Wildfire science

Wildfire figure

Wildfires are increasing in frequency, intensity, and size across many parts of the world, including North America, Europe, Oceania, and Southeast Asia. A reliable way to predict, monitor, and mitigate fire risk and impacts is indispensable.

Our group aims to better:

  • Monitor and understand fire dynamics, including controls from hydroclimate, vegetation dynamcis, and human activities
  • Model fires using AI and satellite data, including probability, risk, and behavior changes
  • Quantify post-fire impacts on humans and ecosystems

Relevant Publications

  1. Li, F., Zhu, Q., Yuan, K., Huang, H., Radeloff, V. C., & Chen, M. (2025). Exacerbating risk in human-ignited large fires over western United States due to lower flammability thresholds and greenhouse gas emissions. PNAS Nexus, 4(2), pgaf012.
  2. Li, F., Zhu, Q., Yuan, K., Ji, F., Paul, A., Lee, P., Radeloff, V. C., & Chen, M. (2024). Projecting large fires in the western US with an interpretable and accurate hybrid machine learning method. Earth’s Future, 12, e2024EF004588.
  3. Li, F., Zhu, Q., Riley, W., Zhao, L., Xu, L., Yuan, K., Chen, M., Wu, H., Gui, Z., Gong, J., & Randerson, J. (2023). AttentionFire v1.0: interpretable machine learning fire model for burned area predictions over the tropics. Geoscientific Model Development, 16(3), 869–884.
  4. Zhu, Q., Li, F., Riley, W. J., Xu, L., Zhao, L., Yuan, K., Wu, H., Gong, J., & Randerson, J. (2022). Building a machine learning surrogate model for wildfire activities within a global Earth system model. Geoscientific Model Development, 15(5), 1899–1911.
  5. Li, F., Zhu, Q., Riley, W. J., Yuan, K., Wu, H., & Gui, Z. (2022). Wetter California projected by CMIP6 models with observational constraints under a high GHG emission scenario. Earth’s Future, 10(4), e2022EF002694.