Abstract: Snow accumulated in mountainous areas is the source of water supply for much of the western United States. Estimates of the expected amounts of annual discharge in rivers are based on snow water equivalence (SWE) measurements and regression models that have climate stability as an underlying assumption. This is likely to be untrue as climate changes. This suggests that assessments of water availability change from statistical models to physically-based models. However, the current data collection network for SWE provides sparse areal coverage. Inexpensive wireless sensor networks and simple estimation techniques could be used to extend the areal coverage of snow data to improve spatial resolution. We report results of deployment of a prototype wireless sensor network, Snowcloud, at the Sagehen Creek, CA experimental field station. The network reported snow depth and temperature from January—May, 2010. A simple estimate of SWE at each node was based on the assumption of stability of the ratio SWE/SD and predicted SWE with reasonable accuracy (average difference of+ 1.0 cm (0.4 in), standard deviation= 3.0 cm (1.2 in)). Regression analysis indicated significant associations (P<. 05) between SWE and% canopy closure to the north, weekly total incoming solar radiation and monthly average temperature. These results indicate that wireless sensor networks measuring SD can be used to extend information from snow measurement sites accurately to give estimates of water availability in snowpack.
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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).