Evaluation of sensitive physical parameter combinations for determining the uncertainty of fire simulations and predictions in China

Abstract

AbstractFires are fundamental and natural phenomena that affect terrestrial ecosystems and the global climate change. However, uncertainties in fire modeling still exist. It is important to improve the ability to simulate fires by tuning model parameters. In this study, a sensitivity analysis (SA) approach based on the conditional nonlinear optimal perturbation related to parameters (CNOP‐P) is employed to find the most sensitive parameter subset. First, the maximum uncertainty in modeling fire is estimated, and it is found that the degrees of uncertainty in modeling fire are different in different regions of China. The extents of the uncertainties in modeling fire in northeastern China and northern China with arid and semiarid climate conditions are greatest, and the magnitude of the uncertainty in southern China is the smallest. The uncertainty in modeling fires in northern China with a semihumid climate condition is between the above values. Second, we find that the most sensitive parameter combination with the number of elements determined by the SA method based on the CNOP‐P approach are different from the top five parameters combination via a sensitivity test using a traditional method (such as the one‐at‐a‐time [OAT] method). The most sensitive parameter combination includes not only the parameters in the fire module (e.g., ) but also the parameters that could cause variations in biomass and soil moisture. The prediction skill of fire by reducing the errors of the sensitive parameter combination using the SA method based on the CNOP‐P method are higher than those using the OAT method. The above results suggest that not only the parameters in the fire module but also the parameters of other physical processes (e.g., biomass and soil moisture) should be corrected and calibrated to improve simulation and prediction of fire.