Integrating hydrological parameters in wildfire risk assessment

A machine learning approach for mapping wildfire probability

Bibliographic Data

ID15550597
AuthorsMahsa Khodaee (0000-0003-4542-1493, The Nature Conservancy, corresponding author), Kelly Easterday (0000-0002-1678-0975, The Nature Conservancy), KIRK R KLAUSMEYER (0009-0008-3339-6263, The Nature Conservancy)
Year2024
Volume19
Issue11
Pages114043-114043
Publication date2024-09-27
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEnvironmental Research Letters (JOURNAL)
Journal identifiersISSN: 1748-9326 • E-ISSN: 1748-9326
PublisherIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/ad80ad
OpenAlexW4402910675
LanguageEN
Citations received1
References cited66

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

Machine learning · Computer Science · Environmental Science · Fire effects on ecosystems · Landslides and related hazards

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