Estimating the Dead Fuel Moisture Content (DFMC) is crucial in wildfire risk management, representing a key component in forest fire danger rating systems and wildfire simulation models. DFMC fluctuates sub-daily and spatially, influenced by local weather and fuel characteristics. This necessitates models that provide sub-daily fuel moisture conditions for improving wildfire risk management. Many forest fire danger rating systems typically rely on daily fuel moisture models that overlook local fuel characteristics, with consequent impact on wildfire management. The semi-empirical parametric DFMC model proposed addresses these issues by providing hourly dead fuel moisture dynamics, with specific parameters to consider local fuel characteristics. A calibration framework is proposed by adopting Particle Swarm Optimization-type algorithm. In the present study, the calibration framework has been tested by using hourly 10-h fuel sticks measurements. Implementing this model in forest fire danger rating systems would enhance detail in forest fire danger conditions, advancing wildfire risk management.

中文翻译：

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适用于各种燃料类型和时间尺度的适应性强的死燃料水分模型，专为野火危险评估量身定制


估计死燃料水分含量 （DFMC） 在野火风险管理中至关重要，是森林火灾危险评级系统和野火模拟模型的关键组成部分。DFMC 受当地天气和燃料特性的影响，在次日内和空间上波动。这需要提供低于每日燃料水分条件的模型，以改善野火风险管理。许多森林火灾危险评级系统通常依赖于每日燃料水分模型，这些模型忽略了当地的燃料特性，从而对野火管理产生影响。提出的半经验参数化 DFMC 模型通过提供每小时死燃料水分动力学来解决这些问题，并具有考虑局部燃料特性的特定参数。采用 Particle Swarm Optimization 型算法提出了一种标定框架。在本研究中，校准框架已使用每小时 10 小时的加油棒测量进行了测试。在森林火灾危险评级系统中实施此模型将增强森林火灾危险情况的细节，从而推进野火风险管理。