scholarly journals Comparing drag partition schemes over a herbaceous Sahelian rangeland

2014 ◽  
Vol 119 (10) ◽  
pp. 2291-2313 ◽  
Author(s):  
C. Pierre ◽  
G. Bergametti ◽  
B. Marticorena ◽  
L. Kergoat ◽  
E. Mougin ◽  
...  
Keyword(s):  
Drag Partition

A scheme for drag partition over rough surfaces

2005 ◽  
Vol 39 (38) ◽  
pp. 7351-7361 ◽  
Author(s):  
Y SHAO ◽  
Y YANG
Keyword(s):  
Rough Surfaces ◽  
Drag Partition

scholarly journals A note on the use of drag partition in aeolian transport models

2020 ◽  
Vol 42 ◽  
pp. 100560 ◽  
Author(s):  
Nicholas P. Webb ◽  
Adrian Chappell ◽  
Sandra L. LeGrand ◽  
Nancy P. Ziegler ◽  
Brandon L. Edwards
Keyword(s):  
Aeolian Transport ◽  
Transport Models ◽  
Drag Partition

scholarly journals A parameterization of momentum roughness length and displacement height for a wide range of canopy densities

1997 ◽  
Vol 1 (1) ◽  
pp. 81-91 ◽  
Author(s):  
A. Verhoef ◽  
K. G. McNaughton ◽  
A. F. G. Jacobs
Keyword(s):  
Roughness Length ◽  
Quality Data ◽  
Simplified Model ◽  
Partition Model ◽  
Area Index ◽  
Row Crops ◽  
Displacement Height ◽  
Wide Range ◽  
Drag Partition ◽  
D Values

Abstract. Values of the momentum roughness length, z0, and displacement height, d, derived from wind profiles and momentum flux measurements, are selected from the literature for a variety of sparse canopies. These include savannah, tiger-bush and several row crops. A quality assessment of these data, conducted using criteria such as available fetch, height of wind speed measurement and homogeneity of the experimental site, reduced the initial total of fourteen sites to eight. These datapoints, combined with values carried forward from earlier studies on the parameterization of z0 and d, led to a maximum number of 16 and 24 datapoints available for d and z0, respectively. The data are compared with estimates of roughness length and displacement height as predicted from a detailed drag partition model, R92 (Raupach, 1992), and a simplified version of this model, R94 (Raupach, 1994). A key parameter in these models is the roughness density or frontal area index, λ. Both the comprehensive and the simplified model give accurate predictions of measured z0 and d values, but the optimal model coefficients are significantly different from the ones originally proposed in R92 and R94. The original model coefficients are based predominantly on measured aerodynamic parameters of relatively closed canopies and they were fitted `by eye'. In this paper, best-fit coefficients are found from a least squares minimization using the z0 and d values of selected good-quality data for sparse canopies and for the added, mainly closed canopies. According to a statistical analysis, based on the coefficient of determination (r2), the number of observations and the number of fitted model coefficients, the simplified model, R94, is deemed to be the most appropriate for future z0 and d predictions. A CR value of 0.35 and a cd1 value of about 20 are found to be appropriate for a large range of canopies varying in density from closed to very sparse. In this case, 99% of the total variance occurring in the d-data across 16 selected canopies can be explained, whereas the analogous value for the z0-data (24 datapoints available) is 81%. This makes the R94 model, with only two coefficients and its relatively simple equations, a useful universal tool for predicting z0 and d values for all kinds of canopies. For comparison, a similar fitting exercise is made using simple linear equations based on obstacle height only (e.g. Brutsaert, 1982) and another formula involving canopy height as well as roughness density (Lettau, 1969). The fitted Brutsaert equations explain 98% and 62% of the variance in the d and z0-data, respectively. Lettau's equation for prediction of z0 performs unsatisfactorily (r2 values <0, even after fitting of the coefficient) and so it is concluded that the drag partition model is definitely the most effective for prediction of the momentum roughness lengths for a wide rang of canopy densities.

Impact of surface roughness on dust occurrence frequency over arid regions

2021 ◽  
Author(s):  
Martina Klose ◽  
Carlos Pérez García-Pando ◽  
Paul Ginoux ◽  
Ron L. Miller
Keyword(s):  
Surface Roughness ◽  
Spatial Patterns ◽  
Arid Regions ◽  
Transport Model ◽  
Dust Emission ◽  
Occurrence Frequency ◽  
Dust Transport ◽  
Drag Partition ◽  
The Impact ◽  
Semi Arid

&lt;p&gt;Soil dust aerosol created by wind erosion of arid and semi-arid surfaces dominates climate effects over large areas of the Earth. To represent the dust cycle, Global Earth System Models (ESMs) typically prescribe preferential dust sources phenomenologically using empirical source scaling functions. While this approach has helped to compensate for a lack or inaccuracy of soil and surface input data to models, it potentially limits progress in the representation of the global dust cycle, because such strong empirical constraints make models less sensitive to parameters known to affect dust emission, and thus potentially insensitive to changes in climate. Here we investigate the link between surface roughness due to non-erodible elements such as vegetation, pebbles and rocks, and the spatial patterns of dust activity. Using two different satellite-based methods to represent roughness within an atmospheric dust transport model, we evaluate the impact of surface roughness on the spatial distribution of dust optical depth occurrence frequency observed from satellite by both reducing the atmospheric momentum available for particle entrainment and protecting the surface from dust emission. We test the variability of our results across conceptually different parameterizations of dust emission and drag partition. Our results suggest that the spatial patterns of dust activity are largely determined by surface roughness, not only in semi-arid, but also in arid regions, where green vegetation is sparse or absent.&amp;#160;&lt;/p&gt;

Modeling of Cube Array Roughness: RANS, LES, and DNS

2021 ◽  
Author(s):  
Samuel Altland ◽  
Haosen H. A. Xu ◽  
Xiang I. A. Yang ◽  
Robert Kunz
Keyword(s):  
Reynolds Stress ◽  
Significant Degree ◽  
Reynolds Stress Model ◽  
Stress Model ◽  
Mean Velocity ◽  
Flow Features ◽  
Drag Partition ◽  
Flow Phenomena ◽  
Rans Models ◽  
Packing Densities

Abstract Flow over arrays of cubes is an extensively studied model problem for rough wall turbulent boundary layers. While considerable research has been performed in computationally investigating these topologies using DNS and LES, the ability of sublayer-resolved RANS to predict the bulk flow phenomena of these systems is relatively unexplored, especially at low and high packing densities. Here, RANS simulations are conducted on six different packing densities of cubes in aligned and staggered configurations. The packing densities investigated span from what would classically be defined as isolated, up to those in the d-type roughness regime, filling in the gap in the present literature. Three different sublayer-resolved turbulence closure models were tested for each case; a low Reynolds number k-ε model, the Menter k-ω SST model, and a full Reynolds stress model. Comparisons of the velocity fields, secondary flow features, and drag coefficients are made between the RANS results and existing LES and DNS results. There is a significant degree of variability in the performance of the various RANS models across all comparison metrics. However, the Reynolds stress model demonstrated the best accuracy in terms of the mean velocity profile as well as drag partition across the range of packing densities.

Drag and drag partition on rough surfaces

1992 ◽  
Vol 60 (4) ◽  
pp. 375-395 ◽  
Author(s):  
M. R. Raupach
Keyword(s):  
Rough Surfaces ◽  
Drag Partition

Drag Partition and Its Possible Implications for Dust Emission

2005 ◽  
Vol 5 (3-6) ◽  
pp. 251-259 ◽  
Author(s):  
Yan Yang ◽  
Yaping Shao
Keyword(s):  
Dust Emission ◽  
Drag Partition

Drag Partition for Regularly-Arrayed Rough Surfaces

2003 ◽  
Vol 107 (2) ◽  
pp. 445-468 ◽  
Author(s):  
D. M. Crawley ◽  
W. G. Nickling
Keyword(s):  
Rough Surfaces ◽  
Drag Partition
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