Predicting tree mortality in the western United States

As climate change continues, the evaporative demand on plant life also increases (Anderegg et al., 2019). This predisposes trees to die through a combination of processes (Choat et al., 2018):

• Carbon starvation
• Hydraulic failure
• Attack by boring insects

Mortality events have been documented worldwide, but are particularly severe in the western US. This project has two goals:

1. Improve annual predictions of the distribution of drought-induced tree mortality.
2. Identify drivers of mortality over time to determine which mechanisms are most important.

Setup

First, clone the repository

git clone https://github.com/s-kganz/ForestLST
cd ForestLST

Then, create the environment. environment.yml is a streamlined version of the CryoCloud Python image, plus a few libraries pip install'd on top.

conda env create -f environment.yml

If you want to use any of the scripts that work with earthaccess, you will have to set up a NASA Earthdata account. This is not necessary unless you want to recreate the steps we took to build the mortality datasets. Once you have done so, do the following:

• Create a file named .netrc in your home directory. Add your earthaccess credentials to the file in the following format machine urs.earthdata.nasa.gov login <your_username> password <your_password> Now you should be able to authenticate with earthacess.login(strategy="netrc").

Datasets

If you don't care about recreating the model-ready datasets we used in this project, the netcdf files are available in the github release and on Zenodo under the mort_datasets directory.

Recreating results

We provided a static, compressed version of the data_out directory at submission time on github/Zenodo. Inflating this and mort_datasets in the project directory will let you recreate all the paper figures with notebooks/plots.ipynb. Other notebooks do the following:

• ads_iou.ipynb: calculate annual overlap between sequential ADS survey polygons.
• gbm_[westmort|soap_teak].ipynb: fit gradient-boosted regression models to the continental and local mortality datasets.
• repeability_iou.ipynb: calculate overlap among the survey polygons in Coleman et al. (2018).
• survey_prop.ipynb: calculate the ratio of survey probability given past mortality history.
• variograms.ipynb: calculate Moran's $I$ and temporal autocorrelation at a variety of spatial and temporal lags.

License

See file LICENSE. This repo uses the MIT license to support collaboration, and I strongly encourage you to reach out to me if you want to work on this problem!