Examining and Reforming the Rothermel Surface Fire Spread Model under No-Wind and Zero-Slope Conditions for the Karst Ecosystems

The Rothermel model, which has been widely used to predict the rate of forest fire spread, has errors that restrict its ability to reflect the actual rate of spread (ROS). In this study, the fuels from seven typical tree species in the Karst ecosystems in southern China were considered as the research objects. Through indoor burning simulation, three methods, namely directly using the Rothermel model, re-estimating the parameters of the Rothermel, and reforming the model, were evaluated for applicability in Karst ecosystems. We found that the direct use of the Rothermel model for predicting the ROS in the Karst ecosystems is not practical, and the relative error can be as high as 50%. However, no significant differences between the prediction effect of re-estimating the parameters of the Rothermel and the reformed model were found, but the reform model showed more evident advantages of being simpler, and the errors were lower. Our research proposes a new method that is more suitable for predicting the rate of forest fire spread of typical fuels in Karst ecosystems under flat and windless conditions, which is of great significance for further understanding and calculating the ROS of forest fires in the region.

Automated summary

This study evaluated the applicability of three methods, direct use of the Rothermel model, re-estimation of Rothermel parameters, and model reform, in predicting forest fire spread in Karst ecosystems. It found that the direct use of the Rothermel model is not practical, with relative errors as high as 50%. However, there were no significant differences between re-estimation of parameters and model reform, but the reform model showed advantages of simplicity and lower errors. The research proposes a new method for predicting fire spread rate in Karst ecosystems, which is significant for understanding and calculating the rate of forest fire spread in the region.