The moisture content of dead fine forest fuels is a central component of nearly all fire behaviour and fire danger rating systems. The main objective of this thesis was to develop operationally applicable fine fuel moisture prediction models for Aleppo pine (Pinus halepensis Mill.) and Dalmatian black pine (Pinus nigra (Arnold) subsp. dalmatica ((Vis.) Franco)) needle litters below the fibre saturation point. Experimental measurements of equilibrium moisture content and response time of dead needles were carried out in a growth chamber for both species. The results of these laboratory experiments and the structure of the HFFMC (Hourly Fine Fuel Moisture Code) model were used to develop hourly fine fuel moisture prediction models, whose predictive ability was tested in selected pine species stands. The predictive ability of the HFFMC model for both litter types was also evaluated. Although the HFFMC model was designed for pine litter, the results suggest that it is unsuitable for operational use in Aleppo pine and Dalmatian black pine forests. In contrast, new models performed better and can be used operationally because the mean absolute error of fine fuel moisture prediction model for Aleppo pine is 1% moisture content, and for Dalmatian black pine it is 0.86 % moisture content. The results suggest that these models should be species-specific in order to achieve the accuracy needed for precise prediction of fire behaviour. This thesis is the first to report equilibrium moisture content and response time data for both species, as well as develop this type of model. Equilibrium moisture content was also determined for the first time for the leaves of holm oak (Quercus ilex L.) and pubescent oak (Quercus pubescens Willd.). The other objective of this thesis, in addition to developing fine fuel moisture prediction models, was to develop regression models that relate forest floor depth to forest floor fuel load. Regression models were given for the OL horizon, the OF and OH horizons, and for the entire forest floor in Aleppo pine, Dalmatian black pine, holm oak and pubescent oak stands. These regression models allow for reliable quantifications of forest floor fuel loads, which is very important in the operative application of fire behaviour prediction models. Regression models, together with fine fuel moisture prediction models, provide a basis for the adoption of existing or development of our own fire behaviour prediction models.