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 km 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.
Highlights
Snow represents the predominant winter land surface cover for 50% of North America, Europe andAsia [1]
We have developed Python and C core libraries to manipulate Sensor Object Library (SOL) objects, including serialization, de-serialization, conversion and validation routines
As demonstrated by [40], better hydrologic information can potentially increase hydropower revenue. This deployment of Wireless Sensor Network (WSN) demonstrates the capability of collecting more comprehensive hydrologic data, which can potentially translate into lower uncertainty in streamflow forecasts at various temporal and spatial scales and improved economic viability of hydropower
Summary
Snow represents the predominant winter land surface cover for 50% of North America, Europe and. Snow provides one-sixth of the world’s population with fresh water [2]. Runoff from snowmelt is an important source of hydropower for populous regions such as the Himalayas in. Asia [4], the Sierra Nevada and Rocky Mountains in the Western United States [5,6], and the Alps in Sensors 2017, 17, 2583; doi:10.3390/s17112583 www.mdpi.com/journal/sensors. As climate change and population growth increase strain on water and energy systems, it is crucial to improve monitoring of the snowpack and snowmelt processes in the world’s mountain regions to enhance control and forecasting of water supplies [8]
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