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Article

Real-Time Alpine Measurement System Using Wireless Sensor Networks

1
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
2
French Institute for Research in Computer Science and Automation (Inria), 2 Rue Simone IFF, 75012 Paris, France
3
Department of Land, Air, and Water Resources, University of California, Davis, CA 95616, USA
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(11), 2583; https://doi.org/10.3390/s17112583
Received: 26 September 2017 / Revised: 29 October 2017 / Accepted: 7 November 2017 / Published: 9 November 2017
(This article belongs to the Special Issue Sensor Networks for Environmental Observations)
Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape. View Full-Text
Keywords: wireless sensor networks; ground measurement system; mountain hydrology; snow pack; internet of things; real-time monitoring system. wireless sensor networks; ground measurement system; mountain hydrology; snow pack; internet of things; real-time monitoring system.
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MDPI and ACS Style

Malek, S.A.; Avanzi, F.; Brun-Laguna, K.; Maurer, T.; Oroza, C.A.; Hartsough, P.C.; Watteyne, T.; Glaser, S.D. Real-Time Alpine Measurement System Using Wireless Sensor Networks. Sensors 2017, 17, 2583. https://doi.org/10.3390/s17112583

AMA Style

Malek SA, Avanzi F, Brun-Laguna K, Maurer T, Oroza CA, Hartsough PC, Watteyne T, Glaser SD. Real-Time Alpine Measurement System Using Wireless Sensor Networks. Sensors. 2017; 17(11):2583. https://doi.org/10.3390/s17112583

Chicago/Turabian Style

Malek, Sami A., Francesco Avanzi, Keoma Brun-Laguna, Tessa Maurer, Carlos A. Oroza, Peter C. Hartsough, Thomas Watteyne, and Steven D. Glaser. 2017. "Real-Time Alpine Measurement System Using Wireless Sensor Networks" Sensors 17, no. 11: 2583. https://doi.org/10.3390/s17112583

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