剧。我们将把重点放在沿海地区，在那里，含水层超吃水造成海水入侵（SWI），从而恶化地下水质量，预计SLR将进一步减少淡水地下水的可用性。这些问题的解决方案将涉及更有效的抽水计划，减少需求和技术干预，如海水淡化。然而，确定这些问题的最佳解决方案提出了极端的计算需求。该项目将大大推进模拟优化（SO）的发展和应用，通过开发计算效率高，鲁棒性强，准确的代理模型为沿海地下水system.The有限的文献SO和代理建模在SWI问题集中在简化的水文地质环境和数学表示的管理策略。然而，现实的SWI问题涉及水文地质的复杂性，包括离散的岩相，断层和裂缝，咸水-淡水混合区动态，地表水-地下水的相互作用，以及非线性目标函数和连续和离散的决策变量，以代表广泛的工程组件。我们假设，这些水文地质和管理功能确定准确和有效的代理人的建设，准确的代理SO模型SWI的问题，至少可以比全尺寸模型快一个数量级。计算成本的降低使得能够调查更广泛的SLR和气候变化影响以及更广泛的管理应对这些影响的措施。本研究的创新之处在于：（a）通过对从简单到复杂问题的全尺度SO模型进行测试，开发了一种系统的方法来建立稳健的替代模型;（B）在一系列水文地质和管理设置中，评估替代模型精度和计算效率之间的权衡;（c）确定管理三个“端元”案例研究含水层沿海地下水资源的健全管理计划;（d）与水资源管理机构合作，制定有用的方案、优化框架和模型输出。三个测试含水层（加州的圣巴巴拉、佛罗里达的比斯坎和巴哈马的圣萨尔瓦多岛）具有不同的水文地质和管理特征，并具有校准良好的地下水流模型。该项目的目标是：（a）开发SO-SWI-SLR测试问题，以提供模型替代物的可靠评估;（B）根据测试案例含水层的管理制定管理目标和限制，并识别与测试案例相关的场景;（c）对“数据驱动”和“模型驱动”替代物的性能进行规划、培训和评估，以确定测试案例含水层的最佳管理方案，一系列SLR率，气候学和地下水需求情景。这项工作将建立在我们的美国-英国小组的补充经验，模拟SLR和气候对SWI的影响，并在其他地下水问题的SO模型开发。

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.