Preprints
https://doi.org/10.5194/egusphere-2024-1380
https://doi.org/10.5194/egusphere-2024-1380
23 May 2024
 | 23 May 2024
Status: this preprint is open for discussion.

Modelling Current and Future Forest Fire Susceptibility in north-east Germany

Katharina Heike Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit

Abstract. Preventing and fighting forest fires has been a challenge worldwide in recent decades. Forest fires alter forest structure and composition, threaten people’s livelihoods, and lead to economic losses, as well as soil erosion and desertification. Climate change and related drought events, paired with anthropogenic activities, have magnified the intensity and frequency of forest fires. It is crucial to identify the conditions that cause the emergence and spread of forest fires to improve prevention and management. We applied Random Forest (RF) machine learning (ML) algorithm to model current and future forest fire susceptibility (FFS) in the federal state of Brandenburg (Germany) using topographic, climatic, anthropogenic, soil, and vegetation predictors. FFS was modelled at a spatial resolution of 50 metres for current (2014–2022) and future scenarios (2081–2100) considering different shared socioeconomic pathways (SSP3.70 and SSP5.85). Model accuracy ranged between 69 % (RFtest) and 71 % (LOYO), showing a moderately high model reliability for predicting FFS. The model results underscore the importance of anthropogenic parameters and vegetation parameters in modelling FFS on a regional level. This study will allow forest managers and environmental planners to better identify areas, which are most susceptible to forest fires, enhancing warning systems and prevention measures.

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Katharina Heike Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit

Status: open (until 04 Jul 2024)

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Katharina Heike Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit
Katharina Heike Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit

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Short summary
In this study we applied Random Forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-east Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % to 71 % showing a moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters for FFS. This study will support regional forest fire prevention and management.