Soil wind erosion rate on rough surfaces: A dynamical model derived from an invariant pattern of the shear-stress probability density function of the soil surface

Zou, Xueyong; Li, Huiru; Kang, Liqiang; Zhang, Chunlai; Jia, Wenru; Gao, Yan; Zhang, Junjie; Yang, Zhicheng; Zhang, Mengcui; Xu, Jing; Cao, Hong; Wu, Xiaoxu

doi:10.1016/j.catena.2022.106633

Cited by 8 publications

(6 citation statements)

References 58 publications

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“…Under the same conditions, the surface shear stress increases with increasing friction velocity u , and as λ increases, the surface shear stress decreases. When λ was less than 0.03, the roughness elements were sparsely distributed on the surface and interfered little with each other, which is similar to the results reported by Zou et al (2022).…”

Section: Resultssupporting

confidence: 88%

“…The probability‐density function can quantitatively express the spatial distribution of surface shear stress. According to Li et al (2021) and Zou et al (2022), the probability‐density can be expressed as a logistic function:

P = \frac{\exp (\frac{τ_{a} - ε}{δ})}{δ {[1 + \exp (\frac{τ_{a} - ε}{δ})]}^{2}}

where p is the probability density, τ a is the surface shear stress in Pa, ε is the location parameter and δ is the scale parameter. The location parameter ε and the scale parameter δ in the logistic function are related to the friction velocity as follows:

ε goodbreak= a_{1} u_{*}^{2}

normalδ goodbreak= b_{1} u_{*}^{2}

where a 1 and b 1 are fitting parameters.…”

Section: Resultsmentioning

confidence: 99%

“…In contrast, the average surface shear stress decreases because more of the total stress is transferred to the roughness elements (Kang et al, 2018). Both density and arrangement of roughness elements influence the spatial heterogeneity of the surface shear stress (Webb et al, 2014;Zou et al, 2022). Marshall (1971) investigated the partitioning of shear stress during wind erosion in a wind tunnel and found that erosion depended mainly on roughness density and was not significantly related to the shape and arrangement of the roughness elements (Brown et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simplifying Raupach's model of shear‐stress partitioning by clarifying the relationship between β and σ and quantifying the shear‐stress probability density based on numerical simulation

Liu

Zhang

Wang

et al. 2023

Earth Surf Processes Landf

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We used computational fluid dynamics to simplify Raupach's model by clarifying the relationship between the parameter β (the ratio of element to surface drag coefficients) and the roughness‐element shape parameter σ using a k‐ω shear‐stress transport (SST) turbulence model with rigid cylindrical roughness elements. Results showed that β ranged between 102 and 242 and the parameter m (a relationship between the average and the peak surface shear stresses) ranged between 0.15 and 0.32 for partitioning of surface shear stress. β decreased with increasing σ, and the relationship followed a power law. We confirmed this relationship by comparing wind tunnel and field measurements for different types of roughness element (blocks, hemispheres, trapezoids and plants based on the results from previous studies). However, the model has some limitations due to the characteristics such as streamline and porosity of plants. The peak mean stress ratio (a) ranged between 2.09 and 3.61. We also confirmed that the probability‐density function for the surface shear stress proposed by Zou et al. (2022) described the distribution of the surface shear stress among roughness elements well, which is essential to accurately estimate surface erosion by wind.

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Section: Resultssupporting

confidence: 88%

P = \frac{\exp (\frac{τ_{a} - ε}{δ})}{δ {[1 + \exp (\frac{τ_{a} - ε}{δ})]}^{2}}

ε goodbreak= a_{1} u_{*}^{2}

normalδ goodbreak= b_{1} u_{*}^{2}

where a 1 and b 1 are fitting parameters.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simplifying Raupach's model of shear‐stress partitioning by clarifying the relationship between β and σ and quantifying the shear‐stress probability density based on numerical simulation

Liu

Zhang

Wang

et al. 2023

Earth Surf Processes Landf

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show abstract

“…Wind is a critical factor for sediment transport and dust emission (Bergametti et al., 2020). In landscapes across Earth prone to sediment transport, the land surfaces are covered with a “canopy” of roughness elements such as vegetation and gravel, which reduce the wind speed acting on the soil surface and thus influence the intensity and spatial distribution of wind erosion (Zou et al., 2022). To best describe the wind erosion process, most aeolian transport models include the wind friction velocity ( u * ) as an important parameter (Bagnold, 1941; Marticorena & Bergametti, 1995; Shao et al., 1996).…”

Section: Introductionmentioning

confidence: 99%

Using Field Measurements Across Land Cover Types to Evaluate Albedo‐Based Wind Friction Velocity and Estimate Sediment Transport

Zhou,

Zhang,

Chappell

et al. 2024

JGR Atmospheres

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The soil surface wind friction velocity (us*) is an essential parameter for predicting sediment transport on rough surfaces. However, this parameter is difficult and time‐consuming to obtain over large areas due to its spatiotemporal heterogeneity. The albedo‐based approach calibrates normalized shadow retrieved from any source of albedo data with laboratory measurements of aerodynamic properties. This enables direct and cross‐scale us* retrieval but has not been evaluated against field measurements for different cover types. We evaluated the approach's performance using wind friction velocity (u*) measurements from ultrasonic anemometers. We retrieved coincident field pyranometer and satellite albedo at 48 sites that were spread over approximately 1,800 km on the Inner Mongolia Plateau, including grassland, artificial shrubland, open shrubland, and gobi land. For all cover types, u* estimates from ultrasonic anemometers were close to the albedo‐based results approach. Our results confirm and extend the findings that the approach works across scales from lab to field measurements and permits large‐area assessments using satellite albedo. We compared the seasonal sediment transport across the region calculated from albedo‐based us* with results from an exemplar traditional transport model (QT) driven by u* with aerodynamic roughness length varying with land cover type and fixed over time. The traditional model could not account for spatiotemporal variation in roughness elements and considerably over‐estimated sediment transport, particularly in partially vegetated and gravel‐covered central and western parts of the Inner Mongolia Plateau. The albedo‐based sediment transport (QA) estimates will enable dynamic monitoring of the interaction between wind and surface roughness to support Earth System models.

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Characterizing sand transport with various micro‐ridge spacings and heights

Jia,

Li,

et al. 2024

Earth Surf Processes Landf

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In arid regions, the use of ridge cover as a traditional agricultural strategy has been effective in mitigating soil wind erosion. However, few studies have focused on the sand transport characteristics of different micro‐ridge spacings and heights. This study aimed to identify the mechanism by which ridges change the near‐surface sand transport. In a controlled wind tunnel environment, the aeolian sand flux structure and sand transport flux (qz) at heights from 0 to 0.7 m were measured. The results showed that, compared with no ridges, ridge covering could significantly modify the structure of aeolian sand flux near the surface, yielding a substantial reduction in the proportion of sand transport to the total sand transport in the 0‐ to 0.1‐m height layer. In the absence of ridges, blown sand was mainly concentrated in the 0‐ to 0.1‐m height layer, and qz decreased exponentially as the height increased. With various micro‐ridge spacings and heights, when H was less than 0.1 m and L was more than 15H, the blown sand also remained concentrated in the 0‐ to 0.1‐m height layer, and qz decreased exponentially as the height increased. When H increased to 0.15 m from 0.1 m and L decreased from 15H to 5H, the height of the blown sand was concentrated in the 0.3‐ to 0.4‐m layer. The blown sand layer of most ridge structures was concentrated at a height of 0.1–0.4 m, and the structure of the aeolian sand flux resembled an ‘elephant nose effect’. The total sand transport rate of all ridge structures was significantly lower than that with no ridges, indicating that a reasonable ridge structure can effectively prevent wind erosion.

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Soil wind erosion rate on rough surfaces: A dynamical model derived from an invariant pattern of the shear-stress probability density function of the soil surface

Cited by 8 publications

References 58 publications

Simplifying Raupach's model of shear‐stress partitioning by clarifying the relationship between β and σ and quantifying the shear‐stress probability density based on numerical simulation

Simplifying Raupach's model of shear‐stress partitioning by clarifying the relationship between β and σ and quantifying the shear‐stress probability density based on numerical simulation

Using Field Measurements Across Land Cover Types to Evaluate Albedo‐Based Wind Friction Velocity and Estimate Sediment Transport

Characterizing sand transport with various micro‐ridge spacings and heights

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