Diurnal variation and prediction method of floor fuel moisture content in a Pinus massoniana-dominated forest in Guizhou province, China

Abstract

Fuel moisture content (FMC) on forest floor exhibits consistent diurnal fluctuations between daytime and night-time, and the construction of a high-precision prediction model is important for estimating the diurnal variation of forest fires and implementing scientific management strategies. In this study, typical fuel from a Pinus massoniana dominated forest in southwestern China was selected as the research object, and its moisture content was monitored during the fire prevention period. Analyze the diurnal variation of FMC and its driving factors, establish a prediction model of moisture content based on the classification of diurnal changes, and discuss the necessity of predicting FMC separate during the daytime and night-time. The results show that: (1) a significant difference exists in the FMC between day and night-time in Pinus massoniana dominated forests, and the daily variation patterns of FMC are similar (2) with an increase in air temperature and wind speed, the FMC showed a significant downward trend, whereas the dynamic changes in humidity and FMC showed a significant positive correlation. Simultaneously, the impact of meteorological factors on the dynamic changes of FMC had a certain lag effect. (3) The Nelson method was the most effective in predicting the diurnal moisture content of Pinus massoniana dominated forests in the southwest forest area. The mean absolute errors for the entire day, daytime, and night-time that were 0.303, 0.329, and 0.197 %, respectively. (4) It is necessary to distinguish between daytime and night-time to predict the FMC separately. We elucidated the law of diurnal variation of FMC, and FMC prediction models should be established separately for daytime and night-time periods, which has important guiding significance for research on diurnal changes in forest fires and night-time fire prediction, and provides basic data support for managing extreme fires that may occur under extreme climate conditions.