Facilitate and tailor Walter & Lieth climatic diagram drawing

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

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

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

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

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

Reference

Guijarro J A (2023). climatol: Climate Tools (Series Homogenization and Derived Products), 4.0.0., https://CRAN.R-project.org/package=climatol.
Pizarro M, Hernangómez D, Fernández-Avilés G (2021). climaemet: Climate AEMET Tools. doi:10.32614/CRAN.package.climaemet.
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.
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.
Walter H & Lieth H (1960): Klimadiagramm Weltatlas. G. Fischer, Jena.

Session information

## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 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-r0.3.26.so;  LAPACK version 3.12.0
## 
## 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_1.4.0 knitr_1.49       dplyr_1.1.4      climplot_0.4.0  
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.9         generics_0.1.3     xml2_1.3.6         class_7.3-22      
##  [5] KernSmooth_2.23-24 stringi_1.8.4      digest_0.6.37      magrittr_2.0.3    
##  [9] evaluate_1.0.3     grid_4.4.2         fastmap_1.2.0      plyr_1.8.9        
## [13] jsonlite_1.8.9     e1071_1.7-16       DBI_1.2.3          viridisLite_0.4.2 
## [17] scales_1.3.0       codetools_0.2-20   textshaping_1.0.0  jquerylib_0.1.4   
## [21] cli_3.6.4          rlang_1.1.5        units_0.8-5        munsell_0.5.1     
## [25] cachem_1.1.0       yaml_2.3.10        tools_4.4.2        colorspace_2.1-1  
## [29] ggplot2_3.5.1      vctrs_0.6.5        R6_2.6.1           proxy_0.4-27      
## [33] lifecycle_1.0.4    classInt_0.4-11    stringr_1.5.1      fs_1.6.5          
## [37] ragg_1.3.3         pkgconfig_2.0.3    desc_1.4.3         pkgdown_2.1.1     
## [41] terra_1.8-21       pillar_1.10.1      bslib_0.9.0        gtable_0.3.6      
## [45] glue_1.8.0         Rcpp_1.0.14        sf_1.0-19          systemfonts_1.2.1 
## [49] xfun_0.50          tibble_3.2.1       tidyselect_1.2.1   rstudioapi_0.17.1 
## [53] htmltools_0.5.8.1  rmarkdown_2.29     svglite_2.1.3      compiler_4.4.2    
## [57] geodata_0.6-2

Auman Chan, PhD Candidate

2025-02-19