Fire risk: an integrated modelling approach

Abstract

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.