Regional estimation of dead fuel moisture content in southwest China based on a practical process-based model
Chunquan Fan A , Binbin He A * , Jianpeng Yin A and Rui Chen A
+ Author Affiliations
- Author Affiliations
A School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China.
* Correspondence to: binbinhe@uestc.edu.cn
International Journal of Wildland Fire 32(7) 1148-1161 https://doi.org/10.1071/WF22209
Submitted: 14 October 2022 Accepted: 18 April 2023 Published: 4 May 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
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.
Keywords: 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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