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22 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 Precipitation and air temperature data observed at the observatory are shown in Figure 8 (e and f). Three additional air temperature stations were installed during 2005 melt season in and next to the catchment area (Figure 1, Nr. 2, 3 and 4). A temporary tipping bucket, for liquid precipitation measurements only, was installed at the catchment outlet (Figure 1, Nr. 4). The tipping bucket was fixed together with the temperature station and the data logger of the discharge gauge. Water levels could only be recorded between July and October at a natural cross section of a lake outlet. To prevent damage the instrumentation has to be removed during winter period. The rating curves were calculated for every melt season separately due to the changes of the hydraulic conditions. Snow depths have been measured automatically in a daily interval at about hundred meters of elevation beneath the observatory (see Figure 1, Nr. 5). Starting in May 2005 four field campaigns for mapping the SWE (snow water equivalent) of the catchment area were performed in monthly steps. Aluminium probes were used to measure the snow depth at about 60 to 140 points irregularly distributed over the entire catchment area. The snow density has been measured at two snow pits following the instructions of Kaser et al. (2003). During the earliest campaign in May eight pits have been dug with regard to the higher variability of the snow layers at that time (Figure 1, grey triangles). From this very detailed data set we interpolated distributed maps of SWE using a spline method to generate 10 m grids of snow depth. Additional sampling points at areas with no snow cover have been set to ensure better interpolation results at border areas. The measured snow density data have been interpolated using inverse distance weighting technique. Finally, SWE were computed from spatialized snow depth and snow density. Snow free areas were mapped in field using GPS. The maps of snow free areas have been overlaid to generate the final depletion maps. This unique set of SWE grid has been available for visual and quantitative comparison with the computation of the hydrological model (Figure 2). As a standard program of the mass balance measurements following the glaciological method (Hoinkes, 1970; Østrem and Brugman, 1991; Kaser et al., 2003) the ice ablation have been measured using ablation stakes, which have been drilled into the bare ice of the glacier. Using 17 ablation stakes distributed over the entire ablation area of glacier Goldbergkees the net ablation of the glacier has been calculated (Hynek and Schöner, 2004).
23 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 Figure 2. Simulated vs. observed distributed SWE data for different dates in 2004 and 2005. Black areas indicate snow free areas. Mentioned values of SWE mean are distributed SWE values averaged over the catchment area (mm) The hydrological model PREVAH The spatially distributed hydrological model PREVAH (Precipitation–Runoff– Evapotranspiration–HRU model, Gurtz et al., 1999) has been used to simulate the processes contributing to runoff. PREVAH has already been used at glacierized sites at different spatial resolution (Badoux, 1999; Gurtz et al., 2003; Zappa et al., 2003; Zappa et al., 2000). The catchment area is subdivided into HRU’s (hydrological response units, Ross et al., 1979) based on DEM and land use data. For every HRU the hydrological response to the meteorological input is simulated using a typical storage cascade approach. The runoff contributions of all units are added to provide the total discharge at the outlet of the entire catchment. Due to the small size of the catchment and steep slopes no routing between the spatial units and the river outlet is carried out.
Page 1 and 2: Proceedings of the 63rd ANNUAL EAST
Page 3 and 4: FOREWORD T his proceedings volume c
Page 5 and 6: CONTENTS Foreword..................
Page 7 and 8: STATEMENT OF PURPOSE The Eastern Sn
Page 9 and 10: EXECUTIVES FOR THE 63rd EASTERN SNO
Page 11 and 12: THE PRESIDENT’S PAGE The 63rd ann
Page 13 and 14: Weisnet Medal for Best Student Pape
Page 15 and 16: 3 63 rd EASTERN SNOW CONFERENCE New
Page 17 and 18: Figure 1. Layer 1 represents the so
Page 19 and 20: Figure 4. The general layout of the
Page 21 and 22: lue color represented no convection
Page 23 and 24: Total Density of Water Profiles The
Page 25 and 26: Figure 11(a). Simulated snow grain
Page 27 and 28: Jordan R, 1991. A one-dimensional t
Page 29 and 30: Campbell Scientific Award for Best
Page 31 and 32: 19 63 rd EASTERN SNOW CONFERENCE Ne
Page 33: 21 63 rd EASTERN SNOW CONFERENCE Ne
Page 41 and 42: R ∑i ∑ n 2 = 1 NS = − n i = 1
Page 49 and 50: Snow and Climate 37
Page 53 and 54: Spatial distributions of changes in
Page 55 and 56: Snow and Climate Posters 43
Page 59 and 60: Figure 2 presents correlation maps
Page 61 and 62: CONCLUSIONS Three regional-continen
Page 65 and 66: Discharge (mm) 20 18 16 14 12 10 8
Page 67 and 68: ABSTRACT 55 63 rd EASTERN SNOW CONF
Page 69 and 70: This paper analyzes the synoptic pa
Page 71 and 72: Cyclones that track west of the App
Page 73 and 74: The synoptic-scale atmospheric circ
Page 75 and 76: CONCLUSIONS The record snowfall in
Page 79 and 80: correlations between April-May snow
Page 81 and 82: Figure 3. Linear correlations betwe
Page 83 and 84: winter/early spring could contribut
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Snow Remote Sensing 73
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75 63 rd EASTERN SNOW CONFERENCE Ne
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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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89 63 rd EASTERN SNOW CONFERENCE Ne
Page 103 and 104:
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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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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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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----- -------------- ------- ------
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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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