Abstract: Measurements from a fixed-bed, Froude-scaled hydraulic model of a stream in northeastern Vermont demonstrate the importance of forested riparian vegetation effects on near-bank turbulence during overbank flows. Sections of the prototype stream, a tributary to Sleepers River, have increased in channel width within the last 40 years in response to passive reforestation of its riparian zone. Previous research found that reaches of small streams with forested riparian zones are commonly wider than adjacent reaches with non-forested, or grassy, vegetation; however, driving mechanisms for this morphologic difference are not fully explained. Flume experiments were performed with a 1:5 scale, simplified model of half a channel and its floodplain, mimicking the typical non-forested channel size. Two types of riparian vegetation were placed on the constructed floodplain: non-forested, with synthetic grass carpeting; and forested, where rigid, randomly distributed, wooden dowels were added. Three-dimensional velocities were measured with an acoustic Doppler velocimeter at 41 locations within the channel and floodplain at near-bed and 0·6-depth elevations. Observations of velocity components and calculations of turbulent kinetic energy (TKE), Reynolds shear stress and boundary shear stress showed significant differences between forested and non-forested runs. Generally, forested runs exhibited a narrow band of high turbulence between the floodplain and main channel, where TKE was roughly two times greater than TKE in non-forested runs. Compared to non-forested runs, the hydraulic characteristics of forested runs appear to create an environment with higher erosion potential. Given that sediment entrainment and transport can be amplified in flows with high turbulence intensity and given that mature forested stream reaches are wider than comparable non-forested reaches, our results demonstrated a possible driving mechanism for channel widening during overbank flow events in stream reaches with recently reforested riparian zones.
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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).