In RS-eco/edc: European Data Cube

knitr::opts_chunk$set(
  collapse = T, comment = NA, warning=F, message=F, eval=T, 
  echo=T, error=F, comment = "#>"
)

Load packages & data

# Load edc & ggplot2 package
library(edc); library(dplyr); library(tidyr); library(ggplot2); library(patchwork)

# Load e-obs climate data from bavDC package
# Rainfall
load(system.file("extdata", "e-obs_rr_ens_mean_0.1deg_eur_yearmon.rda", package = "edc"))
# Mean temperature
load(system.file("extdata", "e-obs_tg_ens_mean_0.1deg_eur_yearmon.rda", package = "edc"))

# WFDE5 re-analysis climate data (0.5°)
data("wfde5_rainf_eur")
data("wfde5_tair_eur") # In Kelvin

# Define standard colour scheme
whitered <- colorRampPalette(c('#fff7ec','#fee8c8','#fdd49e','#fdbb84','#fc8d59','#ef6548','#d7301f','#b30000','#7f0000'))(255)
#pie(rep(1,9),col=c('#fff7ec','#fee8c8','#fdd49e','#fdbb84','#fc8d59','#ef6548','#d7301f','#b30000','#7f0000'))

whiteblue <- colorRampPalette(c('#f7fcf0','#e0f3db','#ccebc5','#a8ddb5','#7bccc4','#4eb3d3','#2b8cbe','#0868ac','#084081'))(255)
#pie(rep(1,9),col=c('#f7fcf0','#e0f3db','#ccebc5','#a8ddb5','#7bccc4','#4eb3d3','#2b8cbe','#0868ac','#084081'))

bluewhitered <- colorRampPalette(c("#009392","#39b185","#9ccb86","#e9e29c","#eeb479","#e88471","#cf597e"))(255)
#pie(rep(1,7),col=c("#009392","#39b185","#9ccb86","#e9e29c","#eeb479","#e88471","#cf597e"))

Plot time-series of monthly climate data

## Plot of tmean
eobs_tmean <- `e-obs_tg_ens_mean_0.1deg_eur_yearmon` %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="tmean") %>%
  group_by(date) %>% summarise(`e-obs`=mean(tmean, na.rm=T))
wfde5_tair_eur <- wfde5_tair_eur %>% group_by(x,y) %>% 
  mutate_at(vars(-group_cols()), .funs=function(x){x - 273.15})
wfde5_tmean <- wfde5_tair_eur  %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="tmean") %>%
  group_by(date) %>% summarise(wfde5=mean(tmean, na.rm=T))
#head(eobs_tmean)
#head(wfde5_tmean)

all_tmean <- full_join(eobs_tmean, wfde5_tmean) %>% 
  tidyr::pivot_longer(cols=-date, names_to="Dataset", values_to="tmean")
all_tmean$date <- zoo::as.yearmon(all_tmean$date, "%B %Y")

p1 <- all_tmean %>% ggplot(aes(x=date, y=tmean, col=Dataset)) +
  geom_line() + theme_bw()

## Plot of precipitation
eobs_pr <- `e-obs_rr_ens_mean_0.1deg_eur_yearmon` %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="pr") %>%
  group_by(date) %>% summarise(`e-obs`=mean(pr, na.rm=T))
wfde5_pr <- wfde5_rainf_eur  %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="pr") %>%
  group_by(date) %>% summarise(wfde5=mean(pr*3600, na.rm=T)) # Turn kg m−2 s−1 into mm (multiply by time)
wfde5_pr$date <- sub(pattern="[.]", replacement=" ", x=wfde5_pr$date)

#head(eobs_pr)
#head(wfde5_pr)

all_pr <- full_join(eobs_pr, wfde5_pr) %>% 
  tidyr::pivot_longer(cols=-date, names_to="Dataset", values_to="pr") 
all_pr$date <- zoo::as.yearmon(all_pr$date, "%B %Y")
p2 <- all_pr %>% ggplot(aes(x=date, y=pr, col=Dataset)) +
  geom_line() + theme_bw()

p1 / p2 + plot_layout(guides="collect")

Plot spatial maps of different data sets

## Plot of tmean
eobs_tmean <- `e-obs_tg_ens_mean_0.1deg_eur_yearmon` %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="e-obs") %>%
  filter(date %in% c("Mär 1980", "Jun 1990", "Sep 2000", "Dez 2010"))
wfde5_tmean <- wfde5_tair_eur  %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="tmean") %>%
  rename(wfde5=tmean) %>% filter(date %in% c("Mär 1980", "Jun 1990", "Sep 2000", "Dez 2010")) 

#head(eobs_tmean)
#head(wfde5_tmean)

all_tmean <- full_join(eobs_tmean, wfde5_tmean) %>%
  tidyr::pivot_longer(cols=-c(x,y,date), names_to="Dataset", values_to="tmean") %>% drop_na()
all_tmean$date <- zoo::as.yearmon(all_tmean$date, "%B %Y")

all_tmean %>% ggplot(aes(x=x, y=y, fill=tmean)) +
  facet_grid(date~Dataset, scales="free") + geom_raster() +
  theme_bw() + scale_fill_gradientn(colours=bluewhitered) + labs(x="", y="")

## Plot of precipitation
eobs_pr <- `e-obs_rr_ens_mean_0.1deg_eur_yearmon` %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="e-obs") %>%
  filter(date %in% c("Mär 1980", "Jun 1990", "Sep 2000", "Dez 2010"))
wfde5_pr <- wfde5_rainf_eur  %>% 
  tidyr::pivot_longer(cols=-c(x,y), names_to="date", values_to="pr")  %>%
  filter(date %in% c("Mär 1980", "Jun 1990", "Sep 2000", "Dez 2010")) %>%
  mutate(wfde5=pr*3600) %>% select(-pr) # Turn kg m−2 s−1 into mm (multiply by time)

head(eobs_pr)
head(wfde5_pr)

all_pr <- full_join(eobs_pr, wfde5_pr) %>% 
  tidyr::pivot_longer(cols=-c(x,y,date), names_to="Dataset", values_to="pr") %>% drop_na()
all_pr$date <- zoo::as.yearmon(all_pr$date, "%B %Y")
all_pr %>% ggplot(aes(x=x, y=y, fill=pr)) +
  facet_grid(date~Dataset, scales="free") + geom_raster() + 
  theme_bw() + scale_fill_gradientn(colours=whiteblue) + labs(x="", y="")