Vegetation effects on spatiotemporal variability in Aeolian mass flux over a range of ecological conditions

TitleVegetation effects on spatiotemporal variability in Aeolian mass flux over a range of ecological conditions
Publication TypeConference Paper
Year of Publication2018
AuthorsEdwards B, Webb N, Van Zee JW, Chappell A
Conference NameInternational Conference on Aeolian Research
Date Published06/2018
Conference LocationBordeaux, France
ARIS Log Number358039

Issues of scale have long been a chief concern in efforts by the aeolian research community to monitor and model aeolian mass flux over spatially large areas. Observations over small sampling scales (< 1 m2 ) dominate physical descriptions of aeolian transport processes and predictive mass flux equations. Consequently, those descriptions are unlikely to represent the controls of transport over space (>> 1 m2 ). Loss of process fidelity is compounded as area increases because additional sources of variance are introduced and unknown synergies occur between controlling factors (Figure 1). Uncertainty is further amplified by increasing landscape heterogeneity, but because monitoring efforts are often limited in scope, few data are available to adequately describe spatial variability of transport to produce unbiased areal estimates, particularly in vegetated landscapes. In this paper, we use transport and vegetation data from US National Wind Erosion Research Network ( sites to investigate spatiotemporal variability of transport and its controls for a range of vegetation conditions. For a given site, vegetation characteristics (e.g., canopy height, vegetative cover fraction, and gap size distribution) are recorded seasonally using standardized methods. Transport is measured using a stratified scheme of 27 MWAC collectors randomly located in groups of 3 within 9 cells in a 100 m2 plot. We use regression co-kriging to map monthly transport and investigate how vegetation as a controlling factor drives variability in transport. Results are expected to inform future modeling efforts and improve analyses of wind erosion and dust emission responses to land use and land cover change.