Supporting code for "Quantifying the effect of precipitation on landslide hazard in urbanized and non-urbanized areas" (https://doi.org/10.1029/2021GL094038)
Elizabeth Johnston (1), Frances Davenport (1), Lijing Wang (2), Jef Caers (2,3), Suresh Muthukrishnan (4,5), Marshall Burke (1,6,7) and Noah Diffenbaugh (1,8)
- Department of Earth System Science, Stanford University, Stanford, CA 94305
- Department of Geological Sciences, Stanford University, Stanford, CA 94305
- Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA 94305
- Department of Earth, Environmental, and Sustainability Sciences, Furman University, Greenville, SC 29613
- GIS and Remote Sensing Center, Furman University, Greenville, SC 29613
- Center on Food Security and the Environment, Stanford University, Stanford, CA 94305
- Environment and Energy Economics, National Bureau of Economic Research, Cambridge, MA 02138
- Woods Institute for the Environment, Stanford University, Stanford, CA 94305
Corresponding author: Elizabeth C. Johnston (ecj@stanford.edu)
Raw datasets are available from the following locations:
- The Cooperative Open Online Landslide Repository is available from NASA (https://maps.nccs.nasa.gov/arcgis/apps/MapAndAppGallery/index.html?appid=574f26408683485799d02e857e5d9521)
- PRISM daily 4 km precipitation is available from the PRISM Climate Group, Oregon State University (http://www.prism.oregonstate.edu/recent)
- The TIGER/Line shapefile of land use designations is available from the US Census (https://catalog.data.gov/dataset/tiger-line-shapefile-2019-2010-nation-u-s-2010-census-urban-area-national)
- Digital Elevation Model – with SRTM voids filled using accurate topographic mapping – is available at 3 arc second resolution from Jonathan de Ferranti (http://viewfinderpanoramas.org/Coverage%20map%20viewfinderpanoramas_org3.htm).
- data: post-processed data
- scripts: code for data processing and analysis
- results: regression coefficients
- func.R: defines functions used throughout data processing
- calc-cumulative-precip.R: calculates ten-day and thirty-day precipitation accumulation
- calc-slope.R: calculates slope from a digital elevation model (DEM)
- buffer-rural.R: subdivides rural areas based on proximity to urbanized areas and urban clusters
- panel-regression-models.R: defines panel regression models
- bootstrap-models.R: bootstraps regression models
Reduced data for the US Pacific Coast region at 4 km spatial resolution (regional daily-scale precipitation data not included due to size):
- pacific_coast_df.rds: dataframe of x/y coordinates within the Pacific Coast region at 4 km resolution
- pacific_coast.asc: raster layer of the Pacific Coast region at 4 km resolution
- bay_area_df.rds: dataframe of x/y coordinates of coastal San Francisco counties of Marin, San Francisco, San Mateo, and Santa Cruz
- land_use.rds: land use designation into urbanized (>50,000 people), urban cluster (2,500 – 50,000 people), and rural (<2,500 people)
- land_use_subdiv.rds: land use classification with rural areas subdivided based on proximity to urban footprint
- slope.rds: slope (in radians)
- landslides.rds: all precipitation-triggered landslides reported by COOLR
- landslides_precip.rds: one-day, ten-day, and thirty-day precipitation accumulation preceding observed landslides
Bootstrapped coefficients from panel regression models
- tidyverse, raster, rgdal, zoo, ncdf4, data.table, lfe