Estimating total horizontal aeolian flux within shrub‐invaded groundwater‐dependent meadows using empirical and mechanistic models

Abstract

[1] Wind erosion is a significant environmental problem that removes soil resources from sensitive ecosystems and contributes to air pollution. In regions of shallow groundwater, friable (puffy) soils are maintained through capillary action, surface evaporation of solute-rich soil moisture, and protection from mobilization by groundwater-dependent grasses and shrubs. When a reduction in vegetation cover occurs through any disturbance process, there is potential for aeolian transport and dust emission. We find that as mean gap size between vegetation elements scaled by vegetation height increases, total horizontal aeolian sediment flux increases and explains 58% of the variation in total horizontal aeolian sediment flux. We also test a probabilistic model of wind erosion based on gap size between vegetation elements scaled by vegetation height (the Okin model), which predicts measured total horizontal aeolian sediment flux more closely than another commonly used model based on the average plant area observed in profile (Raupach model). The threshold shear velocity of bare soil appears to increase as gap size between vegetation elements scaled by vegetation height increases, reflecting either surface armoring or reduced interaction between the groundwater capillary zone and surface sediments. This work advances understanding of the importance of measuring gap size between vegetation elements scaled by vegetation height for empirically estimating Q and for structuring process-based models of desert wind erosion in groundwater-dependent vegetation.Citation: Vest, K. R., A. J. Elmore, J. M. Kaste, G. S. Okin, and J. Li (2013), Estimating total horizontal aeolian flux within shrub-invaded groundwater-dependent meadows using empirical and mechanistic models,

The effect of roughness elements on wind erosion: The importance of surface shear stress distribution

“…It does not follow our predicted flux behavior with respect to λ for the measured τ′ S distributions (Figure ). Nor does this conform to observations from the field, which show that Q may decline by 3 orders of magnitude as λ increases over the range 0.0–0.2 m ( z 0 = 1.0 × 10 −4 to 1.0 × 10 −2 m, u *t = 0.2–0.8 m s –1 ) [ Lancaster and Baas , ; Vest et al ., ]. Calculating z 0 (and therefore u * ) from λ can be problematic and can thus result in highly abnormal flux estimates when applied with u *t scaled by either measured or modeled drag partitions.…”

“…While the accuracy of aeolian transport models may be improved by incorporating the effects of roughness configuration, operationalizing such approaches is a challenge for broad‐scale modeling. However, unlike λ , the fractional cover of vegetation, vegetation height, and the spatial distribution of canopy interspaces can be easily measured in the field and used as input to aeolian transport models [ Vest et al ., ; Webb et al ., ]. McGlynn and Okin [] developed an approach for characterizing the distribution of shrubs in a desert landscape using high spatial resolution (1 m) remote sensing.…”

“…Our results show that using a bulk drag partition coefficient to scale the threshold for soil entrainment ( u *t ) can result in an overestimate of sediment flux by 2 to 3 orders of magnitude (Figures and ). Explicitly accounting for spatial variability in τ S due to vegetation distribution better captures the magnitude and variability in sediment flux over vegetated landscapes [ Li et al ., ; Vest et al ., ].…”

Evaluation of a new model of aeolian transport in the presence of vegetation

“…Recent research by Vest et al . [] supports this view. Alternatively, vegetation characteristics could be obtained by an image‐based technique [ Karl et al ., ; McGlynn and Okin , ], supplemented by knowledge of plant height.…”

“…Gap size distribution, in contrast, may be obtained by a standard transect-based vegetation survey technique [e.g., Herrick et al, 2005]. Recent research by Vest et al [2012] supports this view. Alternatively, vegetation characteristics could be obtained by an image-based technique McGlynn and Okin, 2006], supplemented by knowledge of plant height.…”