Forecasting Dead Fuel Moisture Content at Spatial Scales Using a Process-Based Model with Global Forecast System Data

Dead fuel moisture content (DFMC) was usually involved and being an important part in predicting ignition potential, fireline intensity, flame length, and rate of spread. Previous studies focused on model development and paid little attention to large-scale DFMC forecasting using these models, especially process-based models. In this study, we forecast spatial 1-h (fuel with a diameter less than 0.635 cm) and 10-h (fuel with a diameter between 0.635 cm and 2.54 cm) DFMC in 16 days with meteorological variables interpolated from Global Forecast System (GFS). First, we interpolated meteorological variables including air temperature (T air ), relative humidity (RH), wind speed (W s ) and precipitation (P) to 2 km from GFS data (0.25°) for each site with DFMC measurement in Liangshan Yi Autonomous Prefecture. Then, we forecasted DFMC hourly for 96 sites with three process-based models (Simard, Nelson and fuel stick moisture model (FSMM)). Our results show that the FSMM forecasted more accurate DFMC values (1-h: R ² =0.54, RMSE=6.58%, MAE=5.63%; 10-h: R ² =0.73, RMSE=3.7%, MAE=2.7%) compared to the Nelson and Simard model. Our results suggest that accurate DFMC forecasts from GFS data based on our methods can be used for fire risk assessment and fire behavior prediction.