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CONCLUSION The comparison of the GEOtop SWE results with MODIS snow extent maps shows a fairly good agreement. The problems of the model are mainly due to uncertainties about the distribution of the meteorological forcing in space and time, as only measurements in some points are available. Only total precipitation is measured, and a criterion based on two thresholds on the air temperature has been used to distinguish solid and liquid precipitation. However, threshold values a little higher than the values found in literature were needed to reproduce the snow cover at the bottom of the valley. An hourly varying lapse rate was inferred by the temperature measurements, and was used as input data for the model. Though characterized by some commission errors, MODIS maps can be valuable tools to validate distributed models. However, images of finer resolution and more frequent overpassing time would be better tools. With the results of the model we investigated the relationship between SWE and elevation and aspect in different seasons. It has been shown that the dependence of SWE on elevation is more or less linear, but tends to be weaker in autumn and stronger in spring. Moreover, differential melting in north and south facing slopes occurs all through the winter at lower elevation (approximately below 800 m a.s.l.), whereas it is present only in autumn and in spring at the higher elevations. However, these relations should be verified in other applications to different and wider basins. ACKNOWLEDGEMENT We thank prof. John Albertson who supported the visit of the first author at the Department of Civil and Environmental Engineering of Duke University, and Joseph Tomasi who corrected a first version of the manuscript. REFERENCES Alfnes E, Andreassen LM, Engeset RV, Skaugen T, Udnaes HC. 2004. Temporal variability in snow distribution. Annals of Glaciology 38: 101-105. Anderson EA. 1976. A point energy and mass balance model of a snow cover. Office of Hydrology National Weather Service. Bertoldi G, Rigon R, Over TM. 2006. Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets. Accepted to Journal of Hydrometeorology. Brutsaert W. 2004. On a derivable formula for long-wave radiation from clear sky. Water Resources Research 11: 742-744. Businger JA, Wyngaard JC, Izumi Y, Bradley EF. 2004. Flux-profile relationship in the atmospheric surface layer. Journal of Atmospheric Sciences 28: 181-189. Carroll T, Cline D, Olheiser C, Rost A, Nilsson A, Fall G, Bovitz C, Li L. 2006. NOAA’s national snow analyses. Paper presented at the 74 th Annual Meeting of the Western Snow Conference 2006. Cazorzi F, Dalla Fontana G. 1996. Snowmelt modeling by combining air temperature and a distributed radiation index. Journal of Hydrology 181: 169-187. Cline DW, Bales CB, Dozier J. Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling. Water Resources Research 34(5): 1275- 1285. Cline D, Elder K, Bales R. 1998. Scale effects in a distributed snow water equivalence and snow melt model for mountain basins. Hydrological Processes 12: 1527-1536. 208
Colbeck SC, Anderson EA. 1982. The permeability of a melting snow cover. Water Resources Research 18(4): 904-908. Hall DK, Andrew BT, James LF, Alfred TCC, Milan A. 2000. Intercomparison of satellite-derived snow-cover maps. Annals of Glaciology 31: 369-376. Hall KH, Riggs GA, Salomonson VV, DiGirolamo NE, Bayr KJ. 2002. MODIS snow-cover products. Remote Sensing of Environment 83: 181-194. Idso SB. 1981. A set of equation for full spectrum and 8 to 14 µm and 10.5 to 12.5 µm thermal radiation from cloudless skies. Water Resources Research 17(2): 295-304. Iqbal M. 1983. An introduction to solar radiation. Academic Press. Jordan R. 1991. A one dimensional temperature model for a snowcover: Technical documentation for SNTHERM 89, Spec. Rep. 657, User’s guide. US Army Cold Regions Research and Engineering Laboratory, Hanover, NH, USA. Lee S., Klein AG, Over TM. 2005. A comparison of MODIS and NOHRSC snow-cover products for simulating streamflow using the Snowmelt Runoff Model. Hydrological Processes 19: 2951-2972. Marks D, Domingo J, Susong D, Link T, Garen D. 1999. A spatially distributed energy balance snowmelt model for application in mountain basins. Hydrological Processes 13: 1935-1959. Martinec J, Rango A. 1986. Parameter values for snowmelt runoff modeling. Journal of Hydrology 84: 197-219. Neteler M. 2005. Time series processing of MODIS satellite data for landscape epidemiological applications. International Journal of Geoinformatics 1(1): 133-138. Oleson KW, Dai Y, Bonan G, Bosilovich M, Dickinson R, Dirmeyer P, Hoffman F, Houser P, Levis S, Niu G, Thornton P, Vertenstein M, Yang Z, Zeng X. 2004. Technical description of the Community Land Model (CLM). NCAR, Boulder, CO, USA. Pomeroy JW, Toth B, Granger RJ, Hedstrom NR, Essery RLH. 2003. Variation in Surface Energetics during Snowmelt in a Subartic Mountain Catchment. Journal of Hydrometeorology 4: 702-719. Riggs GA, Hall DH, Salomonson VV. 2003. MODIS Snow Products User Giude. Electronic document: http://modis-snow-ice.gsfc.nasa.gov/sugkc.html. Riggs GA, Hall DK. 2006. Evaluation of snow errors in the MODIS snow data products. Poster presented at the 63 rd Annual Meeting of the Eastern Snow Conference 2006. Rigon R, Bertoldi G, Over TM. 2006. GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets. Accepted to Journal of Hydrometeorology. Satterlund DR. 1979. An improved equation for estimating longwave radiation from the atmosphere. Water Resources Research 15(6): 1649-1650. Turpin O, Ferguson R, Johansson B. 1999. Use of remote sensing to test and update simulated snow cover in hydrological models. Hydrological Processes 13: 2067-2077. US Army Corps of Engineers. 1956. Snow hydrology, Summary report of the snow investigations. US Army Corps of Engineers, North Pacific Division, Portland, OR, USA. Zanotti F, Endrizzi S, Bertoldi G, Rigon R. 2004. The GEOTOP snow module. Hydrological Processes 18: 3667-3679. 209
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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
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METHODS The IMS Product The IMS was
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Figure 1. Microwave spectral charac
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snow depth and SWE on a weekly basi
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Figure 4. Example of blended SWE pr
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Evaluation of the global distributi
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Figure 9. Inter-comparison plots of
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Helfrich, S.R., D. McNamara, B.H.Ra
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105 63 rd EASTERN SNOW CONFERENCE N
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and validated regionally so they ca
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109 Test Site Figure 2. Variation o
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Correlation Coe. Correlation Coe. C
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Figure 8. SSM/I scattering signatur
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Considering the facts mentioned abo
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Besides the temporal validation, th
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RMSE Non linear Azar Chang Goodison
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63 nd EASTERN SNOW CONFERENCE Newar
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http://www.ccin.ca); from simulatio
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over the period 1988-2000. With the
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Figure 3: Explained variance (R 2 )
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correspondence between simulated an
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values. Continued development of ne
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National Climatic Data Center (2005
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Snow Remote Sensing Posters 135
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ABSTRACT 63 rd EASTERN SNOW CONFERE
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day and has a mean pixel resolution
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Table 1. Wheaton River basin EASE-G
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The SSM/I snowmelt onset algorithm
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coarse-resolution pixel with the hi
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Figure 5. (a) Scatterplot of snowpa
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For both 2004 and 2005, the AMSR-E
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threshold of ±18 K that reflects t
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ABSTRACT 153 63 rd EASTERN SNOW CON
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DATA USED SSM/I & QuikSCAT Coverage
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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
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
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
Page 207 and 208: 63 rd EASTERN SNOW CONFERENCE Newar
Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
Page 213 and 214: Figure 3: 8-day composite maps (24
Page 215 and 216: The third pair (Figure 5) represent
Page 217 and 218: Figure 9: Time evolution of SWE ave
Page 219: melting occurs, whereas at the high
Page 223 and 224: ABSTRACT 211 63 rd EASTERN SNOW CON
Page 225 and 226: A B Figure 1. Low-magnification sec
Page 227 and 228: GB ridge GB groove Figure 3. Higher
Page 229 and 230: CONCLUSION Figure 5. Indexed electr
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Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
Page 239 and 240: The daily wind speed can exceed 6.5
Page 241 and 242: NCDC. 2006. National Climate Data C
Page 243 and 244: ABSTRACT 231 63 rd EASTERN SNOW CON
Page 245 and 246: and mass balances for 540 environme
Page 247 and 248: Temperature, precipitation, windspe
Page 249 and 250: the left in response to snow compac
Page 251 and 252: Pinched-cone dimensions for each sl
Page 253 and 254: Terrain slope (degrees) Terrain slo
Page 255 and 256: ACKNOWLEDGEMENTS This work was fund
Page 257 and 258: Glacial and Periglacial Processes 2
Page 261 and 262: From linear regression the constant
Page 265 and 266: DATA COLLECTION Figure 1. Location
Page 267 and 268: Table 1. Comparison of GPS measured
Page 269 and 270: 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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