Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 1
Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-383-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-25-383-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 1
Research article
 | Highlight paper
 | 
27 Jan 2025
Research article | Highlight paper |  | 27 Jan 2025

Modelling current and future forest fire susceptibility in north-eastern Germany

Katharina H. Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit

Viewed

Total article views: 9,732 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
6,433 1,688 1,611 9,732 363 203 296
  • HTML: 6,433
  • PDF: 1,688
  • XML: 1,611
  • Total: 9,732
  • Supplement: 363
  • BibTeX: 203
  • EndNote: 296
Views and downloads (calculated since 23 May 2024)
Cumulative views and downloads (calculated since 23 May 2024)

Viewed (geographical distribution)

Total article views: 9,732 (including HTML, PDF, and XML) Thereof 9,548 with geography defined and 184 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 14 May 2026
Download
Editorial statement
Forest fires have become a major problem in many regions of the world, including parts of Central Europe. The modelling study addresses the different factors for Forest Fire Susceptibility (FFS), making use of high spatial resolution of input data for the state of Brandenburg, Germany. An increasing susceptibility is found under rising greenhouse gas forcing scenarios when other changes are not taken into account. Extreme weather periods are of particular relevance in this respect. However, the importance of anthropogenic and vegetation parameters for modelling FFS on a regional level can outweigh the pure climatic effects. The paper also suggests some recommendations for forest management and environmental planning for a reduction of fire risk.
Read more
Short summary
In this study we applied a random forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-eastern Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % and 71 %, showing moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters. This study will support regional forest fire prevention and management.
Read more
Share
Special issue
Current and future water-related risks in the Berlin–Brandenburg...
Altmetrics
Final-revised paper
Preprint