A comparison of SNOTEL and AMSR-E snow water equivalent datasets in western U.S. watersheds

Abstract

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e.g., precipitation, snow). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western United States. However, the ability to produce accurate forecasts and analyses is dependent upon the quality (accuracy) of these predictors (snowpack). Current snowpack datasets are based upon in-situ telemetry. Recent satellite deployments offer an alternative remote sensing data source of snowpack. The proposed research will investigate (compare) remote sensing datasets in western U.S. watersheds in which snowpack is the primary driver of streamflow. A comparison is made between snow water equivalent (SWE) data from in-situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer -- earth observing system (AMSR-E) aboard NASA's Aqua satellite. Principal component techniques and Singular Value Decomposition are applied to determine similarities and differences between the datasets and investigate regional snowpack behaviors. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine if satellite based remote sensed SWE data provide a comparable option to in-situ datasets. Watersheds investigated include the North Platte River, the Upper Green River, and the Upper Colorado River. The time period analyzed is 2003--2008, due to the recent deployment of the NASA Aqua satellite. Two distinct snow regions were found to behave similarly between both datasets using principal component analysis. Singular Value Decomposition linked both data products with streamflow in the region and found similar behaviors among datasets. However, only 11 of the 84 SNOTEL sites investigated correlated at a significance of 90% or greater with its corresponding AMSR-E cell. Also, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent difference in the magnitude (Snow Water Equivalent) of the datasets. Finally, both datasets were utilized and compared in a statistically based streamflow forecast of several gages.