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24 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 The runoff delay caused by the different glacial reservoirs is managed by the main model storages for the melt simulations. This are the snow-, ice- and firn storage, parameterized using the storage coefficient and the translation time (Badoux, 1999). PREVAH uses different approaches for the snow and icemelt simulations. For this study the radiation based temperature-index approach of Hock (1999) has been applied: ⎧1 ⎪ (MF M = ⎨n ⎪ ⎩ snow / ice + a 0 snow / ice ⋅I ) ⋅T : : T > 0 T ≤ 0 where M is the calculated melt rate (mm h –1 ), MFsnow / ice is the melt factor for snow respectively ice (mm d –1 K –1 ), asnow / ice is the radiation melt factor for snow respectively ice, I is the potential clear-sky direct solar radiation at ice or snow surface (W m –2 ), T is the air temperature (°C) and n is the number of time-steps per day, in this case 24 hours. The melt factors for snow and ice are empirical coefficients and I is a calculated value following Hock (1999). Only air temperature is needed as input to the model, radiation is calculated as the site adjusted potential direct radiation for every HRU, considering exposition and slope. This approach has shown the best results in the comparative study of Zappa et al. (2003), where the performance of an energy balance model and three degree-day-factor models have been investigated for a mesoscale basin. For the calculation of the snow accumulation, the simulation of the surface runoff, the calculation of snow- and icemelt, and the calculation of the evapotranspiration the following input to the PREVAH model is required: air temperature, precipitation, water vapour pressure, global radiation, wind speed and sunshine duration. Model application at Goldbergkees watershed As a major source for the description of the topography to the modelling system a DEM of 10 m resolution (Auer et al., 2002), covering the entire catchment area, has been applied. Such high resolution is required to account for the small scale variability of the investigated processes for such a small basin. The separation into HRU’s has been realized using two land use classes (glacier and rock), 50 m elevation bands (16 classes), nine aspect classes and six slope classes. Thus 722 HRU’s and 197 meteorological units (MU) were generated. MU are the spatial units covering the watershed for which the meteorological data have been interpolated based on hourly data from available stations (Figure 1, stations 1, 2, 3, and 4). Because elevation and exposition are the main factors governing climatological and meteorological variability in such areas, raster elements in the same elevation band and showing similar aspect belong to the same MU. The PREVAH model has been run in an hourly time step. For the interpolation of the meteorological input an inverse distance weighting and altitude-dependent regression approach has been used (Klok et al. 2001). Precipitation has been permanently observed at the observatory and temporary at the catchment outlet (Figure 1, stations 1 and 4). For a better weighting two additional virtual stations at the sites of the stations 2 and 3 (Figure 1) have been applied, where monthly measured precipitation sums have been available. The laps rate for the air temperature has been set to 0.65°K/100 m. This value has been estimated using long term air temperature measurements of two stations in the area. The air temperature input for the melt modelling is taken from 3 stations outside the glacier, which is proposed by Lang and Braun (1990). The simulation of one entire hydrological year with PREVAH, producing tables of hourly runoff and storage output, and daily maps of SWE takes less than 3 minutes computation time using a standard PC. We proceeded therefore with manual tuning of the parameters governing the processes of snow accumulation, snowmelt, icemelt and runoff generation (Zappa et al., 2003; Gurtz et al., 2003) for the hydrological year 2004/2005, where we have additional observations on snow cover and SWE for multi-criteria verification. The verification for the discharge simulation (1)
25 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 has been carried out by the analysis of the model performance for the hydrological year 2003/2004. Calibration procedure First step: Due to the extreme climate conditions during 2003 melt season the catchment area of Goldbergkees glacier was nearly snow free. Hence it has been possible to calibrate the melt parameters for icemelt separated from other processes. Therefore the observed and simulated discharge hydrographs have been compared; Second step: The model has been initialized using spatially distributed SWE data at the time of the maximum snow accumulation (begin of May). Thus the degree day factor for snowmelt has been calibrated comparing the daily results of the internal snow storage with the in field observed spatially distributed SWE data at different points in time; Third step: The model has been initialized using spatially distributed SWE data at the beginning of the accumulation period (equal to the beginning of the hydrological year in the northern hemisphere at the beginning of October). For this reason the solid precipitation has been corrected by plus 18 % more precipitation following Sevruk (1986). The snow accumulation of the PREVAH model has been compared with the in field observed spatially distributed SWE data at the time of the maximum accumulation; Fourth step: Fine tuning of the melt parameters and adaptation of the storage parameters comparing observed and simulated hydrographs. The storage time for snow- and icemelt are calibrated for the Goldbergkees catchment adducting the recession curves of the observed hydrograph, which result when summer snowfalls reduce ablation by raising albedo (Collins, 1982). Three events of this kind have been observed at 2005 melt season (see Figure 4). For the determination of the translation time of snow- and icemelt the diurnal maximums of the simulated runoff have been fitted to the observed hydrograph. Figure 4. Simulated vs. observed snow cover pattern at 29 July 2005. The observed image is generated by a classification of an ASTER image. Black coloured areas indicate snow free areas, white areas are still snow covered. Verification procedure First step: the distributed SWE simulation has been verified at 4 dates of the 2004/2005 period; Second step: the simulation of the snow patterns has been verified with satellite data from ASTER; Third step: the skill of the runoff simulation has been independently verified without further adjustment of the calibrated parameters by comparisons with the hydrological year 2003/2004; Fourth step: observed data on glacier ablation for both 2003/2004 and 2004/2005 have been compared with the simulated ice ablation.
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 35: 23 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
Page 85 and 86: 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
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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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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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