%0 Journal Article %J Journal of Geophysical Research: Earth Surface %D 2023 %T Aeolian Sediment Transport Responses to Vegetation Cover Change: Effects of Sampling Error on Model Uncertainty %A Wojcikiewicz, R.R. %A Webb, N.P. %A Edwards, B.L. %A Van Zee, J.W. %A Courtright, E.M. %A Cooper, B.F. %A Hanan, N.P. %K aeolian sediment transport %K sample design %K sediment flux %K vegetative cover %X

Although it is widely known that observations of aeolian sediment transport are susceptible to large sampling errors, sample designs are frequently used that do not sufficiently reduce the measurement uncertainties inherent in the study of aeolian processes. Here, we examine the influence of sample size (n) and sampling location on uncertainty in models of aeolian sediment transport responses to vegetation cover change. We compare measurements from a stratified random array of 27 horizontal sediment mass flux samplers to vegetative cover data collected at a 1 ha site over a period of nearly 6 years. To assess the sensitivity of modeled relationships between aeolian transport and vegetative cover to sample design, we analyze statistical regressions for all possible combinations of sample size and sampler locations. We show that at least 17 randomly located samplers are needed to consistently capture the sediment mass flux response to vegetative cover change. We found that multiple statistically significant models can describe the sediment flux-vegetative cover relationship when using smaller sample sizes, demonstrating the risks of inferring sediment transport response from an underpowered sample design. Across vegetative functional groups, we found that woody cover generally influenced aeolian sediment transport rates more than herbaceous cover, while model uncertainty at large sample sizes (n > 17) showed the limitation of using vegetative cover as an indicator of aeolian sediment transport. Our results suggest an evaluation of sampling practices in aeolian sediment transport studies may be needed to avoid inferential errors that are likely pervasive in this field of study.

%B Journal of Geophysical Research: Earth Surface %V 128 %G eng %N 12 %& e2023JF007319 %R https://doi.org/10.1029/2023JF007319 %0 Journal Article %J Bulletin of the American Meteorological Society %D 2021 %T Addressing air quality, agriculture, and climate change across the Southwest and Southern Plains: A roadmap for research, extension, and policy %A Dinan, M. %A Elias, E. %A Webb, N.P. %A Zwicke, G. %A Dy, T.S. %A Aney, S. %A Brady, M. %A Brown, J.R. %A Dobos, R.R. %A DuBois, D. %A Edwards, B.L. %A Heimel, S. %A Luke, N. %A Rottler, C.M. %A Steele, C. %K Agriculture %K Air quality %K climate change %K Decision making %K Policy %K Societal impacts %B Bulletin of the American Meteorological Society %G eng %& E1394 %R doi:10.1175/BAMS-D-21-0088.1 %0 Conference Proceedings %B American Meteorological Society 99th Annual Meeting %D 2019 %T Area estimates of wind friction velocity derived from net radiometers and MODIS albedo %A Parker, N.E. %A Webb, N.P. %A Chappell, A. %A LeGrand, S.L. %B American Meteorological Society 99th Annual Meeting %C Phoenix, AZ %G eng %0 Conference Proceedings %B 73rd Soil and Water Conservation Society Annual Conference %D 2018 %T AERO: A wind erosion modeling framework with applications to monitoring data %A Edwards, B.L. %A Nicholas Webb %A McCord, S.E. %X

The Aeolian Erosion Model (AERO) is a versatile aeolian transport and dust emission modeling environment developed to provide a robust interface for fundamental research while also acting as a decision-support tool for land managers. The model simulates size-resolved horizontal and vertical mass flux on the plot scale from user inputs of meteorological, soil and vegetation data. AERO is highly customizable; the model can be run for a single set of conditions, a time series of conditions, conditions over space, or a time series of conditions over space. Drag partitioning, vertical dust emission schemes, and horizontal transport equations are user-selectable. Key variables (e.g., vegetation cover, canopy gap distribution, soil type) can be input as scalars, defined by descriptive statistics, supplied as probability distributions, or, when run spatially, as remote sensing-derived inputs and atmospheric data from the Weather Research and Forecasting (WRF) weather prediction model. As such, the model is adaptable to many research and management applications over a range of site conditions. Here, we detail the model framework and processing options and provide an example of model application to U.S. Bureau of Land Management (BLM) Assessment, Inventory and Monitoring (AIM) plots in New Mexico, USA to assess potential implications of management actions for dust emission rates. The test case demonstrates how the AERO model can leverage emerging large-scale ecological datasets like AIM to provide new opportunities to evaluate aeolian sediment transport responses to land surface conditions, potential interactions with disturbances and ecological change, and impacts of anthropogenic land use and land cover change.

%B 73rd Soil and Water Conservation Society Annual Conference %C Albuquerque, USA %V 29 July – 1 August, 2018 %G eng