Large eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

Abstract. Surface roughness parameters are at the core of every model representation of the coupling and interactions between land-surface and atmosphere, and are used in every model of surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and do not vary them in response to spatial or temporal changes to canopy structure. In part, this is due to the difficulty of reducing the complexity of canopy structure and its spatiotemporal dynamic and heterogeneity to less than a handful of parameters describing its effects of atmosphere–surface interactions. In this study we use large-eddy simulations to explore, in silico, the effects of canopy structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction. We found roughness parameters to be highly variable, but were able to find positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, and between eddy-penetration depth and maximum canopy height and leaf area index. Using a decade of wind and canopy structure observations in a site in Michigan, we tested the effectiveness of our model-resolved parameters in predicting the frictional velocity over heterogeneous and disturbed canopies. We compared it with three other semi-empirical models and with a decade of meteorological observations. We found that parameterizations with fixed representations of roughness performed relatively well. Nonetheless, some empirical approaches that incorporate seasonal and inter-annual changes to the canopy structure performed even better than models with temporally fixed parameters.

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Large-eddy simulations of surface roughness parameter sensitivity to canopy-structure characteristics

Biogeosciences ◽

10.5194/bg-12-2533-2015 ◽

2015 ◽

Vol 12 (8) ◽

pp. 2533-2548 ◽

Cited By ~ 23

Author(s):

K. D. Maurer ◽

G. Bohrer ◽

W. T. Kenny ◽

V. Y. Ivanov

Keyword(s):

Surface Roughness ◽

Leaf Area Index ◽

Leaf Area ◽

Roughness Length ◽

Canopy Structure ◽

Canopy Height ◽

Roughness Parameters ◽

Area Index ◽

Displacement Height ◽

Gap Fraction

Abstract. Surface roughness parameters, namely the roughness length and displacement height, are an integral input used to model surface fluxes. However, most models assume these parameters to be a fixed property of plant functional type and disregard the governing structural heterogeneity and dynamics. In this study, we use large-eddy simulations to explore, in silico, the effects of canopy-structure characteristics on surface roughness parameters. We performed a virtual experiment to test the sensitivity of resolved surface roughness to four axes of canopy structure: (1) leaf area index, (2) the vertical profile of leaf density, (3) canopy height, and (4) canopy gap fraction. We found roughness parameters to be highly variable, but uncovered positive relationships between displacement height and maximum canopy height, aerodynamic canopy height and maximum canopy height and leaf area index, and eddy-penetration depth and gap fraction. We also found negative relationships between aerodynamic canopy height and gap fraction, as well as between eddy-penetration depth and maximum canopy height and leaf area index. We generalized our model results into a virtual "biometric" parameterization that relates roughness length and displacement height to canopy height, leaf area index, and gap fraction. Using a decade of wind and canopy-structure observations in a site in Michigan, we tested the effectiveness of our model-driven biometric parameterization approach in predicting the friction velocity over heterogeneous and disturbed canopies. We compared the accuracy of these predictions with the friction-velocity predictions obtained from the common simple approximation related to canopy height, the values calculated with large-eddy simulations of the explicit canopy structure as measured by airborne and ground-based lidar, two other parameterization approaches that utilize varying canopy-structure inputs, and the annual and decadal means of the surface roughness parameters at the site from meteorological observations. We found that the classical representation of constant roughness parameters (in space and time) as a fraction of canopy height performed relatively well. Nonetheless, of the approaches we tested, most of the empirical approaches that incorporate seasonal and interannual variation of roughness length and displacement height as a function of the dynamics of canopy structure produced more precise and less biased estimates for friction velocity than models with temporally invariable parameters.

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Extracting Leaf Area Index Using Viewing Geometry Effects - A New Perspective on High-Resolution Unmanned Aerial System Photography.

10.31220/osf.io/jcxa9 ◽

2020 ◽

Author(s):

Lukas Roth ◽

Helge Aasen ◽

Achim Walter ◽

Frank Liebisch

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Plant Ecology ◽

Growth And Yield ◽

Unmanned Aerial System ◽

Area Index ◽

Handheld Device ◽

Gap Fraction ◽

Rgb Images ◽

New Perspective

Abstract Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI 14 prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92, RMSE of 0.42 m2 m2) and correlated well with destructive LAI measurements (R2 of 0.89, RMSE of 0.41 m2 m2). These results indicate that, if respecting the range (LAI ≤3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

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