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.
