Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-65-2023
https://doi.org/10.5194/nhess-23-65-2023
Research article
 | 
12 Jan 2023
Research article |  | 12 Jan 2023

A data-driven model for Fennoscandian wildfire danger

Sigrid Jørgensen Bakke, Niko Wanders, Karin van der Wiel, and Lena Merete Tallaksen

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Latest update: 01 Mar 2024
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Short summary
In this study, we developed a machine learning model to identify dominant controls of wildfire in Fennoscandia and produce monthly fire danger probability maps. The dominant control was shallow-soil water anomaly, followed by air temperature and deep soil water. The model proved skilful with a similar performance as the existing Canadian Forest Fire Weather Index (FWI). We highlight the benefit of using data-driven models jointly with other fire models to improve fire monitoring and prediction.
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