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Bohrer, Gil; Wolosin, Michael; Brady, Rachael; Avissar, Roni (2011)
Publisher: Tellus B
Journal: Tellus B
Languages: English
Types: Article
The structure of tree canopies affects turbulence in the atmospheric boundary layer, and light attenuation, reflection and emission from forested areas. Through these effects, canopy structure interacts with fluxes of heat, water, CO2, and volatile organic compounds, and affects patterns of soil moisture and ecosystem dynamics. The effects of canopy structure on the atmosphere are hard to measure and can be studied efficiently with large-eddy simulations. Remote sensing images that can be interpreted for biophysical properties are prone to errors due to effects of canopy structure, such as shading. However, the detailed 3-D canopy structure throughout a large spatial domain (up to several km2) is rarely available. We introduce a new method, namely the virtual canopy generator (V-CaGe), to construct finely detailed, 3-D, virtual forest canopies for use in remote sensing, and atmospheric and other environmental models. These virtual canopies are based on commonly observed mean and variance of biophysical forest properties, and a map (or a remotely-sensed image) of leaf area, or canopy heights, of a canopy subdomain. The canopies are constructed by inverse 2-D Fourier-transform of the observed spatial autocorrelation function and a random phase. The resulting field is expanded to 3-D by using empirical allometric profiles. We demonstrate that the V-CaGe can generate realistic simulation domains.DOI: 10.1111/j.1600-0889.2007.00253.x
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    • Adcroft, A., Hill, C. and Marshall, J. 1997. Representation of topography by shaved cells in a height coordinate ocean model. Mon. Weather Rev. 125, 2293-2315.
    • Albertson, J. D., Katul, G. G., Parlange, M. B. and Eichinger, W. E. 1998. Spectral scaling of static pressure fluctuations in the atmospheric surface layer: The interaction between large and small scales. Phys. Fluids 10, 1725-1732.
    • Asner, G. P., Bateson, C. A., Privette, J. L., El Saleous, N. and Wessman, C. A. 1998. Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling. J. Geophys. Res.- Atmos. 103, 28839-28853.
    • Asner, G. P. and Warner, A. S. 2003. Canopy shadow in IKONOS satellite observations of tropical forests and savannas. Remote Sens. Environ. 87, 521-533.
    • Asner, G. P., Carlson, K. M. and Martin, R. E. 2005. Substrate age and precipitation effects on Hawaiian forest canopies from spaceborne imaging spectroscopy. Remote Sens. Environ. 98, 457-467.
    • Aubinet, M., Heinesch, B. and Yernaux, M. 2003. Horizontal and vertical CO2 advection in a sloping forest. Bound. Layer. Meteor. 108, 397- 417.
    • Avissar, R. and Schmidt, T. 1998. An evaluation of the scale at which ground-surface heat flux patchiness affects the convective boundary layer using large-eddy simulations. J. Atmos. Sci. 55, 2666-2689.
    • Baldocchi, D., Finnigan, J., Wilson, K., Paw, U. K. T. and Falge, E. 2000. On measuring net ecosystem carbon exchange over tall vegetation on complex terrain. Bound. Layer. Meteor. 96, 257-291.
    • Bohrer, G., Mourad, H., Laursen, T. A., Drewry, D., Avissar, R. and co-authors. 2005. Finite-Element Tree Crown Hydrodynamics model (FETCH) using porous media flow within branching elements - a new representation of tree hydrodynamics. Water Resour. Res. 41, W11404, doi:10.11029/12005WR004181.
    • Bou-Zeid, E., Meneveau, C. and Parlange, M. B. 2004. Large-eddy simulation of neutral atmospheric boundary layer flow over heterogeneous surfaces: Blending height and effective surface roughness. Water Resour. Res. 40, WR002475, doi:10.001029/002003WR002475.
    • Burke, E. J., Shuttleworth, W. J. and Houser, P. R. 2004. Impact of horizontal and vertical heterogeneities on retrievals using multiangle microwave brightness temperature data. IEEE Trans. Geosci. Remote Sensing 42, 1495-1501.
    • Chuang, Y.-L., Oren, R., Bertozzi, A. L., Phillips, N. and Katul, G. G. 2006. The porous media model for the hydraulic system of a conifer tree: Linking sap flux data to transpiration rate. Ecol. Model. 191, 447-468.
    • Couteron, P., Pelissier, R., Nicolini, E. A. and Dominique, P. 2005. Predicting tropical forest stand structure parameters from Fourier transform of very high-resolution remotely sensed canopy images. J. Appl. Ecol. 42, 1121-1128.
    • Falkowski, M. J., Gessler, P. E., Morgan, P., Hudak, A. T. and Smith, A. M. S. 2005. Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling. For. Ecol. Manage. 217, 129-146.
    • Farquhar, G. D., Caemmerer, S. V. and Berry, J. A. 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C-3 species. Planta 149, 78-90.
    • Feigenwinter, C., Bernhofer, C. and Vogt, R. 2004. The influence of advection on the short term CO2-budget in and above a forest canopy. Bound. Layer. Meteor. 113, 201-224.
    • Ferraris, L., Gabellani, S., Parodi, U., von Hardenberg, J. and Provenzale, A. 2003. Revisiting multifractality in rainfall fields. J. Hydrometeorol. 4, 544-551.
    • Finnigan, J. 2000. Turbulence in plant canopies. Annu. Rev. Fluid Mech. 32, 519-571.
    • Finnigan, J. 2004. The footprint concept in complex terrain. Agric. For. Meteorol. 127, 117-129.
    • Fitzgerald, G. J., Pinter, P. J., Hunsaker, D. J. and Clarke, T. R. 2005. Multiple shadow fractions in spectral mixture analysis of a cotton canopy. Remote Sens. Environ. 97, 526-539.
    • Foken, T. and Leclerc, M. Y. 2004. Methods and limitations in validation of footprint models. Agric. For. Meteorol. 127, 223-234.
    • Gilbert, K. E., Raspet, R. and Di, X. 1990. Calculation of turbulence effects in an upward-refracting atmosphere. J. Acoust. Soc. Am. 87, 2428-2437.
    • Gong, P., Biging, G. S. and Standiford, R. 2000. Use of digital surface model for hardwood rangeland monitoring. J. Range Manage. 53, 622-626.
    • Gopalakrishnan, S. G., Roy, S. B. and Avissar, R. 2000. An evaluation of the scale at which topographical features affect the convective boundary layer using large eddy simulations. J. Atmos. Sci. 57, 334-351.
    • Gopalakrishnan, S. G. and Avissar, R. 2000. An LES study of the impacts of land surface heterogeneity on dispersion in the convective boundary layer. J. Atmos. Sci. 57, 352-371.
    • Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V., Syed, K. H. and co-authors.1998. Integration of soil moisture remote sensing and hydrologic modeling using data assimilation. Water Resour. Res. 34, 3405-3420.
    • Hurtt, G. C., Dubayah, R., Drake, J., Moorcroft, P. R., Pacala, S. W. and co-authors. 2004. Beyond potential vegetation: combining LiDAR data and a height-structured model for carbon studies. Ecol. Appl. 14, 873-883.
    • Jackson, R. B., Jobbagy, E. G., Avissar, R., Baidya Roy, S., Barrett, D. J. and co-authors. 2005. Trading water for carbon with biological sequestration. Science 310, 1944-1947.
    • Kanda, M., Inagaki, A., Letzel, M. O., Raasch, S. and Watanabe, T. 2004. LES study of the energy imbalance problem with eddy covariance fluxes. Bound. Layer. Meteor. 110, 381-404.
    • Karl, T., Potosnak, M., Guenther, A., Clark, D., Walker, J. and co-authors. 2004. Exchange processes of volatile organic compounds above a tropical rain forest: Implications for modeling tropospheric chemistry above dense vegetation. J. Geophys. Res.-Atmos. 109, D18306, doi:10.11029/12004JD004738.
    • Katul, G. G., Finnigan, J., Poggi, D., Leuning, R. and Belcher, S. E. 2006a. The influence of hilly terrain on canopy-atmosphere carbon dioxide exchange. Bound. Layer. Meteor. 118, 186-216.
    • Katul, G. G., Williams, C. G., Siqueira, M., Poggi, D., Porporato, A. and co-authors. 2006b. Spatial modelling of transgenic conifer pollen. In: Landscapes, Genomics, and Transgenic Conifers (ed. C. G. Williamss). Springer, New York, 265
    • Kruijt, B., Malhi, Y., Lloyd, J., Norbre, A. D., Miranda, A. C. and coauthors. 2000. Turbulence statistics above and within two Amazon rain forest canopies. Bound. Layer. Meteor. 94, 297-331.
    • Kruijt, B., Elbers, J. A., von Randow, C., Araujo, A. C., Oliveira, P. J. and co-authors. 2004. The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecol. Appl. 14, S101-S113.
    • Lefsky, M. A., Cohen, W. B., Parker, G. G. and Harding, D. J. 2002. Lidar remote sensing for ecosystem studies. Bioscience 52, 19- 30.
    • Leuning, R., Kelliher, F. M., Depury, D. G. G. and Schulze, E. D. 1995. Leaf nitrogen, photosynthesis, conductance and transpiration - scaling from leaves to canopies. Plant Cell Environ. 18, 1183-1200.
    • Li, B. and Avissar, R. 1994. The impact of spatial variability of landsurface characteristics on land-surface heat fluxes. J. Clim. 7, 527-537.
    • Markkanen, T., Rannik, U¨., Marcolla, B., Cescatti, A. and Vesala, T. 2003. Footprints and fetches for fluxes over forest canopies with varying structure and density. Bound. Layer. Meteor. 106, 437-459.
    • McCarthy, H. R., Oren, R., Finzi, A. C., Ellsworth, D. S., Kim, H.- S. and co-authors. 2007. Temporal dynamics and spatial variability in the enhancement of canopy leaf area under elevated atmospheric CO2. Glob. Change Biol. (in press).
    • Moorcroft, P. R., Hurtt, G. C. and Pacala, S. W. 2001. A method for scaling vegetation dynamics: The ecosystem demography model (ED). Ecol. Monogr. 71, 557-585.
    • Naidu, S. L., DeLucia, E. H. and Thomas, R. B. 1998. Contrasting patterns of biomass allocation in dominant and suppressed loblolly pine. Can. J. For. Res. 28, 1116-1124.
    • Nathan, R. and Katul, G. G. 2005. Foliage shedding in deciduous forests lifts up long-distance seed dispersal by wind. Proc. Natl. Acad. Sci. USA. 102, 8251-8256.
    • Nathan, R., Sapir, N., Trakhtenbrot, A., Katul, G. G., Bohrer, G. and coauthors. 2005. Long-distance biological transport processes through the air: Can nature's complexity be unfolded in silico? Divers. Distrib. 11, 131-137.
    • Patton, E. G., Shaw, R. H., Judd, M. J. and Raupach, M. R. 1998. Largeeddy simulation of windbreak flow. Bound. Layer. Meteor. 87, 275- 306.
    • Patton, E. G., Davis, K. J., Barth, M. C. and Sullivan, P. P. 2001. Decaying scalars emitted by a forest canopy: A numerical study. Bound. Layer. Meteor. 100, 91-129.
    • Patton, E. G., Sullivan, P. P. and Davis, K. J. 2003. The influence of a forest canopy on top-down and bottom-up diffusion in the planetary boundary layer. Q. J. R. Meteorol. Soc. 129, 1415-1434.
    • Patton, E. G., Sullivan, P. P. and Moeng, C. H. 2005. The influence of idealized heterogeneity on wet and dry planetary boundary layers coupled to the land surface. J. Atmos. Sci. 62, 2078-2097.
    • Patton, E. G., Sullivan, P. P. and Ayotte, K. W. 2006. Flow and transport above and within forests in complex topography. Integrated LandEcosystem Atmosphere Study Conference. Boulder, CO. pp. 376.
    • Peters-Lidard, C. D., Pan, F. and Wood, E. F. 2001. A re-examination of modeled and measured soil moisture spatial variability and its implications for land surface modeling. Adv. Water Resour. 24, 1069-1083.
    • Poggi, D., Porporato, A., Ridolfi, L., Albertson, J. D. and Katul, G. G. 2004a. The effect of vegetation density on canopy sub-layer turbulence. Bound. Layer. Meteor. 111, 565-587.
    • Poggi, D., Katul, G. G. and Albertson, J. D. 2004b. Momentum transfer and turbulent kinetic energy budgets within a dense model canopy. Bound. Layer. Meteor. 111, 589-614.
    • Rannik, U¨., Aubinet, M., Kurbanmuradov, O., Sabelfeld, K. K., Markkanen, T. and co-authors. 2000. Footprint analysis for measurements over a heterogeneous forest. Bound. Layer. Meteor. 97, 137-166.
    • Raupach, M. R. 1989a. Applying Lagrangian fluid-mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agric. For. Meteorol. 47, 85-108.
    • Raupach, M. R. 1989b. A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Q. J. R. Meteorol. Soc. 115, 609-632.
    • Raupach, M. R. 1998. Influences of local feedbacks on land-air exchanges of energy and carbon. Glob. Change Biol. 4, 477-494.
    • Rogallo, R. S. 1981. Numerical experiments in homogeneous turbulence. Pages 1-91. NASA, Ames Research Center.
    • Scanlon, T. M. and Albertson, J. D. 2003. Water availability and the spatial complexity of CO2, water, and energy fluxes over a heterogeneous sparse canopy. J. Hydrometeorol. 4, 798-809.
    • Scha¨fer, K. V. R. 2002. Effects of increased atmospheric CO2 concentrations on water and carbon relations of four co-occurring tree species. PhD thesis. Nicolas School of the Environment and Earth Sciences, Duke University, Durham, NC, pp 208.
    • Shaw, R. H., Denhartog, G. and Neumann, H. H. 1988. Influence of foliar density and thermal-stability on profiles of reynolds stress and turbulence intensity in a deciduous forest. Bound. Layer. Meteor. 45, 391-409.
    • Shaw, R. H. and Patton, E. G. 2003. Canopy element influences on resolved- and subgrid-scale energy within a large-eddy simulation. Agric. For. Meteorol. 115, 5-17.
    • Slaymaker, D., Schultz, H., Hanson, A., Riseman, E., Holmes, C. and co-authors. 1999. Calculating forest biomass with small format aerial photography, videography and a profiling laser. ASPRS Proceedings of the 17th Biennial Workshop on Color Photography and Videography in Resource Assessment. Reno, NV. pp. 241-260.
    • Sogachev, A., Menzhulin, G. V., Heimann, M. and Lloyd, J. 2002. A simple three-dimensional canopy - planetary boundary layer simulation model for scalar concentrations and fluxes. Tellus 54B, 784-819.
    • Sogachev, A., Leclerc, M. Y., Karipot, A., Zhang, G. and Vesala, T. 2005. Effect of clearcuts on footprints and flux measurements above a forest canopy. Agric. For. Meteorol. 133, 182-196.
    • Staebler, R. M. and Fitzjarrald, D. R. 2004. Observing subcanopy CO2 advection. Agric. For. Meteorol. 122, 139-156.
    • Staebler, R. M. and Fitzjarrald, D. R. 2005. Measuring canopy structure and the kinematics of subcanopy flows in two forests. J. Appl. Meteorol. 44, 1161-1179.
    • Stenberg, P. 1995. Penumbra in within-shoot and between-shoot shading in conifers and its significance for photosynthesis. Ecol. Model. 77, 215-231.
    • Styles, J. M., Raupach, M. R., Farquhar, G. D., Kolle, O., Lawton, K. A. and co-authors. 2002. Soil and canopy CO2, 13(CO2), H2O and sensible heat flux partitions in a forest canopy inferred from concentration measurements. Tellus 54B, 655-676.
    • Su, H. B., Shaw, R. H., Paw, U. K. T., Moeng, C. H. and Sullivan, P. P. 1998. Turbulent statistics of neutrally stratified flow within and above a sparse forest from large-eddy simulation and field observations. Bound. Layer. Meteor. 88, 363-397.
    • Toivonen, T., Kalliola, R., Ruokolainen, K. and Malik, R. N. 2006. Across-path DN gradient in Landsat TM imagery of Amazonian forests: A challenge for image interpretation and mosaicking. Remote Sens. Environ. 100, 550-562.
    • Venema, V., Meyer, S., Garcia, S. G., Kniffka, A., Simmer, C. and co-authors. 2006. Surrogate cloud fields generated with the iterative amplitude adapted Fourier transform algorithm. Tellus 58A, 104- 120.
    • Venugopal, V., Basu, S. and Foufoula-Georgiou, E. 2005. A new metric for comparing precipitation patterns with an application to ensemble forecasts. J. Geophys. Res.-Atmos. 110, D08111, doi:10.01029/02004JD005395.
    • Vesala, T., Markkanen, T., Palva, L., Siivola, E., Palmroth, S. and coauthors. 2000. Effect of variations of PAR on CO2 exchange estimation for Scots pine. Agric. For. Meteorol. 100, 337-347.
    • Vinuesa, J. F. and De Arellano, J. V.-G. 2003. Fluxes and (co-)variances of reacting scalars in the convective boundary layer. Tellus 55B, 935- 949.
    • Walko, R. L., Band, L. E., Baron, J., Kittel, T. G. F., Lammers, R. and co-authors. 2000. Coupled atmosphere-biophysics-hydrology models for environmental modeling. J. Appl. Meteorol. 39, 931- 944.
    • Wang, H., Takle, E. S. and Shen, J. M. 2001. Shelterbelts and windbreaks: Mathematical modeling and computer simulations of turbulent flows. Annu. Rev. Fluid Mech. 33, 549-586.
    • Weishampel, J. F., Blair, J. B., Knox, R. G., Dubayah, R. and Clark, D. B. 2000. Volumetric LiDAR return patterns from an old-growth tropical rainforest canopy. Int. J. Remote Sens. 21, 409-415.
    • Wirth, R., Weber, B. and Ryel, R. J. 2001. Spatial and temporal variability of canopy structure in a tropical moist forest. Acta Oecol.-Int. J. Ecol. 22, 235-244.
    • Yang, B., Raupach, M., Shaw, R. H., U, K. T. P. and Morse, A. P. 2006. Large-eddy simulation of turbulent flow across a forest edge. Part I: flow statistics. Bound. Layer. Meteor. 119, DOI: 10.1007/s10546- 10006-19083-10543.
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