Low rates of bedrock outcrop erosion in the Central Appalachian Mountains inferred from in situ 10Be
Geological Society of America Bulletin, 125, 201-215, 2013
Abstract: Bedrock outcrops are common on central Appalachian Mountain ridgelines. Because these ridgelines define watersheds, the rate at which they erode influences the pace of landscape evolution. To estimate ridgeline erosion rates, we sampled 72 quartz-bearing outcrops from the Potomac and Susquehanna River Basins and measured in situ produced Be-10. Ridgeline erosion rates average 9 +/- 1 m m.y.(-1) (median = 6 m m.y.(-1)), similar to Be-10-derived rates previously reported for the region. The range of erosion rates we calculated reflects the wide distribution of samples we collected and the likely inclusion of outcrops affected by episodic loss of thick slabs and periglacial activity. Outcrops on main ridgelines erode slower than those on mountainside spur ridges because ridgelines are less likely to be covered by soil, which reduces the production rate of Be-10 and increases the erosion rate of rock. Ridgeline outcrops erode slower than drainage basins in the Susquehanna and Potomac River watersheds, suggesting a landscape in disequilibrium. Erosion rates are more similar for outcrops meters to tens of meters apart than those at greater distances, yet semivariogram analysis suggests that outcrop erosion rates in the same physiographic province are similar even though they are hundreds of kilometers apart. This similarity may reflect underlying lithological and/or structural properties common to each physiographic province. Average Be-10-derived outcrop erosion rates are similar to denudation rates determined by other means (sediment flux, fission-track thermochronology, [U-Th]/He dating), indicating that the pace of landscape evolution in the central Appalachian Mountains is slow, and has been since post-Triassic rifting events.
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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).