Investigating Drought Events and Their Consequences in Wildfires: An Application in China

Understanding the impact of drought on fire dynamics is crucial for assessing the potential effects of climate change on wildfire activity in China. In this study, we present a series of multiple linear regression (MLR) models linking burned area (BA) during mainland China&rsquo:s fire season from 2001 to 2019, across seven regions, to concurrent drought, antecedent drought, and time trend. We estimated burned area using Collection 6 Moderate Resolution Imaging Spectradiometer (MODIS) and drought indicators using either the Standardized Precipitation Evapotranspiration Index (SPEI) or the self-calibrated Palmer Drought Severity Index (sc-PDSI). Our findings indicate that the wildfire season displays a spatial variation pattern that increases with latitude, with the Northeast China (NEC), North China (NC), and Central China (CC) regions identified as the primary areas of wildfire occurrence. Concurrent and antecedent drought conditions were found to have varying effects across regions, with concurrent drought as the dominant predictor for NEC and Southeast China (SEC) regions and antecedent drought as the key predictor for most regions. We also found that the Northwest China (NWC) and CC regions exhibit a gradual decrease in burned area over time, while the NEC region showed a slight increase. Our multiple linear regression models exhibited a notable level of predictive power, as evidenced by the average correlation coefficient of 0.63 between the leave-one-out cross-validation predictions and observed values. In particular, the NEC, NWC, and CC regions demonstrated strong correlations of 0.88, 0.80, and 0.76, respectively. This indicates the potential of our models to contribute to the prediction of future wildfire occurrences and the development of effective wildfire management and prevention strategies. Nevertheless, the intricate relationship among fire, climate change, human activities, and vegetation distribution may limit the generalizability of these findings to other conditions. Consequently, future research should consider a broad range of factors to develop more comprehensive models.