Abstract

This paper used a data assimilation framework to estimate spatially and temporally continuous snow water equivalent (SWE) from a 27 year reanalysis (from water year 1985 to 2011) of the Landsat-5 record for the Kern River watershed in the Sierra Nevada, California. The data assimilation approach explicitly treats sources of uncertainty from model parameters, meteorological inputs, and observations. The method is comprised of two main components: (1) a coupled land surface model (LSM) and snow depletion curve (SDC) model, which is used to generate an ensemble of predictions of SWE and fractional snow cover area (FSCA) for a given set of prior (uncertain) inputs, and (2) a retrospective reanalysis step, which updates estimation variables to be consistent with the observed fractional snow cover time series. The final posterior SWE estimate is generated from the LSM-SDC using the posterior estimation variables consistently with all postulated sources of uncertainty in the model, inputs, and observations. A reasonable agreement was found between the SWE reanalysis and in situ SWE observations and streamflow data. The data set was studied to evaluate factors controlling SWE spatial and temporal variability. Elevation was found to be the primary control on spatial patterns of peak-SWE and day-of-peak. The easting coordinate had additional explanatory power, which is hypothesized to be related to rain shadow effects due to the prevailing storm track directions. The spatial patterns were found to be interannually inconsistent. However, drier years and lower elevations were found more variable than wetter years and higher elevations, respectively. Only a very small percentage of the Kern River watershed had a significant trend in peak-SWE and day-of-peak. Trends deemed to be significant were found to be positive (peak-SWE is increasing and day-of-peak occurs later) at higher elevations, but negative (peak-SWE is decreasing and day-of-peak occurs earlier) at lower elevations. The reanalysis approach proved to be useful in terms of identifying subwatershed variability and trends, and could be extended to larger regions and areas where in situ data are sparse or unavailable.