path <- "~/OneDrive - University of Reading/UoR/Data/CRU/4.04/"
maarten_path <- "~/Downloads/SMPDSv2/Maarten/"
maarten_path <- "~/Downloads/"
CPUS <- 6
`%>%` <- magrittr::`%>%`
# Maarten reconstructions ------------------------------------------------------
maarten <-
# readxl::read_xlsx("~/Downloads/SMPDSv2/Maarten/Coordinates_Maarten.xlsx") %>%
readxl::read_xlsx("~/Downloads/Coordinates_Maarten.xlsx") %>%
dplyr::rename(latitude = Lat,
longitude = Lon,
elevation = Altituda) %>%
dplyr::mutate(latitude = as.double(latitude),
longitude = as.double(longitude))
## Interpolate climate from the CRU TS dataset
maarten_cld <- maarten %>%
smpds::gwr(varid = "cld",
.ref = file.path(path, "cru_ts4.04.1901.2019.cld.dat-clim-1961-1990-int.nc"),
cpus = CPUS) %>%
progressr::with_progress()
maarten_pre <- maarten %>%
smpds::gwr(varid = "pre",
.ref = file.path(path, "cru_ts4.04.1901.2019.pre.dat-new-clim-1961-1990-int.nc"),
cpus = CPUS)
maarten_tmp <- maarten %>%
smpds::gwr(varid = "tmp",
.ref = file.path(path, "cru_ts4.04-clim-1961-1990-daily.tmp.nc"),
cpus = CPUS)
## Transform climate reconstructions
maarten_cld2 <- maarten_cld %>%
smpds::pivot_data(varname = "cld")
### Calculate sunshine fraction from cloud cover
maarten_sf <- maarten_cld %>%
smpds::pivot_data(scale = -0.01, add = 1, varname = "sf")
maarten_pre2 <- maarten_pre %>%
smpds::pivot_data(varname = "pre")
maarten_tmp2 <- maarten_tmp %>%
smpds::pivot_data(varname = "tmp")
maartenv2 <- maarten_sf %>%
dplyr::left_join(maarten_pre2) %>%
dplyr::left_join(maarten_tmp2)
## Reconstruct climate variables
maartenv3 <- maartenv2 %>%
smpds::mi(cpus = CPUS) %>%
smpds::gdd() %>%
smpds::mat() %>%
smpds::mtco() %>%
smpds::mtwa() %>%
progressr::with_progress()
## Reconstruct potential natural vegetation (PNV)
maartenv4 <- maartenv3 %>%
smpds::parallel_extract_biome() %>%
smpds::biome_name()
maartenv4 %>%
dplyr::select(-sf, -tmp, -pre) %>%
readr::write_excel_csv("~/Downloads/maarten_reconstructions.csv")
## Plots
show_plot <- FALSE
size <- 1.5
stroke <- 0.1
width <- 16
xlim <- c(60, 120)
ylim <- c(45, 60)
p_gdd0 <- smpds::plot_gdd(maartenv4,
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,
show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_gdd0_", Sys.Date(), ".pdf")),
plot = p_gdd0,
device = "pdf",
width = width,
height = 8,
units = "in")
p_mat <- smpds::plot_mat(maartenv4,
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_mat_", Sys.Date(), ".pdf")),
plot = p_mat,
device = "pdf",
width = width,
height = 8,
units = "in")
p_mi <- smpds::plot_mi(maartenv4,
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,
show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_mi_", Sys.Date(), ".pdf")),
plot = p_mi,
device = "pdf",
width = width,
height = 8,
units = "in")
p_mtco <- smpds::plot_mtco(maartenv4,
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,
show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_mtco_", Sys.Date(), ".pdf")),
plot = p_mtco,
device = "pdf",
width = width,
height = 8,
units = "in")
p_mtwa <- smpds::plot_mtwa(maartenv4,
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,
show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_mtwa_", Sys.Date(), ".pdf")),
plot = p_mtwa,
device = "pdf",
width = width,
height = 8,
units = "in")
p_biome <- smpds::plot_biome(maartenv4, #%>%
# dplyr::mutate(ID_BIOME = smpds::pnv_classes()$ID_BIOME %>%
# sample(size = length(ID_BIOME), replace = TRUE)),
size = size,
stroke = stroke,
xlim = xlim,
ylim = ylim,
legend.key.width = ggplot2::unit(1.3, "cm"),
show_plot = show_plot)
ggplot2::ggsave(file.path(maarten_path,
paste0("maarten_PNV_", Sys.Date(), ".pdf")),
plot = p_biome,
device = "pdf",
width = width,
height = 8,
units = "in")
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