ÃÛÌÇÖ±²¥

Dozier, Jeff ¹

¹ Donald Bren School of Environmental Science & Management, University of California at Santa Barbara

In the 21st century, a crucial question for 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 the lack of stationarity will cause uncertainty to get worse without new, more mechanistic approaches. At the same time, we see two emerging trends in science: (i) data-intensive science, The Fourth Paradigm, that goes beyond computational modelling to foster analyses with many large datasets; and (ii) a new science of environmental applications. Applied to the snowmelt runoff problem, interpolations from ground measurements of snow-water equivalent, constrained by measurements of snow-covered area, provide a method to estimate the spatial distribution of snow. An energy balance snow-depletion calculation can be used for validation. The combination of models should improve the accuracy of snowmelt runoff forecasts, even in mountains with sparse data networks.