Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques

Sally S.-C Wang,Yuxuan Wang

doi:10.5194/acp-20-11065-2020

Abstract

Abstract. Occurrences of devastating wildfires have been increasing in the United States for the past decades. While some environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between these factors and wildfires are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model consisting of multiple machine learning algorithms to predict 0.5∘×0.5∘ gridded monthly wildfire burned area over the south central United States during 2002–2015 and then use this model to 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 alleviates the issue of unevenly distributed burned-area data, predicts burned grids with area under the curve (AUC) of 0.82 and 0.83 for the two seasons, and achieves temporal correlations larger than 0.5 for more than 70 % of the grids and spatial correlations larger than 0.5 (p<0.01) for more than 60 % of the months. For the total burned area over the study domain, the model can explain 50 % and 79 % of the observed interannual variability for the winter–spring and summer fire season, respectively. Variable importance measures indicate that relative humidity (RH) anomalies and preceding months' drought severity are the two most important predictor variables controlling the spatial and temporal variation in gridded burned area for both fire seasons. The model represents the effect of climate variability by climate-anomaly variables, and these variables are found to contribute the most to the magnitude of the total burned area across the whole domain for both fire seasons. In addition, antecedent fuel amounts and conditions are found to outweigh the weather effects on the amount of total burned area in the winter–spring fire season, while fire weather is more important for the summer fire season likely due to relatively sufficient vegetation in this season.

Highlights

Wildfire is an important process maintaining the balance of terrestrial ecosystems
As fewer prior studies of a similar nature as ours predicted all possible outcomes at the grid level and none of these studies targeted the south central United States (US), we choose to compare our model performance with previously published models that predicted gridded burned area in terms of the approaches, the temporal and spatial resolution, and the percent of variance explained by the model, regardless of their study regions, periods, methods, and predictors
Our model reveals the response of fire burned area to the changes in relative humidity (RH) anomaly, which is a climate variable as opposed to a weather variable. “rhum” is the actual RH, which can vary by location and season, while RH anomaly measures the departure of rhum from its long-term average due to climate change and/or climate variability

Summary

Introduction

Wildfire is an important process maintaining the balance of terrestrial ecosystems. Wildfire occurrence is controlled by a complex interaction among fuel, weather, and climate (Bowman et al, 2009; Pausas and Keeley, 2009). Many regions of the world have experienced an increase in frequency and intensity of wildfires, which may be possibly connected to changes in regional climate (Balshi et al, 2009; Barbero et al, 2015; Carvalho et al, 2008; Flannigan et al, 2009; Westerling et al, 2006; Westerling, 2016). It is imperative to understand how wildfires would respond to changes in environmental factors in a warming climate. Previous studies revealed the importance of several environmental factors for wildfires. Fuel availability and composition across regions can affect fire developments such as fire likelihood and spread efficiency

Objectives

Results

Conclusion