244 Geomorphology (Posters)

Wednesday, 21 October 2009
9:00 AM - 6:00 PM
Hall A

QUANTIFICATION OF DUST EMISSIONS FROM DESERT LANDFORMS USING THE PI-SWERL, SOUTHERN CALIFORNIA

Paper number: 244-6
Program: Technical Sessions
Time: 9:00 AM – 6:00 PM

Authors

Abstract

The quantification of dust emissions from desert landforms is crucial in the accuracy of global dust models. The PI-SWERL (Portable In Situ Wind Erosion Lab) is a new type of wind tunnel whose dust emissions (PM-10) are comparable to large field wind tunnels. Numerous measurements can be made in a reasonable time and in places not accessible by large wind tunnels, allowing the characterization of the heterogeneity of dust emissions from individual landforms. Here, we present >500 individual measurements from six major desert landforms in southern California: desert pavement, playa, sand dune, dry wash, distal alluvial fan, and lacustrine beach. Playas are further subdivided into salt-crusted, silt/clay-crusted, and playa margin/skirt. We found that the largest emitters of dust are dry washes, followed by playa margins, dunes and distal fans. Low emitters include desert pavements, crusted playas and beaches. Low dust emissions are primarily a function of surface crusting and/or armoring. High dust emissions are a function of available saltating sand that bombards the surface, liberating dust-sized particles for entrainment. The PI-SWERL values, measured in the interspaces of surface roughness elements, represent the potential high-end of dust emission values. Therefore, shear stress partitioning can be applied depending on the size and density of surface roughness elements (i.e., vegetation) in any particular setting. This allows appropriate ranges of friction velocities (u*) at which dust emission occurs to be estimated for any given desert landform. The ranges of dust emissions measured can reasonably be applied to similar landforms world-wide if appropriate shear stress partitioning values are applied. These results can greatly enhance global dust modeling efforts and potentially improve upon climate forecasting associated with atmospheric dust loading.