Analysis of the uncertainty of fuel model parameters in wildland fire modelling of a boreal forest in north-east China.

Fire propagation is inevitably affected by fuel-model parameters during wildfire simulations and the uncertainty of the fuel-model parameters makes forecasting accurate fire behaviour very difficult. In this study, three different methods (Morris screening, first-order analysis and the Monte Carlo method) were used to analyse the uncertainty of fuel-model parameters with FARSITE model. The results of the uncertainty analysis showed that only a few fuel-model parameters markedly influenced the uncertainty of the model outputs, and many of the fuel-model parameters had little or no effect. The fire-spread rate is the driving force behind the uncertainty of other fire behaviours. Thus, the highly uncertain fuel-model parameters associated with spread rate should be used cautiously in wildfire simulations. Monte Carlo results indicated that the relationship between model input and output was non-linear and neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. Additionally, fuel-model parameters have high input uncertainty. Therefore, fuel-model parameters must be calibrated against actual fires. The highly uncertain fuel-model parameters with high spatial-temporal variability consisted of fuel-bed depth, live-shrub loading and 1-h time-lag loading are preferentially chosen as parameters to calibrate several wildfires. Uncertainty analysis in this study shows that neglecting fuel-model parameter uncertainty of the model would magnify fire behaviours. This emphasises the importance of considering fuel-model parameter uncertainty when simulating wildfire behaviours. The highly uncertain fuel-model parameters with highly temporal and spatial variability are preferentially chosen as parameters to adjust the calibration against several actual fires and to effectively improve the fire-prediction capabilities of fuel models. Moreover, the results of fuel-model parameter uncertainty analysis advance the fuel-model classification in boreal forests. [ABSTRACT FROM AUTHOR]