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Development of Snow Covered Area Products
Snow researchers obtained snow-covered area data at a 1-km resolution from Advanced Very High Resolution Radiometer (AVHRR) satellite imagery. Snow is highly reflective in the visible portion of the electromagnetic spectrum but highly absorptive in the infrared portion of the spectrum. Thus, researchers can detect the presence of snow using satellite imagery by calculating how much light reflects off the snow (reflectance) and the temperature of the land surface at different wavelengths (known as brightness temperature).
The surface reflectance for a given wavelength is the ratio of the outgoing radiation and the incoming radiation–basically, how much light bounces back to the satellite as compared to how much sunlight hits the snow surface. Researchers used remotely sensed data from AVHRR to measure the outgoing radiation, and they modeled the incoming solar radiation at the land surface based on seasonal changes and atmospheric conditions. To derive brightness temperatures for the image, researchers selected near-infrared (band 4), used especially for vegetation type identification and health, for outlining bodies of water, and for detecting soil moisture.
Clouds and bodies of water are often similar to snow in their reflective and brightness temperature characteristics. To eliminate erroneous readings for cloud-covered pixels, researchers masked cloud cover by manually interpreting and editing pixels, then using a computer image analysis. They also masked water and highly reflective land features to prevent the computer from interpreting them as snow. Finally, to identify pixels that were only partially snow covered, researchers put brightness temperature and surface reflectance variables into probability models.
Once researchers had a reliable method for estimating snow cover at SNOTEL sites (see Figure 3 for sites in Arizona and New Mexico), they developed models to predict snow cover in areas that did not contain sampling sites. Researchers used data from 231 SNOTEL stations surrounding the Colorado and Rio Grande River basins to estimate snow cover at unknown sites based on elevation, topography, slope angle, and other factors. The technique required developing a linear equation between snow water equivalent and elevation and applying this equation to a digital elevation model to calculate snow water equivalent for 1-square-kilometer grid cells across the entire basin. (Each grid cell represents about 0.4 square miles.)
By doing this, they obtained daily snow water equivalent estimates at a 1-km resolution without the information gaps that had previously existed.
To test the accuracy of this formula calculation, they removed known snow water equivalent values at SNOTEL sites and compared the estimates derived from the formula for the sites with the actual values. They subtracted the actual from the estimated snow water equivalent values, and applied this correction to the entire grid. The final product provides researchers and water managers with snowpack and snow water equivalent estimates across a larger land area than was previously available.
The researchers chose to adapt the snow water equivalent products to fit into the Precipitation-Runoff Modeling System (PRMS) because the U.S. Geological Survey and other agencies use it operationally worldwide. At every grid point within the study watersheds, researchers adjusted the PRMS-modeled snowpack condition to match their snow water equivalent product condition (Figure 4). They then simulated snowmelt and runoff over the landscape throughout the snow accumulation and snowmelt seasons, and compared their results to the observed patterns of streamflow fluctuations.
Snow water equivalent estimates from individual states, as well totals from all states that share water supplies or rivers, provide vital information to water resource managers and users. Managers in Arizona will look at data not only from their state, but from Utah and Colorado as well, since the Colorado River Basin runs through all three states and each states’ actions affect the others’ water supplies.