Real-Time Alpine Measurement System Using Wireless Sensor Networks
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 re...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/17/11/2583 |
| _version_ | 1811184066256437248 |
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| author | Sami A. Malek Francesco Avanzi Keoma Brun-Laguna Tessa Maurer Carlos A. Oroza Peter C. Hartsough Thomas Watteyne Steven D. Glaser |
| author_facet | Sami A. Malek Francesco Avanzi Keoma Brun-Laguna Tessa Maurer Carlos A. Oroza Peter C. Hartsough Thomas Watteyne Steven D. Glaser |
| author_sort | Sami A. Malek |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:06:54Z |
| format | Article |
| id | doaj.art-42a7e3c2dc6348f9927bf6bad261e804 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:06:54Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-42a7e3c2dc6348f9927bf6bad261e8042022-12-22T04:22:41ZengMDPI AGSensors1424-82202017-11-011711258310.3390/s17112583s17112583Real-Time Alpine Measurement System Using Wireless Sensor NetworksSami A. Malek0Francesco Avanzi1Keoma Brun-Laguna2Tessa Maurer3Carlos A. Oroza4Peter C. Hartsough5Thomas Watteyne6Steven D. Glaser7Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USADepartment of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USAFrench Institute for Research in Computer Science and Automation (Inria), 2 Rue Simone IFF, 75012 Paris, FranceDepartment of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USADepartment of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USADepartment of Land, Air, and Water Resources, University of California, Davis, CA 95616, USAFrench Institute for Research in Computer Science and Automation (Inria), 2 Rue Simone IFF, 75012 Paris, FranceDepartment of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USAMonitoring 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.https://www.mdpi.com/1424-8220/17/11/2583wireless sensor networksground measurement systemmountain hydrologysnow packinternet of thingsreal-time monitoring system. |
| spellingShingle | Sami A. Malek Francesco Avanzi Keoma Brun-Laguna Tessa Maurer Carlos A. Oroza Peter C. Hartsough Thomas Watteyne Steven D. Glaser Real-Time Alpine Measurement System Using Wireless Sensor Networks Sensors wireless sensor networks ground measurement system mountain hydrology snow pack internet of things real-time monitoring system. |
| title | Real-Time Alpine Measurement System Using Wireless Sensor Networks |
| title_full | Real-Time Alpine Measurement System Using Wireless Sensor Networks |
| title_fullStr | Real-Time Alpine Measurement System Using Wireless Sensor Networks |
| title_full_unstemmed | Real-Time Alpine Measurement System Using Wireless Sensor Networks |
| title_short | Real-Time Alpine Measurement System Using Wireless Sensor Networks |
| title_sort | real time alpine measurement system using wireless sensor networks |
| topic | wireless sensor networks ground measurement system mountain hydrology snow pack internet of things real-time monitoring system. |
| url | https://www.mdpi.com/1424-8220/17/11/2583 |
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