Authors: Ben Roberts-Pierel*, Oregon State University
Topics: Cryosphere, Remote Sensing, Water Resources and Hydrology
Keywords: cryosphere, cloud computing, google earth engine, MODIS, snowmelt
Session Type: Paper
Start / End Time: 8:00 AM / 9:40 AM
Room: Roosevelt 6, Marriott, Exhibition Level
Presentation File: No File Uploaded
The field of snow remote sensing has relied on a variety of sensors and techniques to quantify aspects of snow-covered area/extent (SCA/SCE), snow water equivalent (SWE) and grain size, among others. While research has looked at inter-annual variability of snow cover, less research has been done on sub-annual variability, particularly the intermittence of snow cover and its relationship to spring melt and runoff. This research relies on a snow cover metric called snow cover frequency (SCF), which was created by members of the Mountain Hydroclimatology Research Group at Oregon State University based on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to investigate these processes. The research aims to quantify snow intermittence from late fall through snowmelt in the spring for each year in the MODIS data archive (~2000 to the present). Work is being conducted in the Columbia River Basin and sub-basins with the goal of investigating the impact that elevation, climatic zones and annual variation have in determining intermittence of snow cover and melt. SCF is calculated in Google Earth Engine (GEE), a cloud computing platform that hosts geospatial data and relies on computing power on Google’s servers. Relying on this platform and the 8-day temporal resolution of MODIS allows for a flexible and powerful approach to understanding change over temporal and spatial scale. While the research is ongoing, the findings could have important implications for the timing of snowmelt and runoff in the basin.