Abstract: In this study, a bias-aware Ensemble Kalman Filter (EnKF) is applied to a scaled aquifer transport experiment (1- cm of the model equals 1-m at field scale and 1-day of model transport equals 1-year of field-scale transport) located at the University of Vermont. The scaled aquifer was constructed using layered porous media within a 2.54-m by 3.56-m by 2.43-m (10-ft by 14-ft by 8-ft) tank. The experimental porous media has been highly characterized and contains 105 sampling locations that are capable of providing concentration data at a high temporal resolution. The scaling of the tank experiment is applicable for advection dominated transport, which has been modeled using the three-dimensional flow-and-transport groundwater models - MODFLOW 2000 and MT3DMS. A 19-day (19-years scaled) ammonia chloride tracer experiment was conducted with concentration data collected at all 105 sampling locations every 17.5-minutes (4.4-days scaled). Results from this research demonstrate how a known bias in the flow-and-transport initial conditions relative to the true experimental conditions can dramatically degrade the EnKF framework's ability to make forward transport forecasts that capture observed breakthrough time series. The bias-aware extension of the EnKF significantly improved the reliability of concentration breakthrough forecasts. In the long term, this research is being used to develop new simulation-optimization frameworks for designing groundwater observation networks. In support of this long-term objective, this study explores the influence of model bias on both the mean and covariance projections provided by EnKF. Moreover, this work explores how the increased computational demands associated with the bias-aware EnKF can be reduced using minimal ensemble sizes and management period formulations. This work advances beyond the commonly employed static Kalman filter formulations employed in prior monitoring design studies (i.e., spatiotemporal kriging) by making forecasts more robust to nonlinearities while also accounting for measurement uncertainties and dynamic, spatiotemporally correlated model structure errors as well as model parameter errors.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).