WiFi Weather Station and Snow Depth Monitoring System for Snow Research at Harvard Forest, Massachusetts

Publication Year:
2010
Usage 137
Abstract Views 137
Repository URL:
https://vc.bridgew.edu/small_grant/12
Author(s):
Hellström, Robert
Tags:
meteorology; environmental geography
artifact description
The PI, Dr. Rob Hellstrom, has been studying snow cover at the Harvard Forest Long Term Ecological Research (HFR) station over the past three years (Hellstrom, 2008). This research has applied leading-edge sensor technology to measure the impact of various types of forest cover on winter and spring season snow accumulation and the newly installed wireless network at HFR provides a unique opportunity for new approaches and to disseminate observations through the Internet.The Pi's research is timely given continuing changes in the regional climate. Dramatic changes in forest coverage in New England over the past two centuries, largely attributed to human causes, such as logging, agriculture, urbanization and infestations, are presumed partially responsible for locally rising temperature trends over the 20th century (Keim et al., 2003; Trombulak and Wolfson, 2004). Jones and Moberg (2003) suggest that the warming may be most pronounced during the winter months. In New England, decreasing trends in snowfall (Huntington et al., 2004) and six-fold increase loss of forest cover by anthropogenic development (Foster, et al., 2005) in the second half of the 20th century, together with this strong regional warming trend, will affect the magnitude, duration and timing of snow cover.This past summer (2010), the HFR staff installed a WiFi network in the forest, the Harvard Forest Field Wireless network (HFFW) to provide high-speed Internet access to areas around various research sites in the forest. These research sites are in close proximity to the Pi's field locations established in 2008 (Hellstrom, 2008), and consequently offers the PI the opportunity to install specialized sensors that will interface with the HFFW, thereby providing remote access for research and integration into meteorology and climate courses taught at BSU. Furthermore, the PI intends to test a new sonar sensor for automatically measuring snow depth throughout the snow season, which will be about 1/10 the cost of off-the-shelf sensors ($1,000), and hence very attractive other snow scientists.