|Building a wind erosion and dust emission model for rangelands using National Wind Erosion Research Network data
|Year of Publication
|Edwards B, Webb N, Cooper B, Courtright E, Duni D, Newingham B, Pierson FB, Scott D, Tatarko J, Tedela NH, Toledo DP, Verburg PS, Williams JC, Van Pelt S, Van Zee J
|2021 LTAR Annual Meeting
|ARIS Log Number
Existing wind erosion and dust emission models are poorly developed for assessing the impacts of land management and environmental change on rangelands. Models that leverage standardized vegetation and soil monitoring data and produce estimates that capture intra- and inter-annual spatial variability in sediment transport rates are needed to develop indicators and benchmarks for wind erosion in support of land health and air quality assessments. It is also important to describe, quantify, and communicate estimates of model uncertainty to users. The Aeolian EROsion (AERO) model was developed in response to these concerns and has been parametrized for rangelands. We used a Generalized Likelihood Uncertainty Estimation (GLUE) framework to calibrate AERO using vegetation, sediment transport, and meteorological data collected from 2015 to 2019 at five grassland and shrubland National Wind Erosion Research Network (NWERN) sites. Results show good agreement for individual sampling periods across the sites and a one-to-one relationship between median predictions and sediment flux observations. In addition, on a site-by-site basis, combined distributions of aeolian sediment flux estimates closely approximate the probability distribution of observed flux at the site over intermediate time scales (seasons to years). These results suggest AERO effectively represents temporal variability in aeolian transport rates and provides robust estimates suitable for assessing rangeland health and better predicting changes in air quality and the impacts of land management activities. Here, we present the AERO wind erosion and dust emission model, the parameterization for rangelands, and example applications of AERO to large monitoring data sets.