RII Track-2 FEC: IGM--A Framework for Harnessing Big Hydrological Datasets for Integrated Groundwater Management

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Groundwater depletion is a major water management problem that is of global concern. Locally, the Southeastern US has experienced increased water stress due to the mismanagement of its water resources, especially during drought periods. Rapid agricultural expansion and unplanned urbanization have further aggravated this problem. Given that water-related industries contribute to over 150 billion of US dollars in annual revenues, the long-term sustainability of freshwater resources is of paramount importance to this region. While mapping the availability of water in topsoil, reservoirs, and rivers continues to receive much attention, mapping of groundwater storage changes at a fine spatiotemporal resolution over large areas is currently lacking. This is important because groundwater contributes around 40 percent of freshwater usage in the conterminous US, and its contribution in some Southeastern states, e.g., Mississippi, is over two-thirds. Groundwater also indirectly sustains surface water resources, and hence its actual contribution to freshwater usage is even larger than reported. The goal of this project is to harness the big data to implement an integrated groundwater management (IGM) framework that will provide new scientific insights and make useful groundwater predictions at an unprecedented fine spatiotemporal resolution. The IGM framework integrates hydrological, geological, and satellite datasets with machine learning tools and high-resolution simulation models. The information generated will be made available to a wide group of stakeholders through a web-based platform to help develop engineering and policy solutions. The research tasks and workforce development efforts will be jointly accomplished by a team of interdisciplinary researchers at five universities: The University of Alabama, Louisiana State University, University of Mississippi, Tuskegee University, and Southern University.Prediction of groundwater storage changes at fine spatiotemporal scales is challenging due to lack of information about recharge fluxes, which are influenced by variations in natural land surface processes (e.g., precipitation and evapotranspiration) and anthropogenic interventions such as irrigation and pumping. The inability to map subsurface heterogeneities is another major limitation. In this study, we will harness big hydrologic datasets using science-based process models and machine learning tools to develop groundwater level and recharge maps at fine spatiotemporal scales. Novel contributions from this effort will include the development of new machine learning algorithms (such as convolutional and long-short term memory networks constrained by conservation principles), a new hydrogeological database derived from well log data, new machine learning tools for developing geological cross-sections from well log data, physically-realistic process models that use novel methods for estimating plant transpiration under climatic stress, and a new web platform for sharing groundwater level and recharge datasets. The integrated groundwater management framework will help answer several important science questions: 1) How well can we predict the groundwater levels and recharge at fine temporal resolution? 2) How different is the efficiency of data driven models compared to process-based models for obtaining groundwater recharge, and what are the advantages of a hybrid approach? and 3) What are the physical controls on groundwater drought-recovery processes?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.