A snow index is a tool that allows you to compare the snow water amount for a specific basin with the historical period. Snow indexes are calculated by summing the snow water amount for the same list of SNOTEL sites for each year. These values can then be sorted to rank the current year versus the historical period to see how the current snowpack conditions compare to the past.
These snow graphs illustrate monthly snow water content and the chance for the snowpack to “recover” for a given year. When the snow accumulation is below average during the core winter months (typically Nov-Feb), users are often interested in the chance that snow water content will increase to normal seasonal levels by early April which is when the snowpack typically reaches its seasonal peak. Snow water data are sorted from low to high, using first of the month values for January, February, and March to compare across the accumulation season, ending on April 1st. Streamflow graphs are included to show a simplified relationship between seasonal snow water and streamflow. Snow data is sorted by the month of April to illustrate how peak snow water correlates with the April-September streamflow.
Click on a snowflake for the historical comparison of snowpack and streamflow for that basin.
Basin or Area
Bear River above ID-UT Stateline
Big Lost River
Big Wood River above Hailey
Little Wood River
Middle Fork Salmon River
Salmon Falls Creek
Snake River above Heise
Snake River above Jackson Lake
Q1. Why are there estimated (E/ST) snow water equivalent (swe) data in historical monthly SNOTEL files?
The estimated data in historical monthly SNOTEL SWE tables (Here’s an example from Atlanta Summit SNOTEL) are back generated values for the SNOTEL site based on measurements from a highly correlated snow course with a longer record. In many cases the SNOTEL was built very close to an older manually measured snow course. For the first 10 years or so after the SNOTEL was installed the snow course was still measured until a reliable regression relationship between the two locations could be developed. If a highly correlated relationship was found (R^2 > 0.90), then that relationship was used to back estimate SWE data for the SNOTEL site based on the historic snow course measurements. At that point the snow course was generally discontinued.