@mastersthesis {44199,
	title = {Semi-distributed snowmelt modeling and regional snow mapping using passive microwave radiometry},
	volume = {Civil and Environmental Engineering},
	year = {2002},
	pages = {228},
	address = {University of Alberta},
	abstract = {Two semi-distributed snowmelt models (SDSM-MTI and SDSM-EBM) developed to model the basin-scale snow accumulation and ablation processes at sub-basin scale, were applied to the Paddle River Basin (PRB) of central Alberta. SDSM-MTI uses a modified temperature index approach that consists of a weighted average of near surface soil (T g ) and air temperature (Ta ) data. SDSM-EBM, a relatively data intensive energy balance model accounts for snowmelt by considering (a) vertical energy exchange in open and forested area separately; (b) snowmelt in terms of liquid and ice phases separately, canopy interception, snow density, sublimation, refreezing, etc, and (c) the snow surface temperature. Other than the "regulatory" effects of beaver dams, both models simulated reasonably accurate snowmelt runoff, SWE and snow depth for PRB. For SDSM-MTI, the advantage of using both T a and Tg is partly attributed to T g showing a stronger correlation with solar and net radiation at PRB than Ta .

Existing algorithms for retrieving snow water equivalent (SWE) from the Special Sensor Microwave/Imager (SSM/I) passive microwave brightness temperature data were assessed and new algorithms were developed for the Red River basin of North Dakota and Minnesota. The frequencies of SSM/I data used are 19 and 37 GHz in both horizontal and vertical polarization. The airborne gamma-ray measurements of SWE for 1989, 1988, and 1997 provided the ground truth for algorithm development and validation. Encouraging calibration results are obtained for the multivariate regression algorithms and dry snow cases of the 1989 and 1988 SSM/I data (from DMSP-F8). Similarly, validation results e.g., 1988 (1989 as calibration data), 1989 (1988 as calibration data), and 1997 (from DMSP-F10 and F13), are also encouraging. The non-parameric, Projection Pursuit Regression technique also gave good results in both stages. However, for the validation stage, adding a shift parameter to all retrieval algorithms was necessary because of possibly different scatter-induced darkening, which could arise even for snowpacks of the same thickness because snowpacks undergo different metamorphism in different winter years.},
	url = {http://search.proquest.com/docview/305510806},
	author = {Singh, Purushottam Raj}
}
