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Harvard Forest Symposium Abstract 2008
- Title: A Cost-Effective Automated Sensor Network for Meteorological and Snow Depth Measurements
- Primary Author: Robert Hellstrom (Bridgewater State University)
- Abstract:
Cost and time requirements for consistently measuring meteorological forcing and snow depth in areas with heterogeneous physiography often limit the potential for snow model and remote sensing validation. This paper reports on the development and testing of an automatic sensor network (ASN) that measures soil and snowpack temperature profiles, snow depth, air temperature, humidity, reflected light intensity, and wind speed. The anemometer, consisting of half ping-pong balls attached to four arms extending orthogonally from the shaft of a low-resistance electric motor, was calibrated in a wind tunnel. Recent deployments (2007-2008) of the ASN at mixed deciduous, mature Hemlock and open sites within the Harvard Forest of northern Massachusetts suggest low maintenance requirements under a broad range of meteorological conditions. Each ASN consists of three HOBO® loggers with internal and external sensors and one outdoor surveillance camera that takes daily snapshots of an 8X8 meter graduated snow stake grid and stores them on a laptop computer. Bi-weekly site visits provided snow pit density profiles of a snowpack with approximately 0.5 m depth at maximum. Comparisons of hourly measurements at the three sites show important differences between soil-snow temperature profiles and other measured variables that will improve the forest-snow algorithms in snow models.
- Research Category: Forest-Atmosphere Exchange
- Figures: C:Documents and Settings hellstrom hellstrom's DocumentsResearchWORKinPROGRESSHarvard_ForestPresentations_AbstractsHFabstract2008HemlockSetupPic.JPG
