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In contrast to the six ET0 models, the BROOK90 model provided more realistic representation of vegetation and soil parameters and allowed evaluation of the hydrologic components affecting ET. The components included infiltration, surface evaporation, transpiration, and canopy intercepted evaporation. RESULTS Field Measurements We report on the results of intercomparisons for dry and wet composite diurnal cycles of air temperature, relative humidity, insolation, precipitation, wind speed and direction, and iButton temperatures. HOBO automated weather station We were particularly careful with our data syntheses from the humidity sensor (naturally aspirated), which was affected by supersaturation during portions of the wet season. We computed the dew point temperatures from humidity and relative humidity measurements and adjusted erroneous humidity values according to our corrections. Table 1 summarizes the results from the HOBO weather station. The ratio of dry to wet was applied to demonstrate differences. Table 1. Comparison of average daily statistics for variables measured by HOBO weather station during the dry and wet periods. HOBO Weather Station (daily statistics from hourly data) Dry Wet Dry/ Wet Air Temp. (°C) 7.6 6.6 1.15 Max. Temp. (°C) 16.0 12.7 1.26 Min. Temp. (°C) 1.5 3.6 0.42 Temp. Range. (°C) 14.5 9.1 1.59 RH (%) 59.5 92.4 0.64 Vap. Press. (kPa) 0.62 0.90 0.69 VP Deficit (kPa) 0.43 0.08 5.38 Insolation (MJ/m 2 ) 16.82 13.21 1.27 Wind (m/s) 1.42 1.18 1.20 Wind Direction (°) 39 233 N/A Wind Constancy 0.54 0.72 0.75 0.10 m Soil Moist. (m 3 /m 3 ) 0.005 0.119 0.04 0.10 m Soil Temp. (°C) 13.8 12.8 1.08 Precip. (mm/day) 0.08 7.36 0.01 Comparisons of the HOBO weather station data for the composite dry and wet periods suggest significant differences between all variables of meteorological forcing. The average day during the wet period is 1°C cooler than the dry period, given the lack of rainfall and greater insolation during the dry period. Because of increased cloud cover associated with higher rainfall, the diurnal temperature range is 5.4°C less during the wet period. The average relative humidity of 59.5% for the dry period is 33% less than that of the wet season. The vapor pressure of 0.62 kPa for the dry period was 69% of that for the wet period, 0.90 kPa. Figs. 7 through 12 illustrate the diurnal cycles for the dry and wet periods. The persistent valley winds (Figs. 10b, 11b and 12b) during the daylight hours of the wet period controls the efficiency of the impact of turbulent sensible and latent heat flux on ET rate. Assuming a saturated evaporating surface, the aerodynamic term of the Penman-Monteith method maximizes ET0 with concurrently high wind speed and vapor pressure deficit. This assumption is good for the wet 270
season, but unacceptable for the dry season when the surface is extremely dry (Fig. 8). The process of near-surface water vapor removal depends to a large extent on wind speed and air turbulence. When vaporizing water, the air above the evaporating surface becomes gradually saturated. If this air is not continuously replaced with drier air, the driving force for water vapor removal and the ET rate decreases. Hence, the dominance of daytime valley winds during the wet season plays a critical role in controlling ET within the pro-glacial valley, particularly given the steep confining walls. Furthermore, drier surface conditions in the lower valley will enhance the potential for ET throughout the valley, which is plausible given the predominantly easterly synoptic flow and the rain shadow effect to the west of the mountain range. (a) Air Temperature (°C) Dry Period Diurnal Variation of Average Air Temperature and Relative Humidity Relative Humidity Air Temperature 20 100 16 12 8 4 0 0:00 2:00 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 80 60 40 20 0 Relative Humidity (%) Air Temperature (°C) 271 20 16 12 8 4 0 0:00 2:00 Wet Period Diurnal Variation of Average Air Temperature and Relative Humidity Relative Humidity Air Temperature 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 Figure 7. Composite diurnal cycle of air temperature and relative humidity for dry (a) and wet (b) periods. Soil Temperature (°C) 30 26 22 18 14 10 6 0:00 2:00 Dry Period Diurnal Variation of Average Soil Temperature and Soil Water Content Soil Water Content Soil Temperature 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 0.11 0.09 0.07 0.05 0.03 0.01 -0.01 Water Content (m 3 /m 3 ) Soil Temperature (°C) 30 26 22 18 14 10 6 0:00 2:00 Wet Period Diurnal Variation of Average Soil Temperature and Soil Water Content Soil Water Content Soil Temperature 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 Figure 8. Composite diurnal cycle of incoming soil moisture and temperature for dry (a) and wet (b) periods. Precipitation, swe (mm) (a) (a) 1 0.8 0.6 0.4 0.2 0 Dry Period Diurnal Variation of Average Precipitation and Insolation Precipitation Insolation 1000 0:00 2:00 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 800 600 400 200 0 Incident Solar (W/m 2 ) Precipitation, swe (mm) 1 0.8 0.6 0.4 0.2 0 100 80 60 40 20 0 0.11 0.09 0.07 0.05 0.03 0.01 -0.01 Wet Period Diurnal Variation of Average Precipitation and Insolation Precipitation Insolation 1000 0:00 2:00 4:00 6:00 8:00 10:00 Time of Day (hours) 12:00 14:00 16:00 18:00 20:00 22:00 Figure 9. Composite diurnal cycle of incoming solar radiation and precipitation for dry (a) and wet (b) periods. 800 600 400 200 0 Water Content (m 3 /m 3 ) Relative Humidity (%) Incident Solar (W/m 2 ) (a) (b)
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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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180 160 140 120 100 80 60 40 20 0 1
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independent of regions. This greatl
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slightly vary the IMS with this pro
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imagery, the higher latitudes rely
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MODIS Land Science Team, NASA Godda
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FUTURE OF IMS Enhancements to the I
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information never present before on
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Brubaker, K.L., R. Pinker and E. De
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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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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
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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
Page 271 and 272: is shown in Figure 4. This variatio
Page 273 and 274: Table 4. The calculated volume flux
Page 277 and 278: Figure 1. Cordillera Blanca regiona
Page 279 and 280: MATERIALS AND METHODS Field Measure
Page 281: Data analysis techniques We synthes
Page 285 and 286: significant, although more informat
Page 287 and 288: (a) Liquid Water (mm) Dry Period: M
Page 289 and 290: Future Work We are installing addit
Page 291 and 292: Shuttleworth, W.J., and J.S. Wallac
Page 293 and 294: Snow and Periglacial Processes Post
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Page 299 and 300: No one to date has shown why snowfl
Page 301 and 302: APPENDIX A Northern Hemisphere Year
Page 303 and 304: 62nd ESC Papers 291
Page 305 and 306: 62 nd EASTERN SNOW CONFERENCE Water
Page 307 and 308: each other; and, they both decrease
Page 309 and 310: 297 62 nd EASTERN SNOW CONFERENCE W
Page 311 and 312: comes in the form of precipitation.
Page 313 and 314: averaged to reduce bias in this var
Page 315 and 316: RESULTS Figure 2. Flow chart of reg
Page 317 and 318: Table 6: Actual-Conditions SWE, dep
Page 319 and 320: 307 Table 9: Regression results bet
Page 321 and 322: Goita, K., A. Walker, B. Goodison,
Page 323: TO ERR IS HUMAN, TO FORGET IT WOULD
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