Unveiling RISICO 2024: Enhancing Wildfire Forecasting through Cutting-Edge Updates

The socio-economic changes over recent decades, marked by rural abandonment and fuel accumulation, coupled with the impact of climate change altering spatio-temporal weather patterns, have created conditions conducive to potential extreme wildfire events. Numerous wildfire management systems have thus faced significant challenges, leading to an additional push to develop or improve decision-support tools. Forest Fire Danger Rating models have been widely used by wildfire management systems in recent decades, aiding in daily operations planning and the production of fire bulletins.

Since the year 2000, independent research programs conducted by the Liguria Region in Italy, and subsequently by the Italian Civil Protection, have led to the creation of the Forest Fire Danger Rating system known as RISICO. The system incorporates meteorological observations and forecasts from Limited Area Models, utilizing vegetation cover and topography as additional inputs to enhance its capabilities. The system is currently adopted at the national level in Italy by the Civil Protection system (Dipartimento della Protezione Civile), supporting the production of the national daily fire danger bulletin, and by several regional authorities.

Over the past year, significant efforts have been made to upgrade the model. Specifically, a new fuel map based on fire susceptibility obtained through Machine Learning techniques has been proposed. This new approach allows for the structured integration of wildfire susceptibility information within the assessment of wildfire danger. Given the importance RISICO places on information about fuel classes, this approach allows a focus on fuel conditions that, when combined with specific meteorological conditions, can lead to extreme wildfire events. Furthermore, the Fine Fuel Moisture component of RISICO has been modified in its dynamics and calibrated using observed data from fuel sticks. This modification aims to better identify prolonged conditions of dry fuel that facilitate the ignition and spread of fires. Finally, the Rate of Spread model has been enhanced through the integration of the PROPAGATOR wildfire spread model's approach, with the goal of providing a more accurate description of the interaction between wind and topography.

The updated model was subsequently validated using fires that occurred in Italy from 2007 to 2022 and compared with the model's performance before the modifications. The results demonstrate an improvement in the model's ability to identify situations particularly dangerous for fire ignition and spread. The updated model, therefore, enhances the prediction of wildfire danger, providing scientific support in the decision-making process and promoting effective wildfire management.