Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE
| Autor: | Judith Winterkamp, Kara M. Yedinak, Brett Williams, Rodman R. Linn, James H. Furman, J. Kevin Hiers, Joseph J. O'Brien, Alexandra Jonko, Scott L. Goodrick |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
040101 forestry
Atmospheric Science 010504 meteorology & atmospheric sciences Meteorology Scale (ratio) model-observation comparison Sampling (statistics) 04 agricultural and veterinary sciences lcsh:QC851-999 Environmental Science (miscellaneous) Combustion Grid 01 natural sciences Field (computer science) Variable (computer science) fire modeling Anemometer low intensity fire 0401 agriculture forestry and fisheries lcsh:Meteorology. Climatology Sensitivity (control systems) prescribed fire Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences |
| Zdroj: | Atmosphere Volume 12 Issue 2 Atmosphere, Vol 12, Iss 139, p 139 (2021) |
| ISSN: | 2073-4433 |
| DOI: | 10.3390/atmos12020139 |
| Popis: | Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes. |
| Databáze: | OpenAIRE |
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