Important meteorological predictors for long-range wildfires in China


Wildfire predictions provide useful information for fire management planning and implementation. Temperature and precipitation have been used as the primary meteorological predictors for wildfires in China. This study is to improve the prediction skills of long-range (monthly, seasonal, and annual) wildfires in China by identifying other important meteorological predictors. Provincial data during 1999–2020 were used to calculate the correlations between fire properties (fire count and burned area) and meteorological variables of maximum temperature, precipitation, relative humidity, wind speed, and vapor pressure deficit (VPD) and drought indices of Keetch-Bryam Drought Index (KBDI), Palmer Drought Severity Index (PDSI), and Standardized Precipitation Index (SPI). The fitting rates of the linear regression fire prediction models were compared among these meteorological variables and drought indices. The results indicate that the number of provinces with significant correlations and / or high fitting rates is the largest with VPD for monthly fires, KBDI for seasonal fires, and KBDI, PDSI, and SPI for annual fires. The number is larger in Northeast, Central, and South China than in other China regions. The number is comparable between spring and other seasons for KBDI but often smaller in spring for meteorological variables. The number is generally smaller for burned areas than fire count. It is concluded that the skills of long-range fire predictions are expected to be improved in many provinces of China by using VPD and KBDI as well as some other drought indices.

  • Citation: Zhao F., Liu Y.-Q.,. 2021. Important meteorological predictors for long-range wildfires in China, Forest Ecology and Management, 499, 119638
  • Keywords: Fire-meteorological relationship, Correlation, Regression model, Fire prediction skill, Drought index
  • Posted Date: September 1, 2021
  • Modified Date: September 27, 2022
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