Low-intensity fires mitigate the risk of catastrophic wildfires in California's forests

This is the data repository for publicly available code and data to conduct analyses in the paper titled "Low-intensity fires mitigate the risk of catastrophic wildfires in California's forests."

We use a synthetic control approach to analyze twenty years of satellite-based fire activity data across 124,186 km2 of forests in California, and provide evidence that low-intensity fires substantially reduce the risk of future high-intensity fires.

Code:

1. data_processing process downloaded geospatial data from various data sources into a tabular for statistical analysis purpose.

2. balancing apply covariate balancing synthetic control approach to obtain control weights to create the synthetic control region.

3. analysis conduct outcome analysis on covariate balanced data to generate main results and result graphs.

4. figures generate figures and tables in the main text and supplementary materials.

Data Source:

Data	Sources	Spatial resolution	Time resolution	Time periods
Active Fires	MODIS FIRMS	1 km2	daily	11/01/2000 -
Meteorological	Daymet	1 km2	daily	01/01/2000 -
Disturbance Agents	Dataverse	30 m2	yearly	2000 -
Fractional Vegetation Cover	Dataverse	30 m2	yearly	2000 -
Vegetation Class	CAL FIRE	30 m2	one time	1990 - 2014
Topography	GMTED	1 km2	one time	2010
Fire Severity	MTBS	30 m2	yearly	2000 -
Fire Severity	RAVG	30 m2	yearly	2012 -
Prescribed fires	Federal FACTS	unspecified	yearly	2000 -
Prescribed fires	CAL FIRE	unspecified	yearly	2000 -

Data:
All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials and Online Repository. Those interested in the original data can contact the corresponding author.

All the analyses are run on Yen Servers with R programming at the Stanford Graduate School of Business. Computational Support was provided by the Data, Analytics, and Research Computing (DARC) group at the Stanford Graduate School of Business (RRID:SCR_022938).

R version 4.2.1 (2022-06-23) -- "Funny-Looking Kid"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

Terms of Use:
Authors/funders retain copyright (where applicable) of code on this Github repo and the article. Anyone who wishes to share, reuse, remix, or adapt this material must obtain permission from the corresponding author.

By using the contents on this Github repo and the article, you agree to cite:

1. Wu, X., Sverdrup, E., Mastrandrea, M.D., Wara, M.W., and Wager, S., 2023. Low-intensity fires mitigate the risk of high-intensity wildfires in California's forests. Science Advances, 9(45), p.eadi4123. DOI: 10.1126/sciadv.adi4123

Contact Us:

• Email: xw2892@cumc.columbia.edu, erikcs@stanford.edu, and swager@stanford.edu

Acknowledgments

We thank Sitong Pan and Henry Zhu for data collection and processing; A. Abadie, C. Knight, X. Nie, and researchers from the Pyregence Consortium for helpful discussions. We are also grateful to C. Knight for sharing data with us, including refined USFS and CAL FIRE’s forest management datasets. All the analyses are run on Yen Servers with R programming at the Stanford Graduate School of Business. Computational support was provided by the Data, Analytics, and Research Computing (DARC) group at the Stanford Graduate School of Business (RRID: SCR_022938).