Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability
A wildfire model for Santa Barbara County shows that both short‑term weather and long‑term hydrologic shifts strongly shape fire probability. Using fine‑scale data and a random forest model, the study highlights how changing water availability influences fuel conditions and provides maps to support mitigation and planning.
Subject Tags
- Data Science and Artificial Intelligence
- Fire management
- Forest
Abstract
The increasing occurrence of catastrophic wildfire across the globe threatens public health, community safety, ecosystem functioning, and biodiversity resilience. Wildfire risk is closely connected to shifting climatic trends and their impacts on fuel availability and flammability. Although previous research has explored the connection between meteorological conditions and wildfire probabilities, there remains a substantial gap in understanding the influence of hydrologic drivers, such as groundwater recharge, on wildfire dynamics. Both short- and long-term variations in these variables are crucial in shaping fuel conditions, and significant changes can create environments more prone to severe wildfires. This study focuses on Santa Barbara County to examine the connection between wildfire probability and various environmental factors, including meteorological and hydrological data from 1994 to 2021, topography, vegetation, and proximity to road. Using a random forest (RF) machine learning model and fine-scale data (270 m resolution) we achieved high predictive accuracy in identifying wildfire probability. Our findings confirm the important roles of short-term meteorological conditions, such as mean precipitation 12 months and relative humidity 1 month before a wildfire event, in predicting wildfire occurrence. In addition, our results emphasize the critical contribution of long-term hydrological components, such as mean deviation from the historical normal in actual evapotranspiration and recharge in the years preceding the fire, in influencing wildfire probability. Partial dependence plots from our RF model revealed that both positive and negative deviations of these hydrological variables can increase the likelihood of wildfire by controlling fuel water availability and productivity. These findings are particularly relevant given the increasing extreme weather patterns in southern California, significantly affecting water availability and fuel conditions. This study provides valuable insights into the complex interactions between wildfire occurrence and hydrometeorological conditions. Additionally, the resulting wildfire probability map, can aid in identifying high-risk areas, contributing to enhanced mitigation planning and prevention strategies.
Citation
Khodaee, M., Easterday, K. and Klausmeyer, K., 2024. Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability. Environmental Research Letters, 19(11), p.114043. https://doi.org/10.1088/1748-9326/ad80ad
TNC Authors
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Kelly Easterday
Conservation Technology Director, PCI, California
The Nature Conservancy
Email: kelly.easterday@tnc.org -
Kirk Klausmeyer
Director of Data Science, California
The Nature Conservancy
Email: kKlausmeyer@tnc.org