Climate predicts wildland fire extent across China

Document Type

Journal Article

Publication Title

Science of The Total Environment



PubMed ID





School of Science / Centre for Ecosystem Management


National Key Research and Development Program of China / Fundamental Research Funds for the Central Universities


Shabbir, A. H., Ji, J., Groninger, J. W., Gueye, G. N., Knouft, J. H., van Etten, E. J. B., & Zhang, J. (2023). Climate predicts wildland fire extent across China. Science of The Total Environment, 896, article 164987. https://doi.org/10.1016/j.scitotenv.2023.164987


Wildland fire extent varies seasonally and interannually in response to climatic and landscape-level drivers, yet predicting wildfires remains a challenge. Existing linear models that characterize climate and wildland fire relationships fail to account for non-stationary and non-linear associations, thus limiting prediction accuracy. To account for non-stationary and non-linear effects, we use time-series climate and wildfire extent data from across China with unit root methods, thus providing an approach for improved wildfire prediction. Results from this approach suggest that wildland area burned is sensitive to vapor pressure deficit (VPD) and maximum temperature changes over short and long-term scenarios. Moreover, repeated fires constrain system variability resulting in non-stationarity responses. We conclude that an autoregressive distributed lag (ARDL) approach to dynamic simulation models better elucidates interactions between climate and wildfire compared to more commonly used linear models. We suggest that this approach will provide insights into a better understanding of complex ecological relationships and represents a significant step toward the development of guidance for regional planners hoping to address climate-driven increases in wildfire incidence and impacts.



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