Boundary-Layer Meteorology

, Volume 132, Issue 3, pp 351–382

Exploring the Effects of Microscale Structural Heterogeneity of Forest Canopies Using Large-Eddy Simulations

  • Gil Bohrer
  • Gabriel G. Katul
  • Robert L. Walko
  • Roni Avissar
Article

Abstract

The Regional Atmospheric Modeling System (RAMS)-based Forest Large-Eddy Simulation (RAFLES), developed and evaluated here, is used to explore the effects of three-dimensional canopy heterogeneity, at the individual tree scale, on the statistical properties of turbulence most pertinent to mass and momentum transfer. In RAFLES, the canopy interacts with air by exerting a drag force, by restricting the open volume and apertures available for flow (i.e. finite porosity), and by acting as a heterogeneous source of heat and moisture. The first and second statistical moments of the velocity and flux profiles computed by RAFLES are compared with turbulent velocity and scalar flux measurements collected during spring and winter days. The observations were made at a meteorological tower situated within a southern hardwood canopy at the Duke Forest site, near Durham, North Carolina, U.S.A. Each of the days analyzed is characterized by distinct regimes of atmospheric stability and canopy foliage distribution conditions. RAFLES results agreed with the 30-min averaged flow statistics profiles measured at this single tower. Following this intercomparison, two case studies are numerically considered representing end-members of foliage and midday atmospheric stability conditions: one representing the winter season with strong winds above a sparse canopy and a slightly unstable boundary layer; the other representing the spring season with a dense canopy, calm conditions, and a strongly convective boundary layer. In each case, results from the control canopy, simulating the observed heterogeneous canopy structure at the Duke Forest hardwood stand, are compared with a test case that also includes heterogeneity commensurate in scale to tree-fall gaps. The effects of such tree-scale canopy heterogeneity on the flow are explored at three levels pertinent to biosphere-atmosphere exchange. The first level (zero-dimensional) considers the effects of such heterogeneity on the common representation of the canopy via length scales such as the zero-plane displacement, the aerodynamic roughness length, the surface-layer depth, and the eddy-penetration depth. The second level (one-dimensional) considers the normalized horizontally-averaged profiles of the first and second moments of the flow to assess how tree-scale heterogeneities disturb the entire planar-averaged profiles from their canonical (and well-studied planar-homogeneous) values inside the canopy and in the surface layer. The third level (three-dimensional) considers the effects of such tree-scale heterogeneities on the spatial variability of the ejection-sweep cycle and its propagation to momentum and mass fluxes. From these comparisons, it is shown that such microscale heterogeneity leads to increased spatial correlations between attributes of the ejection-sweep cycle and measures of canopy heterogeneity, resulting in correlated spatial heterogeneity in fluxes. This heterogeneity persisted up to four times the mean height of the canopy (hc) for some variables. Interestingly, this estimate is in agreement with the working definition of the thickness of the canopy roughness sublayer (2hc–5hc).

Keywords

Atmospheric modelling Atmospheric boundary layer Backscatter Biosphere–atmosphere interactions Land-surface heterogeneity Large-eddy simulation Tree canopy Turbulence Regional Atmospheric Modeling System 

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References

  1. Adcroft A, Hill C, Marshall J (1997) Representation of topography by shaved cells in a height coordinate ocean model. Mon Weather Rev 125: 2293–2315CrossRefGoogle Scholar
  2. Albertson JD, Katul GG, Wiberg P (2001) Relative importance of local and regional controls on coupled water, carbon, and energy fluxes. Adv Water Resour 24: 1103–1118CrossRefGoogle Scholar
  3. Arakawa A, Lamb VR (1977) Computational design of the basic dynamical processes of the UCLA general circulation model. In: Chang J (eds) Methods in computational physics: advances in research and applications. Academic Press, London, pp 174–265Google Scholar
  4. Asner GP, Keller M, Silva JNM (2004) Spatial and temporal dynamics of forest canopy gaps following selective logging in the eastern Amazon. Glob Chang Biol 10: 765–783CrossRefGoogle Scholar
  5. Asner GP, Knapp DE, Broadbent EN, Oliveira PJC, Keller M, Silva JN (2005) Selective logging in the Brazilian Amazon. Science 310: 480–482CrossRefGoogle Scholar
  6. Avissar R, 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: 2666–2689CrossRefGoogle Scholar
  7. Avissar R, Eloranta EW, Gurer K, Tripoli GJ (1998) An evaluation of the large-eddy simulation option of the regional atmospheric modeling system in simulating a convective boundary layer: a FIFE case study. J Atmos Sci 55: 1109–1130CrossRefGoogle Scholar
  8. Baldocchi D, Finnigan J, Wilson K, Paw U KT, Falge E (2000) On measuring net ecosystem carbon exchange over tall vegetation on complex terrain. Boundary-Layer Meteorol 96: 257–291CrossRefGoogle Scholar
  9. Basu S, Porté-Agel F (2006) Large-eddy simulations of stably stratified atmospheric boundary layer turbulence: a scale-dependent dynamic modeling approach. J Atmos Sci 63: 2074–2091CrossRefGoogle Scholar
  10. Belcher SE, Jerram N, Hunt JCR (2003) Adjustment of a turbulent boundary layer to a canopy of roughness elements. J Fluid Mech 488: 369–398CrossRefGoogle Scholar
  11. Belcher SE, Finnigan JJ, Harman IN (2008) Flows through forest canopies in complex terrain. Ecol Appl 18: 1436–1453CrossRefGoogle Scholar
  12. Bhushan S, Warsi ZUA (2005) Large eddy simulation of turbulent channel flow using an algebraic model. Int J Numer Methods Fluids 49: 489–519CrossRefGoogle Scholar
  13. Bhushan S, Warsi ZUA, Walters DK (2006) Modeling of energy backscatter via an algebraic subgrid-stress model. AIAA J 44: 837–847CrossRefGoogle Scholar
  14. Bohrer G, Mourad H, Laursen TA, Drewry D, Avissar R, Poggi D, Oren R, Katul GG (2005a) 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:10.1029/2005WR004181 CrossRefGoogle Scholar
  15. Bohrer G, Nathan R, Volis S (2005b) Effects of long-distance dispersal for metapopulation survival and genetic structure at ecological time and spatial scales. J Ecol 93: 1029–1040CrossRefGoogle Scholar
  16. Bohrer G, Wolosin M, Brady R, Avissar R (2007) A virtual canopy generator (V-CaGe) for modeling complex heterogeneous forest canopies at high resolution. Tellus Ser B Chem Phys Meteorol 59: 566–576CrossRefGoogle Scholar
  17. Bohrer G, Longo M, Zielinski DJ, Brady R (2008a) VR visualisation as an interdisciplinary collaborative data exploration tool for large eddy simulations of biosphere-atmosphere interactions. Advances in visual computing—4th international symposium, ISVC 2008, Las Vegas, NV. Lecture Notes in Computer Science, vol 5358, pp 866–876Google Scholar
  18. Bohrer G, Nathan R, Katul GG, Walko RL, Avissar R (2008b) Effects of canopy heterogeneity, seed abscission, and inertia on wind-driven dispersal kernels of tree seeds. J Ecol 96: 569–580CrossRefGoogle Scholar
  19. Bou-Zeid E, Meneveau C, Parlange MB (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:10.1029/2003WR002475 CrossRefGoogle Scholar
  20. Bou-Zeid E, Parlange MB, Meneveau C (2007) On the parameterization of surface roughness at regional scales. J Atmos Sci 64: 216–227CrossRefGoogle Scholar
  21. Bou-Zeid E, Vercauteren N, Parlange MB, Meneveau C (2008) Scale dependence of subgrid-scale model coefficients: an a priori study. Phys Fluid 20: 115106CrossRefGoogle Scholar
  22. Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW (2005) Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci USA 102: 15144–15148CrossRefGoogle Scholar
  23. Cassiani M, Katul GG, Albertson JD (2008) The effects of canopy leaf area index on airflow across forest edges: large eddy simulation and analytical results. Boundary-Layer Meteorol 126: 433–460CrossRefGoogle Scholar
  24. Cava D, Katul GG (2008) Spectral short-circuiting and wake production within the canopy trunk space of an alpine hardwood forest. Boundary-Layer Meteorol 126: 415–431CrossRefGoogle Scholar
  25. Chambers SD, Beringer J, Randerson JT, Chapin FS (2005) Fire effects on net radiation and energy partitioning: contrasting responses of tundra and boreal forest ecosystems. J Geophys Res 110: D09106. doi:10.1029/2004JD005299 CrossRefGoogle Scholar
  26. Chow FK, Street RL, Xue M, Ferziger JH (2005) Explicit filtering and reconstruction turbulence modeling for large-eddy simulation of neutral boundary layer flow. J Atmos Sci 62: 2058–2077CrossRefGoogle Scholar
  27. Clark DB, Olivas PC, Oberbauer SF, Clark DA, Ryan MG (2008) First direct landscape-scale measurement of tropical rain forest leaf area index, a key driver of global primary productivity. Ecol Lett 11: 163–172Google Scholar
  28. Collins DC, Avissar R (1994) An evaluation with the fourier amplitude sensitivity test (Fast) of which land-surface parameters are of greatest importance in atmospheric modeling. J Clim 7: 681–703CrossRefGoogle Scholar
  29. Deardorff JW (1980) Stratocumulus-capped mixed layers derived from a 3-dimensional model. Boundary-Layer Meteorol 18: 495–527CrossRefGoogle Scholar
  30. Detto M, Montaldo N, Albertson JD, Mancini M, Katul G (2006) Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy. Water Resour Res 42: W08419. doi:10.1029/2005WR004693 CrossRefGoogle Scholar
  31. Detto M, Katul GG, Siqueira M, Juang J-H, Stoy PC (2008) The structure of turbulence near a tall forest edge: the backward facing step flow analogy revisited. Ecol Appl 18: 1420–1435CrossRefGoogle Scholar
  32. Dias MAFS, Regnier P (1996) Simulation of mesoscale circulations in a deforested area of Rondônia in the dry season. In: Gash JHC, Nobre CA, Roberts JM, Victoria RL (eds) Amazonian deforestation and climate. Wiley, Chichester, pp 531–547Google Scholar
  33. Dupont S, Brunet Y (2008a) Influence of foliar density profile on canopy flow: a large-eddy simulation study. Agric Meteorol 148: 976–990CrossRefGoogle Scholar
  34. Dupont S, Brunet Y (2008b) Edge flow and canopy structure: a large-eddy simulation study. Boundary-Layer Meteorol 126: 51–71CrossRefGoogle Scholar
  35. Dwyer MJ, Patton EG, Shaw RH (1997) Turbulent kinetic energy budgets from a large-eddy simulation of airflow above and within a forest canopy. Boundary-Layer Meteorol 84: 23–43CrossRefGoogle Scholar
  36. Eichinger WE, Cooper DI (2007) Using lidar remote sensing for spatially resolved measurements of evaporation and other meteorological parameters. Agron J 99: 255–271CrossRefGoogle Scholar
  37. Finnigan J (2000) Turbulence in plant canopies. Annu Rev Fluid Mech 32: 519–571CrossRefGoogle Scholar
  38. Finnigan J (2004) The footprint concept in complex terrain. Agric Meteorol 127: 117–129CrossRefGoogle Scholar
  39. Fisch G, Culf AD, Nobre CA (1996) Modelling convective boundary layer growth in Rondônia. In: Gash JHC, Nobre CA, Roberts JM, Victoria RL (eds) Amazonian deforestation and climate. Wiley, Chichester, pp 425–435Google Scholar
  40. Flesch TK, Wilson JD (1999) Wind and remnant tree sway in forest cutblocks. I. Measured winds in experimental cutblocks. Agric Meteorol 93: 229–242CrossRefGoogle Scholar
  41. Foken T, Leclerc MY (2004) Methods and limitations in validation of footprint models. Agric Meteorol 127: 223–234CrossRefGoogle Scholar
  42. Hadfield MG, Cotton WR, Pielke RA (1991) Large-eddy simulations of thermally forced circulations in the convective boundary-layer. 1. A small-scale circulation with zero wind. Boundary-Layer Meteorol 57: 79–114CrossRefGoogle Scholar
  43. Haltiner GJ, Williams RT (1980) Numerical prediction and dynamic meteorology. Wiley, New York, p 477Google Scholar
  44. Harman IN, Finnigan JJ (2007) A simple unified theory for flow in the canopy and roughness sublayer. Boundary-Layer Meteorol 123: 339–363CrossRefGoogle Scholar
  45. Hsieh CI, Katul G, Chi T (2000) An approximate analytical model for footprint estimation of scaler fluxes in thermally stratified atmospheric flows. Adv Water Resour 23: 765–772CrossRefGoogle Scholar
  46. Hsieh CI, Siqueira M, Katul G, Chu CR (2003) Predicting scalar source-sink and flux distributions within a forest canopy using a 2-D Lagrangian stochastic dispersion model. Boundary-Layer Meteorol 109: 113–138CrossRefGoogle Scholar
  47. Hurtt GC, Dubayah R, Drake J, Moorcroft PR, Pacala SW, Blair JB, Fearon MG (2004) Beyond potential vegetation: combining lidar data and a height-structured model for carbon studies. Ecol Appl 14: 873–883CrossRefGoogle Scholar
  48. Jackson PS (1981) On the displacement height in the logarithmic velocity profile. J Fluid Mech 111: 15–25CrossRefGoogle Scholar
  49. Juang J-Y, Katul GG, Porporato A, Stoy PC, Siqueira MS, Detto M, Kim HS, Oren R (2007) Eco-hydrological controls on summertime convective rainfall triggers. Glob Chang Biol 13: 887–896Google Scholar
  50. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77: 437–471CrossRefGoogle Scholar
  51. Kanda M, Moriwaki R, Kasamatsu F (2004) Large-eddy simulation of turbulent organized structures within and above explicitly resolved cube arrays. Boundary-Layer Meteorol 112: 343–368CrossRefGoogle Scholar
  52. Katul GG, Albertson JD (1998) An investigation of higher-order closure models for a forested canopy. Boundary-Layer Meteorol 89: 47–74CrossRefGoogle Scholar
  53. Katul GG, Chang WH (1999) Principal length scales in second-order closure models for canopy turbulence. J Appl Meteorol 38: 1631–1643CrossRefGoogle Scholar
  54. Katul GG, Chu CR, Parlange MB, Albertson JD, Ortenburger TA (1995) Low-wave-number spectral characteristics of velocity and temperature in the atmospheric surface-layer. J Geophys Res 100: 14243–14255CrossRefGoogle Scholar
  55. Katul G, Hsieh CI, Kuhn G, Ellsworth DS, Nie DL (1997) Turbulent eddy motion at the forest-atmosphere interface. J Geophys Res 102: 13409–13421CrossRefGoogle Scholar
  56. Katul GG, Geron CD, Hsieh CI, Vidakovic B, Guenther AB (1998) Active turbulence and scalar transport near the forest-atmosphere interface. J Appl Meteorol 37: 1533–1546CrossRefGoogle Scholar
  57. Katul G, Hsieh CI, Bowling D, Clark K, Shurpali N, Turnipseed A, Albertson J, Tu K, Hollinger D, Evans B, Offerle B, Anderson D, Ellsworth D, Vogel C, Oren R (1999) Spatial variability of turbulent fluxes in the roughness sublayer of an even-aged pine forest. Boundary-Layer Meteorol 93: 1–28CrossRefGoogle Scholar
  58. Katul GG, Mahrt L, Poggi D, Sanz C (2004) One- and two-equation models for canopy turbulence. Boundary-Layer Meteorol 113: 81–109CrossRefGoogle Scholar
  59. Katul GG, Poggi D, Cava D, Finnigan J (2006) The relative importance of ejections and sweeps to momentum transfer in the atmospheric boundary layer. Boundary-Layer Meteorol 120: 367–375CrossRefGoogle Scholar
  60. Klemp JB, Wilhelmson RB (1978) Simulation of 3-dimensional convective storm dynamics. J Atmos Sci 35: 1070–1096CrossRefGoogle Scholar
  61. Kruijt B, Elbers JA, von Randow C, Araujo AC, Oliveira PJ, Culf A, Manzi AO, Nobre AD, Kabat P, Moors EJ (2004) The robustness of eddy correlation fluxes for Amazon rain forest conditions. Ecol Appl 14: S101–S113CrossRefGoogle Scholar
  62. Launiainen S, Vesala T, Mölder M, Mammarella I, Smolander S, Rannik Ü, Kolar P, Har P, Lindroth A, Katul GG (2007) Vertical variability and effect of stability on turbulence characteristics down to the floor of a pine forest. Tellus Ser B Chem Phys Meteorol 59: 919–936CrossRefGoogle Scholar
  63. Lefsky MA, Cohen WB, Parker GG, Harding DJ (2002) Lidar remote sensing for ecosystem studies. Bioscience 52: 19–30CrossRefGoogle Scholar
  64. Li B, Avissar R (1994) The impact of spatial variability of land-surface characteristics on land-surface heat fluxes. J Clim 7: 527–537CrossRefGoogle Scholar
  65. Liu YQ, Avissar R (1996) Sensitivity of shallow convective precipitation induced by land surface heterogeneities to dynamical and cloud microphysical parameters. J Geophys Res 101: 7477–7497CrossRefGoogle Scholar
  66. Massman WJ, Weil JC (1999) An analytical one-dimensional second-order closure model of turbulence statistics and the Lagrangian time scale within and above plant canopies of arbitrary structure. Boundary-Layer Meteorol 91: 81–107CrossRefGoogle Scholar
  67. McCarthy HR, Oren R, Finzi AC, Ellsworth DS, Kim H-S, Johnsen KH, Millar B (2007) Temporal dynamics and spatial variability in the enhancement of canopy leaf area under elevated atmospheric CO2. Glob Chang Biol 13: 2479–2497CrossRefGoogle Scholar
  68. Medvigy D, Walko RL, Avissar R (2008) Modeling interannual variability of the Amazon hydroclimate. Geophys Res Lett 35. doi:10.1029/2008GL034941
  69. Meyers TP, Baldocchi DD (1991) The budgets of turbulent kinetic-energy and Reynolds stress within and above a deciduous forest. Agric Meteorol 53: 207–222CrossRefGoogle Scholar
  70. Moorcroft PR, Hurtt GC, Pacala SW (2001) A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol Monogr 71: 557–585Google Scholar
  71. Naidu SL, DeLucia EH, Thomas RB (1998) Contrasting patterns of biomass allocation in dominant and suppressed loblolly pine. Can J Res 28: 1116–1124CrossRefGoogle Scholar
  72. Nakai T, Sumida A, Matsumoto K, Daikoku K, Iida S, Park H, Miyahara M, Kodama Y, Kononov AV, Maximov TC, Yabuki H, Hara T, Ohta T (2008) Aerodynamic scaling for estimating the mean height of dense canopies. Boundary-Layer Meteorol 128: 423–443CrossRefGoogle Scholar
  73. Negrón JF, Wilson JL, Anhold JA (2000) Stand conditions associated with roundheaded pine beetle (Coleoptera: Scolytidae) infestations in Arizona and Utah. Environ Entomol 29: 20–27CrossRefGoogle Scholar
  74. Nepf H, Ghisalberti M, White B, Murphy E (2007) Retention time and dispersion associated with submerged aquatic canopies. Water Resour Res 43: W04422. doi:10.1029/2006WR005362 CrossRefGoogle Scholar
  75. Novick KA, Stoy PC, Katul GG, Ellsworth DS, Siqueira MBS, Juang J, Oren R (2004) Carbon dioxide and water vapor exchange in a warm temperate grassland. Oecologia 138: 259–274CrossRefGoogle Scholar
  76. Oren R, Hseih CI, Stoy P, Albertson J, McCarthy HR, Harrell P, Katul GG (2006) Estimating the uncertainty in annual net ecosystem carbon exchange: spatial variation in turbulent fluxes and sampling errors in eddy-covariance measurements. Glob Chang Biol 12: 883–896CrossRefGoogle Scholar
  77. Orlanski I (1975) A rational subdivision of scales for atmospheric processes. Bull Am Meteorol Soc 56: 527–530Google Scholar
  78. Palace M, Keller M, Asner GP, Hagen S, Braswell B (2008) Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties. Biotropica 40: 141–150CrossRefGoogle Scholar
  79. Patton EG (1997) Large-eddy simulation of turbulent flow above and within a plant canopy. Atmospheric Science, University of California Davis, 132 ppGoogle Scholar
  80. Patton EG, Horst TW, Lenschow DH, Sullivan PP, Oncley S, Burns S, Guenther A, Held A, Karl T, Mayor S, Rizzo L, Spuler S, Sun J, Turnipseed A, Allwine E, Edburg S, Lamb B, Avissar R, Holder HE, Calhoun R, Kleissl J, Massman W, Paw U KT, Weil JC (2008) The canopy horizontal array turbulecne study (CHATS). In: The 18th symposium on boundary layers and turbulenceGoogle Scholar
  81. Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, Grasso LD, Nicholls ME, Moran MD, Wesley DA, Lee TJ, Copeland JH (1992) A comprehensive meteorological modeling system—RAMS. Meteorol Atmos Phys 49: 69–91CrossRefGoogle Scholar
  82. Poggi D, Katul GG (2006) Two-dimensional scalar spectra in the deeper layers of a dense and uniform model canopy. Boundary-Layer Meteorol 121: 267–281CrossRefGoogle Scholar
  83. Poggi D, Katul GG, Albertson JD (2004a) Momentum transfer and turbulent kinetic energy budgets within a dense model canopy. Boundary-Layer Meteorol 111: 589–614CrossRefGoogle Scholar
  84. Poggi D, Porporato A, Ridolfi L, Albertson JD, Katul GG (2004b) Interaction between large and small scales in the canopy sublayer. Geophys Res Lett 31: L05102. doi:10.1029/2003GL018611 CrossRefGoogle Scholar
  85. Poggi D, Porporato A, Ridolfi L, Albertson JD, Katul GG (2004c) The effect of vegetation density on canopy sub-layer turbulence. Boundary-Layer Meteorol 111: 565–587CrossRefGoogle Scholar
  86. Prabha TV, Karipot A, Binford MW (2007) Characteristics of secondary circulations over an inhomogeneous surface simulated with large-eddy simulation. Boundary-Layer Meteorol 123: 239–261CrossRefGoogle Scholar
  87. Rannik Ü, Aubinet M, Kurbanmuradov O, Sabelfeld KK, Markkanen T, Vesala T (2000) Footprint analysis for measurements over a heterogeneous forest. Boundary-Layer Meteorol 97: 137–166CrossRefGoogle Scholar
  88. Raupach MR (1989a) Applying Lagrangian fluid-mechanics to infer scalar source distributions from concentration profiles in plant canopies. Agric Meteorol 47: 85–108CrossRefGoogle Scholar
  89. Raupach MR (1989b) A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Q J Roy Meteorol Soc 115: 609–632CrossRefGoogle Scholar
  90. Raupach MR, Thom AS (1981) Turbulence in and above plant canopies. Annu Rev Fluid Mech 13: 97–129CrossRefGoogle Scholar
  91. Raupach MR, Finnigan JJ, Brunet Y (1996) Coherent eddies and turbulence in vegetation canopies: the mixing-layer analogy. Boundary-Layer Meteorol 78: 351–382CrossRefGoogle Scholar
  92. Scanlon TM, Albertson JD (2003) Water availability and the spatial complexity of CO2, water, and energy fluxes over a heterogeneous sparse canopy. J Hydrometeorol 4: 798–809CrossRefGoogle Scholar
  93. Schäfer KVR (2002) Effects of increased atmospheric CO2 concentrations on water and carbon relations of four co-occurring tree species. Nicolas School of the Environment and Earth Sciences, Duke University, 208 ppGoogle Scholar
  94. Schmid HP (2002) Footprint modeling for vegetation atmosphere exchange studies: a review and perspective. Agric Meteorol 113: 159–183CrossRefGoogle Scholar
  95. Shaw RH, Patton EG (2003) Canopy element influences on resolved- and subgrid-scale energy within a large-eddy simulation. Agric Meteorol 115: 5–17CrossRefGoogle Scholar
  96. Shaw RH, Schumann U (1992) Large-eddy simulation of turbulent-flow above and within a forest. Boundary-Layer Meteorol 61: 47–64CrossRefGoogle Scholar
  97. Shaw RH, Denhartog G, Neumann HH (1988) Influence of foliar density and thermal-stability on profiles of reynolds stress and turbulence intensity in a deciduous forest. Boundary-Layer Meteorol 45: 391–409CrossRefGoogle Scholar
  98. Sogachev A, Leclerc MY, Karipot A, Zhang G, Vesala T (2005) Effect of clearcuts on footprints and flux measurements above a forest canopy. Agric Meteorol 133: 182–196CrossRefGoogle Scholar
  99. Stoll R, Porte-Agel F (2006a) Dynamic subgrid-scale models for momentum and scalar fluxes in large-eddy simulations of neutrally stratified atmospheric boundary layers over heterogeneous terrain. Water Resour Res 42: W01409. doi:10.1029/2005WR003989 CrossRefGoogle Scholar
  100. Stoll R, Porte-Agel F (2006b) Effect of roughness on surface boundary conditions for large-eddy simulation. Boundary-Layer Meteorol 118: 169–187CrossRefGoogle Scholar
  101. Stoll R, Porte-Agel F (2008) Large-eddy simulation of the stable atmospheric boundary layer using dynamic models with different averaging schemes. Boundary-Layer Meteorol 126: 1–28CrossRefGoogle Scholar
  102. Stoy PC, Katul GG, Siqueira MBS, Juang JY, Novick KA, Uebelherr JM, Oren R (2006) An evaluation of models for partitioning eddy covariance-measured net ecosystem exchange into photosynthesis and respiration. Agric Meteorol 141: 2–18CrossRefGoogle Scholar
  103. Su HB, Shaw RH, Paw U KT, Moeng CH, Sullivan PP (1998) Turbulent statistics of neutrally stratified flow within and above a sparse forest from large-eddy simulation and field observations. Boundary-Layer Meteorol 88: 363–397CrossRefGoogle Scholar
  104. Su HB, Schmid HP, Vogel CS, Curtis PS (2008) Effects of canopy morphology and thermal stability on mean flow and turbulence statistics observed inside a mixed hardwood forest. Agric Meteorol 148: 862–882CrossRefGoogle Scholar
  105. Thomas C, Foken T (2007) Flux contribution of coherent structures and its implications for the exchange of energy and matter in a tall spruce canopy. Boundary-Layer Meteorol 123: 317–337CrossRefGoogle Scholar
  106. Thomas C, Mayer JC, Meixner FX, Foken T (2006) Analysis of low-frequency turbulence above tall vegetation using a Doppler sodar. Boundary-Layer Meteorol 119: 563–587CrossRefGoogle Scholar
  107. Tremback CJ, Walko RL, Bell MJ (2004) RAMS/HYPACT evaluation and visualization utilities, version 2.5 user guide. ATMET LLC, Boulder, CO, p 32Google Scholar
  108. Volis S, Bohrer G, Oostermeijer G, Van Tienderen P (2005) Regional consequences of local population demography and genetics in relation to habitat management in Gentiana pneumonanthe. Conserv Biol 19: 357–367CrossRefGoogle Scholar
  109. Walko RL, Avissar R (2008) The ocean-land-atmosphere model (OLAM). Part II: formulation and tests of the nonhydrostatic dynamic core. Mon Weather Rev 136: 4045–4062CrossRefGoogle Scholar
  110. Weishampel JF, Drake JB, Cooper A, Blair JB, Hofton M (2007) Forest canopy recovery from the 1938 hurricane and subsequent salvage damage measured with airborne LiDAR. Remote Sens Environ 109: 142–153CrossRefGoogle Scholar
  111. Wilson NR, Shaw RH (1977) Higher-order closure model for canopy flow. J Appl Meteorol 16: 1197–1205CrossRefGoogle Scholar
  112. Yang B, Morse AP, Shaw RH, U KTP (2006a) Large-eddy simulation of turbulent flow across a forest edge. Part II: momentum and turbulent kinetic energy budgets. Boundary-Layer Meteorol 121: 433–457CrossRefGoogle Scholar
  113. Yang B, Raupach M, Shaw RH, U KTP, Morse AP (2006b) Large-eddy simulation of turbulent flow across a forest edge. Part I: flow statistics. Boundary-Layer Meteorol 119: 377–412. doi:10.1007/s10546-006-9083-3 CrossRefGoogle Scholar
  114. Yue W, Parlange MB, Meneveau C, Zhu W, van Hout R, Katz J (2007) Large-eddy simulations of plant canopy flows using plant-scale representation. Boundary-Layer Meteorol 124: 183–203. doi:10.1007/s10546-007-9173-x CrossRefGoogle Scholar
  115. Yue W, Meneveau C, Parlange MB, Zhu W, Kang HS, Katz J (2008) Turbulent kinetic energy budgets in a model canopy: comparisons between LES and wind-tunnel experiments. Environ Fluid Mech 8: 73–95CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  • Gil Bohrer
    • 1
    • 2
  • Gabriel G. Katul
    • 1
    • 3
  • Robert L. Walko
    • 1
  • Roni Avissar
    • 1
  1. 1.Department of Civil and Environmental EngineeringDuke UniversityDurhamUSA
  2. 2.Department of Civil and Environmental Engineering and Geodetic ScienceOhio State UniversityColumbusUSA
  3. 3.Nicholas School of the EnvironmentDuke UniversityDurhamUSA

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