Meteorological and Land-Cover Controls on Grassland Fire Behaviour by WRF v4.4-SFIRE v0.1 with the computational optimization

Abstract. Under global warming, grassland wildfire risk has increased substantially. The WRF-SFIRE model was applied to the extreme grassland wildfire that occurred in Inner Mongolia in 2023, with satellite-derived fire perimeters used to evaluate the effects of meteorological forcing, land cover, and key driving factors on fire behavior, as well as to optimize model performance. For meteorological forcing, the FNL 6 h two-way coupling configuration achieved the highest spatial agreement with observations, with a recall of 39.1 %, whereas ERA5 was more sensitive to short-term meteorological variability but tended to overestimate fire spread. For land cover, FROM_GLC30 (2017) showed relatively high overall agreement but tended to overexpand the fire, GlobeLand30 (2020) produced more conservative simulations, and GLC_FCS30D (2022) achieved the best overall performance. Fire-atmosphere feedback significantly enhanced fire spread rate and burned area. Wind speed exerted the strongest influence on burned area and spread distance, relative humidity mainly controlled spread rate and flame length, and air temperature played a secondary role. Among the fuel-related parameters, fuel load mainly affected flame intensity, fuel depth dominated burned area variation, and fuel moisture effects mainly regulated spread rate. In addition, the optimized computational configuration, particularly the combined use of PNetCDF and asynchronous I/O, reduced runtime by 29.3 % relative to the baseline case. These results clarify the differential effects of input data and key driving factors on grassland fire behavior simulations and provide more reliable support for fire risk warning and management.