Using Snow Water Equivalent Reconstruction for Operational Use: Two Case Studies

TitleUsing Snow Water Equivalent Reconstruction for Operational Use: Two Case Studies
Publication TypeConference Proceedings
Year of Conference2016
AuthorsBair, Edward H., Rittger Karl, Vuyovich Carrie, McGurk Bruce, and Dozier Jeff
Conference Name84th Annual Western Snow Conference
Date Published2016
Conference LocationSeattle, Washington

In the snow water equivalent (SWE) reconstruction method, the snowpack is built up in reverse from downscaled energy balance forcings and satellite-based estimates of fractional snow covered area (fSCA). For melt modeling, we have developed a full energy balance model called ParBal that relies on satellite-based measurements and does not need ground-based inputs. Using ParBal to compute the melt, SWE reconstruction has been shown to be accurate when compared with a variety of validation sources, most recently by comparison with Airborne Snow Observatory (ASO, Painter et al., 2016) measurements in the Upper Tuolumne Basin, CA USA (Bair et al., 2015; Bair et al., in review). The major disadvantage is that reconstructed SWE estimates are only available retrospectively. While this limitation cannot be overcome, we present two case studies where reconstructed SWE can be used in an operational context. 1) We run ParBal as a melt only model, examining seasonal and daily melt without reconstructing the snowpack. Although we cannot reconstruct snow on the ground in this manner, we can potentially make near real-time estimates of snowmelt if these daily melt estimates are then fed through a routing model and compared with streamflow measurements. ParBal can be driven by: a) Global Data Assimilation System (GDAS, Kleist et al., 2009) meteorological forcings, now available at 1/9° spatial resolution and at 3 hr intervals with almost no latency; and b) Moderate Resolution Imaging Spectrometer Snow Covered Area and Grain Size (MODSCAG, Painter et al., 2009) snow cover data, available daily with one or two day latency. 2) We use machine learning techniques to build statistical relationships between remotely-sensed products, such as the ones mentioned in 1), and reconstructed SWE in Afghanistan. This approach allows real-time SWE prediction in remote areas that previously had little or no baseline for comparison