Real-Time Alpine Measurement System Using Wireless Sensor Networks
| Autor: | Keoma Brun-Laguna, P. C. Hartsough, Carlos A. Oroza, Francesco Avanzi, Sami Malek, Steven D. Glaser, Thomas Watteyne, Tessa Maurer |
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| Přispěvatelé: | Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Wireless Networking for Evolving & Adaptive Applications (EVA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of California [Davis] (UC Davis), University of California, European Project: 688237,H2020 Pilier Industrial Leadership,H2020-ICT-2015,ARMOUR(2016), European Project: 687884,H2020 Pilier Industrial Leadership,H2020-ICT-2015,F-Interop(2015), University of California (UC) |
| Rok vydání: | 2017 |
| Předmět: |
010504 meteorology & atmospheric sciences
Meteorology Environmental Science and Management 0208 environmental biotechnology 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Article Water year Analytical Chemistry ground measurement system lcsh:TP1-1185 [INFO]Computer Science [cs] Precipitation Electrical and Electronic Engineering wireless sensor networks Instrumentation 0105 earth and related environmental sciences Ecology snow pack Elevation Snow internet of things 6. Clean water Atomic and Molecular Physics and Optics 020801 environmental engineering real-time monitoring system 13. Climate action mountain hydrology Snowmelt [SDE]Environmental Sciences Environmental science Spatial variability Surface runoff Distributed Computing Wireless sensor network |
| Zdroj: | Sensors Malek, SA; Avanzi, F; Brun-Laguna, K; Maurer, T; Oroza, CA; Hartsough, PC; et al.(2017). Real-Time alpine measurement system using wireless sensor networks. Sensors (Switzerland), 17(11). doi: 10.3390/s17112583. UC Berkeley: Retrieved from: http://www.escholarship.org/uc/item/859504mg Sensors (Basel, Switzerland) Sensors, MDPI, 2017, 17 (11), pp.1-30. ⟨10.3390/s17112583⟩ Sensors (Basel, Switzerland), vol 17, iss 11 Sensors, 2017, 17 (11), pp.1-30. ⟨10.3390/s17112583⟩ Sensors, Vol 17, Iss 11, p 2583 (2017) Sensors; Volume 17; Issue 11; Pages: 2583 |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s17112583 |
| Popis: | © 2017 by the authors. 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 distributedWireless 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 km2network 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. |
| Databáze: | OpenAIRE |
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