The Aeolian Erosion (AERO) model was developed by the United States Department of Agriculture Agricultural Research Service (ARS) to address the need for a generalizable, physically-based wind erosion and dust emission model that could be applied to existing standardized monitoring datasets across all land cover types. The need for a generalizable and physically-based model arose from recognition that the strengths of available cropland wind erosion models (e.g., WEPS) for assessing management impacts on soil loss do not currently (2018) extend to rangeland applications. Available cropland wind erosion models and global dust models were also seen as being either too empirically tuned to cropland settings or too insensitive to the subtle, and sometimes not so subtle, effects of rangeland management and vegetation state changes on aeolian sediment transport and dust emission. AERO was developed from a selection of the best-available schemes to represent biophysical controls on sediment transport and dust emission processes. Criteria for scheme selection included a desire for a high level of process fidelity, low model complexity, and the ability to be applied directly to available soil and ecological monitoring data collected by the Natural Resources Conservation Service's (NRCS) National Resources Inventory (NRI) and the Bureau of Land Management’s (BLM) Assessment, Inventory and Monitoring (AIM) programs.24
The AERO model draws heavily on the structure of the Shao25 dust model. The threshold wind friction velocity for soil entrainment is estimated using the Iverson and White26 threshold equation. A minimally dispersed soil particle size distribution, identified by geographic location and surface soil texture class, is used as input to the equation to produce a size-resolved entrainment threshold. The Fécan et al.27 scheme is used as a threshold modifier to account for the effects of soil moisture on inter-particle cohesion. The Okin28 drag partition scheme is used to estimate the probability density distribution of wind friction velocity at the soil surface as a function of the freestream wind velocity, mean vegetation canopy height, and the vegetation canopy gap size distribution. A tiered drag partition can be implemented to assess effects of shrubs, grasses and oriented soil roughness (e.g., due to tillage) on surface wind friction velocities and sediment transport. Horizontal sediment mass flux, Q (g m-1 s-1) is estimated when the surface wind friction velocity exceeds the entrainment threshold and is computed for each soil particle size class using the Owen29 sand transport equation. Size-resolved vertical dust flux, F (g m-2 s-1) is calculated using the Shao25 dust emission scheme as a function of saltation bombardment and aggregate disintegration processes. A dispersed soil particle size distribution and surface wind friction velocity are used to estimate the level of soil disaggregation, with F estimated as the volume of fine particles emitted from the soil surface. AERO outputs can be tailored by application and may include total horizontal (saltation) and vertical (dust) mass fluxes, size-resolved dust mass flux, and gross wind erosion.
AERO can be implemented in three modes: (1) a timeseries mode using field measured inputs of meteorological, soil and vegetation properties; (2) a probabilistic mode using a combination of field measured inputs, spatially-explicit soils data and reanalysis wind speed probability densities queried by geographic location; and (3) a spatial mode in which AERO can be run offline or online with a numerical weather model. The primary intended application of AERO is in probabilistic mode using wind speed from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) dataset and soils and vegetation inputs from the NRI and AIM programs and/or collected following the standardized methods of Herrick et al.30 In this mode, AERO estimates are produced at the plot scale (typically measured as ≤ 1 ha) and can be scaled to produce spatially-weighted estimates of Q and F. Spatially-weighted AERO estimates can be produced to assess wind erosion and dust emission responses to treatments (e.g., tillage, vegetation clearing, seeding) and disturbances (e.g., fire) and at different administrative and ecogeomorphic scales. For example, AERO has been applied to county, state, ecological site (to inform Ecological Site Descriptions - ESDs), Major Land Resource Area (MLRA), and ecoregion-level assessments. At the time of writing (December 2018), AERO is being calibrated in the Agricultural Policy / Environmental eXtender (APEX) farming systems model to support management scenario-driven assessments of wind erosion and dust emission for croplands and rangelands. AERO model development was funded by and supports the NRCS Conservation Effects Assessment Project (CEAP) and the BLM.
It is anticipated that AERO will serve as a tool for conservation planners to evaluate aeolian sediment transport patterns and trends, and following land treatments and disturbances under different climatic conditions (drought, extreme weather phenomenon). By both enhancing wind erosion monitoring and evaluating management and disturbance scenarios, conservation planners, ranchers and farmers will be better prepared to recognize and react to projected adverse climate conditions, and where risk is deemed too high- avoid or delay the proposed treatment. The capacity to run AERO using standardized monitoring data (NRI, AIM) will enable wind erosion and management options to be assessed along other land health attributes and resource concerns managed on private and federal lands.
Currently, AERO is being validated using data from the National Wind Erosion Research Network31 cropland and rangeland sites across the Great Plains and Western United States and is expected to be released as a fully functional tool by 2020.
References
24. Toevs GR, Karl JW, Taylor JJ, et al. Consistent Indicators and Methods and a Scalable Sample Design to Meet Assessment, Inventory, and Monitoring Information Needs Across Scales. Rangelands. 2011;33(4):14-20. doi:10.2111/1551-501X-33.4.14
25. Shao Y. Simplification of a dust emission scheme and comparison with data. J Geophys Res Atmospheres. 2004;109(D10). doi:10.1029/2003JD004372
26. Iversen JD, White BR. Saltation threshold on Earth, Mars and Venus. Sedimentology. 1982;29(1):111-119. doi:10.1111/j.1365-3091.1982.tb01713.x
27. Fécan F, Marticorena B, Bergametti G. Parametrization of the increase of the aeolian erosion threshold wind friction velocity due to soil moisture for arid and semi-arid areas. Ann Geophys. 1999;17(1):149-157.
28. Okin GS. A new model of wind erosion in the presence of vegetation. J Geophys Res Earth Surf. 2008;113(F2).
29. Owen PR. Saltation of uniform grains in air. J Fluid Mech. 1964;20(2):225-242. doi:10.1017/S0022112064001173
30. Herrick JE, Van Zee JW, McCord SE, Courtright EM, Karl JW, Burkett LM. Monitoring Manual for Grassland, Shrubland, and Savanna Ecosystems Volume 1: Core Methods. Second Edition Rep. Las Cruces, New Mexico: USDA-ARS Jornada Experimental Range; 2018.
31. National Wind Erosion Research Network Sites. https://winderosionnetwork.org/.