HR: 13:40h
AN: C33A-01    [Abstracts]
TI: Analysis of the Sierra Nevada Snowpack in the 21st Century
AU: * Dozier, J
EM: dozier@bren.ucsb.edu
AF: University of California, Donald Bren School of Environmental Science and Management, Santa Barbara, CA 93106-5131, United States
AU: Famiglietti, J S
EM: jfamigli@uci.edu
AF: University of California, Department of Earth System Science, Irvine, CA 92697-3100, United States
AU: Rice, R
EM: rrice@ucmerced.edu
AF: University of California, School of Engineering, Merced, CA 95344, United States
AU: Molotch, N P
EM: molotch@seas.ucla.edu
AF: University of California, Department of Civil and Environmental Engineering, Los Angeles, CA 90095-1593, United States
AU: Rittger, K
EM: krittger@bren.ucsb.edu
AF: University of California, Donald Bren School of Environmental Science and Management, Santa Barbara, CA 93106-5131, United States
AU: Painter, T H
EM: painter@geog.utah.edu
AF: University of Utah, Department of Geography, Salt Lake City, UT 84112, United States
AU: Bales, R C
EM: rbales@ucmerced.edu
AF: University of California, School of Engineering, Merced, CA 95344, United States
AB: Models of California's future climate postulate greater precipitation variability, warmer temperatures, less snow, earlier runoff, and greater likelihood of droughts and floods; statistical analyses covering the last half-century show that some trends have already started. Accompanying these changes will be greater demand from downstream economic development and population growth. Today's measurements at snow courses and pillows support empirical methods of analyzing the snowpack and forecasting runoff, but they do not cover the highest elevations well and they do not represent snow's topographic distribution. These historical data document that climate is changing, but the changes mean that the empirical relations are becoming less reliable. Blending strategically placed ground measurements with broad-coverage satellite and aircraft data offers the opportunity for continual, accurate estimates of snow and other hydrologic variables. Augmented ground observations include small transmitting sensors along elevation gradients, enhanced sites with energy-balance variables, and improved communications and cyberinfrastructure for making remote observations available in real-time. Some satellite and aircraft observations can be used immediately, while others require further research and implementation. Snow-covered area and albedo products are available now from both research and operational satellites, and these data can be used to forecast the rate of snowmelt when combined with energy- balance measurements, ground-based snow water equivalent measurements, and modeling. Airborne lidar surveys can measure snow depth in small watersheds and along transects, and can be scaled to larger basins. Satellite measurements of time-variable gravity can monitor seasonal changes in total water storage over large regions such as the Sacramento-San Joaquin valley. A decade away, a future hybrid active-passive microwave system could measure the spatial distribution of snow water equivalent. The final critical component of a distributed measurement system is the cyberinfrastructure that links measurements, data processing, models, and users.
DE: 0736 Snow (1827, 1863)
DE: 0740 Snowmelt
DE: 0758 Remote sensing
DE: 1860 Streamflow
DE: 6309 Decision making under uncertainty
SC: Cryosphere [C]
MN: 2007 Fall Meeting