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 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.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 1479347
- Journal Information:
- Sensors, Vol. 17, Issue 11; ISSN 1424-8220
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Snow depth, air temperature, humidity, soil moisture and temperature, and solar radiation data from the basin-scale wireless-sensor network in American River Hydrologic Observatory (ARHO)
|
dataset | January 2020 |
Remote sensing of river corridors: A review of current trends and future directions
|
journal | May 2019 |
Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation
|
journal | May 2018 |
Evaluation of IEEE802.15.4g for Environmental Observations
|
journal | October 2018 |
Similar Records
Anthropogenic Warming Impacts on Today's Sierra Nevada Snowpack and Flood Risk: WARMING IMPACTS ON SNOWPACK AND FLOOD RISK
Final Report: Phase II Nevada Water Resources Data, Modeling, and Visualization (DMV) Center