Prediction of forest fire risk according to climate change in Bhutan using a shared socioeconomic pathways (SSP) scenario and random forest

다국어 초록 (Multilingual Abstract)

Forest fires destroy millions of forest acres globally, damaging ecosystem services, emitting carbon into the atmosphere, and causing billions of dollars in socio-economic losses, including loss of life. Bhutan, despite its small size, is grappling with forest fires compounded by climate extremes, a remote location, and a lack of preparedness. Under such conditions, it is important to develop policy, technology, and action to respond to forest fires based on scientific research. This study used the Random Forest model (RF) and Shared Socioeconomic Pathways (SSP) approach to create a model for predicting forest fires in Bhutan by considering climate data, spatial data, and forest fire occurrence factors. The RF algorithm was used to analyze the correlations between various input data and predict the change in future forest fire risk in Bhutan. Using SSP scenarios that considered changes due to future population and economic growth, it was possible to predict the risk of forest fires accurately and spatially in Bhutan. The study found that the average forest fire risk was highest during the months of October to January, which are also the dry seasons with low precipitation. However, it also showed high variability across climate change scenarios. SSP scenarios also confirmed the possibility of a significant increase in forest fire risk in the future.
The results of this study can be used to support specific policy decisions for forest fire prevention and response in Bhutan and to contribute to the development of forest fire prediction technology in the light of the changing global climate.

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