Plot-level Quantification of Snow Melt for Old-Growth Forest Plots of the Pacific Northwest Using Low-Cost Temperature Sensors

Keywords: Hydrology, Climate Change, Forest Ecology, Remote Sensing
Session Type: Virtual Poster Abstract
Day: Monday
Session Start / End Time: 2/28/2022 11:20 AM (Eastern Time (US & Canada)) - 2/28/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 33

Abstract

Snow melt at high elevations plays a role in the availability of water throughout the summer in lower elevations and helps determine the growing season of small trees in forest ecosystems. A changing climate could affect supplies of fresh water and forest growing seasons by causing earlier melt or less snow cover overall. Although recent changes in timing and quantity of snow melt have been relatively well documented for mountains of the western U.S., within plot variability is likely to be important to forest regeneration processes and has been less well quantified. The aim of this research is to investigate spatial and temporal trends in seasonal snow melt for old-growth forest plots within the HJ Andrews Experimental Forest in the Cascade Mountains in Oregon. We analyzed data from ~120 small temperature sensors (HOBOs) placed on the ground at HJ Andrews over seven years. We assumed that a prolonged reading of 0ºC implied snow cover over the HOBO, and we calculated the number of days of snow cover for each HOBO yearly. We verified our assumption using images from cameras focused on the HOBOs as they recorded snow cover. We found a relationship between global weather patterns and annual snowmelt, but did not find an overall decrease in snow cover or earlier snow melt in later years due to a warming climate. We also discuss the potential for an automated image classification to verify the results of the sensors and to obtain fractional snow cover area (fSCA) as an additional metric.