Land, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022

Abstract

Understanding the key variables that characterise fire propagation is important for a better estimation of fire events and their impacts. This study uses machine learning combined with satellite remote sensing and atmospheric modelled data to enhance estimations of burned areas. It focuses on the intense early summer weather patterns in South Asia during April and May 2022 and explores the relationship between environmental factors and fire spread. The study employs various algorithms, including random forest, extra trees, extreme gradient boosting (XGBoost), gradient boosting regressor, support vector regressor and neural networks. XGBoost proves to be the most accurate approach. An isolation forest algorithm is used to adjust for outliers in burned area estimations. The comprehensive analysis conducted includes the identification of key variables and sensitivity tests incorporating changes of up to 25 % in natural environmental conditions to assess the model’s consistency. The results indicate that integrating vegetation, atmospheric, and human-related variables with the XGBoost algorithm, and incorporating outlier adjustments leads to the most effective performance (R2 ≥ 0.7), with jet stream variables enhancing the accuracy by approximately 11.5 %. The study highlights the notable impact on fire propagation of increases in the value of 300-hPa meridional circulation index flow (MCI300) and a high 500-hPa geopotential height anomaly (ΔZ500), indicating the development of strong atmospheric blocking (upper tropospheric ridge). As compared to other factors, e.g. land surface temperature, vapour pressure deficit, soil moisture and vegetation optical depth, the impact of changes in jet stream metrics (MCI300 and ΔZ500) was more pronounced, indicating greater sensitivity. These insights emphasise the complexity of fire spread, and the importance of using atmospheric factors to estimate burned areas, particularly during severe heatwaves.

This project received the support of a fellowship from the ‘la Caixa’ Foundation (ID 100010434), with fellowship code LCF/BQ/DI21/ 11860028. This work was also supported by Project PID2020-114623RB-C32 funded by the Spanish Ministry of Science and Innovation (MCIN/AEI /10.13039/501100011033). In addition, D. Chaparro has received funding from the XXXIII Ramón Areces Postdoctoral grant.

Peer Reviewed

Postprint (published version)