Understanding WorldClim and the Challenge of Isolating Earth’s Terrestrial Surface

WorldClim is a comprehensive dataset providing climate information for the world. One of its features is the ability to query data using R, but there’s a crucial distinction that needs to be made: distinguishing between land and ocean surfaces.

In this response, we will delve into the specifics of how to separate these two surface types when working with WorldClim data in R.

Setting Up for Success

To tackle this problem effectively, you’ll need some basic setup. First and foremost, ensure that you have the necessary libraries installed:

library(spatialEco)
library(sf)
library(ggOceanMaps)
library(rgeos)
library(scattermore)
library(maptools)
library(rgdal)
library(sp)
library(plyr)
library(dplyr)
library(raster)
library(rgdal)
library(geodata)
library(exactextractr)

Next, download the required spatial data from Natural Earth. The specific files you’ll need are:

• ne_10m_land.shp
• dd_land.rda
• ne_10m_glaciated_areas.shp
• dd_lake.rda

Reading and Preparing Data

Let’s start by reading in the required data using R:

continental <- st_read(file.path(NEDPath, 
                                  "ne_10m_land/ne_10m_land.shp"))
islands <- st_read(file.path(NEDPath, "ne_10m_minor_islands/ne_10m_minor_islands.shp"))
world <- rbind(continental,islands)
dd_land <- clip_shapefile(world, c(-180, 180, -90, 90))
save(dd_land, file = paste(outPath, "ggOceanMapsData/dd_land.rda", sep = "/"), compress = "xz")

For glacier coverage:

glaciers <- st_read(file.path(NEDPath, "ne_10m_glaciated_areas/ne_10m_glaciated_areas.shp"))
glaciers <- as_Spatial(glaciers)
glaciers <- gBuffer(glaciers, byid = TRUE, width = 0)
dd_glacier <- clip_shapefile(glaciers, c(-180, 180, -90, 90))
dd_glacier <- gBuffer(dd_glacier, byid = FALSE, width = 0.1)
dd_glacier <- gBuffer(dd_glacier, byid = FALSE, width = -0.1)
save(dd_glacier, file = paste(outPath, "ggOceanMapsData/dd_glacier.rda", sep = "/"), compress = "xz")

For lake coverage:

lake <- st_read(file.path(NEDPath, "ne_10m_lakes/ne_10m_lakes.shp"))
lake <- as_Spatial(lake)
lake <- gBuffer(lake, byid = TRUE, width = 0)
dd_lake <- clip_shapefile(lake, c(-180, 180, -90, 90))
dd_lake <- gBuffer(dd_lake, byid = FALSE, width = 0.1)
dd_lake <- gBuffer(dd_lake, byid = FALSE, width = -0.1)
save(dd_lake, file = paste(outPath, "ggOceanMapsData/dd_lake.rda", sep = "/"), compress = "xz")

Finding the Terrestrial Surface

We’re now at a point where we can calculate the surface area that is not covered by glaciers or lakes.

terrestrial <- gDifference(dd_land, dd_lake)
terrestrial_ice_free <- gDifference(terrestrial, dd_glacier)
save(terrestrial_ice_free, file = paste(outPath, "ggOceanMapsData/landsurface.rda", sep = "/"), compress = "xz")

Using the Terrestrial Surface

You can use this surface data as needed in your research. You can save it to a raster format, apply additional processing techniques, or integrate it into other geospatial projects.

Conclusion

This approach may require adjustments based on the specific requirements of your project. Nonetheless, by isolating the terrestrial surface from glaciers and lakes, you’ll have access to clean data that accurately represents the land area globally.

With this information, researchers can now focus on extracting valuable insights about Earth’s surface without the complications introduced by water bodies or glacial regions.

Additional Considerations

For users who are new to geospatial analysis, keep in mind that handling large spatial datasets might require additional computational resources and planning. Be sure to consider these factors when designing your workflow.

Similarly, if you’re dealing with other environmental variables like elevation changes or precipitation patterns, integrating them into this model may lead to even more accurate results.

For those interested in exploring the broader implications of such a method, consider the long-term possibilities of developing more efficient tools for extracting terrestrial surface data and how that might impact our understanding of climate dynamics and geological processes.

Last modified on 2024-10-31