Forest fire is one of the most important disturbances in boreal forests in China, and it plays an important role in shaping forest species composition, age structure and ecosystem processes. In the past more than half a century, China's forest fire fighting policy has led to a large accumulation of forest fuel. The increasing occurrence probability of high-intensity fires threatens the structure and function of boreal forests. To reduce the losses of wildfires, it is highly important to study forest fuel which is the only factor that humans can manipulate for wildfire control. Many factors such as limitation of parameter measurement, environmental variability and lack of data result in the uncertainty of fuel-model parameters, and the uncertainty of fire behavior prediction. To better predict the impacts of fire disturbance on China boreal forest ecosystems in the context of climate change, it is necessary to analyze the uncertainty of fuel-model parameters first.

To obtain reliable fuel-model parameters, Dr. CAI Longyan, LIANG Yu and their colleagues from the Landscape Process Group of the Institute of Applied Ecology (IAE), Chinese Academy of Sciences, simulated forest fire behaviors with the FARSITE model using different combinations of fuel-model parameters, and performed sensitivity analysis of the model parameters using three different methods: Morris screening, first-order analysis and Monte Carlo method.

The researchers found that different fuel-model parameters have different sensitivity effects on fire behaviours. The 1-hour time-lag loading, 1-hour time-lag SAV (surface area-to-volume), fuel-bed depth, dead heat content, live heat content, live-shrub SAV are highly sensitive parameters (Figure 1). Among them, 1-hour time-lag loading and fuel-bed depth, both with highly spatial- and temporal-variability, are selected by the researchers as priority parameters to calibrate.

The study entitled “Analysis of the uncertainty of fuel model parameters in wildland fire modeling of a boreal forest in north-east China” has been published in International Journal of Wildland Fire. The research is financially supported by the National Key Research and Development Program and the National Natural Science Foundation of China.