The importance of antecedent vegetation and drought conditions as global drivers of burnt area

Abstract

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

A machine learning approach to quantify meteorological drivers of ozone pollution in China from 2015 to 2019

“…deseasonalized MDA8 ozone) (e.g. Menze et al, 2009;Kuhn-Régnier et al, 2021). Since we train the RFR five times given each possible set of 4-year training/validation data, we average the Gini importance scores for each meteorological predictor across all five runs for our final discussion below.…”

Climate drivers of global wildfire burned area

“…Wildfire activity is controlled by weather and climate parameters either in a time window of several months by controlling biomass growth and snowpack accumulation and hence fire fuel availability, or in shorter term by determining vegetation and duff layer moisture content and, hence, their flammability [7][8][9][10]. Analyzing wildfire activity at a global scale for different ecoregions, Abatzoglou et al [11] showed that climate variability can explain one-third of the interannual variability in burned area (BA), highlighting the controlling role of climate parameters.…”

Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area

“…Inclusion of summer conditions results in only a slight bias reduction for the extreme 2020 fire season which is underestimated by GAMs (figure 2). Failure to capture the extreme 2020 burned area extent is likely due to the rarity of the event which is not reflected in the training data [20,56]. Recent research suggests the extreme 2020 fire season was enabled by anomalously dry conditions [5]; however, burned area underestimation from our climate-based statistical models motivate future research to further understand the respective contributions of large-scale climate and factors such as local fire weather, fuel availability and ignitions to this extreme fire year.…”

“…Since the early 2000s, a suite of statistical analyses has quantified relationships between summer fire activity and various climate conditions in the western U.S. [3,5,[9][10][11][20][21][22]. Many of these relationships have been reviewed by Littell et al [23,24].…”