Location: Range Management ResearchTitle: How to detect change in aeolian sediment transport
|WEBB, NICHOLAS - New Mexico State University|
|CHAPPELL, ADRIAN - New Mexico State University|
|Van Zee, Justin|
|EDWARDS, BRANDON - New Mexico State University|
Submitted to: International Conference on Aeolian Research
Publication Type: Abstract Only
Publication Acceptance Date: 4/30/2018
Publication Date: 6/25/2018
Citation: Webb, N., Chappell, A., Van Zee, J.W., Edwards, B., James, D.K. 2018. How to detect change in aeolian sediment transport [abstract]. International Conference on Aeolian Research. June 25-29, 2018, Bordeaux, France.
Technical Abstract: The detection of change in aeolian sediment transport is key to explaining cause. For example, anthropogenic land use and land cover change (LULCC) influence global rates of wind erosion and dust emission. However, current approaches to measuring wind erosion and dust emission are typically highly uncertain, because they inadequately account for the large spatial and temporal variation in aeolian sediment transport. Field measurements need to reduce uncertainty sufficiently to enable change to be detected at some specified confidence level. Here, we use field measurements of aeolian sediment transport to show (Figure 1) that: i) the spatial variance in aeolian sediment transport may be many times larger than the temporal variance depending on the land surface aerodynamics and sediment supply; and ii) too few samplers, or poorly positioned (biased, e.g., by avoiding vegetation) samplers inflate uncertainty causing change in aeolian sediment transport to be undetectable. The straightforward application of an unbiased sampling design combined with a rigorous statistical framework enables minimum detectable change over time in aeolian sediment transport responding to e.g., land use and management across land cover types. We examine how robust field measurements can be combined with new modelling approaches that resolve the spatial variation in transport controls to improve dust model sensitivity and reduce uncertainty in assessments of the impacts of LULCC.