I-Corps: Cloud-based Water Resources Simulations

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The project team plans to further develop a new enabling technology for groundwater modeling - one that has the potential to substantially reduce the costs of groundwater sustainability investigations and could change how groundwater is managed, how data is collected, and even how people collaborate. Under this initiative, the project team has developed a cloud-based groundwater simulation system, that is delivered as a service, not as a product, and is live-linked to a massive pre-processed spatial database, allowing one to zoom in anywhere covered by the database to make a preliminary prediction of groundwater flow and contaminant transport. Additionally, the software allows users to add their own data to that provided by the service, refining model predictions and allows aggregating user data, essentially turning users into co-developers/collaborators and building value as a side-effect of their use of the application.As sustainability is among the greatest challenges in the 21st century and recent IT and data revolution offers unprecedented opportunities for potential breakthroughs in the ability to understand and manage complex systems, interactions, and sustainability. These possibilities, however, are largely unrealized, primarily because the technologies to produce big data are not matched by technologies to analyze them. The project team recently partnered with the Michigan Department of Environmental Quality to develop this integrated computational infrastructure in groundwater science and engineering. The collaboration takes advantage of existing programs - statewide database development and data integration from a network of state, federal, and other institutional storehouses on the part of the State and computational advancements, specifically in hierarchical GIS-based modeling, stochastic modeling, and real-time visual simulation and data analytics, on the part of the project team - and exploits the synergies of integrating these efforts to significantly improve our knowledge-creating infrastructure to understand, characterize, model, and visualize complex groundwater systems.