Chapter 8 microclimUS

8.1 How to use microclimUS

Go to the publication.
From “Open Research” tab at the bottom, click the link to the data stored in the knb (Knowledge Base for Biodiversity).
Select the data type you want to download and click “download” on the far right.
Open the zip folder and move the file for the year you are interested into the same directory as your Rproject.
Install ncdf4 package in Rstudio. install.packages("ncdf4").
Open the file using nc <- nc_open("filename") and get the values using ncvar_get(nc). Pay attention to the dimension of the data. Oftentimes, it is arranged as array[longitude, latitude, time]. nc$dim$longitude$vals and nc$dim$latitude$vals retrieve the values for longitude and latitude respectively, which are necessary to obtain the index of the coordinates you want.
The units can be viewed using nc$var$[VARIABLE_NAME]$units or opening nc in the viewer.

8.2 Code example

Getting data for soil temperature 1 m below ground (0% shade) in Lind, WA (-118.57°, 47°) for June 1-31 in 2015

Download soil100cm_0pctShade.zip from here.

nc <- nc_open(PATH_TO_NCFILE)

ncvar <- ncvar_get(nc)

lonInd <- match.closest(-118.57, nc$dim$longitude$vals)
lat <- sort(nc$dim$latitude$vals)[match.closest(47, sort(nc$dim$latitude$vals))]
latInd <- match(lat, nc$dim$latitude$vals)
  
vals <- c()
extra <- 24 * 151 # The data are ordered from January to December. June 1st is day 151 in 2015.
for (i in 1 : (24 * 31)) {
  vals <- c(vals, ncvar[lonInd, latInd, i + extra])
}
  
days <- c()
for (i in 1:31) {
  days <- c(days, paste0("2015-06-", i))
}

df <- data.frame("Date" = rep(days, each = 24), 
                 "Hour" = 0:23,
                 "Data" = vals / 10)  # Converting units

df$Date <- format(as.POSIXct(paste0(df$Date, " ", df$Hour, ":00")), format = "%Y-%m-%d %H:%M")