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Incorporating the Wind Erosion Prediction System (WEPS) for Dust into a Regional Air Quality Modeling System

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  International Symposium on Erosion and Landscape Evolution (ISELE), 18-21 September 2011, Anchorage, Alaska  711P0311cd Paper #11035.(doi:10.13031/2013.39235)
Authors:   Brian Lamb, Serena Chung, Joseph Vaughan, Jincheng Gao, Larry Wagner
Keywords:   Wind erosion, Regional modeling, PM10, Air quality

Wind erosion of soil is a major concern of the agricultural community as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, suspension of eroded soil particles results in dust emissions into the atmosphere, contributing to poor air quality, reduced visibility, and perturbations to regional radiation budgets. An important aspect of understanding the impact of agricultural activities is the ability to model windblown dust emissions within the framework of a regional air-quality system that considers atmospheric constituents from a variety of sources. The Wind Erosion Prediction System (WEPS) is a new tool for treating erosion from agricultural fields. As a process-based model, WEPS represents a significant improvement in comparison to existing empirical windblown dust modeling algorithms. WEPS includes several submodels to account for the effects of crop growth, crop management practices, soil conditions and surface cover. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, WEPS has been modified so that it can be incorporated into a gridded regional air quality forecasting system. The modified WEPS model is incorporated into the WRF/CMAQ modeling framework to study the impact of windblown dust on air quality in the state of Washington (Figure 1). Preliminary results indicate that the modeling framework performs well in predicting the onset of dust storm events although the exact timing of events is off by as much as several hours and the framework appear to underestimate atmospheric PM10 concentrations. Future work will include more quantitative and comprehensive evaluation to improve the modeling framework.

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