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Title: Real-Time Alpine Measurement System Using Wireless Sensor Networks

Abstract

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 duemore » 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. In conclusion, 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.« less

Authors:
 [1];  [1];  [2]; ORCiD logo [1];  [1];  [3];  [2];  [1]
  1. Univ. of California, Berkeley, CA (United States)
  2. French Institute for Research in Computer Science and Automation (Inria), Paris (France)
  3. Univ. of California, Davis, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1479347
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Sensors
Additional Journal Information:
Journal Volume: 17; Journal Issue: 11; Journal ID: ISSN 1424-8220
Publisher:
MDPI AG
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 58 GEOSCIENCES; wireless sensor networks; ground measurement system; mountain hydrology; snow pack; internet of things; real-time monitoring system

Citation Formats

Malek, Sami A., Avanzi, Francesco, Brun-Laguna, Keoma, Maurer, Tessa, Oroza, Carlos A., Hartsough, Peter C., Watteyne, Thomas, and Glaser, Steven D. Real-Time Alpine Measurement System Using Wireless Sensor Networks. United States: N. p., 2017. Web. doi:10.3390/s17112583.
Malek, Sami A., Avanzi, Francesco, Brun-Laguna, Keoma, Maurer, Tessa, Oroza, Carlos A., Hartsough, Peter C., Watteyne, Thomas, & Glaser, Steven D. Real-Time Alpine Measurement System Using Wireless Sensor Networks. United States. doi:10.3390/s17112583.
Malek, Sami A., Avanzi, Francesco, Brun-Laguna, Keoma, Maurer, Tessa, Oroza, Carlos A., Hartsough, Peter C., Watteyne, Thomas, and Glaser, Steven D. Thu . "Real-Time Alpine Measurement System Using Wireless Sensor Networks". United States. doi:10.3390/s17112583. https://www.osti.gov/servlets/purl/1479347.
@article{osti_1479347,
title = {Real-Time Alpine Measurement System Using Wireless Sensor Networks},
author = {Malek, Sami A. and Avanzi, Francesco and Brun-Laguna, Keoma and Maurer, Tessa and Oroza, Carlos A. and Hartsough, Peter C. and Watteyne, Thomas and Glaser, Steven D.},
abstractNote = {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 km2 network location. In conclusion, 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.},
doi = {10.3390/s17112583},
journal = {Sensors},
number = 11,
volume = 17,
place = {United States},
year = {2017},
month = {11}
}

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