Spatiotemporal modeling of wildfire extremes

This repository contains code to build spatiotemporal models of wildfire extremes in the contiguous United States.

Hardware requirements

We recommend at least 4 physical CPUs and 30 GB of RAM.

Reproducing the analysis

Spinning up the computational environment

We have provided a Docker container that bundles up the software dependencies for this project, and provides an RStudio server instance that can be used in a web browser. To launch the container, run the following:

docker run -e PASSWORD=yourpassword -d -p 8787:8787 mbjoseph/wildfire-extremes

Then, navigate to port 8787 on a web browser (e.g., localhost:8787, or <Insert EC2 IP addres heres>:8787 if running on AWS) and log in with username rstudio, and the password you provided in your call to docker run.

Optional: creating an RStudio project

If you plan to interact much with the code, you may want to create an RStudio project. To do so, after connecting to your RStudio server, choose File > New Project..., then select "Existing Directory" > Browse..., and choose wildfire-extremes, and finally click Create Project. This will create and then open a project associated with this repository.

Running the analysis

To run everything, you can type the following command from a terminal (e.g., the terminal pane in RStudio server):

make

This takes ~3-4 days on a machine with 4 cores, assuming that you're making everything from scratch. Fitting the models takes the longest due to data volume and parameter dimensionality.

Overview of workflow

1. Data processing

We define targets for the input data to the models in the Makefile as follows:

• data/processed/ecoregion_summaries.csv: Summaries of climate data for every EPA level 3 ecoregion for each month from 1984-2015.

• data/processed/housing_density.csv: Summary of housing density for each ecoregion, each month.

• data/processed/stan_d.rds: A serialized rds object that bundles all input data for the model fitting step.

We also generate some ancillary data to be used downstream in the analysis of model results, including data/processed/mtbs.rds and data/processed/ecoregions.rds.

2. Model training and evaluation

Once the serialized model input data (data/processed/stan_d.rds) exist, then all of the models can be fit. These include burn area models and wildfire count models, all of which have the suffix _fit.rds, e.g., nb_fit.rds, ba_gamma_fit.rds. Each model fit object corresponds to a separate target in the Makefile.

With these model fits, a set of figures and tables (figs and tables in the Makefile) are generated. These are passed into the manuscript in the next step.

3. Manuscript generation

The manuscript main.pdf is generated from the main.Rmd file, and all of the tables and figures. The source code for the manuscript is an R Markdown document, which dynamically inserts the figures, tables, and summary statistics into the paper in an automated way.