Estimating aerodynamic roughness over complex surface terrain

Abstract

[1] Surface roughness plays a key role in determining aerodynamic roughness length (z o ) and shear velocity, both of which are fundamental for determining wind erosion threshold and potential. While z o can be quantified from wind measurements, large proportions of wind erosion prone surfaces remain too remote for this to be a viable approach. Alternative approaches therefore seek to relate z o to morphological roughness metrics. However, dust-emitting landscapes typically consist of complex small-scale surface roughness patterns and few metrics exist for these surfaces which can be used to predict z o for modeling wind erosion potential. In this study terrestrial laser scanning was used to characterize the roughness of typical dust-emitting surfaces (playa and sandar) where element protrusion heights ranged from 1 to 199 mm, over which vertical wind velocity profiles were collected to enable estimation of z o . Our data suggest that, although a reasonable relationship (R 2 > 0.79) is apparent between 3-D roughness density and z o , the spacing of morphological elements is far less powerful in explaining variations in z o than metrics based on surface roughness height (R 2 > 0.92). This finding is in juxtaposition to wind erosion models that assume the spacing of larger-scale isolated roughness elements is most important in determining z o . Rather, our data show that any metric based on element protrusion height has a higher likelihood of successfully predicting z o . This finding has important implications for the development of wind erosion and dust emission models that seek to predict the efficiency of aeolian processes in remote terrestrial and planetary environments.

Increased Aerodynamic Roughness Owing to Surfzone Foam

“…The open-ocean estimate of z f being similar to k is most likely an overestimate in the surfzone, owing to the foam not moving. Reducing z f by ;k/3, as suggested by land relationships by Nield et al (2013), results in a C dN10 O(1.5 3 10 23 ) (Fig. 3c) similar to the observations (Fig.…”

Vegetation in Drylands: Effects on Wind Flow and Aeolian Sediment Transport

“…Some wind tunnel studies (e.g., [60,78]) have used creative methods, such as coloured sand, for tracking erosion and deposition around roughness elements, but detailed exploration of flow field behaviour and the evolution of bedforms around plants is still required. For instance, high-resolution terrestrial laser scanning could be used to detect mm-scale changes in height around partially vegetated erodible surfaces [186], thus helping to identify erosional and depositional regions to a high level of accuracy. In terms of modelling vegetation/sediment interactions, Raupach and Lu [87] identify problems of scaling over heterogeneous surfaces, and raise questions over how sediment deposition processes in the presence of vegetation could be adequately measured.…”

Debris-covered glacier energy balance model for Imja–Lhotse Shar Glacier in the Everest region of Nepal

“…On the other hand, the estimations of z 0 remain relatively constant (±0.004 m) as the DEM resolution is reduced to 0.04 m. The consistency of this method despite variations in DEM resolution and obstacle thresholds and the objective approach for deriving the obstacle threshold using a 30 % obstacle density, lends confidence to this method. Furthermore, Nield et al (2013) found that measures regarding surface heights are the best predictor of aerodynamic roughness, specifically for surfaces that comprise large elements or have patches of large and small elements. Therefore, it is expected that the obstacle threshold should vary for different sites as a function of their largest elements, which is consistent with the inter-site variability and the obstacle thresholds reported in this study.…”