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

人口稠密沿海地区的水资源管理是全球最紧迫的可持续性挑战之一。沿海地下水特别容易受到气候变化和海平面上升的影响，因为海水有可能侵入地下蓄水层。海水入侵减少了美国所有沿海地区的供水。这给社会带来了高昂的代价。受海水入侵影响的地下水需要经过昂贵的脱盐过程才能饮用，而灌溉用水可能无法使用，导致农田荒废。未来的气候预测表明，海水入侵的问题将进一步恶化。然而，问题的严重程度尚不清楚，因此难以制定对策。虽然沿海地下水含水层的计算机模型可以用于预测海水入侵，但这些建模工作挑战了即使是最快的计算机的能力。我们建议通过开发比当前模型快几个数量级的模型来应对这一挑战。这将有助于更广泛地考虑潜在的解决办法。这些建模的进展将与供水机构合作进行，目的是提高沿海社区地下水建模的实用性。这些方法的成功开发和采用将有助于负责保护沿海含水层的机构制定可持续的管理战略，以保护稀缺的水资源。海水入侵问题的解决方案涉及更有效的抽水计划，减少需求和技术干预，如脱盐。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。该项目将通过为沿海地下水系统开发计算效率高、鲁棒性强、准确的替代模型，大大推进模拟优化（SO）的开发和应用。有限的文献SO和替代建模海水入侵问题的重点是简化水文地质环境和数学表示的管理策略。然而，现实的海水入侵问题涉及水文地质的复杂性，包括离散的岩相，断层和裂缝，盐水-淡水混合带动力学。解决方案需要非线性目标函数和连续和离散的决策变量，代表了广泛的工程组件。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模型海水入侵问题可以至少比全尺寸模型快一个数量级。计算工作量的减少将使我们能够调查更广泛的潜在海平面上升和气候变化影响，以及更广泛的潜在管理对策。为了实现这一目标，具体的项目目标是：一）开发SO测试问题，提供强大的模型替代品的评价;二）制定管理目标和约束的基础上管理的测试用例含水层，并确定相关的测试用例的情况下;和iii）程序，培训，并评估性能？数据驱动？然后呢？模型驱动？该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Water management in densely populated coastal regions is one of the most pressing sustainability challenges worldwide. Coastal groundwater is especially vulnerable to climate change and sea level rise due to the potential for seawater intrusion into groundwater aquifers. Seawater intrusion has reduced water supply in all coastal regions of the US. This has resulted in high costs to society. Groundwater affected by seawater intrusion requires expensive desalination processes to be made drinkable, while irrigation water could be rendered unusable leading to the abandonment of farmland. Future climate projections suggest the problem of seawater intrusion will worsen. However, the scale of the problem is unclear, making it difficult to devise responses. While computer models of coastal groundwater aquifers can be useful for predicting seawater intrusion, these modeling efforts challenge the capability of even the fastest computers. We propose to address this challenge by developing models that are orders of magnitude faster than current models. This will allow for a much broader consideration of potential solutions. These modeling advances will be made in collaboration with water supply agencies, with the goal of increasing the utility of groundwater modeling for coastal communities. Successful development and adoption of these approaches will help agencies tasked with the protection of coastal aquifers devise sustainable management strategies to protect scarce water resources.Solutions to seawater intrusion problems 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 seawater intrusion problems has focused on simplified hydrogeological settings and mathematical representations of management strategies. However, realistic seawater intrusion problems involve hydrogeological complexities, including discrete lithological facies, faults and fractures, and saltwater-freshwater mixing zone dynamics. Solutions necessitate nonlinear objective functions and continuous and discrete decision variables, representing 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 seawater intrusion problems can be at least an order of magnitude faster than full-scale models. The reduction in computational effort will allow us to investigate a broader range of potential sea level rise and climate change impacts and a wider range of potential management responses to these impacts. To achieve this goal, the specific project objectives are to: i) develop SO test problems to provide robust evaluation of model surrogates; ii) formulate management objectives and constraints based on management of the test case aquifers, and identify scenarios relevant to the test cases; and iii) 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 sea level rates, climatology, and groundwater demand scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.