Prescribed fires in forest ecosystems can negatively impact human health and safety by transporting smoke downwind into nearby communities. Smoke transport to communities is known to occur around Bend, Oregon, United States of America (USA), where burning at the wildland–urban interface in the Deschutes National Forest resulted in smoke intrusions into populated areas. The number of suitable days for prescribed fires is limited due to the necessity for moderate weather conditions, as well as wind directions that do not carry smoke into Bend. To better understand the conditions leading to these intrusions and to assess predictions of smoke dispersion from prescribed fires, we collected data from an array of weather and particulate monitors over the autumn of 2014 and spring of 2015 and historical weather data from nearby remote automated weather stations (RAWS). We characterized the observed winds to compare with meteorological and smoke dispersion models using the BlueSky smoke modeling framework. The results from this study indicated that 1–6 days per month in the spring and 2–4 days per month in the fall met the general meteorological prescription parameters for conducting prescribed fires in the National Forest. Of those, 13% of days in the spring and 5% of days in the fall had “ideal” wind patterns, when north winds occurred during the day and south winds did not occur at night. The analysis of smoke intrusions demonstrated that dispersion modeling can be useful for anticipating the timing and location of smoke impacts, but substantial errors in wind speed and direction of the meteorological models can lead to mischaracterizations of intrusion events. Additionally, for the intrusion event modeled using a higher-resolution 1-km meteorological and dispersion model, we found improved predictions of both the timing and location of smoke delivery to Bend compared with the 4-km meteorological model. The 1-km-resolution model prediction fell within 1 h of the observed event, although with underpredicted concentrations, and demonstrated promise for high-resolution modeling in areas of complex terrain.
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