Development of Modeling Tools for Predicting Smoke Dispersion from Low-Intensity Fires

Publication Year:
2013
Usage 483
Downloads 434
Abstract Views 49
Repository URL:
https://digitalcommons.unl.edu/jfspresearch/51
Author(s):
Heilman, Warren E.; Zhong, Shiyuan; Hom, John L., Dr.; Charney, Joseph J.
Tags:
Forest Biology; Forest Management; Forest Sciences; Natural Resources and Conservation; Natural Resources Management and Policy; Other Environmental Sciences; Other Forestry and Forest Sciences; Sustainability; Wood Science and Pulp, Paper Technology
article description
Of particular concern to fire and air-quality management communities throughout the U.S. are the behavior and air-quality impacts of low-intensity prescribed fires for fuels management. For example, smoke from prescribed fires, which often occur in wildland-urban interface (WUI) areas and in areas where forest vegetation has a significant impact on the local meteorology, can linger for relatively long periods of time and have an adverse effect on human health. Smoke from wildland fires can also reduce visibility over roads and highways in the vicinity of and downwind of these fires, reducing the safety of our transportation system. The planning for and tactical management of low-intensity prescribed fires can be enhanced with models and decision support tools developed with a fundamental understanding of how the atmosphere interacts with these types of fires and the smoke they generate. This particular study focused on (1) an evaluation of several existing coupled meteorological and atmospheric dispersion modeling systems for their potential use as tools to predict the local meteorological and air-quality impacts of low-intensity wildland fires in forested environments, (2) the further development of those modeling systems deemed most appropriate for low-intensity wildland fire applications to enhance their local meteorological and air-quality predictive capabilities within forested environments, and (3) the development and analysis of new observational data sets that can be used to evaluate current and future modeling systems and to improve our understanding of fundamental fire-fuel-atmosphere interactions.