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Large-eddy simulation of turbulence and particle dispersion inside the canopy roughness sublayer

Published online by Cambridge University Press:  25 July 2014

Ying Pan ,
Marcelo Chamecki  and
Scott A. Isard
Ying Pan
Affiliation:
Department of Meteorology, The Pennsylvania State University, University Park, PA 16802, USA
Marcelo Chamecki*
Affiliation:
Department of Meteorology, The Pennsylvania State University, University Park, PA 16802, USA
Scott A. Isard
Affiliation:
Department of Meteorology, The Pennsylvania State University, University Park, PA 16802, USA Department of Plant Pathology and Environmental Microbiology, The Pennsylvania State University, University Park, PA 16802, USA
*
†Email address for correspondence: chamecki@psu.edu
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Abstract

Modelling the dispersion of small particles such as fungal spores, pollens and small seeds inside and above plant canopies is important for many applications. Transport of these particles is driven by strongly inhomogeneous and non-Gaussian turbulent flows inside the canopy roughness sublayer, the region that extends from the ground to approximately three canopy heights. A large-eddy simulation (LES) approach is refined to study particle dispersion within and above the canopy region. Effects of plant reconfiguration are parameterized through a velocity-dependent drag coefficient, which is shown to be critical for accurate reproduction of velocity statistics and mean spore concentrations. The model yields predictions of turbulence statistics that are in good agreement with measurements. This is particularly true of the stress fractions carried by strong events, as revealed by standard quadrant analysis of the resolved velocity fluctuations, which is a known weakness of earlier LES studies of canopy flow using a constant drag coefficient. Experimental data on spore dispersal inside and above a maize canopy are reproduced successfully as well. Characteristics of the particle plume are analysed using LES results, and a pre-existing theoretical framework is adapted to model particle dispersal above the canopy. The results suggest that the plume above the canopy can be approximated using a simple analytical solution if the fraction of spores that escape the canopy region is known. Source height and gravitational settling have strong effects on the plume inside the canopy region and consequently determine the escape fraction. These effects are parameterized in the theoretical model by using the escape fraction to rescale the source strength.

JFM classification

Geophysical and Geological Flows: Atmospheric flows
Type
Papers
Information
Journal of Fluid Mechanics , Volume 753 , 25 August 2014 , pp. 499 - 534
Copyright
© 2014 Cambridge University Press 

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References

Albayrak, I., Nikora, V., Miler, O. & O’Hare, M. 2012 Flow-plant interactions at a leaf scale: effects of leaf shape, serration, roughness and flexural rigidity. Aquat. Sci. 74, 267–286.CrossRefGoogle Scholar
Alben, S., Shelley, M. & Zhang, J. 2002 Drag reduction through self-similar bending of a flexible body. Nature 420, 479–481.Google Scholar
Aylor, D. E. 1982 Modeling spore dispersal in a barley crop. Agric. Meteorol. 26, 215–219.Google Scholar
Aylor, D. E. 1986 A framework for examining inter-regional aerial transport of fungal spores. Agric. Forest Meteorol. 38, 263–288.CrossRefGoogle Scholar
Aylor, D. E. 1989 Aerial spore dispersal close to a focus of disease. Agric. Forest Meteorol. 47, 109–122.Google Scholar
Aylor, D. E. 1990 The role of intermittent wind in the dispersal of fungal pathogens. Annu. Rev. Phytopathol. 28, 73–92.CrossRefGoogle Scholar
Aylor, D. E. 1999 Biophysical scaling and the passive dispersal of fungus spores: relationship to integrated pest management strategies. Agric. Forest Meteorol. 97, 275–292.Google Scholar
Aylor, D. E. 2005 Quantifying maize pollen movement in a maize canopy. Agric. Forest Meteorol. 131, 247–256.Google Scholar
Aylor, D. E. & Ferrandino, F. J. 1989 Dispersion of spores released from an elevated line source within a wheat canopy. Boundary-Layer Meteorol. 46, 251–273.CrossRefGoogle Scholar
Aylor, D. E. & Flesch, T. K. 2001 Estimating spore release rates using a Lagrangian stochastic simulation model. J. Appl. Meteorol. 40, 1196–1208.Google Scholar
Aylor, D. E., Fry, W. E., Mayton, H. & Andrade-Piedra, J. L. 2001 Quantifying the rate of release and escape of Phytophthora infestans sporangia from a potato canopy. Phytopathology 91 (12), 1189–1196.CrossRefGoogle ScholarPubMed
Bailey, B. N. & Stoll, R. 2013 Turbulence in sparse, organized vegetative canopies: a large-eddy simulation study. Boundary-Layer Meteorol. 147, 369–400.Google Scholar
Bouvet, T., Loubet, B., Wilson, J. D. & Tuzet, A. 2007 Filtering of windborne particles by a natural windbreak. Boundary-Layer Meteorol. 123, 481–509.CrossRefGoogle Scholar
Bou-Zeid, E., Meneveau, C. & Parlange, M. B. 2005 A scale-dependent Lagrangian dynamic model for large eddy simulation of complex turbulent flows. Phys. Fluids 17, 025105.Google Scholar
Brown, J. K. M. & Hovmøller, M. S. 2002 Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297, 537–541.Google Scholar
Brunet, Y., Finnigan, J. J. & Raupach, M. R. 1994 A wind tunnel study of air flow in waving wheat: single-point velocity statistics. Boundary-Layer Meteorol. 70, 95–132.Google Scholar
Brutsaert, W. 1982 Evaporation into the Atmosphere: Theory, History, and Applications. Reidel/Springer.Google Scholar
Carrington, E. 1990 Drag and dislodgment of an intertidal macroalga: consequences of morphological variation in Mastocarpus papillatus Kützing. J. Expl Mar. Biol. Ecol. 139, 185–200.CrossRefGoogle Scholar
Cescatti, A. & Marcolla, B. 2004 Drag coefficient and turbulence intensity in conifer canopies. Agric. Forest Meteorol. 121, 197–206.Google Scholar
Chamecki, M. 2012 An analytical solution for dispersion of biological particles emitted from area sources: inclusion of dispersion in the crosswind direction. Agric. Forest Meteorol. 157, 30–38.CrossRefGoogle Scholar
Chamecki, M. 2013 Persistence of velocity fluctuations in non-Gaussian turbulence within and above plant canopies. Phys. Fluids 25, 1–14.Google Scholar
Chamecki, M. & Meneveau, C. 2011 Particle boundary layer above and downstream of an area source: scaling, simulations, and pollen transport. J. Fluid Mech. 683, 1–26.Google Scholar
Chamecki, M., Meneveau, C. & Parlange, M. B. 2008 A hybrid spectral/finite-volume algorithm for large-eddy simulation of scalars in the atmospheric boundary layer. Boundary-Layer Meteorol. 128, 473–484.Google Scholar
Chamecki, M., Meneveau, C. & Parlange, M. B. 2009 Large eddy simulation of pollen transport in the atmospheric boundary layer. J. Aerosol Sci. 40, 241–255.Google Scholar
Chang, J. C. & Hanna, S. R. 2004 Air quality model performance evaluation. Meteorol. Atmos. Phys. 87, 167–196.Google Scholar
Denmead, O. T. & Bradley, E. F. 1987 On scalar transport in plant canopies. Irrig. Sci. 8, 131–149.Google Scholar
Dupont, S. & Brunet, Y. 2008 Influence of foliar density profile on canopy flow: a large-eddy simulation study. Agric. Forest Meteorol. 148, 976–990.Google Scholar
Dupont, S., Brunet, Y. & Jarosz, N. 2006 Eulerian modelling of pollen dispersal over heterogeneous vegetation canopies. Agric. Forest Meteorol. 141, 82–104.Google Scholar
Dupont, S., Gosselin, F., Py, C., de Langre, E., Hemon, P. & Brunet, Y. 2010 Modelling waving crops using large-eddy simulation: comparison with experiments and a linear stability analysis. J. Fluid Mech. 652, 5–44.Google Scholar
Dupont, S. & Patton, E. G. 2012 Influence of stability and seasonal canopy changes on micrometeorology within and above an orchard canopy: the CHATS experiment. Agric. Forest Meteorol. 157, 11–29.Google Scholar
Edburg, S. L., Allwine, G., Lamb, B., Stock, D., Thistle, H., Peterson, H. & Strom, B. 2010 A simple model to predict scalar dispersion within a successively thinned loblolly pine canopy. J. Appl. Meteorol. Climatol. 49, 1913–1926.Google Scholar
Etnier, S. A. & Vogel, S. 2000 Reorientation of daffodil (Narcissus: Amaryllidaceae) flowers in wind: drag reduction andtorsional flexibility. Am. J. Bot. 87, 29–32.Google Scholar
Finnigan, J. J. 2000 Turbulence in plant canopies. Annu. Rev. Fluid Mech. 32, 519–571.Google Scholar
Finnigan, J. J., Shaw, R. H. & Patton, E. G. 2009 Turbulence structure above a vegetation canopy. J. Fluid Mech. 637, 387–424.Google Scholar
Flesch, T. K. & Wilson, J. D. 1992 A two-dimensional trajectory-simulation model for non-Gaussian, inhomogeneous turbulence within plant canopies. Boundary-Layer Meteorol. 61, 349–374.Google Scholar
Gavrilov, K., Morvan, D., Accary, G., Lyubimov, D. & Meradji, S. 2013 Numerical simulation of coherent turbulent structures and of passive scalar dispersion in a canopy sublayer. Comput. Fluids 78, 54–62.Google Scholar
Gaylord, B., Blanchette, C. A. & Denny, M. W. 1994 Mechanical consequences of size in wave-swept algae. Ecol. Monograph 64, 287–313.Google Scholar
Gillies, J. A., Nickling, W. G. & King, J. 2002 Drag coefficient and plant form response to wind speed in three plant species: Burning Bush (Euonymus alatus), Colorado Blue Spruce (Picea pungens glauca.), and Fountain Grass (Pennisetum setaceum). J. Geophys. Res. 107 (D24), 4760 doi:10.1029/2001JD001259.Google Scholar
Gleicher, S. C., Chamecki, M., Isard, S. A., Pan, Y. & Katul, G. G. 2014 Interpreting three-dimensional spore concentration measurements and escape fraction in a crop canopy using a coupled Eulerian–Lagrangian stochastic model. Agric. Forest Meteorol. 194, 118–131.Google Scholar
Gosselin, F., de Langre, E. & Machado-Almeida, B. A. 2010 Drag reduction of flexible plates by reconfiguration. J. Fluid Mech. 650, 319–341.Google Scholar
Hanna, S. R., Chang, J. C. & Strimaitis, D. G. 1993 Hazardous gas model evaluation with field observations. Atmos. Environ. 27, 2265–2285.Google Scholar
Harder, D. L., Speck, O., Hurd, C. L. & Speck, T. 2004 Reconfiguration as a prerequisite for survival in highly unstable flow-dominated habitats. J. Plant Growth Regul. 23, 98–107.Google Scholar
de Jong, M. D., Aylor, D. E. & Bourdôt, G. W. 1999 A methodology for risk analysis of plurivorous fungi in biological weed control: Sclerotinia sclerotiorum as a model. BioControl 43, 397–419.Google Scholar
Kanda, M. & Hino, M. 1994 Organized structures in developing turbulent flow within and above a plant canopy, using a large eddy simulation. Boundary-Layer Meteorol. 68, 237–257.Google Scholar
Katul, G. G. & Albertson, J. D. 1999 Modeling ${\rm CO}_{2}$ sources, sinks, and fluxes within a forest canopy. J. Geophys. Res. 104, 6081–6091.Google Scholar
Katul, G. G., Grönholm, T., Launiainen, S. & Vesala, T. 2011 The effects of the canopy medium on dry deposition velocities of aerosol particles in the canopy sub-layer above forested ecosystems. Atmos. Environ. 45, 1203–1212.Google Scholar
Katul, G. G., Mahrt, L., Poggi, D. & Sanz, C. 2004 One- and two-equation models for canopy turbulence. Boundary-Layer Meteorol. 113, 81–109.Google Scholar
Koizumi, A., Motoyama, J., Sawata, K., Sasaki, Y. & Hirai, T. 2010 Evaluation of drag coefficients of poplar-tree crowns by a field test method. J. Wood Sci. 56, 189–193.Google Scholar
de Langre, E., Gutierrez, A. & Cossé, J. 2012 On the scaling of drag reduction by reconfiguration in plants. C. R. Méc. 340, 35–40.Google Scholar
Legg, B. J. & Powell, F. A. 1979 Spore dispersal in a barley crop: a mathematical model. Agric. Meteorol. 20, 47–67.Google Scholar
Legg, B. J., Raupach, M. R. & Coppin, P. A. 1986 Experiments on scalar dispersion within a model plant canopy, part III: an elevated line source. Boundary-Layer Meteorol. 35 (3), 277–302.Google Scholar
Lu, S. S. & Willmarth, W. W. 1973 Measurements of the structure of the Reynolds stress in a turbulent boundary layer. J. Fluid Mech. 60, 481–511.Google Scholar
Luhar, M. & Nepf, H. M. 2011 Flow-induced reconfiguration of buoyant and flexible aquatic vegetation. Limnol. Oceanogr. 56, 2003–2017.Google Scholar
May, K. R. & Clifford, R. 1967 The impaction of aerosol particles on cylinders, spheres, ribbons and discs. Ann. Occup. Hyg. 10, 83–95.Google Scholar
Pan, Y., Chamecki, M. & Isard, S. A. 2013 Dispersion of heavy particles emitted from area sources in the unstable atmospheric boundary layer. Boundary-Layer Meteorol. 146, 235–256.Google Scholar
Pan, Y., Follett, E., Chamecki, M. & Nepf, H. 2014 Strong and weak, unsteady reconfiguration and its impact on turbulence structure with plant canopies. Phys. Fluids (submitted).Google Scholar
Parker, S., Nally, J., Foat, T. & Preston, S. 2010 Refinement and testing of the drift-flux model for indoor aerosol dispersion and deposition modelling. J. Aerosol Sci. 41, 921–934.Google Scholar
Patton, E. G., Sullivan, P. P. & 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.Google Scholar
Pinard, J. D. J. P. & Wilson, J. D. 2001 First- and second-order closure models for wind in a plant canopy. J. Appl. Meteorol. 40, 1762–1768.Google Scholar
Poggi, D., Katul, G. & Albertson, J. 2006 Scalar dispersion within a model canopy: measurements and three-dimensional Lagrangian models. Adv. Water Resour. 29, 326–335.Google Scholar
Poggi, D., Porporato, A., Ridolfi, L., Albertson, J. D. & Katul, G. G. 2004 The effect of vegetation density on canopy sub-layer turbulence. Boundary-Layer Meteorol. 111, 565–587.Google Scholar
Prabha, T. V., Leclerc, M. Y. & Baldocchi, D. 2008 Comparison of in-canopy flux footprints between large-eddy simulation and the Lagrangian simulation. J. Appl. Meteorol. Climatol. 47, 2115–2128.CrossRefGoogle Scholar
Puijalon, S., Bornette, G. & Sagnes, P. 2005 Adaptations to increasing hydraulic stress: morphology, hydrodynamics and fitness of two higher aquatic plant species. J. Expl Bot. 56, 777–786.Google Scholar
Queck, R., Bienert, A., Maas, H. G., Harmansa, S., Goldberg, V. & Bernhofer, C. 2012 Wind fields in heterogeneous conifer canopies: parameterisation of momentum absorption using high-resolution 3D vegetation scans. Eur. J. Forest Res. 131, 165–176.CrossRefGoogle Scholar
Raupach, M. R. 1989 A practical Lagrangian method for relating scalar concentrations to source distributions in vegetation canopies. Q. J. R. Meteorol. Soc. 115 (487), 609–632.Google Scholar
Raupach, M. R., Finnigan, J. J. & Brunet, Y. 1996 Coherent eddies and turbulence in vegetation canopies: the mixing-layer analogy. Boundary-Layer Meteorol. 78, 351–382.Google Scholar
Raupach, M. R. & Thom, A. S. 1981 Turbulence in and above plant canopies. Annu. Rev. Fluid Mech. 13, 97–129.Google Scholar
Reynolds, A. M. 1998 On the formulation of Lagrangian stochastic models of scalar dispersion within plant canopies. Boundary-Layer Meteorol. 86, 333–344.Google Scholar
Reynolds, A. M. 2012 Incorporating sweeps and ejections into Lagrangian stochastic models of spore trajectories within plant canopy turbulence: modeled contact distributions are heavy-tailed. Phytopathology 102, 1026–1033.Google Scholar
Roelfs, A. P. 1985 Epidemiology in North America. In The Cereal Rusts, Volume II: Diseases, Distribution, Epidemiology, and Control (ed. Bushnell, W. R. & Roelfs, A. P.), pp. 395–424. Academic Press.Google Scholar
Saari, E. E. & Prescott, J. M. 1985 World distribution in relation to economic losses. In The Cereal Rusts, Volume II: Diseases, Distribution, Epidemiology, and Control (ed. Bushnell, W. R. & Roelfs, A. P.), pp. 257–293. Academic Press.Google Scholar
Shaw, R. H. & Schumann, U. 1992 Large-eddy simulation of turbulent flow above and within a forest. Boundary-Layer Meteorol. 61, 47–64.CrossRefGoogle Scholar
Shaw, R. H., Silversides, R. H. & Thurtell, G. W. 1974 Some observations of turbulence and turbulent transport within and above plant canopies. Boundary-Layer Meteorol. 5, 429–449.Google Scholar
Shaw, R. H., Tavangar, J. & Ward, D. P. 1983 Structure of the Reynolds stress in a canopy layer. J. Clim. Appl. Meteorol. 22, 1922–1931.Google Scholar
Skelsey, P., Holtslag, A. A. M. & van der Werf, W. 2008 Development and validation of a quasi-Gaussian plume model for the transport of botanical spores. Agric. Forest Meteorol. 148, 1383–1394.Google Scholar
Su, H. B., Shaw, R. H., Paw, K. T., Moeng, C. H. & Sullivan, P. P. 1998 Turbulent statistics of neutrally stratified flow within and above a sparse forest from large-eddy simulation and field observations. Boundary-Layer Meteorol. 88, 363–397.Google Scholar
Sutton, O. G. 1953 Micrometeorology: A Study of Physical Processes in the Lowest Layers of the Earth’s Atmosphere,. McGraw-Hill.Google Scholar
Taylor, G. I. 1921 Diffusion by continuous movements. Proc. Lond. Math. Soc. 20, 196–211.Google Scholar
Telewski, F. W. & Jaffe, M. J. 1986 Thigmomorphogenesis: field and laboratory studies of Abies fraseri in response to wind or mechanical perturbation. Physiol. Plant. 66, 211–218.Google Scholar
Usherwood, J. R., Ennos, A. R. & Ball, D. J. 1997 Mechanical and anatomical adaptations in terrestrial and aquatic buttercups to their respective environments. J. Expl Bot. 48, 1469–1475.Google Scholar
Vogel, S. 1984 Drag and flexibility in sessile organisms. Am. Zool. 24, 37–44.Google Scholar
Vogel, S. 1989 Drag and reconfiguration of broad leaves in high winds. J. Expl Bot. 40, 941–948.Google Scholar
Willmarth, W. W. 1975 Structure of turbulence in boundary layers. Adv. Appl. Mech. 15, 159–254.Google Scholar
Wilson, J. D. 1988 A second-order closure model for flow through vegetation. Boundary-Layer Meteorol. 42, 371–392.Google Scholar
Wilson, J. D. 2000 Trajectory models for heavy particles in atmospheric turbulence: comparison with observations. J. Appl. Meteorol. 39, 1894–1912.Google Scholar
Wilson, J. D., Ward, D. P., Thurtell, G. W. & Kidd, G. E. 1982 Statistics of atmospheric turbulence within and above a corn canopy. Boundary-Layer Meteorol. 24, 495–519.Google Scholar
Yue, W., Meneveau, C., Parlange, M. B., Zhu, W., Van Hout, R. & Katz, J. 2007a A comparative quadrant analysis of turbulence in a plant canopy. Water Resour. Res. 43, W05422.Google Scholar
Yue, W., Parlange, M. B., Meneveau, C., Zhu, W., Van Hout, R. & Katz, J. 2007b Large-eddy simulation of plant canopy flows using plant-scale representation. Boundary-Layer Meteorol. 124, 183–203.Google Scholar