ESTIMATING THE SPATIAL-DISTRIBUTION OF SNOW IN MOUNTAIN BASINS USING REMOTE-SENSING AND ENERGY-BALANCE MODELING

We present a modeling approach that couples information about snow cov er duration from remote sensing with a distributed energy balance mode l to calculate the spatial distribution of snow water equivalence (SWE ) in a 1.2 km(2) mountain basin at the peak of the accumulation season . In situ measurements of incident solar radiation, incident longwave radiation, air temperature, relative humidity, and wind speed were dis tributed around the basin on the basis of topography. Snow surface alb edo was assumed to be spatially constant and to decrease with time. Di stributed snow surface temperature was estimated as a function of mode led air temperature. We computed the energy balance for each pixel at hourly intervals using the estimated radiative fluxes and bulk-aerodyn amic turbulent-energy flux algorithms from a snowpack energy and mass balance model. Fractional snow cover within each pixel was estimated f rom three multispectral images (Landsat thematic mapper), one at peak accumulation and two during snowmelt, using decision trees and a spect ral mixture model; from these we computed snow cover duration at subpi xel resolution. The total cumulative energy for snowmelt at each remot e sensing date was weighted by the fraction of each pixel's area that lost its snow cover by that date to determine an initial SWE for each pixel. We tested the modeling approach in the well-studied Emerald Lak e basin in the southern Sierra Nevada. With no parameter fitting the m odeled spatial pattern of SWE and the mean basin SWE agreed with inten sive field survey data. As the modeling approach requires only a remot e sensing time series and an ability to estimate the energy balance ov er the model domain, it should prove useful for computing SWE distribu tions at peak accumulation over larger areas, where extensive field me asurements of SWE are not practical.