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Facilitate and tailor Walter & Lieth climatic diagram drawing

Auman Chan, PhD Candidate

2023-10-13

Source: vignettes/climplot_Intro.Rmd
climplot_Intro.Rmd

climplot aims to collect crucial climate data for global locations and draw the Walter & Lieth climatic diagrams in a more user-friendly and personalized manner.

The main features of the package are:

  • The automatic obtainment and arrangement of standardized and reliable data for drawing Walter & Lieth climatic diagrams
  • The provision for more parameter to customize the plots and display comprehensive information

The package offers functions for:

  • Download and arrange worldwide climate data to draw Walter & Lieth climatic diagram for global locations
  • Draw the Walter & Lieth climatic diagram
  • Revise the color scheme and information presentation of the diagram

Installing and loading

To install the latest developmental version from github and gitee, you will need the R packages remotes and git2r. If you want to install the vignettes of this package, please add build_vignettes = TRUE.

install.packages("remotes")

# from github
remotes::install_github("auman-chan/climplot")
# from gitee
install.packages("git2r")
remotes::install_git("https://gitee.com/auman-chan/climplot.git")

# add vignettes
remotes::install_github("auman-chan/climplot", build_vignettes = TRUE)

remotes::install_git("https://gitee.com/auman-chan/climplot.git",
  build_vignettes = TRUE
)

# add vignettes
remotes::install_github("auman-chan/climplot", build_vignettes = TRUE)

remotes::install_git("https://gitee.com/auman-chan/climplot.git",
  build_vignettes = TRUE
)

Plotting data extraction

The task of searching for and processing climate data from multiple locations is a challenging endeavor. Function clim_extract retrieves data from Worldclim Historical monthly weather data(Version of 30 seconds). These data would be downloaded automatically according to your locations with package geodata, and arranged for diagrams visualization.

Location information preparation

In order to extract climate data for specific locations, precise coordinates are essential. Additionally, the diagram should display other relevant information such as location names and altitudes. Therefore, a data.frame containing information of the target locations is necessary for clim_extract. The imported data.frame must contain five columns in the following order:

  • No: Serial number of the locations
  • location: Abbreviation of the locations
  • lon: Longitude of the locations in decimal digit (negative numbers indicating west longitude)
  • lat: Latitude of the locations in decimal digit (negative numbers indicating south latitude)
  • altitude: Altitude of the locations

The data locdata in this package can be an example of the import data.frame. Other extra columns with information is allowed behind the columns above, but would be discarded in following process.

Table1 The example of the import data.frame
No location lon lat altitude name
1 Motuo 95.3536 29.30420 2025 墨脱县仁钦崩寺
2 Wulianshan 100.5000 24.50000 1301 无量山自然保护区
3 Wawushan 102.9167 29.50000 2082 四川省眉山市洪雅县瓦屋山
4 Leibo 103.4667 28.45000 1204 四川省凉山州雷波县
5 Longcanggou 102.8333 29.61667 1764 四川省雅安市荥经县龙苍沟国家森林公园
6 Jinfoshan 107.1933 29.00017 1917 重庆市南川区金佛山国家级自然保护区
7 Xishui 106.4667 28.30000 863 贵州省习水良村镇羊化村
8 Tonglingshan 119.8598 27.82128 724 浙江省铜铃山国家森林公园
9 Qinglongshan 112.5341 23.17020 320 广东省鼎湖山自然保护区的百丈岭、青龙山
10 Tiantongshan 121.7855 29.80710 199 浙江省天童山

Extraction of climate data

After preparing the information of locations, this function will check the coordinates, and download the corresponding raster tiles of climate data from Worldclim. At last it exports a data.frame.

#Example data in this package
data("locdata")

#extraction of climate data
plotdata <- clim_extract(locdata)
Table2 The example of the output data.frame
No Altitude Location Lon Lat Type 1 2 3 4 5 6 7 8 9 10 11 12
1 2025 Motuo 95.3536 29.30420 prec 10.00 22.00 38.00 93.0 114.0 230.00 241.00 216.00 187.0 72.0 12.0 7.00
1 2025 Motuo 95.3536 29.30420 mean_temp 10.90 11.60 15.10 18.6 21.9 24.00 24.40 24.60 23.1 20.1 16.0 12.40
1 2025 Motuo 95.3536 29.30420 min_temp -1.90 -0.50 3.10 6.2 9.9 12.50 13.60 13.30 12.7 9.2 2.9 -0.30
1 2025 Motuo 95.3536 29.30420 max_temp 10.90 11.60 15.10 18.6 21.9 24.00 24.40 24.60 23.1 20.1 16.0 12.40
2 1301 Wulianshan 100.5000 24.50000 prec 12.00 16.00 20.00 35.0 75.0 173.00 204.00 193.00 126.0 98.0 47.0 18.00
2 1301 Wulianshan 100.5000 24.50000 mean_temp 20.80 23.20 26.50 29.0 29.5 28.60 28.30 28.50 27.3 25.2 22.2 19.90
2 1301 Wulianshan 100.5000 24.50000 min_temp 5.70 7.40 10.70 14.4 17.8 20.40 20.60 20.10 18.6 16.0 11.4 7.10
2 1301 Wulianshan 100.5000 24.50000 max_temp 20.80 23.20 26.50 29.0 29.5 28.60 28.30 28.50 27.3 25.2 22.2 19.90
3 2082 Wawushan 102.9167 29.50000 prec 12.00 13.00 21.00 53.0 104.0 168.00 191.00 180.00 145.0 73.0 27.0 14.00
3 2082 Wawushan 102.9167 29.50000 mean_temp 4.20 5.90 10.90 15.6 18.3 19.80 22.00 21.80 17.6 13.7 9.9 5.90
3 2082 Wawushan 102.9167 29.50000 min_temp -4.10 -2.80 1.00 5.7 9.1 11.90 14.70 14.30 11.0 7.0 2.0 -2.20
3 2082 Wawushan 102.9167 29.50000 max_temp 4.20 5.90 10.90 15.6 18.3 19.80 22.00 21.80 17.6 13.7 9.9 5.90
4 1204 Leibo 103.4667 28.45000 prec 13.00 16.00 21.00 53.0 97.0 156.00 180.00 195.00 121.0 72.0 31.0 15.00
4 1204 Leibo 103.4667 28.45000 mean_temp 7.20 9.30 14.70 19.4 22.4 24.10 26.50 26.40 21.9 17.4 13.1 8.70
4 1204 Leibo 103.4667 28.45000 min_temp -1.00 0.60 4.90 9.5 13.4 16.00 18.50 17.80 14.8 10.6 5.6 0.70
4 1204 Leibo 103.4667 28.45000 max_temp 7.20 9.30 14.70 19.4 22.4 24.10 26.50 26.40 21.9 17.4 13.1 8.70
5 1764 Longcanggou 102.8333 29.61667 prec 14.00 16.00 24.00 54.0 104.0 166.00 211.00 209.00 152.0 76.0 30.0 16.00
5 1764 Longcanggou 102.8333 29.61667 mean_temp 5.90 7.80 13.00 17.7 20.7 22.40 24.60 24.30 19.9 15.9 11.7 7.50
5 1764 Longcanggou 102.8333 29.61667 min_temp -2.30 -0.80 3.40 7.7 11.3 14.20 16.80 16.20 13.0 8.7 4.0 -0.50
5 1764 Longcanggou 102.8333 29.61667 max_temp 5.90 7.80 13.00 17.7 20.7 22.40 24.60 24.30 19.9 15.9 11.7 7.50
6 1917 Jinfoshan 107.1933 29.00017 prec 27.00 28.00 55.00 125.0 188.0 202.00 180.00 167.00 152.0 116.0 67.0 30.00
6 1917 Jinfoshan 107.1933 29.00017 mean_temp 0.90 2.00 7.20 12.6 16.3 18.70 21.90 21.90 17.4 12.7 7.6 3.20
6 1917 Jinfoshan 107.1933 29.00017 min_temp -4.20 -3.10 0.90 5.6 9.7 12.60 15.10 14.80 11.5 7.2 2.2 -2.10
6 1917 Jinfoshan 107.1933 29.00017 max_temp 0.90 2.00 7.20 12.6 16.3 18.70 21.90 21.90 17.4 12.7 7.6 3.20
7 863 Xishui 106.4667 28.30000 prec 16.00 17.00 35.00 90.0 158.0 175.00 154.00 141.00 116.0 92.0 49.0 21.00
7 863 Xishui 106.4667 28.30000 mean_temp 6.70 8.10 13.20 19.0 22.9 25.00 28.30 28.40 23.7 18.3 13.3 8.60
7 863 Xishui 106.4667 28.30000 min_temp 1.60 2.70 6.40 10.7 14.4 17.60 20.00 19.20 16.3 12.1 7.4 3.10
7 863 Xishui 106.4667 28.30000 max_temp 6.70 8.10 13.20 19.0 22.9 25.00 28.30 28.40 23.7 18.3 13.3 8.60
8 724 Tonglingshan 119.8598 27.82128 prec 57.00 92.00 149.00 169.0 238.0 279.00 158.00 213.00 206.0 93.0 66.0 39.00
8 724 Tonglingshan 119.8598 27.82128 mean_temp 8.60 9.40 13.00 18.1 21.7 24.90 28.50 27.90 24.5 20.2 15.4 11.10
8 724 Tonglingshan 119.8598 27.82128 min_temp 0.60 2.00 5.30 10.3 14.5 18.10 20.80 20.30 17.4 12.5 7.3 2.20
8 724 Tonglingshan 119.8598 27.82128 max_temp 8.60 9.40 13.00 18.1 21.7 24.90 28.50 27.90 24.5 20.2 15.4 11.10
9 320 Qinglongshan 112.5341 23.17020 prec 44.00 73.00 112.00 198.0 314.0 288.00 221.00 260.00 153.0 77.0 40.0 38.00
9 320 Qinglongshan 112.5341 23.17020 mean_temp 15.80 16.30 19.50 23.7 27.5 29.60 31.00 30.60 29.3 26.3 22.1 18.30
9 320 Qinglongshan 112.5341 23.17020 min_temp 8.30 9.80 13.30 17.8 21.1 23.10 23.60 23.50 22.2 18.9 13.9 9.50
9 320 Qinglongshan 112.5341 23.17020 max_temp 15.80 16.30 19.50 23.7 27.5 29.60 31.00 30.60 29.3 26.3 22.1 18.30
10 199 Tiantongshan 121.7855 29.80710 prec 52.00 74.00 106.00 119.0 141.0 178.00 113.00 151.00 173.0 78.0 64.0 44.00
10 199 Tiantongshan 121.7855 29.80710 mean_temp 4.45 5.45 8.85 14.2 18.8 22.75 26.75 26.55 22.9 18.1 12.7 7.05
10 199 Tiantongshan 121.7855 29.80710 min_temp 1.20 2.10 5.40 10.5 15.3 19.70 23.50 23.40 19.8 14.8 9.1 3.30
10 199 Tiantongshan 121.7855 29.80710 max_temp 7.70 8.80 12.30 17.9 22.3 25.80 30.00 29.70 26.0 21.4 16.3 10.80

The exported data.frame includes 5 kinds of information of locations(as which in the data.frame imported), and values of 4 kinds of climate factors across 12 months. Note that some locations don’t have data of annual average temperature(not included in geodata), so they would be replace by values averaged by annual average minimum temperature and annual average maximum temperature, as the method in Walter & Lieth climatic diagram.

An data.frame stores in the data plotdata of this package, as an example of the function export.

Except the temp folders, the storage path could be modified to prevent the delection or redownloading of climate data.

# add your path
plotdata <- clim_extract(locdata, path= "G:/climplot")

Hereto clim_extact have obtained all essential information for the Walter & Lieth climatic diagram drawing.

Climatic diagram drawing

The function clim_plot can draw the Walter & Lieth climatic diagram plotting in different styles of color scheme and information presentation. It refers to the function diagwl()of a CRAN package climatol.

Walter & Lieth climatic diagram drawing of a single location

Take the data plotdata as an example, and import them into clim_plot.

data("plotdata")
loc <- subset(plotdata, No == 2)
clim_plot(loc)
Fig 1 The example of diagram drawing

Fig 1 The example of diagram drawing

In the figure above:

  • The red curve represents the annual variation of temperature, and blue one represents of precipitation variation. These two curves form two kinds of patches indicating humidity and aridity levels. The line-filled patches represent humid seasons while those with scattered points represent arid seasons. The polygon filled with the color same as precipitation curve indicates months with precipitation over 100mm, displaying the wet season.

  • The information on the left top includes the name, the altitude and the coordinate of the location. The right top are the values of annual mean temperature and mean precipitation.

Solution to drawing for serveral location

clim_plotonly supports drawing diagrams for one location at a time, as we recommend checking each plot individually, and importing multiple vectors of climate data increases the risk of errors. Therefore, if you require automated plotting for several locations, utilizing a loop is recommended:

data("plotdata")
list <- unique(plotdata$No)
par(mfrow = c(1, 1))
for (i in 1:5) {
  k <- list[i]
  sub <- subset(plotdata, No == k)
  clim_plot(
    data = sub, ylabel = TRUE,
    ylab1 = "Temperature(\U{00B0}C)",
    ylab2 = "Precipitation(mm)",
    line_p50 = TRUE
  )
}

To save the plot you can build a frame for picture before plotting:

for (i in 1:3) {
  
  sub <- subset(b, No == i)
  png(filename = paste0(i, "_plot", ".png"),
      width=847,
      height=738,res=100
      )
  #save the plot
  clim_plot(
    data = sub, ylabel = TRUE,
    ylab1 = "Temperature(\U{00B0}C)",
    ylab2 = "precipitation(mm)",
    xlab="Month"
  )
  dev.off()
  }

Other tips of climatic diagram plotting

Plotting the frosty months 

data("plotdata")
loc <- subset(plotdata, No == 3)
clim_plot(data = loc, showfrost = TRUE)
Fig 2 The example of marking frosty months

Fig 2 The example of marking frosty months

The blocks with dark blue color on x-axis represent the months with confirmed frost.

Color and axis modification

The color and the axis labels could be customized to meet specific requirements.

It is possible to adjust the colors of line of temperature and precipitation, as well as patches of humidity, aridity, wet season and frosty month blocks.

loc <- subset(plotdata, No == 1)
clim_plot(loc,
  pcol = "#8DB6CD", tcol = "#FF6A6A", wcol = "#4EEE94",
  dcol = "#EEB422", fcol = "#8A2BE2", showfrost = TRUE)
Fig 3 The example of color modification

Fig 3 The example of color modification

Additionally, the display of axis labels can be controlled and custom labels can be imported using parameters ylabel, ylab1, ylab2 mlab and xlab .

loc <- subset(plotdata, No == 1)
clim_plot(loc,
  xlab = "月份", mlab = "en", ylabel = TRUE,
  ylab1 = "Temperature(\U{00B0}C)",
  ylab2 = "Precipitation(mm)", showfrost = TRUE
)
Fig 4 The example of axis labels modification

Fig 4 The example of axis labels modification

Auxiliary marker

Displaying extreme temperatures, the auxiliary line of precipitation curve and position of 50°C-100mm are optional.

loc <- subset(plotdata, No == 1)
clim_plot(loc,
  line_p3 = TRUE, line_p50 = TRUE, temp_extreme = TRUE,
  showfrost = TRUE
)
Fig 5 The example of auxiliary marker

Fig 5 The example of auxiliary marker

Reference

  1. Guijarro J A (2023). climatol: Climate Tools (Series Homogenization and Derived Products), 4.0.0., https://CRAN.R-project.org/package=climatol.

  2. Fick, S.E. and R.J. Hijmans, (2017). WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

  3. Harris, I., Osborn, T.J., Jones, P.D., Lister, D.H. (2020). Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data 7: 109.

  4. Walter H & Lieth H (1960): Klimadiagramm Weltatlas. G. Fischer, Jena.

Session information

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] kableExtra_1.3.4 knitr_1.44       dplyr_1.1.3      climplot_0.3.0  
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.7         utf8_1.2.3         generics_0.1.3     xml2_1.3.5        
##  [5] class_7.3-22       KernSmooth_2.23-21 stringi_1.7.12     digest_0.6.33     
##  [9] magrittr_2.0.3     grid_4.3.1         evaluate_0.22      fastmap_1.1.1     
## [13] rprojroot_2.0.3    plyr_1.8.9         jsonlite_1.8.7     e1071_1.7-13      
## [17] DBI_1.1.3          httr_1.4.7         rvest_1.0.3        purrr_1.0.2       
## [21] fansi_1.0.5        viridisLite_0.4.2  scales_1.2.1       codetools_0.2-19  
## [25] textshaping_0.3.7  jquerylib_0.1.4    cli_3.6.1          rlang_1.1.1       
## [29] units_0.8-4        munsell_0.5.0      cachem_1.0.8       yaml_2.3.7        
## [33] tools_4.3.1        memoise_2.0.1      colorspace_2.1-0   webshot_0.5.5     
## [37] vctrs_0.6.3        R6_2.5.1           proxy_0.4-27       lifecycle_1.0.3   
## [41] classInt_0.4-10    stringr_1.5.0      fs_1.6.3           ragg_1.2.6        
## [45] pkgconfig_2.0.3    desc_1.4.2         pkgdown_2.0.7      terra_1.7-46      
## [49] pillar_1.9.0       bslib_0.5.1        glue_1.6.2         Rcpp_1.0.11       
## [53] sf_1.0-14          systemfonts_1.0.5  xfun_0.40          tibble_3.2.1      
## [57] tidyselect_1.2.0   rstudioapi_0.15.0  htmltools_0.5.6.1  svglite_2.1.2     
## [61] rmarkdown_2.25     compiler_4.3.1     geodata_0.5-8