Again, I used the raster::stack function I had used for importing the data for the global map. y = evapotranspiration_destfile, ~ download.file( url =. y, method = 'curl')) #- Evapotranspiration # Construct a vector of URLs to download data # First chunk of the URL evapotranspiration_lead <- '' # Piece together the precipitation_lead and year and add the file extension (.nc) evapotranspiration_urls <- paste0(evapotranspiration_lead, years, '.nc') # Construct a vector of destination files evapotranspiration_destfile <- paste0( 'TerraClimate_pet_', years, '.nc') # Download using the purrr::map2 functions map2(. y = precipitation_destfile, # Input 2 to apply a function over ~ download.file( url =. x = precipitation_urls, # Input 1 to apply a function over. #- Load packages -# library(purrr) #- Precipitation -# Construct a vector of URLs to download data # Years to download years <- c( seq( from = 1995, to = 2015, by = 5), 2019) # First chunk of the URL precipitation_lead <- '' # Piece together the precipitation_lead and year and add the file extension (.nc) precipitation_urls <- paste0(precipitation_lead, years, '.nc') # Construct a vector of destination files precipitation_destfile <- paste0( 'TerraClimate_ppt_', years, '.nc') # Download using the purrr::map2 functions map2(. Seeing this gradient from hyper-arid to semi-arid across the Southern African region, I wanted to see if there has been any changes in this gradient over the past 25 years. ![]() The same goes for the Southern African region, which I am from, where the West coast is hyper-arid, and most of the rest of the region is arid or semi-arid. In fact, most of Australia is hyper-arid or arid. As you can see large swaths of North Africa, the Arabian Peninsula, and central Australia are deemed hyper-arid regions. ![]() I think that is a fairly good reproduction of the original figure from the IPCC report, updated to use 2019 data. This blog post details how I accomplished the task, and in the process provides me with a “note to self” on how I did it. So, I took up the challenge to reproduce the plot. I had used the mapping functions in R before (for example, see here), but never to plot raster data. The smaller the ratio, the more arid a region is.įigure: Geographical distribution of drylands, delimited based on the aridity index That is, what is the water gain (precipitation) relative to the amount of water loss (evapotranspiration) in a region. The aridity index is the ratio of the total amount of precipitation in an area and the amount of evapotranspiration. The original figure uses 2015 data, and they wanted to use 2019 data. I was asked by my wife, Prof Andrea Fuller, head of the Wildlife Conservation Physiology Lab, University of the Witwatersrand, South Africa, to assist her and some colleagues who were writing a paper on how dryland mammals may respond to climate change 1 to update a figure (see below) on the global aridity index, which appears in the 2019 IPCC report on climate change 2. ![]() Using raster data to calculate and then map aridity indices Peter Kamerman 15 December 2020
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