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Figure 5. (a) ¾” PVC ventilated radiation shield for iButton Thermochron logger. Note that the elbow sections are detachable for installation in vegetation. (b) Typical installation of iButton temperature loggers, one atop 1.5 m PVC post (top) and one embedded at 1.5 m inside a Queñual tree. One iButton was calibrated under actual field conditions against the air temperature from the HOBO weather station (Fig. 6). The calibration is within the tolerance of the iButton sensors (approximately 3%). Every two months, each iButton was checked and the data were downloaded to a laptop computer using the iButton USB port adaptor (Jesus Gomez, INRENA, Perú). iButton Temperature (°C) 16 14 12 10 8 6 4 2 (a) iButton Temperature Callibration iButton = 1.0211(HOBO) - 0.0561 R 2 = 0.988 2 4 6 8 10 12 14 16 HOBO Temperature (°C) Figure 6. Calibration for iButton sensor inside PVC radiation shield. 268 (b) (c)
Data analysis techniques We synthesized all data from the 8 iButton temperature loggers on the western slope, the HOBO weather station, and the precipitation logger into hourly values to create 24-day periods during the 2005 dry, 17 June to 11 July, and wet, 7 to 31 December, seasons. These sample periods were chosen based on data availability and central proximity to the dry and wet seasons. We created composite averages of 24 hourly measurements for all 24 days making up the wet and dry periods—the diurnal cycle. Hence, composites are averages for each hour over the 24-day periods. The vector components of wind speed were calculated to create composite averages of speed and direction. Because we were interested in microscale variability, particularly elevation effects, we concentrated on analyzing and comparing the composite diurnal cycles of meteorological components for the wet and dry periods. Estimating Evapotranspiration It is difficult to measure ET accurately, particularly in remote mountainous regions with steep topography; most measurements require expensive equipment and frequent maintenance (refs). Consequently, there are a multitude of methods for estimating ET, most of which require site specific parameters and basic meteorological measurements. Evaporation rates, ignoring transpiration, are commonly estimated by energy balance, aerodynamic, and a combination of both methods, such as Penman (1945; 1963) and Penman-Monteith. Xu and Singh (1998; 2002) report on meteorological forcing of and compares methods of estimating ET based on input data from Changines station in Switzerland. We used a similar modeling approach and applied it to the Llanganuco valley. Allen et al. (1989) reports on the FAO methods for estimating potential ET (ET0) based on Penman combination equations for reference surfaces, such as uniform 120 mm tall grass used herein. Because of it’s inclusion of several commonly used models for estimating ET0, we elected to use the REF-ET software developed by Allen et al. (1998) and selected the Penman-Monteith FAO method for estimating hourly ET0 during the dry and wet periods. This will serve as a base-line for comparison to a more realistic physics-based model for ET. The equations for the FAO methods are explained in detail by Allen et al. (1998). We selected the BROOK90 (v.4.4e) model (Federer et al., 2003; Federer, 1995) to estimate the actual ET during the dry and wet season composite days. BROOK90 includes strong physicallybased determination of ET, a graphic user interface, and the visual basic code is available for modification. The model simulates deposition and sublimation of frozen water and assumes snowfall for near-surface air temperature below –1.5°C. The model meteorological input includes solar radiation, air temperature, vapor pressure, wind speed, and precipitation. Soil and vegetation parameters were measured in the field. Additional parameters were taken from the literature as reported by Federer et al. (2003). Model output includes hydrological balance components of the vegetation canopy and soil surface; we here report on the ET components. BROOK90 simulates evaporation and soil-water movement using a process-oriented approach for sparse canopies at a single location within a watershed. The model estimates interception and transpiration from a single-layer plant canopy, soil and snow evaporation/sublimation, snow accumulation and melt, and soil water movement through multiple soil layers. Potential evaporation rates are obtained using the Shuttleworth and Wallace (1985) modification of the Penman-Monteith combination equation. Actual transpiration is the lesser of potential transpiration and a soil water supply rate determined by the resistance to liquid water flow in the plants and on root distribution and soil water potential in the soil layers (Federer, 1979). For potential transpiration, canopy resistance depends on maximum leaf conductance, reduced for humidity, temperature, and light penetration. Each soil layer and the roots of vegetation have a resistance to water flow based on field observations and published results. Aerodynamic resistances are modified from Shuttleworth and Gurney (1990); they depend on leaf area index (LAI), which can vary seasonally, and on canopy height, which determines stem area index (SAI). Potential transpiration or potential interception are obtained using the actual or existing soil surface wetness in the Shuttleworth-Wallace equations. The equations then provide the total soil or ground evaporation. The total ET is the sum of precipitation evaporated from the canopy, soil evaporation, and transpiration from the canopy. 269
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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
Page 229 and 230: CONCLUSION Figure 5. Indexed electr
Page 231 and 232: 219 63rd EASTERN SNOW CONFERENCE De
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 259 and 260: 247 63 rd EASTERN SNOW CONFERENCE N
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: MATERIALS AND METHODS Field Measure
Page 283 and 284: season, but unacceptable for the dr
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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