Minimization of Cost, Sediment Load, and Sensitivity to Climate Change in a Watershed Management Application
Environmental modelling & software, 50, 158-168, 2013
Abstract: One challenge of climate change adaptation is to design watershed-based stormwater management plans that meet current total maximum daily load targets and also take into consideration anticipated changes in future precipitation patterns. We present a multi-scale, multiobjective framework for generating a diverse family of stormwater best management practice (BMP) plans for entire watersheds. Each of these alternative BMP configurations are non-dominated by any other identified solution with respect to cost of the implementation of the management plan and sediment loading predicted at the outflow of the watershed; those solutions are then pruned with respect to dominance in sensitivity to predicted changes in precipitation patterns. We first use GIS data to automatically precompute a set of cost-optimal BMP configurations for each subwatershed, over its entire range of possible treatment levels. We then formulate each solution as a real-valued vector of treatment levels for the subwatersheds and employ a staged multiobjective optimization approach using differential evolution to generate sets of non- dominated solutions. Finally, selected solutions are mapped back to the corresponding preoptimized BMP configurations for each subwatershed. The integrated method is demonstrated on the Bartlett Brook mixed-used impaired watershed in South Burlington, VT, and patterns in BMP configurations along the non-dominated front are investigated. Watershed managers and other stakeholders could use this approach to assess the relative trade-offs of alternative stormwater BMP configurations.
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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).