rISIMIP - R package for accessing and analysing ISIMIP Environmental Data

Overview

rISIMIP is an R package for accessing and analysing data provided by the Inter-sectoral Impact Model Intercomparison Project (ISIMIP). Data from the different simulation rounds (ISIMIP2a, ISIMIP2b, ISIMIP3a, ISIMIP3b) is available from here. For more information on the different data types and data input and output products have a look at the ISIMIP Website. The package currently consists of two functions:

readISIMIP() reads and pre-processes ISIMIP data
listISIMIP() creates a list of requested ISIMIP data files

You can learn more about them in vignette("rISIMIP").

An example of extracting country-specific data from ISIMIP2b can be found in vignette("country-specific").

Installation

To use the package, install it directly from GitHub using the remotes package:

# Install remotes if not previously installed
if(!"remotes" %in% installed.packages()[,"Package"]) install.packages("remotes")

# Install rISIMIP from Github if not previously installed
if(!"rISIMIP" %in% installed.packages()[,"Package"]) remotes::install_github("RS-eco/rISIMIP", build_vignettes = TRUE)

If you encounter a bug or if you have any problems, please file an issue on Github.

Usage

# Load rISIMIP package
library(rISIMIP)

List ISIMIP files

The function listISIMIP just lists all climate files for the desired time period, model and variable. The files can then be put into the aggregateNC function of the processNC package for processing the required NetCDF files.

# List urban area files for histsoc scenario - ISIMIP2b
listISIMIP(path="I:/", version="ISIMIP2b", type="landuse", 
           scenario="histsoc", var="urbanareas", startyear=1861, endyear=2005)

# List crop data files for histsoc scenario - ISIMIP3b
listISIMIP(path="I:/", version="ISIMIP3b", type="landuse", 
           scenario="histsoc", var="5crops", startyear=1861, endyear=2005)

Note: The path must lead to a file directory on your computer, which contains the required ISIMIP files. You can download the required ISIMIP data files from: https://esg.pik-potsdam.de/search/isimip/

Read ISIMIP files

With readISIMIP you can read one or multiple ISIMIP datafiles into a raster stack.

# Read urban area file for 2005soc scenario - ISIMIP2b
(urbanareas_1970_1999 <- readISIMIP(path="I:/", type="landuse", scenario="2005soc", 
                                    var="urbanareas", startyear=1970, endyear=1999))

# Read pasture file for 2015soc scenario - ISIMIP3b
(pastures_1970_1999 <- readISIMIP(path="I:/", type="landuse", scenario="2015soc", 
                                    var="pastures", startyear=1970, endyear=1999))

However, this is not useful if you are interested in long time periods, as one datafile is about 7 GB in size and you will quickly run into memory limitations.

Internal data

rISIMIP contains various pre-processed data. See the data-raw folder for how we derived the included datasets.

Temperature thresholds

Annual global mean temperature as well as the 31-year running mean were calculated for each GCM and four RCPs (RCP2.6, RCP4.5, RCP6.0 and RCP8.5). Furthermore, the year when the 31-year runnning mean of global mean temperature crosses a certain temperature threshold has been calculated. The data has been provided by ISIMIP and a summary of it can be accessed from the vignette("temperature-thresholds") vignette and is also available from the ISIMIP Website.

Landseamask

The landseamask used by ISIMIP has been included in the package and can be accessed by:

data("landseamask_generic")

Bioclimatic data

The code for calculating global bioclimatic data from ISIMIP2b and ISIMIP3b mnodel output can be found in vignette("global-landonly") and vignette("global-landonly-isimip3b") respectively.

ISIMIP2b & EWEMBI

Current and future bioclimatic data for three 30-yr periods (1995, 2050, 2080) was derived from the EWEMBI (https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input_secondary&dataset_type=Climate+atmosphere+observed) and ISIMIP2b data (https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP2b&product=input&dataset_type=Climate+atmosphere+simulated) and is included in this package.

EWEMBI - 1995

data("bioclim_ewembi_1995_landonly")

library(dplyr); library(sf); library(ggplot2)
data(outline, package="ggmap2")
outline <- sf::st_as_sf(outline)
col_val <- scales::rescale(unique(c(seq(min(bioclim_ewembi_1995_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_ewembi_1995_landonly$bio1), length=5))))

bioclim_ewembi_1995_landonly %>% select(x,y,bio1) %>% 
  ggplot() + geom_tile(aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_ewembi_1995_landonly$bio1)-2, 
             max(bioclim_ewembi_1995_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_ewembi_1995_landonly$x), 
                  max(bioclim_ewembi_1995_landonly$x)), 
           ylim=c(min(bioclim_ewembi_1995_landonly$y),
                  max(bioclim_ewembi_1995_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

RCP2.6 - 2080

data("bioclim_gfdl-esm2m_rcp26_2080_landonly")
data("bioclim_hadgem2-es_rcp26_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp26_2080_landonly")
data("bioclim_miroc5_rcp26_2080_landonly")

bioclim_rcp26_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp26_2080_landonly`, 
                                         `bioclim_hadgem2-es_rcp26_2080_landonly`, 
                                         `bioclim_ipsl-cm5a-lr_rcp26_2080_landonly`, 
                                         `bioclim_miroc5_rcp26_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_rcp26_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_rcp26_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_rcp26_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_rcp26_2080_landonly$bio1)-2, 
             max(bioclim_rcp26_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_rcp26_2080_landonly$x), 
                  max(bioclim_rcp26_2080_landonly$x)), 
           ylim=c(min(bioclim_rcp26_2080_landonly$y),
                  max(bioclim_rcp26_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

RCP6.0 - 2080

data("bioclim_gfdl-esm2m_rcp60_2080_landonly")
data("bioclim_hadgem2-es_rcp60_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp60_2080_landonly")
data("bioclim_miroc5_rcp60_2080_landonly")

bioclim_rcp60_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp60_2080_landonly`, 
                                         `bioclim_hadgem2-es_rcp60_2080_landonly`, 
                                         `bioclim_ipsl-cm5a-lr_rcp60_2080_landonly`,
                                         `bioclim_miroc5_rcp60_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_rcp60_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_rcp60_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_rcp60_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_rcp60_2080_landonly$bio1)-2, 
             max(bioclim_rcp60_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_rcp60_2080_landonly$x), 
                  max(bioclim_rcp60_2080_landonly$x)), 
           ylim=c(min(bioclim_rcp60_2080_landonly$y),
                  max(bioclim_rcp60_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

RCP8.5 - 2080

data("bioclim_gfdl-esm2m_rcp85_2080_landonly")
data("bioclim_hadgem2-es_rcp85_2080_landonly")
data("bioclim_ipsl-cm5a-lr_rcp85_2080_landonly")
data("bioclim_miroc5_rcp85_2080_landonly")

bioclim_rcp85_2080_landonly <- bind_rows(`bioclim_gfdl-esm2m_rcp85_2080_landonly`, 
                                         `bioclim_hadgem2-es_rcp85_2080_landonly`, 
                                         `bioclim_ipsl-cm5a-lr_rcp85_2080_landonly`,
                                         `bioclim_miroc5_rcp85_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_rcp85_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_rcp85_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_rcp85_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_rcp85_2080_landonly$bio1)-2, 
             max(bioclim_rcp85_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_rcp85_2080_landonly$x), 
                  max(bioclim_rcp85_2080_landonly$x)), 
           ylim=c(min(bioclim_rcp85_2080_landonly$y),
                  max(bioclim_rcp85_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

ISIMIP3b & GSWP3-W5E5

Current and future bioclimatic data for five 30-yr periods (1995, 2000, 2005, 2050, 2080) was derived from the GSWP3-W5E5 and ISIMIP3b data (https://esg.pik-potsdam.de/search/isimip/?project=ISIMIP3b&product=input&dataset_type=Climate+atmosphere+simulated) and is included in this package.

GSWP3_W5E5 - 2005

data("bioclim_gswp3-w5e5_obsclim_2005_landonly")

library(dplyr); library(sf); library(ggplot2)
data(outline, package="ggmap2")
outline <- sf::st_as_sf(outline)
col_val <- scales::rescale(unique(c(seq(min(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$bio1), 0, length=5),
                                    seq(0, max(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$bio1), length=5))))

`bioclim_gswp3-w5e5_obsclim_2005_landonly` %>% select(x,y,bio1) %>% 
  ggplot() + geom_tile(aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$bio1)-2, 
             max(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$x), 
                  max(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$x)), 
           ylim=c(min(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$y),
                  max(`bioclim_gswp3-w5e5_obsclim_2005_landonly`$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

SSP126 - 2080

data("bioclim_gfdl-esm4_ssp126_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp126_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp126_2080_landonly")
data("bioclim_mri-esm2-0_ssp126_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp126_2080_landonly")

bioclim_ssp126_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp126_2080_landonly`, 
                                         `bioclim_ipsl-cm6a-lr_ssp126_2080_landonly`, 
                                         `bioclim_mpi-esm1-2-hr_ssp126_2080_landonly`, 
                                         `bioclim_mri-esm2-0_ssp126_2080_landonly`,
                                         `bioclim_ukesm1-0-ll_ssp126_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_ssp126_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_ssp126_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_ssp126_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_ssp126_2080_landonly$bio1)-2, 
             max(bioclim_ssp126_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_ssp126_2080_landonly$x), 
                  max(bioclim_ssp126_2080_landonly$x)), 
           ylim=c(min(bioclim_ssp126_2080_landonly$y),
                  max(bioclim_ssp126_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

SSP370 - 2080

data("bioclim_gfdl-esm4_ssp370_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp370_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp370_2080_landonly")
data("bioclim_mri-esm2-0_ssp370_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp370_2080_landonly")

bioclim_ssp370_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp370_2080_landonly`, 
                                         `bioclim_ipsl-cm6a-lr_ssp370_2080_landonly`, 
                                         `bioclim_mpi-esm1-2-hr_ssp370_2080_landonly`, 
                                         `bioclim_mri-esm2-0_ssp370_2080_landonly`,
                                         `bioclim_ukesm1-0-ll_ssp370_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_ssp370_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_ssp370_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_ssp370_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_ssp370_2080_landonly$bio1)-2, 
             max(bioclim_ssp370_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_ssp370_2080_landonly$x), 
                  max(bioclim_ssp370_2080_landonly$x)), 
           ylim=c(min(bioclim_ssp370_2080_landonly$y),
                  max(bioclim_ssp370_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

SSP585 - 2080

data("bioclim_gfdl-esm4_ssp585_2080_landonly")
data("bioclim_ipsl-cm6a-lr_ssp585_2080_landonly")
data("bioclim_mpi-esm1-2-hr_ssp585_2080_landonly")
data("bioclim_mri-esm2-0_ssp585_2080_landonly")
data("bioclim_ukesm1-0-ll_ssp585_2080_landonly")

bioclim_ssp585_2080_landonly <- bind_rows(`bioclim_gfdl-esm4_ssp585_2080_landonly`, 
                                         `bioclim_ipsl-cm6a-lr_ssp585_2080_landonly`, 
                                         `bioclim_mpi-esm1-2-hr_ssp585_2080_landonly`, 
                                         `bioclim_mri-esm2-0_ssp585_2080_landonly`,
                                         `bioclim_ukesm1-0-ll_ssp585_2080_landonly`) %>% 
  select(x,y,bio1) %>% group_by(x,y) %>% summarise(bio1=mean(bio1, na.rm=T))
col_val <- scales::rescale(unique(c(seq(min(bioclim_ssp585_2080_landonly$bio1), 0, length=5),
                                    seq(0, max(bioclim_ssp585_2080_landonly$bio1), length=5))))

ggplot() + geom_tile(data=bioclim_ssp585_2080_landonly, aes(x=x, y=y, fill=bio1)) + 
  geom_sf(data=outline, fill="transparent", colour="black") + 
  scale_fill_gradientn(name="tmean (°C)", colours=rev(colorRampPalette(
    c("#00007F", "blue", "#007FFF", "cyan", 
      "white", "yellow", "#FF7F00", "red", "#7F0000"))(255)),
    na.value="transparent", values=col_val, 
    limits=c(min(bioclim_ssp585_2080_landonly$bio1)-2, 
             max(bioclim_ssp585_2080_landonly$bio1)+2)) + 
  coord_sf(expand=F, 
           xlim=c(min(bioclim_ssp585_2080_landonly$x), 
                  max(bioclim_ssp585_2080_landonly$x)), 
           ylim=c(min(bioclim_ssp585_2080_landonly$y),
                  max(bioclim_ssp585_2080_landonly$y)), 
           ndiscr=0) + theme_classic() + 
  theme(axis.title = element_blank(), axis.line = element_blank(),
        axis.ticks = element_blank(), axis.text = element_blank(),
        plot.background = element_rect(fill = "transparent"), 
        legend.background = element_rect(fill = "transparent"), 
        legend.box.background = element_rect(fill = "transparent", colour=NA))

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
R		R
data-raw		data-raw
data		data
figures		figures
man		man
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
DESCRIPTION		DESCRIPTION
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
README.Rmd		README.Rmd
README.md		README.md
rISIMIP.Rproj		rISIMIP.Rproj

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rISIMIP - R package for accessing and analysing ISIMIP Environmental Data

Overview

Installation

Usage

List ISIMIP files

Read ISIMIP files

Internal data

Temperature thresholds

Landseamask

Bioclimatic data

ISIMIP2b & EWEMBI

ISIMIP3b & GSWP3-W5E5

About

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RS-eco/rISIMIP

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rISIMIP - R package for accessing and analysing ISIMIP Environmental Data

Overview

Installation

Usage

List ISIMIP files

Read ISIMIP files

Internal data

Temperature thresholds

Landseamask

Bioclimatic data

ISIMIP2b & EWEMBI

ISIMIP3b & GSWP3-W5E5

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