Abstract: Combined results from field-based investigations and flume experiments demonstrated key mechanisms driving channel widening following the reforestation of riparian zones in small streams. Riparian reforestation is a common occurrence either due to restoration efforts, intended to improve water quality, temperature regimes, and in-stream physical habitat or due to passive reforestation that is common when agricultural land uses decline. Previous studies have documented the influence of riparian vegetation on channel size, but driving mechanisms and the timescales at which they operate have not been evaluated. Field-based investigations were conducted in the Sleepers River basin in northeastern Vermont to revisit streams that were previously surveyed in the 1960s. We measured channel dimensions, large woody debris (LWD), and steam velocities in reaches with non-forested and forested riparian vegetation, in reaches currently in transition between vegetation types, and reaches with no change in riparian vegetation over the last 40 years. Flume experiments were performed with a 1:5 scale, fixed-bed model of a tributary to Sleepers River. Two types of riparian vegetation scenarios were simulated: 1) forested, with rigid, wooden dowels; and 2) non-forested, with synthetic grass carpeting. Three-dimensional velocities were measured during flume runs to determine turbulent kinetic energy (TKE) during overbank flows. Results showed that stream reaches with recently reforested vegetation have widened since the mid 1960s, but are not as wide as reaches with older riparian forests. LWD was more abundant in reaches with older riparian forests than in reaches with younger forests; however, scour around LWD did not appear to be a significant driving mechanism for channel widening. Velocity and TKE measurements from the prototype stream and the flume model indicate that TKE was significantly elevated in reforested reaches. Given that bed and bank erosion can be amplified in flows with high TKE, channel widening may be driven by increased turbulence generation in reforested reaches and may operate on a much shorter timescale than previously thought. Understanding the driving mechanisms and the timing of this channel widening phenomenon is important to predict geomorphic change due to riparian reforestation efforts, inform stream restoration designs, and evaluate the ultimate impact on aquatic ecosystems.
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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).