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elation to the AMSR-E data gridded to the EASE-Grid 12.5 x 12.5 km 2 resolution, the Wheaton River basin is covered by parts of 14 pixels, improving the spatial scale at which differences in snowmelt characteristics can be investigated (Fig. 1b). Adjacent pixels in this area appear to have similar Tb signatures that differ only slightly due to differences in land cover as well as elevation and topography. Seasonal variations related to frost, vegetation, and lakes are also factors in this complex landscape, but snowmelt has the largest influence on Tb. Figure 1. Wheaton River basin EASE-Grid (a) 25 x 25 km 2 pixels and (b) 12.5 x 12.5 km 2 pixels superimposed on a 1:250,000 (90 m) digital elevation model (Environment Yukon, 2000). While the actual sensor footprints are more elliptical in shape, these square pixels provide a good representation for the land area covered. The inset map shows the location of the basin within the Yukon Territory. The basin (black outline) is represented by parts of six 25 x 25 km 2 pixels and fourteen 12.5 x 12.5 km 2 pixels. The Wheaton River near Carcross stream gauge is located near the river’s mouth at an elevation of 668 m (white dot in pixels B02 and B02B). 140
Table 1. Wheaton River basin EASE-Grid 25 x 25 km 2 pixel characteristics (from Ramage et al., 2006) Near-surface air temperatures for the Wheaton River basin were acquired in 2004 and 2005 using a HOBO automatic temperature logger located within pixel B02 at 668 m, approximately 10 m from Environment Canada’s “Wheaton River near Carcross” stream gauge (60°08′05″N, 134°53′45″W). The HOBO recorded hourly air temperature and relative humidity and was situated about 1.5 m above the forest floor on the north facing side of a spruce tree (shaded most of the time). It was installed under a protective platform in August 2004, and data were downloaded periodically by Pat Maltais of the Water Survey of Canada. A field campaign was carried out during the spring of 2005 to collect a variety of in situ snowpack measurements from snowpits, including snow density and the dielectric properties of the snow at various depths, at times that coincided with AMSR-E satellite overpasses. The dielectric properties of the snow, and ultimately the snow liquid water content as a percentage of the volume, were derived from these measurements to relate to AMSR-E brightness temperature data. Other observations of the snowpack in the upper Yukon River basin provided support to show that the response in Tb could be attributed to snowmelt instead of liquid precipitation events. METHODS Ramage et al. (2006) developed a snowmelt onset algorithm for use with SSM/I 37V GHz data to detect the timing of snowmelt onset within the Wheaton River basin. This algorithm determines the onset of snowmelt based on the day of the year when certain thresholds for both brightness temperature (Tb) and diurnal amplitude variation (DAV) are met (Table 2). For the SSM/I data, ‘DAV Melt’ occurs when DAV is greater than ±10 K, and ‘Melt Onset’ occurs on the day when Tb is greater than 246 K and DAV is greater than ±10 K, indicating the snow is both melting periodically and experiencing strong melt-refreeze cycles (Ramage and Isacks, 2002, 2003). In addition, ‘End DAV’ represents the end of the transition when DAV decreases below ±10 K, and the duration of the transition period is calculated as the number of days between ‘Melt Onset’ and ‘End DAV’. Table 2. Snowmelt onset thresholds 141
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Proceedings of the 63rd ANNUAL EAST
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FOREWORD T his proceedings volume c
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CONTENTS Foreword..................
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STATEMENT OF PURPOSE The Eastern Sn
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EXECUTIVES FOR THE 63rd EASTERN SNO
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THE PRESIDENT’S PAGE The 63rd ann
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Weisnet Medal for Best Student Pape
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3 63 rd EASTERN SNOW CONFERENCE New
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Figure 1. Layer 1 represents the so
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Figure 4. The general layout of the
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lue color represented no convection
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Total Density of Water Profiles The
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Figure 11(a). Simulated snow grain
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Jordan R, 1991. A one-dimensional t
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Campbell Scientific Award for Best
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19 63 rd EASTERN SNOW CONFERENCE Ne
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R ∑i ∑ n 2 = 1 NS = − n i = 1
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Snow and Climate 37
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Spatial distributions of changes in
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Snow and Climate Posters 43
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Figure 2 presents correlation maps
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CONCLUSIONS Three regional-continen
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Discharge (mm) 20 18 16 14 12 10 8
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ABSTRACT 55 63 rd EASTERN SNOW CONF
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This paper analyzes the synoptic pa
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Cyclones that track west of the App
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The synoptic-scale atmospheric circ
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CONCLUSIONS The record snowfall in
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correlations between April-May snow
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Figure 3. Linear correlations betwe
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winter/early spring could contribut
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Snow Remote Sensing 73
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height averaged 11.4 m with an aver
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strings were buried 20 - 30 cm belo
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Although the λE/Rn fraction was on
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estimated snow depth based on the 2
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important to note that the average
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Schmidt, R. A., C. A. Troendle, and
Page 101 and 102: 89 63 rd EASTERN SNOW CONFERENCE Ne
Page 103 and 104: METHODS The IMS Product The IMS was
Page 105 and 106: Figure 1. Microwave spectral charac
Page 107 and 108: snow depth and SWE on a weekly basi
Page 109 and 110: Figure 4. Example of blended SWE pr
Page 111 and 112: Evaluation of the global distributi
Page 113 and 114: Figure 9. Inter-comparison plots of
Page 115 and 116: Helfrich, S.R., D. McNamara, B.H.Ra
Page 117 and 118: 105 63 rd EASTERN SNOW CONFERENCE N
Page 119 and 120: and validated regionally so they ca
Page 121 and 122: 109 Test Site Figure 2. Variation o
Page 123 and 124: Correlation Coe. Correlation Coe. C
Page 125 and 126: Figure 8. SSM/I scattering signatur
Page 127 and 128: Considering the facts mentioned abo
Page 129 and 130: Besides the temporal validation, th
Page 131 and 132: RMSE Non linear Azar Chang Goodison
Page 133 and 134: 63 nd EASTERN SNOW CONFERENCE Newar
Page 135 and 136: http://www.ccin.ca); from simulatio
Page 137 and 138: over the period 1988-2000. With the
Page 139 and 140: Figure 3: Explained variance (R 2 )
Page 141 and 142: correspondence between simulated an
Page 143 and 144: values. Continued development of ne
Page 145 and 146: National Climatic Data Center (2005
Page 147 and 148: Snow Remote Sensing Posters 135
Page 149 and 150: ABSTRACT 63 rd EASTERN SNOW CONFERE
Page 151: day and has a mean pixel resolution
Page 155 and 156: The SSM/I snowmelt onset algorithm
Page 157 and 158: coarse-resolution pixel with the hi
Page 159 and 160: Figure 5. (a) Scatterplot of snowpa
Page 161 and 162: For both 2004 and 2005, the AMSR-E
Page 163 and 164: threshold of ±18 K that reflects t
Page 165 and 166: ABSTRACT 153 63 rd EASTERN SNOW CON
Page 167 and 168: DATA USED SSM/I & QuikSCAT Coverage
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Page 179 and 180: independent of regions. This greatl
Page 181 and 182: slightly vary the IMS with this pro
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: MODIS Land Science Team, NASA Godda
Page 187 and 188: FUTURE OF IMS Enhancements to the I
Page 189 and 190: information never present before on
Page 191 and 192: Brubaker, K.L., R. Pinker and E. De
Page 195 and 196: The Sierra Nevada del Cocuy region
Page 197 and 198: Colombia Venezuela Glacier Series R
Page 199 and 200: Table 4. Glacier Areas of the Ruiz-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
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Linder W, Jordan E, 1991. Ice-mass
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Snowpack Processes 193
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63 rd EASTERN SNOW CONFERENCE Newar
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In the following paragraph the main
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Figure 1: Variation of the season a
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Figure 3: 8-day composite maps (24
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The third pair (Figure 5) represent
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Figure 9: Time evolution of SWE ave
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melting occurs, whereas at the high
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Colbeck SC, Anderson EA. 1982. The
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ABSTRACT 211 63 rd EASTERN SNOW CON
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A B Figure 1. Low-magnification sec
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GB ridge GB groove Figure 3. Higher
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CONCLUSION Figure 5. Indexed electr
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219 63rd EASTERN SNOW CONFERENCE De
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Meteorological data for the winter
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monthly probability adjusted data 4
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225 Pullman WA Rawlins WY Leadville
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The daily wind speed can exceed 6.5
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NCDC. 2006. National Climate Data C
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ABSTRACT 231 63 rd EASTERN SNOW CON
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and mass balances for 540 environme
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Temperature, precipitation, windspe
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the left in response to snow compac
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Pinched-cone dimensions for each sl
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Terrain slope (degrees) Terrain slo
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ACKNOWLEDGEMENTS This work was fund
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Glacial and Periglacial Processes 2
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247 63 rd EASTERN SNOW CONFERENCE N
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From linear regression the constant
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DATA COLLECTION Figure 1. Location
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Table 1. Comparison of GPS measured
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Transverse Velocity Profiles Surfac
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is shown in Figure 4. This variatio
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Table 4. The calculated volume flux
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Figure 1. Cordillera Blanca regiona
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MATERIALS AND METHODS Field Measure
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Data analysis techniques We synthes
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season, but unacceptable for the dr
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significant, although more informat
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(a) Liquid Water (mm) Dry Period: M
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Future Work We are installing addit
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Shuttleworth, W.J., and J.S. Wallac
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Snow and Periglacial Processes Post
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No one to date has shown why snowfl
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APPENDIX A Northern Hemisphere Year
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62nd ESC Papers 291
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62 nd EASTERN SNOW CONFERENCE Water
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each other; and, they both decrease
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297 62 nd EASTERN SNOW CONFERENCE W
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comes in the form of precipitation.
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averaged to reduce bias in this var
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RESULTS Figure 2. Flow chart of reg
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Table 6: Actual-Conditions SWE, dep
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307 Table 9: Regression results bet
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Goita, K., A. Walker, B. Goodison,
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TO ERR IS HUMAN, TO FORGET IT WOULD
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