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Importance of diurnal cycles in the Tropics Diurnal variation is related to the large and well-defined cycle in solar heating during a 24-hour period, and it represents one of the most fundamental components accounting for the variability of the climate system. Numerous observation studies have documented diurnal variation of deep convection, precipitation, cloudiness, and outgoing longwave radiation over the tropics (e.g., Gray and Jacobson, 1977; Duvel and Kandel, 1985; Hendon and Woodberry, 1993; Chen and Houze, 1997; Yang and Slingo, 2001; Nesbitt and Zipser, 2003; among many others). Mapes et al. (2003) observed an afternoon maximum rainfall over most of South and Central America that is typically composed of relatively small convective cloud systems. Furthermore, Mapes et al. found a nocturnal maximum of rainfall over some large valleys in the Andes, and this would include the larger hanging valleys, such as Llanganuco. Poveda et al. (2005) use hourly records from 51 rain gages in the Tropical Andes of Columbia to find clear diurnal cycles in precipitation, with minima between 0900 and 1100 local time, and nocturnal maxima on the western flank of the Central Andes. In addition, Porveda concluded no relation between the timing of the strong seasonal variability of rainfall maxima and elevation. Bendix et al. (2006) used K-band rain-radar to study convection in a valley with east-west orientation between the southern Equadorean Andes and the Amazon basin. Results revealed that a great portion of rainfall is of stratiform character, and discovered the existence of embedded convection and/or showers produced by local heating for the overall amount of rainfall. Bendix et al. (2006) further suggests that cold air drainage flow from the Andes and low-level confluence due to the concavity of the Andean chain in this area leads to convective instability in the nocturnal Amazonian boundary layer, which is extended to the east-west oriented Andean valley the predominant easterlies in the mid-troposphere. Rain clouds with at least embedded shallow convection can overflow the bordering ridges of the San Francisco valley providing rains of higher intensity at the ECSF research station. On the contrary, Bendix et al. found that afternoon convective precipitation can be caused by locally induced thermal convection at the bordering slopes (up-slope breeze system) where the ECSF station profits from precipitation off the edge of these local cells due to the narrow valley. Recent remote sensing studies demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales (Sorooshian et al., 2002; Bowman et al., 2005). Hence, evidence from various scales of observation suggest a strong influence of diurnal cycles in tropical regions, including the Andes Mountains. CONCLUSION From our meteorological and model results, we make the following conclusions for this proglacial valley. Additional years and observations of nearby valleys are anticipated. It is important to note that we are using the term lapse rate in an unconventional sense, not the free atmosphere, but rather the effects within the surface boundary layer up the valley floor. First, the steepest lapse rates occur below the lakes for both wet and dry seasons, and the lapse rate is significantly smaller above the glacial lakes. We partially attribute this elevation effect to the heterogeneous vegetation and topographic characteristics of pro-glacial valleys. Fruthermroe, the dry season nocturnal inversion below the lakes is not evident during wet season. Most precipitation (and cloud cover) occurs between sunset and sunrise during the wet season, hence insolation at the ground is strong during both seasons. Up-valley winds dominate during sunlight hours in both wet and dry seasons, but abruptly shift to katabatic winds during the dry season at sunset. The combination of valley winds and steep lapse rate below the lakes suggest local warm air advection within the surface boundary layer will contribute to evapotranspiration and lower glacial melt. Furthermore, the up-slope winds may enhance convective precipitation, particularly during the wet season after sunset. The BROOK90 output suggests that transpiration is a significant source of ET when soil moisture is available during the wet season. The model results suggest that the predominance of cloud-free daylight conditions and relatively high solar input enhance ET during the wet season. ET was insignificant throughout the dry season. 276
Future Work We are installing additional humidity, wind, solar, and soil moisture sensors to evaluate spatial variation of ET. We plan to make direct measurements of ET using lysimeters and evaporation pans, and to use BROOK90 to perform a formal sensitivity analysis of ET to meteorological forcing. In addition to site-specific parameters, the meteorological input includes solar radiation, air temperature, vapor pressure, wind speed, and precipitation. Model output includes various hydrological balance components of the vegetation canopy and soil surface. We plan to evaluate the reliability and realism of estimated ET and to determine the sensitivity of ET to variables measured at the HOBO site in the Llanganuco valley. Future modeling will focus on more accurate measurements of parameters for BROOK90 and the elevation effects on ET and total ET for the valley. ACKNOWLEDGEMENTS Funding for this project was provided by The Ohio State University, Department of Geography and Office of International Affairs. We are grateful for the collaboration with Peruvian Institute of Natural Resources (INRENA), especially the timely collection and transmission of data by Jesús Gómez, INRENA-Huaraz, Perú. REFERENCES Allen, R.G., Jensen, M.E., Wright, J.L., and Burman, R.D. 1989. Operational Estimates of Reference Evapotranspiration. Agronomy Journal, 81: 650–662. Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. 1998. Crop evapotranspiration-Guidelines for computing crop water requirements, FAO irrigation and drainage paper 56, FAO. ISBN 92-5- 104219-5. Ames, A., 1998. A documentation of glacier tongue variations and lake developments in the Cordillera Blanca. Zeitschrift fur Gletscherkunde und Glazialgeologie, 34(1): 1–26. Barry, R. and Seimon, A., 2000. Research for mountain area development: Climatic fluctuations in the mountains of the Americas and their significance. Ambio, 29(7): 364–370. Bendix, J., Rollenbeck, R., and Reudenbach, C., 2006. Diurnal patterns of rainfall in a tropical Andean valley of southern Ecuador as seen by a vertically pointing K-band Doppler radar. International Journal of Climatology, Volume 26, Issue 6, 829–846. Bowman, KP, Collier, JC, North, GR, Wu, Q, Ha, E, Hardin, J, 2005. Diurnal cycle of tropical precipitation in Tropical Rainfall Measuring Mission (TRMM) satellite and ocean buoy rain gauge data. Journal of Geophysical Research, 110(d21): D21104-. Caballero, Y., Chevallier, P., Gallaire, R. and Pillco, R., 2004. Flow modelling in a high mountain valley equipped with hydropower plants: Rio zongo valley, Cordillera Real, Bolivia. Hydrological Processes, 18(5): 939–957. Caballero, Y., Jomelli, V., Chevallier, P. and Ribstein, P., 2002. Hydrological characteristics of slope deposits in high tropical mountains (Cordillera Real, Bolivia). Catena, 47(2): 101–116. Chen, S. S., and R. A. Houze Jr., 1997: Diurnal variation and life-cycle of deep convective systems over the tropical Pacific warm pool. Quart. J. Roy. Meteor. Soc., 123, 357–388. Duvel, J. P., and R. S. Kandel, 1985: Regional-scale diurnal variations of outgoing infrared radiation observed by METEOSAT. J. Climate Appl. Meteor., 24, 335–349. Federer, C.A. 1979. A soil–plant–atmosphere model for transpiration and availability of soil water. Water Resour Res., 15:555–562. Federer, C.A. 1995. BROOK90. A Simulation Model for Evaporation, Soil Water, and Streamflow. Version 3.1 Computer Freeware and Documentation. USDA Forest Service, P.O. Box 640, Durham, N.H. 03824. Federer, C.A., C. Vörösmarty, and B. Fekete. 1996. Intercomparison of methods for calculating potential evaporation in regional and global water balance models. Water Resour Res., 32: 2315–2321. 277
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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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219 63rd EASTERN SNOW CONFERENCE De
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Meteorological data for the winter
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monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
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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 and 282: Data analysis techniques We synthes
Page 283 and 284: season, but unacceptable for the dr
Page 285 and 286: significant, although more informat
Page 287: (a) Liquid Water (mm) Dry Period: M
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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