Preprints
https://doi.org/10.5194/egusphere-2022-92
https://doi.org/10.5194/egusphere-2022-92
 
11 Apr 2022
11 Apr 2022
Status: this preprint is open for discussion.

Assessing the performance of various fire weather indices for wildfire occurrence in Northern Switzerland

Daniel Steinfeld1, Adrian Peter2, Olivia Martius1, and Stefan Brönnimann1 Daniel Steinfeld et al.
  • 1Institute of Geography and Oeschger Centre for Climate Change Research, University of Bern, Switzerland
  • 2Office for Forests and Natural Hazards of the Canton of Bern

Abstract. Fire weather indices are widely used to understand and assess meteorological fire hazard. However, in complex regions such as Switzerland with mountainous and hilly terrain, it is difficult to select an appropriate index. In this study, we validate the performance of 14 fire weather indices, four meteorological variables, and a logistic regression model to predict wildfire occurrence for different ecoregions in the canton of Bern in Northern Switzerland with respect to historical fire records from 1981 to 2020. We find that the performance of the indices varies seasonally and regionally. The spring season (March–May) shows that the Canadian Fine Fuel Moisture Content and other indices that respond readily to weather changes perform best. In summer (June–August) and autumn (September–November), the Canadian Buildup Index and Drought Code – indices that describe persistent hot and dry conditions – perform best. Overall, seasonal differences in performance are larger than inter-regional differences. Finally, we show that a logistic regression model trained on local historical fire activity can outperform existing fire weather indices and can be used for medium-range weather forecasting or climate change studies, using only daily averages of meteorological variables as input.

Daniel Steinfeld et al.

Status: open (until 10 Jun 2022)

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Daniel Steinfeld et al.

Daniel Steinfeld et al.

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Short summary
We assess the performance of various fire weather indices to predict wildfire occurrence in Northern Switzerland. We find that indices responding readily to weather changes have the best performance during spring; in the summer and autumn seasons, indices that describe persistent hot and dry conditions perform best. We demonstrate that a logistic regression model trained on local historical fire activity can outperform existing fire weather indices.