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Discussion papers
https://doi.org/10.5194/acp-2019-885
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-885
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Nov 2019

Submitted as: research article | 04 Nov 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Predicting wildfire burned area in South Central US using integrated machine learning techniques

Sing-Chun Wang and Yuxuan Wang Sing-Chun Wang and Yuxuan Wang
  • Department of Earth and Atmospheric Sciences, University of Houston, Houston, Texas 77024, USA

Abstract. Occurrences of devastating wildfires have been on the rise in the United States for the past decades. While the environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between fires and the environmental controls are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area during 2002–2015 over the South Central United States and identify the relative importance of the environmental drivers on the burned area for both the winter-spring and summer fire seasons of that region. The developed model is able to alleviate the issue of unevenly-distributed burned area data and achieve a cross-validation (CV) R2 value of 0.42 and 0.40 for the two fire seasons. For the total burned area over the study domain, the model can explain 50 % and 79 % of interannual total burned area for the winter-spring and summer fire season, respectively. The prediction model ranks relative humidity (RH) anomalies and preceding months’ drought severity as the top two most important predictors on the gridded burned area for both fire seasons. Sensitivity experiments with the model show that the effect of climate change represented by a group of climate-anomaly variables contributes the most to the burned area for both fire seasons. Antecedent fuel amount and conditions are found to outweigh weather effects for the burned area in the winter-spring fire season, while the current-month fire weather is more important for the summer fire season likely due to the controlling effect of weather on fuel moisture in this season. This developed model allows us to predict gridded burned area and to access specific fire management strategies for different fire mechanisms in the two seasons.

Sing-Chun Wang and Yuxuan Wang
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Status: open (until 30 Dec 2019)
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Sing-Chun Wang and Yuxuan Wang
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Short summary
Occurrences of devastating wildfires have been on the rise in the United States for the past decades. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area over the South Central United States. This developed model allows us to predict gridded burned area more accurately, which is beneficial for potential applications of studying climate-driven changes of wildfire behaviors and their impact on air quality.
Occurrences of devastating wildfires have been on the rise in the United States for the past...
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