Comparison of four methods to calculate aeolian sediment transport threshold from field data: Implications for transport prediction and discussion of method evolution

Aeolian sediment transport threshold is commonly defined as the minimum wind speed (or shear stress) required for wind-driven sediment transport. Accurate and consistent quantification of this threshold is essential because it is an input variable in models used to predict wind erosion, dune activity, and dust emissions. The majority of threshold quantification has been performed with analogs (analytical models or wind tunnels); however, in the past few decades field-based approaches to threshold parameterization have become more common. Although several methods of calculating transport threshold from field data are available, their comparability is unknown. To address this issue we collected high resolution sediment transport and wind measurements (1 Hz) on an active sand dune for 11 days and compared four different methods of calculating threshold: (i) time fraction equivalence method (TFEM); (ii) Gaussian time fraction equivalence method (GTFEM); (iii) instantaneous method; and (iv) regression method. Time-paired measurements from the two most widely used methods (TFEM and GTFEM) were strongly correlated (r = 0.977); however, correlations between other methods varied (from r = 0.861 to r = 0.261). To demonstrate the implications of using different threshold calculation methods we predicted mass transport, which ranged from 63.6 (instantaneous method) to 126.6 kg per crosswind meter (regression method). This inconsistency suggests that the threshold calculation method could have an appreciable impact on transport predictions. Threshold values are similarly inconsistent when the measurement interval is modified. As such, we do not recommend comparing any measured threshold with another. We discuss several strategies that may mitigate the impact of this issue such as clarification of semantics and method standardization. We also discuss several criticisms of field-based threshold measurements and re-conceptualizations that could allow investigators to develop a better understanding of field-based measurements. Overall, results from this study could allow future investigators to improve threshold (and transport) predictions.

Research highlights► We used raw field data to compare four methods of calculating threshold. ► We found differences that are unexplained, but affect transport predictions. ► We do not recommend comparing any threshold value with another. ► We discuss criticisms, clarifications and solutions to these challenges.