Title: Groundwater Science and Engineering in the Era of Big Data and Networks
Speaker: Dr. Shu-Guang Li
Professor of Civil and Environmental Engineering, Michigan State University
Abstract: In recent decades, the world has undergone dramatic transformation, including mega-scale urbanization, industrialization, and intensive farming. This human civilization, however, was not achieved without a price. Environmental degradation, particularly groundwater pollution and depletion, threatens sustainability. In parts of the world, eighty percent of shallow water wells are believed to be polluted / contaminated. Aquifer mining is occurring at an alarming rate and on a regional scale – to the extent that the depletion is “visible” from orbiting satellites. The rapidly declining groundwater storage further aggravates contamination which, in turn, reduces water availability.
Recognizing the severe environmental challenges, world governments have invested heavily in environmental protection – with activities in research, education, management, and cleanup. The effectiveness of these activities, however, has been limited by fundamental issues that can be ultimately related to data. On the one hand, there is “too little data” - this is probably the most familiar “complaint” from researchers and practitioners alike dealing with the invisible groundwater resource. But, in this talk, I argue there is also “too much data”. In fact, one of the major reasons we have difficulty making sense of the precious little site-specific data is because we are not taking full advantage of the vast existing data available.
While the “too little data” problem has been echoed for decades, today is not the 1980s, ‘90s, or even the 2000s. We live in a dramatically different world – in terms of information, technologies, and communication (ITC). Amid this ITC revolution, a number of new possibilities / opportunities are afforded: a) popularization of remote sensing technologies: creating an extremely spatially rich world and a seamless global hydrological spatial framework; b) emerging drone / unmanned aerial vehicle (UAV) technologies: offering ultra-high-resolution land use/cover and land elevations invaluable for local/site scale hydrological / environmental system modeling; c) wireless sensor network technologies: enabling detailed, automated, continuous monitoring of temporal dynamics in water level/qualities; d) popularization of GIS technologies: triggering global movement of spatial data integration, including integration of vast derived / repurposed data that can be used for groundwater investigations; e) cloud / parallel computing technologies: dramatically enhancing raw computing power, and available on demand for 3D integrated system simulations across scales; and f) 5G Internet communications technologies: providing unprecedented interconnectedness and information sharing in ways previously unimaginable, delivering high fidelity videos, images, and data at lightning speed.
Although groundwater occurs underground, movement is essentially controlled by surface features / conditions and topographic elevations. These surface features / conditions can now be delineated with unprecedented fidelity and details, seamlessly, and on a global scale. This “big data” essentially controls distributed spatial stress / system dynamics. Local “small data”, including data from sensors, provide valuable information for site-specific model calibration and help to characterize site-specific variability and controls, which can only be properly / meaningfully evaluated within a local-regional system context. Taking full advantage of the global spatial data revolution is, however, “easier said than done”! Big Data, though highly valuable, is difficult to use. It is scattered, of variable formats and qualities / resolutions, and highly dimensional (many attributes / parameters). Because of its massive size, big data is increasingly immovable – it is simply too time-consuming, too slow, and too expensive for most to use on a routine basis. Too often, we spend 90% of our project time just on data processing and manipulation. The result is a severe underutilization of big data.
We have truly yet to figure out how to take full advantage of the global spatial data revolution because we have a “paradigm problem”. The way we work – which we often take for granted – is preventing us from taking full advantage of new opportunities. We, as a community, “reinvent the wheel” on a massive scale! Although water resources / pollution problems at different places are different in context, the process of solving them is substantially the same. All studies involve first understanding the system – just at different locations, different scales / resolutions, or levels of details. All studies apply the same universal principles, equations, and conservation laws governing subsurface processes – now using the same universal data (global spatial data). In fact, the long, data-intensive modeling process only differs toward the end (model application). This begs the question: does it make sense for everybody to go through the same process over and over again? In other words, why do we all repeatably integrate, download, curate, filter, process, analyze, program, store, model, visualize and transmit the same data? This grossly inefficient process - while tolerable for small data – is becoming impractical / untenable in the era of big data.
In this talk, I introduce an initiative called “MAGNET4WATER” - Multiscale, Adaptive Global Network for WATER, a deliverable of a recent National Science Foundation I-Corps project. The initiative scrutinizes the entire process of big data-based problem solving and modeling - from data integration to processing, representation, storage, access, modeling, simulation, transmission, assimilation, cognition, and communication. The project integrates hydrology, environmental engineering, computational science/mathematics, software engineering, communications, and network technologies. In particular, I introduce a big data enabled, cloud powered, and realtime interactive global modeling platform – one that allows zooming to anywhere and almost instantly create of a preliminary groundwater flow and transport model that can be further refined with local data and expertise. The big data enabled platform is constantly evolving and dynamically adaptive with respect to both the data linked and the modeling capabilities. The platform is particularly useful in guiding site-specific data collection and evaluate data worth. The platform is currently being expanded to support a comprehensive suite of water resources community models, including USGS MODFLOW/MT3D and SEAWAT for groundwater modeling, SWAT and HECHMS for watershed modeling, HECRAS for channel modeling, SWMM for stormwater modeling, and EPANET for water distribution network modeling.
Biographic Sketch of the Speaker:
Prof. Li earned his Ph.D. in Water Resources and Environmental Engineering from the Massachusetts Institute of Technology. His research covers a range of technical interests in hydrology and water resources, from theoretical to computational to technological, on fundamental as well as applied problems. His innovative integration of scientific hydrology, applied mathematics, computational sciences, “big data”, and information technologies has advanced the ability to model complex groundwater systems and expanded the utility of modeling as a tool for research, education, and professional investigation. Prof. Li's research has been funded by the National Science Foundation (NSF) through a number of cross-cutting programs, including: Hydrological Sciences, Environmental Engineering, Computer Sciences and Information Engineering, Engineering Education & Centers, Undergraduate Education, and Industrial Innovations and Partnerships. Prof. Li's research has also been funded by the Michigan Department of Environmental Quality, the Michigan Department of Agriculture for Rural Service, the Michigan Department of Military and Veteran Affairs, the US Fish and Wildlife Service, the US Environmental Protection Agency, the US Geological Survey, the Great Lakes Protection Fund, and local government agencies, industries, corporations, law firms, and citizen groups. Prof. Li is an associate editor for the ASCE Journal of Hydrologic Engineering, the National Groundwater Association's Journal of Ground Water, and the Journal of Stochastic Environmental Research and Risk Assessment. He is a registered professional engineer and an elected Fellow of the American Society of Civil Engineers and of the Geological Society of America.
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