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 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 km2 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.

Evaluation of IEEE802.15.4g for Environmental Observations

“…Each node is mounted on a 1.8 m PVC tube. From Watteyne et al [ 1 ] and Malek et al [ 4 ], we see their choice of putting their sensors at a height of 4 m on top of fixed wooden poles, reducing the impact the ground poses as an obstacle to the radio links. In our case, 1.8 m tubes reach the same height at which smart meters are located in a house.…”

“…Low-power wireless mesh (sensor) networks drastically decrease the cost of implementing monitoring/control systems, enabling a wide range of applications in the industrial, environmental and urban context. Wireless mesh networks are used over a wide spectrum of environmental observation applications, including smart agriculture [ 1 ], fire monitoring [ 2 ], seismic activity logging [ 3 ], and snow-pack monitoring [ 4 ].…”

“…When sensors need to cover a large area, repeater nodes often need to be added to ensure that the deployment is dense enough. Malek et al [ 4 ] for example reported the need to install three times more repeaters than sensor nodes, increasing the cost and time of deployment. With IEEE802.15.4 at 2.4 GHz, radio links are typically 100 m, sometimes up to 300 m, but with a range that drastically decreases when obstacles (such as trees) are between the nodes.…”

Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation

“…HS can be also measured using ultrasonic [3] or laser [4] sensors, while SWE can be monitored using snow pillows [5] or cosmic rays [4]. The significance of local measurements has been often debated [6][7][8][9][10], especially in view of the marked spatial variability of snow processes [11][12][13][14]. To partially take this variability into account, snow manual measurements are often performed along snow courses and then averaged to provide a more representative estimation of available SWE and snow depth [15].…”

Remote sensing of river corridors: A review of current trends and future directions

“…Similar installations on smaller scales include active river and wetland management for water treatment (Wang et al, ), real‐time sewage monitoring in the United Kingdom to mitigate flooding scenarios (Edmondson et al, ), as well as conceptual designs of flood embankment monitoring systems (Michta, Szulim, Sojka‐Piotrowska, & Piotrowski, ). Uses for research include groundwater and river monitoring to better inform hydrological traits related to climatic variables, infiltration, and surface run off (Malek et al, ; Shi, Zhang, & Wei, ). Being able to effectively utilise the data captured over an IoT infrastructure may see the greatest development.…”