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<br />Acknowledgments. Funding for this research was
<br />provided by the NASA Southwest Regional Earth Sci-
<br />ence Applications Center (NASA Grant NAG13-
<br />99005), the National Science Foundation's Center for
<br />the Sustain ability of semi-Arid Hydrology and Riparian
<br />. Areas (NSF EAR9876800), the NOAA Office of Glob-
<br />al Programs supported Climate Assessment for the
<br />Southwest (CLlMAS) (NA116GP2758), and the UC
<br />Merced School of Engineering.
<br />
<br />::ne), depending nn whieh d.:O: Re:~~n:t~nn::: RaMI E TE 0 R 0 LO G :EFERENCES
<br />
<br />SWE data are considered; however, both datasets pro-
<br />Daly, S. F., R. Davis, E. Ochs, and T. Pangburn, 2000: An ap-
<br />duced comparable interpolation errors. proach 0 spatially distributed snow modeling of the Sacra-
<br />Variogram analysis indicates that snow course mea- mento and San Joaquin basins, California. Hydrol. Process.,
<br />surements have less short-scale variability and possibly 14,3257-3271.
<br />less measurement error along with a longer correlation Day, G. N., 1985: Extended streamflow forecasting using NWSRFS.
<br />length scale. It is not clear from this analysis which I ASCE J. Water Res. Plann. Manage., 111, 157-170.
<br />d. f' I" h Dressler, K. A., G. H. Leavesley, R. C. Bales, and S. R. Fassnacht,
<br />ataset IS more accurate or use In app IcatlOns suc as 2006: Evaluation of gridded snow water equivalent and sat-
<br />water supply forecasting. However, for basin-wide ellite snow cover products for mountain basins in a hydro-
<br />SWE estimates, snow course data are more represen- logic model. Hydrol. Process., 20, 673-688.
<br />tative of the elevation distribution in the basin. Consid- Fassnacht, S. R., K. A. Dressler, and R. C. Bales, 2003: Snow wa-
<br />eration of both datasets and combinations of the two ter equivalent interpolation for the Colorado River Basin
<br />from snow telemetry (SNOTEL) data. Water Resour. Res.,
<br />are good for characterizing high and low estimates of 39, 1208, doi:1O.1029/2002WROOI512.
<br />peak basin-wide SWE, especially for hydrologic mod- Garen, D. C., 1992: Improved techniques in regression-based
<br />eling on spatial scales of the Colorado basin (104_105 I streamflow forecasting. J. Water Resour. Plann. Manage., 118,
<br />km2), for which small differences in point estimates 654--670.
<br />translate to large discrepancies in area estimates from Goodison, B. E., H. L. Ferguson, and G. A. McKay, 1981: Mea-
<br />interpolation. Using SWE values from the two datasets surement and data analysis. The Handbook of Snow: Prin-
<br />ciples, Processes, Management and Use, D. M. Gray and
<br />and their combinations is important in the context of D. M. Male, Eds., Pergamon Press, 191-274.
<br />water supply forecasting to simply provide a range in Isaaks, E. H., and R. M. Srivastava, 1989: An Introduction to Ap-
<br />SWE estimates, because the current network was not I plied Geostatistics. Oxford University Press, 561 pp.
<br />designed for the purpose of calculating basin-averaged Johnson, J. B., 2004: A theory of pressure sensor performance in
<br />SWE. I snow. Hydrol. Process., 18, 53-64.
<br />Although the range of SWE values generated by the -, and G. L. Schaefer, 2002: The influence of thermal, hydro-
<br />logic, and snow deformation mechanisms on snow water
<br />two data sources is valuable, it is diffi'cult to ignore the equivalent pressure sensor accuracy. Hydrol. Process., 16,
<br />utility in the automatic, daily sampling of SNOTEL 3529-3542.
<br />data. Snow course data are infrequent (once or twice a Leavesley, G. H., S. L. Markstrom, P. J. Restrepo, and R. J. Viger,
<br />month) and dependent on weather conditions in many 2002: A modular approach to addressing model design, scale,
<br />cases, but they are more representative of basin ele- and parameter estimation issues in distributed hydrological
<br />modeling. Hydrol. Process., 16,173-187.
<br />vation (a strong influence on SWE). Additionally, McGinnis, D. L., 1997: Estimating climate-change impacts on Col-
<br />snow course SWE is a more independent value than I orado Plateau snowpack using downscaling methods. Prof
<br />SNOTEL (i.e., precipitation and temperature data Geogr., 49, 117-125.
<br />from SNOTEL are often used for model forcing and Pagano, T., D. C. Garen, and S. Sorooshian, 2004: Evaluation of
<br />SWE at the SNOTEL sites is related to the precipita- I official western U.S. seasonal water supply outlooks, 1922-
<br />tion data). These trade-offs in usability must be consid- 2002. J. Hydrometeor., 5, 896-909.
<br />Palmer, P. L., 1986: Estimating snow course water equivalent
<br />ered in modeling, because the impact of assimilating I from SNOTEL pillow telemetry: An analysis of accuracy.
<br />snow information for water supply forecasting is in- Proc. West. Snow Conf, 54, 81-86.
<br />creased with higher-frequency observations in loca- Peck, E. L., 1972: Snow measurement predicament. Water Resour.
<br />tions representative of elevation and associated SWE I Res., 8, 244-248.
<br />amount. Schaefer, G. L., and D. E. Johnson, 1992: Development and op-
<br />eration of the SNOTEL system in the western United States.
<br />Proc. United States/People's Republic of China Flood Fore-
<br />casting Symp., Vol. 1, Portland, OR, Office of Hydrology,
<br />National Weather Service, 29-48.
<br />Serreze, M. c., M. P. Clark, R. L. Armstrong, D. A. McGinnis,
<br />I and R. S. Pulwarty, 1999: Characteristics of the western
<br />United States snowpack from snowpack telemetry (SNOTEL)
<br />I data. Water Resour. Res., 35,2145-2160.
<br />Smith, F. W., and H. S. Boyne, 1981: Snow pillow behavior under
<br />controlled laboratory conditions. Proc. Western Snow Conf,
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