Dust emission from crusted surfaces: Insights from field measurements and modelling

Authors: KLOSE, MARTINA - Agricultural Institute Of Spain; GILL, THOMAS - University Of Texas - El Paso; ETYEMEZIAN, VICKEN - Desert Research Institute; NIKOLICH, GEORGE - Desert Research Institute; ZADEH, ZAHRA - New Mexico State University; WEBB, NICHOLAS - US Department Of Agriculture (USDA); Van Pelt, Robert - Scott

Submitted to: Aeolian Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/18/2019
Publication Date: 6/1/2019
Citation: Klose, M., Gill, T.E., Etyemezian, V., Nikolich, G., Zadeh, Z.G., Webb, N.P., Van Pelt, R.S. 2019. Dust emission from crusted surfaces: Insights from field measurements and modelling. Aeolian Research. 1875-9637. https://doi.org/10.1016/j.aeolia.2019.05.001.
DOI: https://doi.org/10.1016/j.aeolia.2019.05.001

Interpretive Summary: Fugitive dust from wind-eroded soils degrades air quality, creates hazards to commerce, and negatively impacts human and environmental health. One of the controlling factors for dust emission that is poorly understood is the role that surface crusting plays in protecting the soil surface and limiting particulate emissions. In this study, scientists from ARS (Lubbock, TX), New Mexico State University, University of Texas at El Paso, Barcelona Supercomputing Center, and Desert Research Institute examined the surface vegetation, soil physical and biological crusts, soil texture, and loose erodible material on the crust surface and determined how the interactions of these factors affected fugitive dust emissions during several natural wind events. We also compared the observations with a computer model developed in 2004 and revised in 2011 that was parameterized with site characteristics mentioned above. We found that by entering the easily measured parameters mentioned above, revised 2011 computer model adequately predicted the dust emissions from the surface. This information coupled with the model leads to a better understanding on soil surface conditions affect dust emissions.

Technical Abstract: Crusted surfaces, such as crusted playas or ephemeral lakes, can be major sources of mineral dust emission. Quantitative understanding of dust emission from crusted surfaces is limited, because (1) theories on dust emission are not well tested for such surfaces; and (2) modelling is hampered by a lack of input data sufficient to describe the surface conditions. Combining detailed measurements collected in the southwestern United States in 2016 with physics-based numerical modelling, we present new insights in dust emission from crusted surfaces. Our measurements confirm that crust erodibility and dust emission intensity can increase or decrease after previous erosion events. To support interpretation of the measurements and to test the applicability of a state-of-the-art parameterisation to simulate dust emission from crusted surfaces, we apply the dust emission scheme of Shao (2004). Saltation flux, which is input to the dust emission scheme, is approximated using the parameterization of Kawamura (1964) and a scaling factor obtained from observations. Limitations of this approach are discussed. Our results show that the dust emission scheme is suitable to estimate dust emission from crusted surfaces if accurate input data describing the soil-surface condition are provided, i.e. particle-size distributions of the surface crust and loose erodible material, and parameters such as aggregate stability and soil plastic pressure. The parameters were optimized for each dust event to achieve a best estimate. The variation of the resulting parameter values confirms the observed variability of dust emission efficiency between the events and provides further evidence that these were caused by variations in crust erodibility. Our study demonstrates that available physics-based dust emission parameterisations are able to simulate dust emissions under complicated conditions, but also that there is a need to refine information on the soil-surface conditions provided as input to the model. Using new surface data sets which start to become available, we hope that our findings can be expanded and generalised to improve large-scale dust emission estimates in the future.