20 Feb 2023
 | 20 Feb 2023
Status: this preprint is open for discussion.

Fire risk: an integrated modelling approach

Alba Marquez Torres, Giovanni Signorello, Sudeshna Kumar, Greta Adamo, Ferdinando Villa, and Stefano Balbi

Abstract. Wildfires are key to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by fires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time-step, providing spatially and temporally explicit fashion results through the k.LAB platform. K.LAB provides an environment for input data integration, employing modeling methods such as Geographic Information System, Remote Sensing and Bayesian Network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (Findable, Accessible, Interoperable and Reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily are at high probability of danger in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aid in landscape management and preventing wildfires due to climate change.

Alba Marquez Torres et al.

Status: open (until 03 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-138', Marj Tonini, 09 Mar 2023 reply
  • RC2: 'Comment on egusphere-2023-138', Anonymous Referee #2, 27 Mar 2023 reply

Alba Marquez Torres et al.

Data sets

Fire Sicily Alba Marquez Torres

Model code and software

Fire Sicily Alba Marquez Torres

Alba Marquez Torres et al.


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Short summary
Only by mapping fire risks can we manage our forest and prevent fires under current and future climate conditions. We have developed a fire risk map based on k.LAB: an AI-powered and open-source software integrating multidisciplinary knowledge in near real-time. Through an easy-to-use web application, we have modelled the hazard with 84 % accuracy for Sicily, a representative Mediterranean region. The fire risk analysis reveals 45 % of vulnerable areas are at high probability of danger in 2050.