Predicting hourly litter moisture content of larch stands in Daxinganling Region, China using three vapour-exchange methods

Fuel moisture affects fuel ignition potential and fire behaviour. To accurately model fire behaviour, predict fuel ignition potential and plan fuel reduction, fuel moisture content must be assessed regularly and often. To establish models for Daxinganling Region, which has the most severe forest fires in China, hourly measurements were taken of moisture content in litter beds of larch stands sampled under different shading and slope conditions. Models were established using three vapour-exchange methods. The Nelson and Simard methods employed a direct timelag method using a timelag concept and the Nelson and Simard equilibrium moisture content (EMC) functions and estimating model parameters directly from fuel moisture and weather observation data in the field. The direct regression method used equations directly derived from linear regression of fuel moisture and field weather variation. The mean absolute error and mean relative error were determined for the Nelson (0.78%, 4.98%), Simard (1.04%, 5.57%) and direct regression (1.48%, 9.01%) methods. Only the models established using the direct timelag methods met the 1% accuracy requirement using either the Nelson or Simard EMC function, confirming the suitability and robustness of the direct timelag methods. The Simard and Nelson methods had similar accuracy, but Simard was more robust and only needed estimation of one parameter and hence is recommended for predicting litter moisture in this region.