Monitoring snow water equivalent using the phase of RFID signals

Le Breton, Mathieu ; Larose, Éric ; Baillet, Laurent ; Lejeune, Yves ; van Herwijnen, Alec

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<p align=justify>The amount of water contained in a snowpack, known as snow water equivalent (SWE), is used to anticipate the amount of snowmelt that could supply hydroelectric power plants, fill water reservoirs, or sometimes cause flooding. This work introduces a wireless, non-destructive method for monitoring the SWE of a dry snowpack. The system is based on an array of low-cost passive radiofrequency identification (RFID) tags, placed under the snow and read at 865-868 MHz by a reader located above the snow. The SWE was deduced from the phase delay of the tag's backscattered response, which increases with the amount of snow traversed by the radiofrequency wave. Measurements taken in the laboratory, during snowfall events and over 4.5 months at the Col de Porte test field, were consistent with reference measurements of cosmic rays, precipitation and snow pits. SWE accuracy was <span class="inline-formula">±18</span> kg m<span class="inline-formula"><sup>-2</sup></span> throughout the season (averaged over three tags) and <span class="inline-formula">±3</span> kg m<span class="inline-formula"><sup>-2</sup></span> during dry snowfall events (averaged over data from two antennas and four or five tags). The overall uncertainty compared to snow weighing was <span class="inline-formula">±10 <i>%</i></span> for snow density in the range 61-390 kg m<span class="inline-formula"><sup>-3</sup></span>. The main limitations observed were measurement bias caused by wet snow (biased data were discarded) and the need for phase unwrapping. The method has a number of advantages: it allows for continuous measurement (1 min sampling rate in dry snow), it can provide complementary measurement of tag temperature, it does not require the reception of external data, and it opens the way towards spatialized measurements. The results presented also demonstrate that RFID propagation-based sensing can remotely monitor the permittivity of a low-loss dielectric material with scientific-level accuracy.</p>
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