Regional estimation of dead fuel moisture content in southwest China based on a practical process-based model

Abstract

Background Dead fuel moisture content (DFMC) is crucial for quantifying fire danger, fire behaviour, fuel consumption, and smoke production. Several previous studies estimating DFMC employed robust process-based models. However, these models can involve extensive computational time to process long time-series data with multiple iterations, and are not always practical at larger spatial scales. Aims Our aim was to provide a more time-efficient method to run a previously established process-based model and apply it to Pinus yunnanensis forests in southwest China. Methods We first determined the minimum processing time the process-based model required to estimate DFMC with a range of initial DFMC values. Then a long time series process was divided into parallel tasks. Finally, we estimated 1-h DFMC (verified with field-based observations) at regional scales using minimum required meteorological time-series data. Key results The results show that the calibration time and validation time of the model-in-parallel are 1.3 and 0.3% of the original model, respectively. The model-in-parallel can be generalised on regional scales, and its estimated 1-h DFMC agreed well with field-based measurements. Conclusions Our findings indicate that our model-in-parallel is time-efficient and its application in regional areas is promising. Implications Our practical model-in-parallel may contribute to improving wildfire risk assessment.