Preprints
https://doi.org/10.5194/egusphere-2022-92
https://doi.org/10.5194/egusphere-2022-92
11 Apr 2022
 | 11 Apr 2022

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

Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann

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, Adrian Peter, Olivia Martius, and Stefan Brönnimann

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-92', Anonymous Referee #1, 27 Jul 2022
  • RC2: 'Comment on egusphere-2022-92', Anonymous Referee #2, 28 Jul 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-92', Anonymous Referee #1, 27 Jul 2022
  • RC2: 'Comment on egusphere-2022-92', Anonymous Referee #2, 28 Jul 2022
Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann
Daniel Steinfeld, Adrian Peter, Olivia Martius, and Stefan Brönnimann

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Latest update: 01 Mar 2024
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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.