A comparison of SNOTEL and AMSR-E snow water equivalent data sets in western US watersheds

It is a consensus among earth scientists that climate change will result in an increased frequency of extreme events (e. g. floods, droughts). Streamflow forecasts and flood/drought analyses, given this high variability in the climatic driver (snowpack), are vital in the western USA. However, the ability to produce accurate forecasts and analyses is dependent upon the quality of these predictors. Run-off and stream volume analysis in the region is currently based upon in situ telemetry snow data products. Recent satellite deployments offer an alternative data source of regional snowpack. The proposed research investigates and compares remotely sensed snow water equivalent (SWE) data sets in western US watersheds in which snowpack is the primary driver of streamflow. Watersheds investigated include the North Platte, Upper Green and Upper Colorado. SWE data sets incorporated are in situ snowpack telemetry (SNOTEL) sites and the advanced microwave scanning radiometer-earth observing system (AMSR-E) aboard NASA's Aqua satellite. The time period analysed is 2003-2008, coincident with the deployment of the NASA Aqua satellite. Bivariate techniques between data sets are performed to provide valuable information on the time series of the snow products. Multivariate techniques including principal component analysis (PCA) and singular value decomposition (SVD) are also applied to determine similarities and differences between the data sets and investigate regional snowpack behaviours. Given the challenges (including costs, operation and maintenance) of deploying SNOTEL stations, the objective of the research is to determine whether remotely sensed SWE data provide a comparable option to in situ data sets. Correlation analysis resulted in only 11 of the 84 SNOTEL sites investigated being significant at 90% or greater with a corresponding AMSR-E cell. Agreement between SWE products was found to increase in lower elevation areas and later in the snowpack season. Two distinct snow regions were found to behave similarly between both data sets using a rotated PCA approach. Additionally, SVD linked both data products with streamflow in the region and found similar behaviour among data sets. However, when comparing SNOTEL data with the corresponding satellite cell, there was a consistent bias in the absolute magnitude (SWE) of the data sets. The streamflow forecasting results conclude regions that have few (or zero) land-based weather stations can incorporate the AMSR-E SWE product into a streamflow forecast model and obtain accurate values.