Essential Site Maintenance: Authorea-powered sites will be updated circa 15:00-17:00 Eastern on Tuesday 5 November.
There should be no interruption to normal services, but please contact us at [email protected] in case you face any issues.

loading page

Complementary observations aid identification of the mountain rain-snow transition elevation
  • +6
  • W. Tyler Brandt,
  • Forest Cannon,
  • Ava Cooper,
  • Luca Delle Monache,
  • Kayden Haleakala,
  • Benjamin J Hatchett,
  • Bruce McGurk,
  • Ming Pan,
  • F. Martin Ralph
W. Tyler Brandt
Scripps Institution of Oceanography

Corresponding Author:[email protected]

Author Profile
Forest Cannon
Scripps Institution of Oceanography
Author Profile
Ava Cooper
Scripps Institution of Oceanography
Author Profile
Luca Delle Monache
University of California San Diego
Author Profile
Kayden Haleakala
University of California Los Angeles
Author Profile
Benjamin J Hatchett
Desert Research Institute
Author Profile
Bruce McGurk
Self-employed
Author Profile
Ming Pan
University of California San Diego
Author Profile
F. Martin Ralph
SIO
Author Profile

Abstract

The elevation of the mountain rain-snow transition is critical for short-term hazard forecasting and longer-term water supply considerations. Despite the transition’s importance, direct in-situ observations are rare. Here we present two new methods that utilize “anomalous” snow observations to detect rainfall during rain-on-snow: (1) a mass fluctuation at snow pillow sites, and (2) inflated remotely sensed snow grain sizes. Using auxiliary data, we show snow pillows respond to rain-on-snow with distinct perturbations that appear as pulses, collapses and declines within the snow water equivalent. We use these responses to identify mountain-scale rain-snow transitions across California’s Sierra Nevada. We also show how a threshold approach (>200 mm) for remotely sensed snow grain size can identify rain-on-snow as snow grain sizes artificially inflate due to a liquid water film. While the methods are not predictive, if paired retroactively with hydrometeorological models, these new methods have the potential to improve predictive streamflow capabilities.