Reconstructing the space-time variability of California Snow Water Equivalent
Wednesday, April 22, 2015
Mu Xiao1, Ned Bair2, Yixin Mao3, Jeff Dozier4, Dennis P Lettenmaier1 and Karl Rittger5, (1)University of California Los Angeles, Los Angeles, CA, United States, (2)University of California, Earth Research Institute, Santa Barbara, CA, United States, (3)University of Washington Seattle Campus, Seattle, WA, United States, (4)University of California, Mammoth Lakes, CA, United States, (5)National Snow and Ice Data Center, Boulder, CO, United States
Abstract:
Given the magnitude of the California economy and its dependence on water, there has been great interest in the California drought of 2012-14 and its magnitude relative to previous drought events. Although measurements of snow water equivalent (SWE) in California date to 1910, reconstruction of the aggregate snowpack in the Sierra Nevada headwaters for most of the state’s streams based on these observations alone is challenging, because the observations represent points, the siting of which was based on objectives other than representation of aggregate SWE. However, alternative methods have become available for reconstructing aggregate SWE. These include using the output of land surface models driven by gridded precipitation, temperature, and other reconstructed forcings, reconstruction of seasonal maximum SWE using remote sensing estimates of melt energy combined with estimates of the last date of accumulated snow, and interpolation of time-continuous SWE observations from snow pillows. We compare simulated SWE from the Variable Infiltration Capacity (VIC) land surface model, NOAA’s SNODAS (the output of an operational snow model adjusted to snow pillow observations), SWE reconstruction from remotely sensed data, and an interpolation of the California Department of Water Resources’ snow pillow observations, all for the snow seasons of 2003-04 through 2013-14, for which all estimates exist. We give particular attention to differences in aggregate estimates of SWE (e.g., in km3 for the entire Sierra range), as well as differences in the spatial distribution of SWE, and elevation differences. We focus not only on differences in the mean fields over the period of record, but on differences between heavy winters (2005-06 and 2010-11) and near-record low snow winter 2013-14.
