Improvement of human-induced wildfire occurrence modeling from a spatial variation of anthropogenic ignition factor in the CLM5

Abstract

Wildfire is an essential form of natural disturbance for the Earth system, and it is challenging for the current numerical models to accurately retrieve the spatiotemporal distributions of wildfire occurrence. One of the deficiencies could result from the parameterization of anthropogenic impact on wildfire occurrences. This study develops an approach to advance human-induced wildfire modeling by calibrating the parameter of Human Ignition Count (HIC) in the fire module of the Community Land Model version 5 (CLM5). This study modifies the source code to allow a grid-scale variation of HIC. Sensitivity experiments with different grid-uniform HIC values are conducted to quantify the model biases with satellite-based observation data as the reference. The theoretically optimal HIC for each grid is obtained by linear rescaling the HIC based on the model biases in the sensitivity tests. The model evaluation takes place in southwest China where there is complex terrain and land use/land cover features. The involvement of grid-scale HIC significantly reduces the model bias in the climatology of wildfire occurrence. The Pattern Correlation Coefficient (PCC) increases from 0.57 to 0.78, and the Root Mean Squared Error (RMSE) decreases from 0.58 to 0.18. The correlation coefficient of the annual sums of wildfire occurrences increases from 0.69 to 0.77, and the RMSE decreases from 560.8 to 146.4. A global-scale test verifies that such an approach can be extended to multiple regions with a reasonable scale of population density and economy.