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Feb–Mar Min, Max, and Mean Coefficient of Variation, R2 SWE Data SWE (mm) %neg. All North Am. W. Eurasia E. Eurasia CCIN N/A 15.6 N/A 0.00, 0.87, 0.35 N/A N/A PWBM/ERA-40 103 8.5 0.00, 0.91, 0.28 0.00, 0.91, 0.36 0.00, 0.45, 0.15 0.00, 0.66, 0.27 PWBM/NNR 109 12.6 0.00, 0.87, 0.25 0.00, 0.87, 0.33 0.00, 0.56, 0.15 0.00, 0.71, 0.23 PWBM/WM 109 11.9 0.00, 0.91, 0.26 0.00, 0.91, 0.33 0.00, 0.75, 0.22 0.00, 0.53, 0.17 LaD 144 20.1 0.00, 0.83, 0.24 0.00, 0.83, 0.33 0.00, 0.49, 0.12 0.00, 0.69, 0.16 SSM/I 80 72.1 0.00, 0.76, 0.20 0.00, 0.76, 0.26 0.00, 0.40, 0.10 0.00, 0.57, 0.14 SimRO 103 5.3 0.00, 0.92, 0.46 0.01, 0.91, 0.44 0.00, 0.79, 0.35 0.05, 0.92, 0.57 PWBM/ERA-40a 103 10.0 0.00, 0.80, 0.27 0.00, 0.80, 0.33 0.00, 0.61, 0.22 0.00, 0.64, 0.22 PWBM/ERA-40b 103 18.7 0.00, 0.93, 0.34 0.00, 0.93, 0.37 0.00, 0.86, 0.38 0.00, 0.70, 0.26 PWBM/ERA-40c 103 10.0 0.00, 0.80, 0.27 0.80, 0.00, 0.33 0.61, 0.00, 0.22 0.00, 0.64, 0.22 PWBM/ERA-40d 103 10.0 0.00, 0.80, 0.27 0.80, 0.00, 0.33 0.61, 0.00, 0.22 0.00, 0.64, 0.22 PWBM/ERA-40e 103 22.8 0.00, 0.76, 0.25 0.00, 0.76, 0.35 0.00, 0.28, 0.12 0.00, 0.58, 0.19 128 Table 1: Mean February-March SWE (mm), percent negative correlations, and minimum, maximum, and mean coefficient of determination (R2 ) from the pre-melt SWE and spring Q comparisons. SWE is taken from data sets described in Data and Methods and shown in Figure 3. Percentage of negative correlations, and mean explained variance is also tabulated for simulated spring Q vs. observed spring Q (row SimRO), where simulated Q is from PWBM/ERA-40 model simulation. Mean February-March SWE is averaged across the terrestrial arctic basin, excluding Greenland. Individual R2 values for each study basin (shown in Figure 3) are averaged (excluding negative correlations) over all 179 river basins (All), and the basins across North America, western Eurasia, and eastern Eurasia, with the latter two separated by the 90◦E meridian. Mean R2 for CCIN SWE are determined for North American sector only. PWBM/ERA-40a−e represent the alternate comparisons, defined in Results section.
correspondence between simulated and observed spring Q suggests the model—to some degree—is accounting for processes connecting the snowpack and spring river flow, e.g. sublimation, rainfall, and soil infiltration. To better understand the covariance between SWE and Q, alternate comparisons were made using PWBM/ERA-40 monthly SWE and an estimate of when thaw is assumed to have occurred. The month of thaw (TM,withTM− 1, and TM+ 1 indicating the month preceding and postceding the thaw month, respectively) was determined with a step edge detection scheme applied to SSM/I brightness temperatures (McDonald et al., 2004). Then, SWETM becomes monthly basin SWE during TM (or TM− 1), and QTM is discharge in month TM. These alternate comparisons (across all 179 basins) are defined (a) SWETM vs. spring Q, (b)SWETM-1 vs. spring Q, (c)SWETM-1 vs. QTM+1, (d)SWETM-1 vs. QTM+1,2. R 2 s are highest for alternate comparison (b), which compared SWE in the month before thaw (TM − 1) with spring (April–June) Q (Table 1). Yet, despite the fact that the mean R 2 across western Eurasia improves from 0.15 (using default PWBM/ERA-40) to 0.38 (alternate comparison b), little difference is noted with the remianing alternate comparisons and other regions. Lastly, we scaled spring Q using a factor S, whereS = PWBM monthly snow melt–runoff ratio, with 0 < S < 1. Then, snowmelt Q each month is Qs =Q·S. Each occurrence of QS was then summed resulting in a total QS each spring, for each basin. Using QS in place of the default Q (and PWBM/ERA-40 SWE), we note a decrease in agreement across eastern Eurasia, with no change across most of the domain. And although SWE from simulations with ERA-40, in general, explains more than a third of the variation in Q, a large proportion of the interannual variability is not due to SWE variability. When considering these results, it is interesting to note that Lammers et al. (2006) recently found that annual simulated discharge across Alaska (drawn from three separate models) was in poor agreement with observed discharge data between 1980–2001. Better agreements across northwestern North America, eastern Eurasia (EE in Figure 3), and parts of western Eurasia (WE) in this study are attributable to relatively higher snowfall rates and a greater interannual variability in spring discharge (Figure 4b). Conversely, the region of eastern Eurasia with numerous negative correlations is characterized by low spring discharge variability. Delays in snowmelt water reaching river systems, which can be significant (Hinzman and Kane, 1991), are likely an additional influence on these reported correlations. For large arctic basins, comparisons between snow storage and discharge volume are complicated by the large temporal variation in basin thaw and the delays in snowmelt water reaching the gauge. More meaningful comparisons between spatial SWE and river discharge are possible through the use of hydrograph separation to partition discharge into overland and baseflow components. This, however, requires the use of daily discharge data which are more limited for the Pan-Arctic region. CONCLUSIONS In our comparisons of interannual variations in pre-melt SWE and spring Q, R 2 values are highest (mean of 0.25 to 0.28 over all basins) when PWBM is driven by ERA-40, NNR or WM climate data. Similar agreements are noted when SWE from the observed data analysis scheme are used, which suggests that the hydrological model is capturing as much variability in the spring flow as does the observed SWE scheme. Average R 2 determined from the SSM/I SWE and spring Q comparisons are generally low, and a sizable majority (over 72%) of these correlations are negative. The low variability and magnitude is likely related to saturation of the SSM/I algorithm at high SWE 129
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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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Page 89 and 90: height averaged 11.4 m with an aver
Page 91 and 92: strings were buried 20 - 30 cm belo
Page 93 and 94: Although the λE/Rn fraction was on
Page 95 and 96: estimated snow depth based on the 2
Page 97 and 98: important to note that the average
Page 99 and 100: 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: Figure 3: Explained variance (R 2 )
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 and 152: day and has a mean pixel resolution
Page 153 and 154: Table 1. Wheaton River basin EASE-G
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
Page 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
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
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Brubaker, K.L., R. Pinker and E. De
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181 63 rd EASTERN SNOW CONFERENCE N
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The Sierra Nevada del Cocuy region
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Colombia Venezuela Glacier Series R
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Table 4. Glacier Areas of the Ruiz-
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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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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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