Preprints
https://doi.org/10.5194/egusphere-2024-1914
https://doi.org/10.5194/egusphere-2024-1914
13 Aug 2024
 | 13 Aug 2024

Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements

Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke

Abstract. Since its development in 2010, the SPITFIRE global fire model has had a substantial impact on the field of fire modelling using dynamic global vegetation models. It includes process-based representations of fire dynamics, including ignitions, fire spread and fire effects, resulting in a holistic representation of fire on a global scale. Previously, work has been undertaken to understand the strengths and weaknesses of SPITFIRE and similar models by comparing their outputs against remotely sensed data. We seek to augment this work with new validation methods and extend it by completing a thorough review of the theory underlying the SPITFIRE model to better identify and understand sources of modelling uncertainty. We find several points of improvement in the model, the most impactful being an incorrect implementation of the Rothermel fire spread model that results in strong upward biases in fire rate of spread, and a live grass moisture parametrization that results in substantially too low live grass moisture contents. The combination of these issues leads to excessively large and intense fires, particularly on grasslands, that bias SPITFIRE toward high tree mortality. We resolve these issues by correcting the implementation of the Rothermel model and implementing a new live grass moisture parametrization, in addition to several other improvements, including a multi-day fire spread algorithm, and evaluate these changes in the European domain. Our model developments allow SPITFIRE to incorporate more realistic live grass moisture contents, and result in more accurate burnt area on grasslands and reduced tree mortality. This work provides a crucial improvement on the theoretical basis of the SPITFIRE model, and a foundation upon which future model improvements may be built. In addition, this work further supports these model developments by highlighting areas in the model where high amounts of uncertainty remain, based on new analysis and existing knowledge about the SPITIFRE model, and identifying potential means of mitigating them to a greater extent.

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Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-1914', Anonymous Referee #1, 24 Sep 2024
    • AC1: 'Comment on egusphere-2024-1914', Luke Oberhagemann, 29 Nov 2024
  • RC2: 'Comment on egusphere-2024-1914', Anonymous Referee #2, 10 Oct 2024
    • AC1: 'Comment on egusphere-2024-1914', Luke Oberhagemann, 29 Nov 2024
  • AC1: 'Comment on egusphere-2024-1914', Luke Oberhagemann, 29 Nov 2024
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke

Data sets

Model Code and Data for "Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements" Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drueke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke https://doi.org/10.5281/zenodo.11473451

Model code and software

Model Code and Data for "Sources of Uncertainty in the Global Fire Model SPITFIRE: Development of LPJmL-SPITFIRE1.9 and Directions for Future Improvements" Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drueke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke https://doi.org/10.5281/zenodo.11473451

Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke

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Short summary
Under climate change, the conditions for wildfires to form are becoming more frequent in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a crucial platform for future developments.