Please join us Tuesday, August 7th, 4-5pm in EE403. Refreshments (drinks and pizza) will be provided.
Please note the room change for this lecture.
Jeff Dozier (UCSB)
Snow hydrology at the scale of mountain range
Over a billion people – including those in western North America – depend on winter snowfall and subsequent spring melt for their water. In the mountains themselves, the distribution and duration of snow drive ecological processes. So how do we measure the topographic and temporal variability of snow (its water equivalent) and subsequent melt, at scales of whole mountain ranges? Direct measurements with satellites currently flying or available in the next couple of decades are not feasible. Instead, we track the seasonal progression of snow cover and its albedo with remote sensing, and we model melt rates by combining that information with assimilated climate data. When the snow disappears, we can run the model backwards to estimate how much snow existed at peak accumulation, everywhere. This information is great for scientific analysis, but not much use in forecasting. Are there patterns we can observe earlier in the season that correlate with eventual runoff? This end-to-end scenario illustrates eScience problems that combine hydrologic and computer science: physics of the processes, interpretation of surface properties from satellite measurements, management of many disparate data records that are themselves big, computations that cover dimensions of 3D spatial coordinates and time, pattern recognition and correlation, and possibilities for people with special expertise to contribute to parts of the whole. Our goal is to assess seasonal snow resources, relative to historical trends and extremes, in mountains with meager infrastructure, sparse gauging, challenges of accessibility, and emerging or enduring insecurity related to water resources.
Jeff Dozier has been on the UCSB faculty since 1974 and was the founding dean of the Bren School. He has led interdisciplinary studies in two areas: one addresses hydrologic science, environmental engineering, and social science in the water environment; the other is in the integration of environmental science and remote sensing with computer science and technology.