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Mark C. Mastin, Katherine J. Chase, and R. W. Dudley

-term planning for reservoir design and water-management strategies ( Adeloye et al. 1999 ; Draper and Kundell 2007 ), despite incomplete knowledge about the volumetric change in snowpack that can be expected and how snowpack changes may vary regionally and locally. A key measure of snowpack condition used by resource managers in the western United States is the snow-water equivalent (SWE) on 1 April ( Serreze et al. 1999 ). Researchers have adopted this measure in climate-change studies to characterize the

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Gregory J. McCabe, Julio L. Betancourt, Gregory T. Pederson, and Mark D. Schwartz

1. Introduction Across the western United States, accumulated snowpack [snow water equivalent (SWE)] and the phenological timing of spring onset (as measured by plant leaf-out dates) are both sensitive to variations in cool season climate ( Cayan 1996 ; Cayan et al. 2001 ; Ault et al. 2011 ). Snowpack has been shown to integrate changes in temperature and precipitation throughout the cool season in a regionally and physiographically complex manner (e.g., Cayan 1996 ; McCabe and Wolock 1999

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Bruce Ellingwood and Robert K. Redfield

JUNE 1984 BRUCE ELLINGWOOD AND ROBERT K. REDFIELD 1153Probability Models for Annual Extreme Water-Equivalent Ground Snow BRUCE ELLINGWOOD Center for Building Technology, National Bureau of Standards, Washington, DC 20234 ROBERT K. REDFIELD U.S. Army Cold Regions Research and Engineering Laboratory, Hanover, NH 03755 (Manuscript received 10 March 1983, in final form 29 February 1984

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Andrew J. Grundstein, Qi Qi Lu, and Robert Lund

1. Introduction The depth of water resulting from a total melt of snow cover, or snow water equivalent (SWE), provides estimates of water that will become available for use in the spring. In addition to farming and agricultural ramifications, SWE information is vitally important to planners assessing snowmelt flood potential and to engineers designing structures to support snow weight. The SWE prior to spring melt off, which is frequently the annual maximum SWE, is a key design parameter

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Steven A. Margulis, Manuela Girotto, Gonzalo Cortés, and Michael Durand

conditions, but heterogeneity in surface conditions are due to a complex mosaic of factors: Variations in elevation contribute directly to snow water equivalent SWE accumulation variations as a result of orographic effects. Vegetation patterns contribute to variability in snowfall interception losses, radiative fluxes, and exposure, which can impact wind-driven redistribution. Slope, aspect, and shading by terrain variability directly impact the incoming shortwave radiation, which causes variability in

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L. R. Mudryk, C. Derksen, P. J. Kushner, and R. Brown

1. Introduction The seasonal cycle of terrestrial snow cover and snow mass has a notable influence on the Northern Hemisphere energy budget, water balance, and geochemical cycles. Snow water equivalent (SWE) is expected to respond in a complex way to projected temperature and precipitation changes with the magnitude and sign of the response varying with climate regime and elevation ( Brown and Mote 2009 ). Verification of such responses in climate models and the initialization of snow in

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Matthew Sturm, Brian Taras, Glen E. Liston, Chris Derksen, Tobias Jonas, and Jon Lea

1. Introduction As global temperatures rise, the world’s snow resources are predicted to change in significant ways ( Hosaka et al. 2005 ; Christensen et al. 2007 ; Räisänen 2008 ; Deser et al. 2010 ). Long-term changes in global, regional, and local snow depth ( h s ), snow water equivalent (SWE), and extent will ultimately have major ramifications for ecosystem function, human utilization of snow resources, and the climate itself through feedback mechanisms like snow albedo ( Barry 1996

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A. Langlois, J. Kohn, A. Royer, P. Cliche, L. Brucker, G. Picard, M. Fily, C. Derksen, and J. M. Willemet

balance information (such as SWE). Results showed that the models mentioned earlier did provide reasonable simulations of snow water equivalent for the various study regions ( Etchevers et al. 2004 ). Although satellite microwave brightness temperatures exhibit strong sensitivity to the scattering properties of terrestrial snow, SWE retrieval solutions based solely on empirical relationships between microwave brightness temperature and SWE remain elusive. However, data assimilation approaches that can

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April 1966 H. C. S. Thorn 265DISTRIBUTION OF MAXIMUM ANNUAL WATER EQUIVALENT OF SNOW ON THE GROUND*H. C. S. THOMEnvironmental Data Service, ESSA, Washington, D.C.ABSTRACT For later development of design snow loads, the water equivalent of snow on the ground appears to be thv bestmeteorological variable for determining design values. The appropriate c1im:ttological series for this is the winterseason maximum accumulated water equivalent series. Among many distributions investig

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Nicholas Dawson, Patrick Broxton, and Xubin Zeng

is on the ground is challenged by uncertainties of satellite measurements of snow as well as difficulties in scaling up point measurements of snow. As a consequence, there are substantial errors of snow states in operational weather forecasting models, reanalyses, and land data assimilation systems. For example, previous research ( Dawson et al. 2016 ; Broxton et al. 2016a ) found that these products tend to underestimate snow depth and snow water equivalent (SWE), which can lead to systematic

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