Mapping annual wildfire probabilities across California
Statistical modeling highlights predictive importance of local climate
and human activity

Date:
November 3, 2021
Source:
PLOS
Summary:
Researchers have uncovered new insights into the dynamics that
underlie the probabilities of wildfire across the state of
California.



FULL STORY ========================================================================== Researchers have uncovered new insights into the dynamics that underlie
the probabilities of wildfire across the state of California. Isaac
Park of the University of California, Santa Barbara, and colleagues
present their method and findings in the open-access journal PLOS ONE
on Nov. 3, 2021.


========================================================================== Recent wildfires in California and nearby states have demonstrated the
need to better understand the dynamics that determine where and when
wildfires occur.

However, the factors and conditions that interact to contribute to the probability of wildfire -- such as the interplay between local vegetation, precipitation, human land use, and more -- are diverse and complex,
and they vary between locations and over time.

To improve understanding of those relationships, Park and colleagues
used a statistical approach known as generalized additive modeling to
explore and map annual wildfire probabilities throughout California from
1970 to 2016. This work built on previous research that employed the
same technique for longer time scales. In this case, the researchers
tailored the method for annual probabilities by incorporating relevant information on local climate variation, human activity, and the amount
of time since the previous fire event for each location and year --
all at a geographic scale of 1 kilometer.

This analysis uncovered several new insights into wildfire probabilities
in California. For instance, the researchers found, both local climate
and human activity -- such as the dryness of fuel available to burn and
housing density - - play key roles in determining wildfire probabilities throughout the state.

For example, portions of the Southern California mountains such as the
Angeles and Los Padres National Forests were at high risk, having plenty
of vegetation and therefore fuel availability as well as being close to
and at risk from ignitions starting in high-density housing in the Los
Angeles metropolitan area.

In addition, in certain environments, the amount of time since the last
fire has an important influence; as do short-term climate variations
involving extreme conditions, especially in fire-prone shrublands and
forests in southern California.

The researchers also showed that their broad-scale, state-wide approach
for predicting wildfire probabilities outperformed statistical models
developed for certain localized regions. The researchers suggest that
this work -- and further refinements to their modeling method -- could
prove valuable for a variety of research and practical applications in
such areas as wildfire emissions and hazard mapping for implementation
of fire-resistant building codes.

The authors add: "This study presents a powerful tool for mapping
the probability of wildfire across the state of California
under a variety of historical climate regimes. By leveraging
machine learning methods, it demonstrates the distinct ways in
which local climate, human development, and prior fire history
each contribute to the yearly risk of wildfire over space and time." ========================================================================== Story Source: Materials provided by PLOS. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Isaac W. Park, Michael L. Mann, Lorraine E. Flint, Alan L. Flint,
Max
Moritz. Relationships of climate, human activity, and fire history
to spatiotemporal variation in annual fire probability across
California.

PLOS ONE, 2021; 16 (11): e0254723 DOI: 10.1371/journal.pone.0254723 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211103150830.htm

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