Abstract
Abstract. The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q.
Highlights
The mass and energy balance of the snowpack govern its evolution
As it relates to snow hydrology and albedo degradation, large portions of Mammoth Mountain are coved by tephra or pumice
CUES is the only site where the full energy and mass balance is measured on Mammoth Mountain; it is ideal for a full mass/energy balance simulation
Summary
The mass and energy balance of the snowpack govern its evolution. Direct measurement of the variables that comprise these balances is critical to our understanding of the snowpack. Monitoring of the snowpack energy and mass balance has broad utility as the timing and rate of snowmelt affect over 60 million people in the western US (Bales et al, 2006) and a billion people worldwide (Barnett et al, 2005). There are some variables, such as the broadband snow albedo, which require substantial and nontrivial adjustments that require detailed information on measurement location. This partly explains why high-quality datasets of snow albedo, a driver for snowmelt for many parts of the world (e.g., Marks and Dozier, 1992; Painter et al, 2018; van den Broeke et al, 2011), are rare. Bair et al.: Hourly mass and snow energy balance measurements from Mammoth Mountain
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
