A Data-driven Evaluation of Post-wildfire Landslide Hazards

Contains the code and configuration files necessary to reproduce a global analysis of landslide-triggering hydrologic conditions. This analysis will be published in the journal NHESS.

Data

This analysis uses the following data with no preprocessing:

• The NASA Global Landslide Catalog
  • Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., and Lerner-Lam, A. (2010), A global landslide catalog for hazard applications: method, results, and limitations. Natural Hazards, 52,561-575. doi: 1007/s11069-009-9401-4.
  • Data accessed from Global Landslide Catalog Downloadable Products Gallery
  • Data should be placed in 00-data > raw > GLC20201204.csv to run the 02-analysis > glc.Rmd notebook without modifications

This analysis uses the following data preprocessed to extract values at landslide locations and calculat precipitation percentile:

• MODIS Burned Area (Global, 2004-2019)
  • Giglio, L., Justice, C., Boschetti, L., Roy, D. (2015). MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2019-12-04 from https://doi.org/10.5067/MODIS/MCD64A1.006
  • Data accessed from OPeNDAP
• CHIRPS Precipitation
  • Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. ftp://chg-ftpout.geog.ucsb.edu/pub/org/chg/products/CHIRPS-2.0/docs/USGS-DS832.CHIRPS.pdf
  • Data accessed from the University of California Santa Barbara
• Daymet Daily Surface Weather Data Precipitation and Snow Water Equivalent (SWE)
  • Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S-C. Kao, and B.E. Wilson. 2022. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2129
  • Data accessed from the Oak Ridge National Laboratory DAAC

The preprocessed dataset are available from zenodo and should be placed in 00-data > processed to runthe 02-analysis > glc.Rmd notebook without modifications

To run:

Some preprocessing steps are performed using the land-surface-modeling-utilities package using configuration files in 01-preprocessing/cfg:

• Mosaicing, reprojecting, and converting MODIS Burned Area data to netCDF format
• Downloading Daymet data over THREDDS

Additional pre-processing was performed using command line utilities cdo, ncrcat, and ncks. Instructions are included in 01-preprocessing/bash-instructions:

• Clipping, concatenating, and calculating percentiles of CHIRPS data

Python scripts may be run in a docker container duplicated using the supplied Dockerfile and environment.yml. Example run scripts are provided in 01-preprocessing/bin:

• burn_global.py determines if a fire has occurred nearby the landslide site
• precip_dayment.py and precip_global.py files determine the timeline of antecedent precipitation for various datasets
• precip_frequency.py calculates a rolling window of precipitation frequency
• swe_dayment.py determines the timeline of antecedent SWE at landslide sites
• precip_global_monthly.py determines precipitation climatology at landslide sites

Further analysis of the preprocessed data is performed using an RMarkdown file available at 02-analysis/glc.Rmd. 02-analysis/glc.html contains the knitted analysis.

Software

This analysis uses the following software:

• Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2021). maps: Draw Geographical Maps. R package version 3.4.0, <URL: https://CRAN.R-project.org/package=maps>.
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• Grolemund G, Wickham H (2011). “Dates and Times Made Easy with lubridate.” Journal of Statistical Software, 40(3), 1-25. <URL: https://www.jstatsoft.org/v40/i03/>.
• Henry L, Wickham H (2020). purrr: Functional Programming Tools. R package version 0.3.4, <URL: https://CRAN.R-project.org/package=purrr>.
• Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: https://doi.org/10.5334/jors.148
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• Makowski D, Lüdecke D, Patil I, Thériault R (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” CRAN. <URL: https://easystats.github.io/report/>.
• McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).
• Müller K, Wickham H (2021). tibble: Simple Data Frames. R package version 3.1.5, <URL: https://CRAN.R-project.org/package=tibble>.
• Neuwirth E (2014). RColorBrewer: ColorBrewer Palettes. R package version 1.1-2, <URL: https://CRAN.R-project.org/package=RColorBrewer>.
• Pebesma E (2018). “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal, 10(1), 439-446. doi: 10.32614/RJ-2018-009 (URL: https://doi.org/10.32614/RJ-2018-009), <URL: https://doi.org/10.32614/RJ-2018-009>.
• R Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. <URL: https://www.R-project.org/>.
• Robinson D, Hayes A, Couch S (2021). broom: Convert Statistical Objects into Tidy Tibbles. R package version 0.7.9, <URL: https://CRAN.R-project.org/package=broom>.
• Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.
• Vaughan D, Dancho M (2022). furrr: Apply Mapping Functions in Parallel using Futures. R package version 0.3.1, <URL: https://CRAN.R-project.org/package=furrr>.
• Walker K (2022). tigris: Load Census TIGER/Line Shapefiles. R package version 1.6.1, <URL: https://CRAN.R-project.org/package=tigris>.
• Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, <URL: https://ggplot2.tidyverse.org>.
• Wickham H (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0, <URL: https://CRAN.R-project.org/package=stringr>.
• Wickham H (2021). forcats: Tools for Working with Categorical Variables (Factors). R package version 0.5.1, <URL: https://CRAN.R-project.org/package=forcats>.
• Wickham H (2021). tidyr: Tidy Messy Data. R package version 1.1.4, <URL: https://CRAN.R-project.org/package=tidyr>.
• Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi: 10.21105/joss.01686 (URL: https://doi.org/10.21105/joss.01686).
• Wickham H, François R, Henry L, Müller K (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.7, <URL: https://CRAN.R-project.org/package=dplyr>.
• Wickham H, Hester J (2021). readr: Read Rectangular Text Data. R package version 2.0.2, <URL: https://CRAN.R-project.org/package=readr>.
• Wickham H, Pedersen T (2019). gtable: Arrange 'Grobs' in Tables. R package version 0.3.0, <URL: https://CRAN.R-project.org/package=gtable>.
• Wickham H, Seidel D (2020). scales: Scale Functions for Visualization. R package version 1.1.1, <URL: https://CRAN.R-project.org/package=scales>.
• Wilke C (2020). cowplot: Streamlined Plot Theme and Plot Annotations for 'ggplot2'. R package version 1.1.1, <URL: https://CRAN.R-project.org/package=cowplot>.
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