EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Tailored Fine Fuel Moisture Content Model for Improving Wildfire Danger Rating Systems

Nicolò Perello1,2, Andrea Trucchia2, Mirko D'Andrea2, Giorgio Meschi2, Silvia degli Esposti2, and Paolo Fiorucci2
Nicolò Perello et al.
  • 1University of Genoa, Genova, Italy
  • 2CIMA Research Foundation, Savona, Italy

A change in wildfire regimes in several regions around the Earth has been acknowledged in recent decades, with an increase in the frequency of particularly severe events. Consequently, many wildfires management systems have been challenged, renewing interest in Forest Fire Danger Rating (FFDR) models to support preparedness and response phases. The Liguria Region (Italy) and the Italian Civil Protection supported independent research programs that led in 2003 to the development of the FFDR model RISICO. Nowadays the model is used as a decision-support tool by Italian civil protection systems at national and regional levels. RISICO model integrates weather conditions with vegetation types, topography, vegetation indices from satellite and ML-based wildfires susceptibility maps, in order to provide all information available.

One of the main component of RISICO is the Fine Fuel Moisture Content (FFMC) model. Indeed, fine fuel moisture conditions influence the ignition and spread of wildfires, and particularly low FFMC values are often associated with the occurrence of severe events. A new formulation of the FFMC model has therefore been performed to increase its forecasting capabilities and the abilities to discriminate severe wildfire conditions. The FFMC model depends on vegetation types, differentiating the fine fuel moisture behavior through a different response time to weather conditions. This aspect makes it possible to consider the structural peculiarities of each vegetation type, differentiating then forest fire fire risk behavior. The model is also able to simulate fine fuel moisture content at different temporal resolution, ranging from hours to minutes. This makes it possible to describe in detail the fast dynamics of FFMC, which is of particular interest in environments characterized by a rapidly changing forest fire risk such as the Mediterranean environment. A reformulation and parameters calibration of the FFMC model has then been performed, to increase the reliability of the model. The use of the revised FFMC model to simulate moisture conditions in case of wildfires occurred in Italy in the last 15 years shows an increase in the model's ability to discriminate against severe events, characterized by particularly low fine fuel moisture values.


How to cite: Perello, N., Trucchia, A., D'Andrea, M., Meschi, G., degli Esposti, S., and Fiorucci, P.: A Tailored Fine Fuel Moisture Content Model for Improving Wildfire Danger Rating Systems, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-14313,, 2023.

Supplementary materials

Supplementary material file