HR: 11:20h
AN: H32A-05    [Abstracts]
TI: A Bayesian approach to snow water equivalent reconstruction
AU: * Durand, M
EM: durand@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AU: Molotch, N P
EM: molotch@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AU: Margulis, S A
EM: margulis@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, University of California, Los Angeles, 5731 Boelter Hall Box 951593, Los Angeles, CA 90095-1593, United States
AB: For nearly three decades, remotely sensed observations of snow cover depletion have been used to forecast seasonal snowmelt runoff and (indirectly) seasonal snow water equivalent (SWE) accumulation. The use of these snow covered area (SCA) data to reconstruct snow accumulation is based on the simple concept that deeper snow takes more time (or energy) to melt than shallower snow. Traditional reconstruction methodologies do not extract all of the available information from the remote sensing measurements, however. Indeed, snow cover states show considerable temporal autocorrelation due to the fact that accumulation and ablation take place over several months during each winter. Furthermore, there is not a convenient method for taking advantage of the spatial correlations in the remote sensing measurements, which stymies exploitation of mutual information in neighboring pixels. Finally, the uncertainty in the remote sensing observations and snowmelt models cannot be treated in a rigorous way within the current schemes. These uncertainties are time-dependent, and vary with geophysical factors such as elevation and forest cover. To address the drawbacks of traditional reconstruction methods, we demonstrate a Bayesian approach to estimating the spatial distribution of snow accumulation by using the Ensemble Kalman Smoother (EnKS) to combine a land surface model (LSM) with remote sensing SCA observations in a synthetic test. Specifically, the weights used to interpolate gage-based precipitation measurements to the model pixel resolution are estimated using the EnKS. All synthetic SCA measurements are applied in a single end-of-winter single analysis step, which capitalizes on the significant SWE autocorrelation time, allowing for the full exploitation of the time series of SCA measurements. The SWE at each pixel is updated based on the SCA estimates at all pixels within the assumed correlation length, which allows for the exploitation of the mutual information of neighboring pixels. The uncertainty of the various mass and energy products and model parameterizations are modeled explicitly. The potential of this Bayesian approach to SWE reconstruction is explored by probing the sensitivity of the estimates to the uncertainty of the various inputs.
DE: 0736 Snow (1827, 1863)
DE: 0758 Remote sensing
DE: 1854 Precipitation (3354)
DE: 1863 Snow and ice (0736, 0738, 0776, 1827)
DE: 3315 Data assimilation
SC: Hydrology [H]
MN: 2007 Joint Assembly