Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Li, Aihua; Zhao, Wei; Mitchell, Jessica J.; Glenn, Nancy F.; Germino, Matthew J.; Sankey, Joel B.; Allen, Richard G.

doi:10.14358/pers.83.6.415

“…To minimise potential errors and to obtain roughness lengths representative of the conditions at each site, an extensive series of filters were applied to the 30 min values (see Fitzpatrick et al, 2017, for full details). These filters included a 90 • wind direction window centred on the main axis of the EC sensor (to minimise the influence of flow distortion due to the station structure), minimum values for wind speed (> 3 m s −1 ) and u * ec (> 0.1 m s −1 ), minimum differences between measurement and surface height values of air temperature (> 1 • C) and vapour pressure (> 66 Pa) (Calanca, 2001;Conway and Cullen, 2013), a minimum scalar roughness length value of 1 × 10 −7 m based on the mean free path length of molecules (Li et al, 2016), and a precipitation filter. A test for stationarity of the turbulence, following Foken (2008), was also applied.…”

Section: In Situ Roughness Length Valuesmentioning

confidence: 99%

A multi-season investigation of glacier surface roughness lengths through in situ and remote observation

Fitzpatrick

¹

,

Radić

²

,

Menounos

³

2019

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Abstract. The roughness length values for momentum, temperature, and water vapour are key inputs to the bulk aerodynamic method for estimating turbulent heat flux. Measurements of site-specific roughness length are rare for glacier surfaces, and substantial uncertainty remains in the values and ratios commonly assumed when parameterising turbulence. Over three melt seasons, eddy covariance observations were implemented to derive the momentum and scalar roughness lengths at several locations on two mid-latitude mountain glaciers. In addition, two techniques were developed in this study for the remote estimation of momentum roughness length, utilising lidar-derived digital elevation models with a 1×1 m resolution. Seasonal mean momentum roughness length values derived from eddy covariance observations at each location ranged from 0.7 to 4.5 mm for ice surfaces and 0.5 to 2.4 mm for snow surfaces. From one season to the next, mean momentum roughness length values over ice remained relatively consistent at a given location (0–1 mm difference between seasonal mean values), while within a season, temporal variability in momentum roughness length over melting snow was found to be substantial (> an order of magnitude). The two remote techniques were able to differentiate between ice and snow cover and return momentum roughness lengths that were within 1–2 mm (≪ an order of magnitude) of the in situ eddy covariance values. Changes in wind direction affected the magnitude of the momentum roughness length due to the anisotropic nature of features on a melting glacier surface. Persistence in downslope wind direction on the glacier surfaces, however, reduced the influence of this variability. Scalar roughness length values showed considerable variation (up to 2.5 orders of magnitude) between locations and seasons and no evidence of a constant ratio with momentum roughness length or each other. Of the tested estimation methods, the Andreas (1987) surface renewal model returned scalar roughness lengths closest to those derived from eddy covariance observations. Combining this scalar method with the remote techniques developed here for estimating momentum roughness length may facilitate the distributed parameterisation of turbulent heat flux over glacier surfaces without in situ measurements.

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“…Efforts have been made in previous boundary-layer studies over different land surfaces to determine momentum roughness length values for large areas, including over forestry, scrubland, and outwash plains (e.g. Nield et al, 2013;Li et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A multi-season investigation of glacier surface roughness lengths through in situ and remote observation

Fitzpatrick¹,

Radić²,

Menounos³

2018

Preprint

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Abstract. The roughness length values for momentum, temperature, and water vapour are key inputs to the bulk aerodynamic method for estimating turbulent heat flux. Measurements of site-specific roughness length are rare for glacier surfaces, and substantial uncertainty remains in the values and ratios commonly assumed when parameterising turbulence. Over three melt seasons, eddy covariance observations were implemented to derive the momentum and scalar roughness lengths at several locations on two mid-latitude mountain glaciers. In addition, two techniques were developed in this study for the remote estimation of momentum roughness length, utilising LiDAR-derived digital elevation models with a 1 × 1 m resolution. Seasonal mean momentum roughness length values derived from eddy covariance observations at each location ranged from 0.7–4.5 mm for ice surfaces, and 0.5–2.4 mm for snow surfaces. From one season to the next, mean momentum roughness length values over ice remained relatively consistent at a given location (0–1 mm difference between seasonal mean values), while within a season, temporal variability in momentum roughness length over melting snow was found to be substantial (> an order of magnitude). The two remote techniques were able to differentiate between ice and snow cover, and return momentum roughness lengths that were within 1–2 mm (

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“…The frontal area index (fai) can be defined by integrating positive height changes (∆y) over a cross-sectional line divided by the distance (∆x) in that section length, assuming an isotropic surface [25,56]. fai = ( ∑ ∆y)/( ∑ ∆x) for ∆y > 0 (4)…”

Section: Roughness Length Based On Vegetation Geometry and Wind Conditionsmentioning

confidence: 99%

“…where N is the number of subcells within each grid cell; σi,j is the standard deviation of the LiDAR-derived vegetation height (hi,j) per each subcell I; j. h avg is the average vegetation height calculated from the LiDAR's CHM. It was documented that coarser grid cells reduce the standard deviation of height regardless of the size of the subcells, while larger subcells lead to higher values of Z 0 [25]. Based on these observations, the size of each grid cell was chosen to be equal to 1 m and the segment size inside each grid was 0.25 m, reflecting the maximum expected variance in plant height within a 1 × 1 m cell.…”

Section: Roughness Length Based On Vegetation Height Variabilitymentioning

confidence: 99%

“…The representation of a mixed grassland prairie by ALS datasets revealed that up to 76% of the variation in Z 0 was due to the height variability of vegetation and up to 65% of the variation could be explained by estimates of vegetation height [24]. Li et al [25] found that the accuracy of the estimated Z 0 of a semi-arid shrubland using ALS data depends on the adopted morphometric models and the precise representation of shrub height in these models. In short and dense canopies, the estimation of vegetation height using ALS is prone to errors [26], mainly due to the lack of identifiable referenced objects and of detectable differences between first (i.e., vegetation) and last (i.e., ground) LiDAR returns [27,28].…”

Section: Introductionmentioning

confidence: 99%

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Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

Trepekli

¹

,

Friborg

²

2021

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The aerodynamic roughness length (Z0) and surface geometry at ultra-high resolution in precision agriculture and agroforestry have substantial potential to improve aerodynamic process modeling for sustainable farming practices and recreational activities. We explored the potential of unmanned aerial vehicle (UAV)-borne LiDAR systems to provide Z0 maps with the level of spatiotemporal resolution demanded by precision agriculture by generating the 3D structure of vegetated surfaces and linking the derived geometry with morphometric roughness models. We evaluated the performance of three filtering algorithms to segment the LiDAR-derived point clouds into vegetation and ground points in order to obtain the vegetation height metrics and density at a 0.10 m resolution. The effectiveness of three morphometric models to determine the Z0 maps of Danish cropland and the surrounding evergreen trees was assessed by comparing the results with corresponding Z0 values from a nearby eddy covariance tower (Z0_EC). A morphological filter performed satisfactorily over a homogeneous surface, whereas the progressive triangulated irregular network densification algorithm produced fewer errors with a heterogeneous surface. Z0 from UAV-LiDAR-driven models converged with Z0_EC at the source area scale. The Raupach roughness model appropriately simulated temporal variations in Z0 conditioned by vertical and horizontal vegetation density. The Z0 calculated as a fraction of vegetation height or as a function of vegetation height variability resulted in greater differences with the Z0_EC. Deriving Z0 in this manner could be highly useful in the context of surface energy balance and wind profile estimations for micrometeorological, hydrologic, and ecologic applications in similar sites.

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Aerodynamic Roughness Length Estimation with Lidar and Imaging Spectroscopy in a Shrub-Dominated Dryland

Abstract: The aerodynamic roughness length (Z 0

Cited by 6 publications

References 45 publications

A multi-season investigation of glacier surface roughness lengths through in situ and remote observation

A multi-season investigation of glacier surface roughness lengths through in situ and remote observation

A multi-season investigation of glacier surface roughness lengths through in situ and remote observation

Deriving Aerodynamic Roughness Length at Ultra-High Resolution in Agricultural Areas Using UAV-Borne LiDAR

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