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	Project Overview
	Research Methods
	Status of Project
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Photo by: Lynn Betts
USDA NRCS

Research and Products:
Snow Cover Mapping

Status of Project

Researchers have produced 1-km resolution snow-covered area products from 1995–2002 for the Colorado and Rio Grande River basins for select cloud-free time periods. They also produced daily un-masked 1-km resolution snow water equivalent (SWE) products for the same time period. Masked snow water equivalent products were produced for all dates when snow-covered area was available. Due to cloud cover and poor satellite viewing, researchers were able to process only 3 to 17 remotely sensed scenes for snow-covered area each month. Each snow-covered area scene covers approximately 1.3 million km2.

In addition to preparing products, researchers have analyzed the time series of snow-covered area and masked snow water equivalent data to assess seasonal and annual variability in snow cover. In the upper Rio Grande watershed, snow-covered area was seen historically as being most persistent during February. Analysis of the masked snow water equivalent time series in the upper Rio Grande showed that maximum basin-wide snow water equivalent occurs in March, when the higher elevations of the watershed are still accumulating snow and snowmelt is not yet substantial at the lower elevations.  Moreover, the researchers found that ground-based snowpack telemetry (SNOTEL) stations preferentially represent densely forested areas and are located relatively close to mountain barriers; thus, snow cover, as measured by SNOTEL sites, appears to persist longer than the watershed average. Their research provides key insights to the development of observation networks, using both remotely-sensed and ground-based data, which are ideally suited for evaluating spatially distributed estimates of snowpack properties.

Researchers have also developed new methodologies to estimate SWE in alpine basins using digital elevation models (DEMs) and regression tree models. This research has shown that differences in DEMs make significant differences in modeled snow distribution. New techniques were also developed to estimate SWE from remotely sensed satellite data during periods when cloud cover interferes with observations. This research has demonstrated that surface temperature data can be useful in determining snow cover beneath clouds.  

This CLIMAS portion of this research element has been completed. Former CLIMAS investigators Roger Bales (University of California, Merced), Noah Molotch (University of California, Los Angeles), and Kevin Dressler (Penn State University) continue to investigate spatially-distributed snow estimates, from remotely-sensed and ground observations, to improve hydrologic forecasting at their respective institutions.