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Modelling aeolian sand transport using a dynamic mass balancing approach
- Mayaud, Jerome R., Bailey, Richard M., Wiggs, Giles F.S., Weaver, Corinne M.
- Geomorphology 2017 v.280 pp. 108-121
- arid lands, coasts, data collection, landscapes, models, prediction, sand, sediment transport, space and time, time series analysis, turbulent flow, wind speed, Namibia
- Knowledge of the changing rate of sediment flux in space and time is essential for quantifying surface erosion and deposition in desert landscapes. Whilst many aeolian studies have relied on time-averaged parameters such as wind velocity (U) and wind shear velocity (u⁎) to determine sediment flux, there is increasing field evidence that high-frequency turbulence is an important driving force behind the entrainment and transport of sand. At this scale of analysis, inertia in the saltation system causes changes in sediment transport to lag behind de/accelerations in flow. However, saltation inertia has yet to be incorporated into a functional sand transport model that can be used for predictive purposes. In this study, we present a new transport model that dynamically balances the sand mass being transported in the wind flow. The ‘dynamic mass balance’ (DMB) model we present accounts for high-frequency variations in the horizontal (u) component of wind flow, as saltation is most strongly associated with the positive u component of the wind. The performance of the DMB model is tested by fitting it to two field-derived (Namibia's Skeleton Coast) datasets of wind velocity and sediment transport: (i) a 10-min (10Hz measurement resolution) dataset; (ii) a 2-h (1Hz measurement resolution) dataset. The DMB model is shown to outperform two existing models that rely on time-averaged wind velocity data (e.g. Radok, 1977; Dong et al., 2003), when predicting sand transport over the two experiments. For all measurement averaging intervals presented in this study (10Hz–10min), the DMB model predicted total saltation count to within at least 0.48%, whereas the Radok and Dong models over- or underestimated total count by up to 5.50% and 20.53% respectively. The DMB model also produced more realistic (less ‘peaky’) time series of sand flux than the other two models, and a more accurate distribution of sand flux data. The best predictions of total sand transport are achieved using our DMB model at a temporal resolution of 4s in cases where the temporal scale of investigation is relatively short (on the order of minutes), and at a resolution of 1min for longer wind and transport datasets (on the order of hours). The proposed new sand transport model could prove to be significant for integrating turbulence-scale transport processes into longer-term, macro-scale landscape modelling of drylands.