Diurnal variation models for fine fuel moisture content in boreal forests in China

A comparison of five models in predicting surface dead fine fuel moisture content of typical forests in Northeast China

“…This is due to the difference sizes of the fuels, we took the fine fuels of 1-h as the research object, and its speed of water loss and water absorption is faster than that of 10-h fuels. One of the potential reasons for the highest accuracy of semi-physical models may be the short time interval, and the accuracy will decrease with the time interval increases in practical use (de Groot and Wang, 2005;Matthews, 2014;Zhang et al, 2021). Although the accuracy of the machine learning models is slightly lower, it can introduce new variables and data in the future to continuously develop the models, which has great potential to be widely used.…”

“…Temperature and relative humidity are the main meteorological factors affecting the FFMC, which directly affect the water vapor exchange between the fuel and the atmospheric environment, and the two have a synergistic effect (Viney, 1991;Matthews and McCaw, 2006;Alves et al, 2009;Masinda et al, 2021). Other meteorological factors, such as wind, precipitation, and solar radiation, also directly or indirectly affect the FFMC, which make the change process more complex (Slijepcevic et al, 2018;Zhang and Sun, 2020;Lindberg et al, 2021;Zhang et al, 2021;Lei et al, 2022).…”

“…There are many methods to measure the FFMC, the most common being the drying method and the direct measurement method, but these methods still have some limitations (Viney, 1991;Matthews, 2010;Schunk et al, 2016;Yan et al, 2018;Cawson et al, 2020;Lei et al, 2022). Therefore, it is the focus of current research to analyze the relationship between meteorological factors and variations in FFMC in order to build accurate prediction models (Aguado et al, 2007;Pellizzaro et al, 2007;Bovill et al, 2015;Zhang et al, 2021). Previous studies have developed using many traditional models to predict FFMC, including empirical model, semi-physical model, and physical model (Simard, 1968;Catchpole et al, 2001;Matthews, 2006;Masinda et al, 2021;Rakhmatulina et al, 2021).…”

“…Its main prediction equation is derived from the physics-based diffusion equation, and the estimation of related parameters is mainly obtained by experiments, which belongs to semi-physical model (Jin and Li, 2010;Slijepcevic et al, 2013). However, its prediction error increases with increasing time intervals (de Groot and Wang, 2005;Matthews, 2014;Zhang et al, 2021). Zhang and Sun (2020) believe that the oversimplification of the diurnal variation of dead FFMC will increase the model error, which reminds us to study it in a shorter time step (1 h or less) (Jin and Li, 2010;Masinda et al, 2022).…”

The Effects of Fire Disturbance on Litter Decomposition and C:N:P Stoichiometry in a Larix gmelinii Forest Ecosystem of Boreal China

“…Although much research has been done on litter decomposition and the nutrient elements of litter [40][41][42][43], there is still no conclusion on the change in k and the nutrient return patterns of forest ecosystems in boreal China after disturbance by fire. In this experiment, we selected the Larix gmelinii forest, a typical forest ecosystem in boreal China [44], to explore the influence of high burn severity fire disturbance on the decomposition rate (k) of leaf litter and the C, N, and P stoichiometric characteristics. We hypothesized that (1) after high burn severity fire disturbance, k value will accelerate; (2) high burn severity fire disturbance will retard the return of N and P nutrients to forest litter; and (3) the main driving factors affecting litter decomposition in the boreal forest after fire were litter C, N, and C:N. In order to verify the above hypotheses, the objectives of this study were to clarify the long-term variation pattern of litter decomposition in the boreal forest of China after high burn severity fire disturbance, to explore the relationship between litter decomposition and litter C:N:P stoichiometry after fire disturbance, and ultimately to provide data support for the post-fire restoration in boreal forest ecosystems.…”