Edward H. Bair

Validating reconstruction of snow water equivalent in California's Sierra Nevada using measurements from the NASA Airborne Snow Observatory

Author(s): Bair, EH; Rittger, K; Davis, RE; Painter, TH; Dozier, J | Abstract:

CUES—a study site for measuring snowpack energy balance in the Sierra Nevada

Accurate measurement and modeling of the snowpack energy balance are critical to understanding the terrestrial water cycle. Most of the water resources in the western US come from snowmelt, yet statistical runoff models that rely on the historical record are becoming less reliable because of a changing climate. For physically based snow melt models that do not depend on past conditions, ground based measurements of the energy balance components are imperative for verification. For this purpose, the US Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL) and the University of California, Santa Barbara (UCSB) established the “CUES” snow study site (CRREL/UCSB Energy Site, http://www.snow.ucsb.edu/) at 2940 m elevation on Mammoth Mountain, California. We describe CUES, provide an overview of research, share our experience with scientific measurements, and encourage future collaborative research. Snow measurements began near the current CUES site for ski area operations in 1969. In the 1970s, researchers began taking scientific measurements. Today, CUES benefits from year round gondola access and a fiber optic internet connection. Data loggers and computers automatically record and store over 100 measurements from more than 50 instruments each minute. CUES is one of only five high altitude mountain sites in the Western US where a full suite of energy balance components are measured. In addition to measuring snow on the ground at multiple locations, extensive radiometric and meteorological measurements are recorded. Some of the more novel measurements include scans by an automated terrestrial LiDAR, passive and active microwave imaging of snow stratigraphy, microscopic imaging of snow grains, snowflake imaging with a multi-angle camera, fluxes from upward and downward looking radiometers, snow water equivalent from different types of snow pillows, snowmelt from lysimeters, and concentration of impurities in the snowpack. We give an example of terrain-corrected snow albedo measurements compared to several models and of sublimation measured from lysimeter and snow pillow melt. We conclude with some thoughts on the future of CUES.

Avalanche Fatalities during Atmospheric River Events in the Western United States

AbstractThe occurrence of atmospheric rivers (ARs) in association with avalanche fatalities is evaluated in the conterminous western United States between 1998 and 2014 using archived avalanche reports, atmospheric reanalysis products, an existing AR catalog, and weather station observations. AR conditions were present during or preceding 105 unique avalanche incidents resulting in 123 fatalities, thus comprising 31% of western U.S. avalanche fatalities. Coastal snow avalanche climates had the highest percentage of avalanche fatalities coinciding with AR conditions (31%–65%), followed by intermountain (25%–46%) and continental snow avalanche climates (<25%). Ratios of avalanche deaths during AR conditions to total AR days increased with distance from the coast. Frequent heavy to extreme precipitation (85th–99th percentile) during ARs favored critical snowpack loading rates with mean snow water equivalent increases of 46 mm. Results demonstrate that there exists regional consistency between snow avalanche ...

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

10 In many mountains, snowmelt provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely-sensed information as predictors and 15 reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer’s lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques—bagged regression trees and feed-forward neural networks— produced similar mean results, with 0–14% bias and 46–48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the 20 methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.