Using low cost open source temperature sensors to investigate the effects of vegetation type on snow depth and redistribution at ND-LEEF

  • Funded By: Notre Dame Environmental Change Initiative
  • ECI Investigators: Salvatore Curasi, Bethany Blakely, Adrian Rocha

Using low cost open source temperature sensors to investigate the effects of vegetation type on snow depth and redistribution at ND-LEEF

Snow plays a fundamental role in local hydrology, with implications for vegetation phenology, water availability, and human recreation. At small scales, the primary determinant of snow cover is redistribution by wind such that local snow depth is often entirely decoupled from measured precipitation. Vegetation creates local patterns of snow erosion and deposition that have important hydrologic and ecological consequences. However, these patterns remain poorly understood due to limited information on the complex interactions of vegetation cover, surface roughness and wind speed.

Snow depth and other ecological variables often vary at high spatial frequencies. Professionally designed automated snow measurement systems are extremely expensive (~$800 - $17,000) prohibiting the collection of large numbers of data points clustered in space. Open source and DIY sensors allow for a large number of high frequency data points to be collected while forgoing the markup associated with professional systems. 

This research aims to better understand snow redistribution along a forest-grassland boundary at ND-LEEF, while also developing a DIY system to conduct snow depth measurements at extremely high spatial resolution.
 

 

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