An Examination of Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent Reconstruction

Abstract

Snow albedo is a dominant control on snowmelt in many parts of the world. An empirical albedo decay equation, developed over 60 years ago, is still used in snowmelt models. Several empirical snow albedo models developed since show wide spread in results. Remotely sensed snow albedos have been used in a few studies, but validations are scarce because of the difficulty in making accurate in situ measurements. Reconstruction of snow water equivalent (SWE), where the snowpack is built in reverse, is especially sensitive to albedo. We present two new contributions: (1) an updated albedo model where grain size and light absorbing particle content are solved for simultaneously and (2) multiyear comparisons of remotely sensed and in situ albedo measurements from three high‐altitude sites in the western United States. Our remotely sensed albedos show 4 to 6% RMSE and negligible bias. In comparison, empirical albedo decay models, which require extensive in situ measurements, show RMSE values of 7 to 17% with biases of −6 to −14%. We examine the sensitivity of SWE reconstructions to albedo error at two sites. With no simulated error in albedo, reconstructed SWE had MAE values of 7 to 13% and 5–6% bias. The accuracy actually improved with some simulated added error, likely because of a fundamental bias in the reconstruction approach. Conversely, the best age‐based decay model showed an 18–20% MAE and bias in reconstructed SWE. We conclude that remotely sensed albedos where available are superior to age‐based approaches in all aspects except simplicity.

Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

“…From recent work (Bair et al, 2018b), we have shown that the SNOWPACK (Lehning et al, 2002a, b;Bartelt and Lehning, 2002) model is capable of accurate SWE prediction when supplied only with snow depth for precipitation as well as the other requisite forcings (i.e., radiation, snow albedo, temperatures, and wind speed). Over a 5-year period using hourly in situ measured energy balance forcings and a snow pillow for validation at a high-elevation site in the western US, the SWE modeled by the numerical snow cover model SNOWPACK showed a bias of −17 mm, or 1 % (Bair et al, 2018b).…”

“…The authors report overestimation of the measured snow depth at the calibration station, even after questionable adjustments to the snow albedo and other model parameters. For example, the snow and ice albedo is given as 0.20 to 0.30 (Table 3, Shakoor and Ejaz, 2019), which would make it 0.10 to 0.20 lower than some of the lowest measured broadband albedo values for dirty snow (Bair et al, 2019b;Skiles and Painter, 2016). They attribute the overestimation to problems with the precipitation measurements, common for high-elevation stations.…”

“…Unlike during snow accumulation events, SNOWPACK does not force its modeled snow ablation to match the measured snow depth decreases. Uncertainty in SWE during the ablation season is then largely dependent on radiative forcings (Marks and Dozier, 1992) and the broadband snow albedo (Bair et al, 2019b). Here, 5 % uncertainty is used, based on the MAE from SWE reconstructions using the same remotely sensed forcings at a continental subalpine site (Bair et al, 2019b).…”

“…Uncertainty in SWE during the ablation season is then largely dependent on radiative forcings (Marks and Dozier, 1992) and the broadband snow albedo (Bair et al, 2019b). Here, 5 % uncertainty is used, based on the MAE from SWE reconstructions using the same remotely sensed forcings at a continental subalpine site (Bair et al, 2019b). In the same study, a small (3 %) bias in SWE was also found, but this is likely due to shortcomings with the reconstruction method and not applicable to SWE modeled with SNOW-PACK.…”

Autonomous Aerial Vehicles (AAVs) as a Tool for Improving the Spatial Resolution of Snow Albedo Measurements in Mountainous Regions

“…Specifically, the Decadal Strategy identifies that key goals are improving measurements and modeling of albedo as a component of Earth's land surface, and understanding hazards in mountainous terrain [13]. Even with opportunities to improve satellite measurements of albedo in the future, Bair et al, [14] demonstrated that current remotely-sensed albedos are of value and provide better results than traditional age-decay model approaches.…”