Lecture Series

Nye Lecture 2010

Dr. Jeff DozierDr. Jeff Dozier

Donald Bren School of Environmental Science & Management
University of California, Santa Barbara
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Mountain Hydrology, The Fourth Paradigm, and the Color of Snow

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In the 21st century, a crucial question for seasonal mountain snowpacks worldwide is: How do we reliably predict snowmelt runoff and associated demand as climate changes, populations grow, land use evolves, and individual and societal choices are made? Our traditional forecasting methods are based on statistical relations developed when human impacts were less intense and the pace of climate change was slower. The rich, hard-won, long-term data that we have document trends already, but uncertainty will get worse without more physically based approaches that account for topographic heterogeneity. At the same time, we can take advantage of two emerging trends: (i) data-intensive science, The Fourth Paradigm, which goes beyond computational modeling to foster discoveries and analyses from large data sets; and (ii) an ability to measure snow properties suitable for energy balance models at a spatial scale appropriate for mountain regions. The snow-covered area has been the initial snow property measured from the satellite. We know now that the spectral albedo of snow varies in response to grain growth, impurities, and depth; it, therefore, varies with topography differentially with time. In fact, were our eyes sensitive to radiation through the whole solar spectrum, snow would be one of the most “colorful” surface covers in nature. To get at snow-water equivalent, the variable most difficult to measure in snow hydrology, interpolations from ground measurements, constrained by measurements of snow-covered area, provide a method to estimate the spatial distribution of snow, which can also be estimated independently from an energy balance snow-depletion calculation. The combination of field and satellite measurements with models should improve the accuracy of snowmelt runoff forecasts, even in basins with sparse river gauging.

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