Regional Estimation of Dead Fuel Moisture Content in Southwest China Based on a Practical Process-Based Model
INTERNATIONAL JOURNAL OF WILDLAND FIRE(2023)
摘要
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.
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关键词
dead fuel moisture content (DFMC),ERA5-Land,fuel stick moisture model (FSMM),parallel computing,process-based model,regional areas application,time series iteration,wildfires
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