The Impact of Fuel Moisture Initialization on WRF-SFIRE Simulations of Mediterranean Wildfires

In recent years, wildfires have increasingly impacted southern Europe. Although fire is a natural component of Mediterranean ecosystems, the expansion of recreational use of natural and forest areas has increased the number of human-caused ignitions. Climate change further exacerbates this situation by intensifying extreme temperatures and droughts, thereby altering two of the three primary drivers of wildfires: fuel and weather.  The Canadian Forest Fire Weather Index (FWI) is a meteorologically based index widely adopted to estimate fire danger. It requires only temperature, wind speed, relative humidity, and precipitation as input, and consists of six components: three fuel-moisture codes and three fire behavior indices. While originally developed for Canadian boreal conditions, the FWI has been successfully applied in many countries, including southern Europe, where studies have demonstrated its ability to capture fire danger in Mediterranean environments, though further evaluation has been recommended, especially in drier landscapes. In this work, we simulate a wildfire in Italy and treat it as a test case to investigate the effects of integrating the FWI into a coupled fire-atmosphere model WRF-SFIRE. WRF-SFIRE leverages an integrated fuel moisture model to account for the influence of spatial and temporal variability of fuel flammability on fire behavior. We compare simulations executed using two approaches: (i) static fuel moisture initialization, and (ii) dynamic fuel moisture modeling. The comparison between these simulations and observational data shows that dynamic fuel moisture modeling improves the fidelity of both the simulated burned area and the meteorological variables, highlighting the importance of accounting for fuel conditions in operational fire modeling. By default, the fuel moisture model simulates the evolution of dead fuel moisture contents according to a time-lag differential equation, and requires a spin-up phase before the fire event for fuel preconditioning. Alternatively, it can be initialized with external operational fuel moisture data, bypassing this requirement - but such observations are sparse and difficult to obtain routinely. In principle, fuel moisture contents can be estimated from FWI codes using empirical relationships. However, a comparison between FWI-based fuel moisture estimates and a dead fuel moisture reanalysis dataset for California reveals that the FWI-based values systematically overestimate the 100-hr fuel moisture content, consistent with findings reported in the literature. This indicates that the empirical relationship linking the Duff Moisture Code to fuel moisture content requires recalibration before it can be reliably applied. To address this, we developed a framework to derive fuel moisture estimates from FWI codes, enabling WRF-SFIRE initialization without the need for in-situ observations or long model spin-up times, and relying only on routinely available fire danger indices. This approach has the potential to enhance the operational applicability of WRF-SFIRE in data-sparse regions, supporting more timely and accurate fire risk assessment across Mediterranean Europe.