topotools

Coding tools for working with local copies of ETOPO data and GEBCO data.

ETOPO Citation

NOAA National Centers for Environmental Information. 2022: ETOPO 2022 15 Arc-Second Global Relief Model. NOAA National Centers for Environmental Information. DOI: 10.25921/fd45-gt74. Accessed 2023-03-17.

GEBCO Citation

GEBCO Compilation Group (2022) GEBCO_2022 Grid (doi:10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c) Accessed 2023-03-17.

Requirements

• terra

• stars

• sf

• dplyr

• ncdf4

Installation

devtools::install_github("BigelowLab/topotools")

Usage

library(topotools)
library(stars)

Paths

ETOPO and GEBCO datasets are very large. We suggest that you download them to a directory for bathymetry/topography datasets. Then store the path description to this directory in a hidden text file called ~/.topodata in your home directory. We can help with that. Let’s say your path is /mnt/s1/projects/ecocast/coredata/bathy. Then just call the set_root_path() function from the topotools package like this.

set_root_path(path = "/mnt/s1/projects/ecocast/coredata/bathy", filename = "~/.topodata")

You don’t need to do this each time you use the package; just once before your first use, and, of course, again if you move your data.

The Data

Within this data directory create two subdirectories: gebco and etopo.

Into the gebco directory store the “GEBCO_YYYY Grid (ice surface elevation)” NetCDF file, where YYYY is obviously a 4 digit year. It may download as a zipped file, but be sure to unzip it. You can find the GEBCO data here.

Into the etopo directory store either the 30 arc-second or 60 arc-second (or both!) NetCDF file. You can find ETOPO data here.

Now you are all set to use the package.

Read in a region

While it is possible to read in the entire dataset for each source, generally the practice is to read in a portion defined by a bounding box specified in [west, east, south, north] order.

bb <- c( -72,  -63,   39,   46)
(etopo_files = list_etopo())

## [1] "ETOPO_2022_v1_30s_N90W180_surface.nc"
## [2] "ETOPO_2022_v1_60s_N90W180_surface.nc"

etopo <- read_etopo("ETOPO_2022_v1_60s_N90W180_surface.nc", bb = bb)
etopo

## stars object with 2 dimensions and 1 attribute
## attribute(s):
##         Min.   1st Qu.   Median      Mean  3rd Qu.     Max.
## z  -5021.792 -1730.825 -95.4375 -908.8038 38.10166 1653.262
## dimension(s):
##   from  to   offset      delta refsys x/y
## x    1 542 -72.0167  0.0166667 WGS 84 [x]
## y    1 422  46.0167 -0.0166667 WGS 84 [y]

(gebco_files = list_gebco())

## [1] "GEBCO_2022.nc"

gebco <- read_gebco("GEBCO_2022.nc", bb = bb)

gebco

## stars object with 2 dimensions and 1 attribute
## attribute(s), summary of first 1e+05 cells:
##    Min. 1st Qu. Median     Mean 3rd Qu. Max.
## z   -78      47    154 195.0478     326 1502
## dimension(s):
##   from   to   offset       delta                     refsys x/y
## x    1 2162 -72.0042  0.00416667 +proj=longlat +datum=WGS84 [x]
## y    1 1682  46.0042 -0.00416667 +proj=longlat +datum=WGS84 [y]

Note that the GEBCO data provides approximately 4x the resolution of the ETOPO1 data.

For display purposes, it is helpful to clip each raster into a small range of values.

plot(etopo, 
     main = "ETOPO1",
     axes = TRUE)

plot(gebco, 
     main = "GEBCO", 
     axes = TRUE)

## downsample set to 2

Masking

The mask_topo function will work with either data set (well, any terra::SpatRaster or stars::stars object.) It has optional arguments, but at its simplest…

masked_etopo = mask_topo(etopo)
plot(masked_etopo, axes = TRUE)