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exp_figs.R
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exp_figs.R
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###
#Export figures for manusript melTim
#Erwin Rottler, University of Potsdam
###
pacman::p_load(devtools, leaflet, raster, tmap, sf, prettymapr, meltimr, alptempr, rfs, viridis,
shape, scales, emdbook, zoo, zyp, maptools, rworldmap, parallel, doParallel,
DescTools, POT)
#set base direcoty
base_dir <- "U:/rhine_snow/"
data_dir <- "D:/nrc_user/rottler/"
grdc_dir <- "D:/nrc_user/rottler/GRDC_DAY/"
#Load results snow simulations
load("U:/rhine_snow/R/figs_exp/sim_scf_exp.RData")
load("U:/rhine_snow/R/figs_exp/temp_prec_exp.RData")
#map_over----
dem = raster(paste0(data_dir, "basin_data/eu_dem/processed/eu_dem_500.tif"))
rivers <- rgdal::readOGR(dsn = paste0(data_dir, "basin_data/eu_hydro/wise_rivers_lakes/Large_rivers.shp"))
tribus <- rgdal::readOGR(dsn = paste0(data_dir, "basin_data/eu_hydro/wise_rivers_lakes/Other_large_rivers_and_tributaries.shp"))
lakes <- rgdal::readOGR(dsn = paste0(data_dir, "basin_data/eu_hydro/wise_rivers_lakes/Large_lakes.shp"))
rhin_riv <- rivers[rivers@data$NAME == "Rhine",]
aare_riv <- tribus[tribus@data$NAME == "Aare",]
brienzersee <- lakes[which(lakes@data$WPLKNM == "BRIENZER SEE" ),]
thunersee <- lakes[which(lakes@data$WPLKNM == "THUNER SEE" ),]
bielersee <- lakes[which(lakes@data$WPLKNM == "BIELER SEE" ),]
bodensee <- lakes[which(lakes@data$WPLKNM == "BODENSEE" ),]
basins <- rgdal::readOGR(dsn = paste0(data_dir, "basin_data/EZG_Schweiz_BAFU/ezg_kombiniert.shp"), encoding = "UTF8")
basin_base <- spTransform(basins[basins@data$Ort == "Basel, Rheinhalle",], CRS = crs(dem, asText = T))
basin_unte <- spTransform(basins[basins@data$Ort == "Untersiggenthal, Stilli",], CRS = crs(dem, asText = T))
basin_brug <- spTransform(basins[basins@data$Ort == "Brugg",], CRS = crs(dem, asText = T))
basin_bern <- spTransform(basins[basins@data$Ort == "Bern, Schönau",], CRS = crs(dem, asText = T))
basin_neuh <- spTransform(basins[basins@data$Ort == "Neuhausen, Flurlingerbrücke",], CRS = crs(dem, asText = T))
basin_reki <- spTransform(basins[basins@data$Ort == "Rekingen",], CRS = crs(dem, asText = T))
basin_diep <- spTransform(basins[basins@data$Ort == "Diepoldsau, Rietbrücke",], CRS = crs(dem, asText = T))
#7.6167, 47.5594 Basel
#8.33, 47.5704 Rekingen
#8.6259, 47.6823 Neuhausen
#8.2348, 47.5166 Untersiggenthal
#8.1949, 47.4825 Brugg
#7.448, 46.9331 Bern
#9.6409, 47.3831 Diepoldsau
crswgs84 <- sp::CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
gauges_84 <- sp::SpatialPoints(data.frame(lon = c(7.6167, 8.33, 8.6259, 8.2348, 8.1949, 7.448, 9.6409),
lat = c(47.5594, 47.5704, 47.6823, 47.5166, 47.4825, 46.9331, 47.3831)),
proj4string = crswgs84)
gauges <- sp::spTransform(gauges_84, CRS = crs(basin_base, asText = T))
# basin_base_84 <- sp::spTransform(basin_base, CRS = crswgs84)
#corp DEM sub-basin area
my_ext <- extent(basin_base)
my_ext_buf <- my_ext + c(-20000, +30000, -40000, +13000) #xmin, xmax, ymin, ymax
my_box <- as(my_ext_buf, 'SpatialPolygons')
dem_cro_swiss <- raster::crop(dem, extent(my_box))
dem_sub_swiss <- mask(dem_cro_swiss, my_box)
#Meteo stations
stat_id <- c("WFJ", "SAE", "ARO", "BIV", "SIA", "GRC", "DAV", "ANT", "MVE", "NAP", "SMM", "ABO", "SCU", "DIS", "CHM",
"ROB", "GTT", "CDF", "CHD", "ELM", "EIN", "SMA", "BER", "BAS", "ZER", "5WJ", "4ZE", "5DF", "2AN")
stat_na <- c("Weißfluhjoch", "Saentis", "Arosa", "Bivio", "Segl-Maria", "Graechen", "Davos", "Andermatt", "Montana", "Napf",
"Sta. Maria, Val Muestair", "Adelboden", "Scuol", "Disentis", "Chaumant", "Poschiavo/Robbia",
"Guttannen", "La Chaux-de-Fonds", "Chateaux-d'Oez", "Elm", "Einsiedeln", "Zuerich/Fluntern",
"Bern, Zollikofen", "Basel Binningen", "Zermatt", "Weissfluhjoch", "Zermatt", "Davos Flueestr.", "Andermatt")
stat_al <- c(2691, 2502, 1878, 1856, 1804, 1606, 1594, 1438, 1427, 1404, 1386, 1322, 1303, 1197, 1136, 1078, 1055, 1017,
1028, 957, 910, 555, 552, 316, 1638, 2540, 1600, 1560, 1440)
stat_lo <- c(9.8000, 9.3500, 9.6833, 9.6666, 9.7666, 7.8333, 9.8500, 8.5833, 7.4666, 7.94, 10.4333, 7.5666, 10.2833, 8.8500, 6.9833,
10.0666, 8.3000, 6.8000, 7.1333, 9.1833, 8.7500, 8.5666, 7.4666, 7.5836, 7.7531, 9.8093, 7.7512, 9.8482, 8.5919)
stat_la <- c(46.8333, 47.2500, 46.8000, 46.4666, 46.4333, 46.2000, 46.8166, 46.6333, 46.3000, 47.0047222, 46.6000, 46.5000, 46.8000, 46.7000,
47.0500, 46.3500, 46.6500, 47.0833, 46.4833, 46.9166, 47.1333, 47.3833, 46.9833, 47.5411, 46.0291667, 46.8294, 46.0234,
46.8125, 46.6329)
stat_nu <- 1:length(stat_id)
stat_meta <- data.frame(V0 = stat_nu,
V1 = stat_id,
V2 = stat_na,
V3 = stat_al,
V4 = stat_lo,
V5 = stat_la)
colnames(stat_meta) <- c("Number", "ID", "Name", "Alt. [m]", "Longitude", "Latitude")
#Select stations
stats_used <- c("WFJ", "ARO", "SIA", "GRC", "DAV", "ANT", "SMM", "ABO", "DIS", "ELM", "SMA", "NAP", "EIN", "ZER",
"BER", "BAS", "5WJ", "4ZE", "5DF", "2AN")
stat_meta <- stat_meta[stat_meta$ID %in% stats_used, ]
snow_84 <- sp::SpatialPoints(data.frame(lon = stat_meta$Longitude,
lat = stat_meta$Latitude),
proj4string = crswgs84)
snow <- sp::spTransform(snow_84, CRS = crs(basin_base, asText = T))
pdf(paste0(base_dir,"R/figs_exp/map_over_raw.pdf"), width = 10, height = 6)
par(bg=NA, mar=c(0,0,0,0), oma=c(0,0,0,0))
par(family = "serif")
col_elev <- colorRampPalette(c("white", "black"))(200)
# col_elev <- viridis::viridis(n = 200, direction = -1)
# col_elev <- terrain.colors(200)
# cols_min <- colorRampPalette(c("darkred", "firebrick4", "orangered4", "gold3", "grey98"))(n_min)
# cols_max <- colorRampPalette(c("grey98", "lightcyan3", viridis::viridis(9, direction = 1)[c(4,3,2,1)]))(n_max)
plot(dem_sub_swiss, axes = F, legend = F, col = col_elev, box = F)
plot(basin_bern, add =T, lwd = 0.025, col = alpha("steelblue4", alpha = 0.45))
plot(basin_brug, add =T, lwd = 0.025, col = alpha("steelblue4", alpha = 0.25))
plot(basin_unte, add =T, lwd = 0.025, col = alpha("steelblue4", alpha = 0.35))
plot(basin_diep, add =T, lwd = 0.025, col = alpha("darkolivegreen", alpha = 0.45))
plot(basin_neuh, add =T, lwd = 0.025, col = alpha("darkolivegreen", alpha = 0.25))
plot(basin_reki, add =T, lwd = 0.025, col = alpha("darkolivegreen", alpha = 0.35))
plot(basin_bern, add =T, lwd = 0.8, border = "black")
plot(basin_brug, add =T, lwd = 0.8, border = "black")
plot(basin_unte, add =T, lwd = 0.8, border = "black")
plot(basin_diep, add =T, lwd = 0.8, border = "black")
plot(basin_neuh, add =T, lwd = 0.8, border = "black")
plot(basin_reki, add =T, lwd = 0.8, border = "black")
plot(basin_base, add =T, lwd = 2.5, border = "gold3")
plot(rhin_riv, add = T, col = "blue4", lwd = 1.5)
plot(aare_riv, add = T, col = "blue4", lwd = 1.5)
plot(brienzersee, add = T, col = "blue4")
plot(thunersee, add = T, col = "blue4")
plot(bielersee, add = T, col = "blue4")
plot(bodensee, add = T, col = "blue4")
plot(gauges, add = T, pch = 23, cex = 1.7,
bg = alpha(c("gold3", "darkolivegreen", "darkolivegreen", "steelblue4",
"steelblue4", "steelblue4", "darkolivegreen"), alpha = 1.0))
plot(gauges, add = T, pch = 19, cex = 0.5)
plot(snow, add = T, pch = "*", cex = 1.3)
lab_mov <- 5200
lab_pos_1 <- c(lab_mov, -lab_mov, rep(lab_mov, 2), +lab_mov, rep(lab_mov, 7), +lab_mov, lab_mov, 0, -lab_mov, -lab_mov-4000, lab_mov+3000, lab_mov+4000, -lab_mov)
lab_pos_2 <- c(lab_mov, -lab_mov, rep(lab_mov, 2), -lab_mov, rep(lab_mov, 7), -lab_mov, lab_mov, -lab_mov, lab_mov, lab_mov, lab_mov, 0, -lab_mov)
text(snow@coords[, 1]+lab_pos_1, snow@coords[, 2]+lab_pos_2, labels = stat_meta$ID, col = "black", cex = 0.9)
prettymapr::addscalebar(plotunit = "m", widthhint = 0.25, htin = 0.15, pos = "topleft",
padin = c(0.15, 0.15))
dev.off()
#map_ins----
dem = raster(paste0(data_dir, "basin_data/eu_dem/processed/eu_dem_1000.tif"))
# pdf(paste0(base_dir,"R/figs_exp/map_ins_raw.pdf"), width = 10, height = 6)
# tiff(paste0(base_dir,"R/figs_exp/map_ins_raw.tiff"), width = 10, height = 6,
# units = "in", res = 800)
png(paste0(base_dir,"R/figs_exp/map_ins_raw.png"), width = 1000, height = 600)
#Create polygons for extends overview maps
boxes <- rbind(c(5.0, 40.0, 5.0, 55.0, 10.0, 55.0, 10.0, 40.0, 5.0, 40.0),
c(my_ext@xmin, my_ext@ymin, my_ext@xmin, my_ext@ymax, my_ext@xmax, my_ext@ymax,
my_ext@xmax, my_ext@ymin, my_ext@xmin, my_ext@ymin))
matrix_1 <- matrix(boxes[1, ], ncol=2, byrow=TRUE)
matrix_2 <- matrix(boxes[2, ], ncol=2, byrow=TRUE)
ID <- c("box_1", "box_2")
polys <- SpatialPolygons(list(
Polygons(list(Polygon(matrix_1)), ID[1]),
Polygons(list(Polygon(matrix(boxes[2, ], ncol=2, byrow=TRUE))), ID[2])
))
plot(dem, col = alpha("grey35", alpha = 1.0), axes = F, legend = F, box = F)
plot(polys[2], col = alpha("grey35", alpha = 1.0), border = "red3", add = T, lwd = 4)
dev.off()
#elev_dist----
pdf(paste0(base_dir,"R/figs_exp/elev_dist.pdf"), width = 6, height = 2)
#Load DEM
dem = raster(paste0(data_dir, "basin_data/eu_dem/processed/eu_dem_500.tif"))
#Load basin boundaries
basins <- rgdal::readOGR(dsn = paste0(data_dir, "basin_data/EZG_Schweiz_BAFU/ezg_kombiniert.shp"), encoding = "UTF8")
basin_base <- spTransform(basins[basins@data$Ort == "Basel, Rheinhalle",], CRS = crs(dem, asText = T))
# basin_unte <- spTransform(basins[basins@data$Ort == "Untersiggenthal, Stilli",], CRS = crs(dem, asText = T))
# basin_brug <- spTransform(basins[basins@data$Ort == "Brugg",], CRS = crs(dem, asText = T))
# basin_bern <- spTransform(basins[basins@data$Ort == "Bern, Schönau",], CRS = crs(dem, asText = T))
# basin_neuh <- spTransform(basins[basins@data$Ort == "Neuhausen, Flurlingerbrücke",], CRS = crs(dem, asText = T))
# basin_reki <- spTransform(basins[basins@data$Ort == "Rekingen",], CRS = crs(dem, asText = T))
# basin_diep <- spTransform(basins[basins@data$Ort == "Diepoldsau, Rietbrücke",], CRS = crs(dem, asText = T))
#corp DEM sub-basin area
dem_cro_base <- raster::crop(dem, extent(basin_base))
dem_sub_base <- mask(dem_cro_base, basin_base)
# dem_cro_unte <- raster::crop(dem, extent(basin_unte))
# dem_sub_unte <- mask(dem_cro_unte, basin_unte)
# dem_cro_brug <- raster::crop(dem, extent(basin_brug))
# dem_sub_brug <- mask(dem_cro_brug, basin_brug)
# dem_cro_bern <- raster::crop(dem, extent(basin_bern))
# dem_sub_bern <- mask(dem_cro_bern, basin_bern)
# dem_cro_reki <- raster::crop(dem, extent(basin_reki))
# dem_sub_reki <- mask(dem_cro_reki, basin_reki)
# dem_cro_neuh <- raster::crop(dem, extent(basin_neuh))
# dem_sub_neuh <- mask(dem_cro_neuh, basin_neuh)
# dem_cro_diep <- raster::crop(dem, extent(basin_diep))
# dem_sub_diep <- mask(dem_cro_diep, basin_diep)
#get elevations of cells cropped dem
dem_ele_NA_base <- dem_sub_base@data@values
dem_ele_base <- dem_ele_NA_base[!is.na(dem_ele_NA_base)]
# dem_ele_NA_unte <- dem_sub_unte@data@values
# dem_ele_unte <- dem_ele_NA_unte[!is.na(dem_ele_NA_unte)]
# dem_ele_NA_brug <- dem_sub_brug@data@values
# dem_ele_brug <- dem_ele_NA_brug[!is.na(dem_ele_NA_brug)]
# dem_ele_NA_bern <- dem_sub_bern@data@values
# dem_ele_bern <- dem_ele_NA_bern[!is.na(dem_ele_NA_bern)]
# dem_ele_NA_reki <- dem_sub_reki@data@values
# dem_ele_reki <- dem_ele_NA_reki[!is.na(dem_ele_NA_reki)]
# dem_ele_NA_neuh <- dem_sub_neuh@data@values
# dem_ele_neuh <- dem_ele_NA_neuh[!is.na(dem_ele_NA_neuh)]
# dem_ele_NA_diep <- dem_sub_diep@data@values
# dem_ele_diep <- dem_ele_NA_diep[!is.na(dem_ele_NA_diep)]
hist_breaks <- seq(4000, 200, -50)
par(mar = c(1, 2.2, 2.0, 1))
par(family = "serif")
hist_res <- hist(dem_ele_base, breaks = hist_breaks, plot = F)
ylabs <- (hist_res$counts / length(dem_ele_base)) * 100
areal_perc <- c(round(length(which(dem_ele_base < 1000)) / length(dem_ele_base) * 100, digits = 2),
round(length(which(dem_ele_base > 1000 & dem_ele_base < 2000)) / length(dem_ele_base) * 100, digits = 2),
round(length(which(dem_ele_base > 2000 & dem_ele_base < 3000)) / length(dem_ele_base) * 100, digits = 2),
round(length(which(dem_ele_base > 3000)) / length(dem_ele_base) * 100, digits = 2))
hist(dem_ele_base, breaks = hist_breaks, axes = F, ylab = "", xlab = "", main = "",
col = alpha("gold3", alpha = 0.8), yaxs = "i", ylim = c(0,16000), xlim = c(4000, 0))
# rect(xleft = 6000, xright = -300, ytop = 150000, ybottom = 0 ,col = alpha("grey", alpha = 0.30), border = NA)
box()
axis(3, mgp=c(3, 0.08, 0), tck = -0.01, cex.axis = 0.8)
axis(2, mgp=c(3, 0.08, 0), tck = -0.01, cex.axis = 0.8,
at = c(length(dem_ele_base) * c(0.01, 0.025, 0.05, 0.075, 0.1)), labels = c("1.0", "2.5", "5.0", "7.5", "10.0"))
abline(v = c(0, 1000, 2000, 3000, 4000), col = "black", lty = "dashed", lwd = 1.0)
mtext("Elevation [m]", side = 3, line = 1.1, adj = 0.5, cex = 1.2)
mtext("Areal fraction [%]", side = 2, line = 1.2, adj = 0.5, cex = 1.2)
mtext(paste(areal_perc[1], "%"), side = 2, line = -21.0, adj = 0.65, col = "black")
mtext(paste(areal_perc[2], "%"), side = 2, line = -17.0, adj = 0.65, col = "black")
mtext(paste(areal_perc[3], "%"), side = 2, line = -11.0, adj = 0.65, col = "black")
mtext(paste(areal_perc[4], "%"), side = 2, line = -4.5, adj = 0.65, col = "black")
dev.off()
#meteo_plots----
layout(matrix(c(rep(1, 8), 2, rep(3, 8), 4,
rep(5, 8), 6, rep(7, 8), 8),
2, 18, byrow = T), widths=c(), heights=c())
x_axis_lab <- c(16,46,74,105,135,166,196,227,258,288,319,349)
x_axis_tic <- c(16,46,74,105,135,166,196,227,258,288,319,349,380)-15
#Plot: Temperature mean
data_day <- tmea_band
cols_max <- colorRampPalette(c("grey98", "gold3", "darkgoldenrod3", "darkorange4", "darkred"))(100)
cols_min <- colorRampPalette(c(viridis::viridis(9, direction = 1)[1:4], "grey98"))(100)
cols_hydro <- c(cols_min, cols_max)
breaks_hydro <- c(seq(min_na(data_day), 0, length.out = 100),
seq(0, max_na(data_day), length.out = 100+1))
par(mar = c(1.7, 3.7, 2.5, 0.2))
image(x = 1:nrow(data_day),
y = seq(250, 3150, 50),
z = data_day,
col = cols_hydro,
breaks = breaks_hydro,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
axis(2, mgp=c(3, 0.20, 0), tck = -0.03, cex.axis = 1.5)
mtext("a) Temperature mean", side= 3, line = 0.3, cex = 1.6, adj = 0.0)
mtext("[°C]", side= 3, line = 0.3, cex = 1.6, adj = 1.0)
mtext("Elevation", side= 2, line = 1.9, cex = 1.6)
box()
par(mar = c(1.7, 0.2, 2.5, 1.7))
alptempr::image_scale(as.matrix(data_day), col = cols_hydro, breaks = breaks_hydro, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.20, 0), tck = -0.10, cex.axis = 1.4)
box()
#Plot: Temperature trend
data_day <- tslo_band
cols_max <- colorRampPalette(c("grey98", "gold3", "darkgoldenrod3", "darkorange4", "darkred"))(100)
cols_min <- colorRampPalette(c(viridis::viridis(9, direction = 1)[1:4], "grey98"))(100)
cols_hydro <- c(cols_min, cols_max)
breaks_hydro <- seq(-max_na(abs(data_day)), max_na(abs(data_day)), length.out = length(cols_hydro)+1)
par(mar = c(1.7, 3.7, 2.5, 0.2))
image(x = 1:nrow(data_day),
y = seq(250, 3150, 50),
z = data_day,
col = cols_hydro,
breaks = breaks_hydro,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
axis(2, mgp=c(3, 0.20, 0), tck = -0.03, cex.axis = 1.5)
mtext("b) Temperature trend", side= 3, line = 0.3, cex = 1.6, adj = 0.0)
mtext(expression(paste("[°C ", "dec"^"-1", "]")), side= 3, line = 0.3, cex = 1.6, adj = 1.0)
mtext("Elevation", side= 2, line = 1.9, cex = 1.6)
box()
par(mar = c(1.7, 0.2, 2.5, 1.7))
alptempr::image_scale(as.matrix(data_day), col = cols_hydro, breaks = breaks_hydro, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.20, 0), tck = -0.10, cex.axis = 1.4)
box()
#Plot: Precipitation mean
data_day <- pmea_band
cols_hydro <- grDevices::colorRampPalette(c("grey98", viridis::viridis(9, direction = 1)[c(4,3,2,1,1)]))(100)
breaks_hydro <- c(seq(min_na(data_day), max_na(data_day), length.out = 100+1))
cols_hydro <- colorRampPalette(c(viridis::viridis(9, direction = 1)[9:1]))(200)
breaks_hydro <- seq(0, alptempr::max_na(data_day), length.out = length(cols_hydro)+1)
x_axis_lab <- c(16,46,74,105,135,166,196,227,258,288,319,349)
x_axis_tic <- c(16,46,74,105,135,166,196,227,258,288,319,349,380)-15
par(mar = c(1.7, 3.7, 2.5, 0.2))
image(x = 1:nrow(data_day),
y = seq(250, 3150, 50),
z = data_day,
col = cols_hydro,
breaks = breaks_hydro,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
axis(2, mgp=c(3, 0.20, 0), tck = -0.03, cex.axis = 1.5)
mtext("c) Precipitation mean", side= 3, line = 0.3, cex = 1.6, adj = 0.0)
mtext("[mm]", side= 3, line = 0.3, cex = 1.6, adj = 1.0)
mtext("Elevation", side= 2, line = 1.9, cex = 1.6)
box()
par(mar = c(1.7, 0.2, 2.5, 1.7))
alptempr::image_scale(as.matrix(data_day), col = cols_hydro, breaks = breaks_hydro, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.20, 0), tck = -0.10, cex.axis = 1.4)
# mtext("cm", side = 3, line = 0.2, cex = 1.0)
box()
#Plot: Precipitation trend
data_day <- pslo_band
cols_max <- colorRampPalette(c("grey98", "gold3", "darkgoldenrod3", "darkorange4", "darkred"))(100)
cols_min <- colorRampPalette(c(viridis::viridis(9, direction = 1)[1:4], "grey98"))(100)
cols_hydro <- c(cols_min, cols_max)
breaks_hydro <- seq(-max_na(abs(data_day)), max_na(abs(data_day)), length.out = length(cols_hydro)+1)
par(mar = c(1.7, 3.7, 2.5, 0.2))
image(x = 1:nrow(data_day),
y = seq(250, 3150, 50),
z = data_day,
col = cols_hydro,
breaks = breaks_hydro,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
axis(2, mgp=c(3, 0.20, 0), tck = -0.03, cex.axis = 1.5)
mtext("b) Precipitation trend", side= 3, line = 0.3, cex = 1.6, adj = 0.0)
mtext(expression(paste("[mm ", "dec"^"-1", "]")), side= 3, line = 0.3, cex = 1.6, adj = 1.0)
mtext("Elevation", side= 2, line = 1.9, cex = 1.6)
box()
par(mar = c(1.7, 0.2, 2.5, 1.7))
alptempr::image_scale(as.matrix(data_day), col = cols_hydro, breaks = breaks_hydro, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.20, 0), tck = -0.10, cex.axis = 1.4)
box()
#snow_stats----
sta_yea <- 1954
end_yea <- 2018
#Read snow station data
snow_data_1 <- read.table(paste0(base_dir,"data/snow/order_73106_data.txt"), sep = ";", skip = 2, stringsAsFactors = F, header = T, na.strings = "-")
snow_data_2 <- read.table(paste0(base_dir,"data/snow/order_73164_data.txt"), sep = ";", skip = 2, stringsAsFactors = F, header = T, na.strings = "-")
snow_data_3 <- read.table(paste0(base_dir,"data/snow/order_74297_data.txt"), sep = ";", skip = 2, stringsAsFactors = F, header = T, na.strings = "-")
snow_data_all <- rbind(snow_data_1, snow_data_2, snow_data_3)
id_all <- unique(snow_data_all$stn); id_all <- id_all[-which(id_all == "stn")]
snow_all <- NULL
stat_col <- NULL
for(i in 1: length(id_all)){
print(i)
sel_ind <- which(snow_data_all$stn == id_all[i])
snow_valu <- as.numeric(snow_data_all$hto000d0[sel_ind])
snow_date <- as.Date(strptime(snow_data_all$time[sel_ind], "%Y%m%d", tz = "UTC"))
snow_data <- data.frame(date = snow_date,
valu = snow_valu)
#start/end year selected snow recordings
sta_yea_sno <- as.numeric(format(snow_date[1], "%Y"))
end_yea_sno <- as.numeric(format(snow_date[length(snow_date)], "%Y"))
#1.Criteria: Recordings at least from 1969
if(sta_yea_sno <= 1969){
#Fill possible gaps
sta_day <- paste0(sta_yea, "-01-01")
end_day <- paste0(end_yea, "-12-31")
sta_date <- as.POSIXct(strptime(sta_day, "%Y-%m-%d", tz="UTC"))
end_date <- as.POSIXct(strptime(end_day, "%Y-%m-%d", tz="UTC"))
snow_stat_date <- as.Date(seq(sta_date, end_date, by="day"))
snow_sel <- data.frame(date = snow_stat_date,
values = with(snow_data, valu[match(as.Date(snow_stat_date), as.Date(date))]))
if(id_all[i] == "SAE"){#Data Saentis only from 1965 on
snow_sel$values[which(format(snow_sel$date, "%Y") < 1965)] <- NA
}
#2.Criteria: not more than 15 years in total missing
if(length(which(is.na(snow_sel$values))) < length(snow_sel$values) *(15/60)){
snow_all <- cbind(snow_all, snow_sel$values)
stat_col <- c(stat_col, id_all[i])
}
}
}
colnames(snow_all) <- stat_col
#Plot observational stations
# pdf(paste0(base_dir,"R/figs_exp/snow_stats.pdf"), width = 12, height = 14.5)
png(paste0(base_dir,"R/figs_exp/snow_stats.png"), width = 12, height = 14.5,
units = "in", res = 300)
# tiff(paste0(base_dir,"R/figs_exp/snow_stats.tiff"), width = 12, height = 14.5,
# units = "in", res = 300)
par(family = "serif")
sing_snow <- function(stat_sel, do_head = F, sta_yea = 1958, bre_yea = 1988, end_yea = 2018, bre_day = 274){
snow_stat <- snow_all[, which(colnames(snow_all) == stat_sel)]
#Calculate mean yearly cycles
data_day_1 <- ord_day(data_in = snow_stat,
date = snow_stat_date,
start_y = sta_yea,
end_y = bre_yea,
break_day = bre_day,
do_ma = T,
window_width = 30)
data_day_2 <- ord_day(data_in = snow_stat,
date = snow_stat_date,
start_y = bre_yea,
end_y = end_yea,
break_day = bre_day,
do_ma = T,
window_width = 30)
sno_mea_1 <- apply(data_day_1, 2, mea_na)
sno_mea_2 <- apply(data_day_2, 2, mea_na)
#Snow meanaccumulation/melt rates
snow_stat_diff <- c(diff(snow_stat), NA)
data_day_diff_1 <- ord_day(data_in = snow_stat_diff,
date = snow_stat_date,
start_y = sta_yea,
end_y = bre_yea,
break_day = bre_day,
do_ma = T,
window_width = 30)
data_day_diff_2 <- ord_day(data_in = snow_stat_diff,
date = snow_stat_date,
start_y = bre_yea,
end_y = end_yea,
break_day = bre_day,
do_ma = T,
window_width = 30)
diff_mea_1 <- apply(data_day_diff_1, 2, mea_na)
diff_mea_2 <- apply(data_day_diff_2, 2, mea_na)
#vertical ablines at maximum/zero
doy_max_1 <- which(sno_mea_1 == max_na(sno_mea_1))
doy_max_2 <- which(sno_mea_2 == max_na(sno_mea_2))
#Plot 1: Raster graph
data_day <- ord_day(data_in = snow_stat,
date = snow_stat_date,
start_y = sta_yea,
end_y = end_yea,
break_day = bre_day,
do_ma = F,
window_width = 30)
x_axis_lab <- c(16,46,74,105,135,166,196,227,258,288,319,349)
x_axis_tic <- c(16,46,74,105,135,166,196,227,258,288,319,349,380)-15
# cols_hydro <- c(colorRampPalette(c("white", viridis::viridis(20, direction = -1)))(200))
# cols_hydro <- colorRampPalette(c("grey85", "cadetblue3", viridis::viridis(9, direction = 1)[4:1]))(200)
cols_hydro <- colorRampPalette(c("grey90", viridis::viridis(9, direction = 1)[4:1]))(200)
breaks_hydro <- seq(alptempr::min_na(data_day), alptempr::max_na(data_day), length.out = length(cols_hydro)+1)
par(mar = c(1.7, 1.7, 0.5, 0.2))
image(x = 1:ncol(data_day),
y = 1958:2017,
z = t(data_day),
col = cols_hydro,
breaks = breaks_hydro,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
axis(2, mgp=c(3, 0.20, 0), tck = -0.03, cex.axis = 1.5)
if(do_head){
mtext("a) Snow depth raster", side= 3, line = 0.5, cex = 1.6, adj = 0.0)
mtext("[cm]", side= 3, line = 0.3, cex = 1.4, adj = 1.0)
# legend("topleft", c("1959-1988", "1989-2018"), pch = 19, cex = 1.0, col = c(col_1, col_2), bg = "white")
}
box()
par(mar = c(1.7, 0.2, 0.5, 1.7))
alptempr::image_scale(as.matrix(data_day), col = cols_hydro, breaks = breaks_hydro, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.20, 0), tck = -0.10, cex.axis = 1.4)
# mtext("cm", side = 3, line = 0.2, cex = 1.0)
box()
#Plot 2: Mean annual cycle
par(mar =c(1.7, 1.7, 0.5, 1.0))
x_axis_lab <- c(16,46,74,105,135,166,196,227,258,288,319,349)
x_axis_tic <- c(16,46,74,105,135,166,196,227,258,288,319,349,380)-15
col_1 <- "steelblue4"
col_2 <- "darkred"
col_3 <- "black"
my_lwd <- 2.5
lwd_ab <- 2.5
y_max <- max(c(sno_mea_1, sno_mea_2))
y_min <- min(c(sno_mea_1, sno_mea_2))
plot(sno_mea_1, type = "n", col =col_1, axes = F, ylab = "", xlab = "", ylim = c(y_min, y_max))
abline(v = x_axis_tic, col = "grey55", lty = "dashed", lwd = 0.7)
lines(sno_mea_1, col = col_1, lwd = my_lwd)
lines(sno_mea_2, col = col_2, lwd = my_lwd)
# abline(v = doy_max_1, col = col_1, lty = "dashed", lwd = lwd_ab)
# abline(v = doy_max_2, col = col_2, lty = "dashed", lwd = lwd_ab)
axis(2, mgp=c(3, 0.2, 0), tck = -0.03, cex.axis = 1.5)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
if(do_head){
mtext("b) Snow depth mean", side= 3, line = 0.5, cex = 1.6, adj = 0.0)
mtext("[cm]", side= 3, line = 0.3, cex = 1.4, adj = 1.0)
legend("topright", c("1958-1988", "1988-2018"), pch = 19, cex = 1.25, col = c(col_1, col_2), bg = "white")
}
box(lwd = 0.7)
#Plot 3: Annaul cycle meand diff.
y_max <- max(c(diff_mea_1, diff_mea_2))
y_min <- min(c(diff_mea_1, diff_mea_2))
plot(diff_mea_1, type = "n", col = col_1, axes = F, ylab = "", xlab = "", ylim = c(y_min, y_max))
abline(h = 0, col = "grey55", lty = "dashed", lwd = 0.7)
# abline(v = doy_max_1, col = col_1, lty = "dashed", lwd = lwd_ab)
# abline(v = doy_max_2, col = col_2, lty = "dashed", lwd = lwd_ab)
abline(v = x_axis_tic, col = "grey55", lty = "dashed", lwd = 0.7)
lines(diff_mea_1, col = col_1, lwd = my_lwd)
lines(diff_mea_2, col = col_2, lwd = my_lwd)
axis(2, mgp=c(3, 0.2, 0), tck = -0.03, cex.axis = 1.5)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.08)#plot ticks
axis(1, at = x_axis_lab, c("O","N","D","J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.4)#plot labels
if(do_head){
mtext("c) Acc./Melt rate", side= 3, line = 0.5, cex = 1.6, adj = 0.0)
mtext(expression(paste("[cm ","day"^"-1", "]")), side= 3, line = 0.15, cex = 1.4, adj = 1.0)
}
# abline(v = x_axis_tic, col = "grey55", lty = "dashed", lwd = 0.8)
# grid(nx = 0, ny = 5, lty = "dashed", col = "grey55", lwd = 0.8)
box(lwd = 0.7)
}
layout(matrix(c(46, rep(45, 11),
rep(c(46, 1, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41), 8),
46, 2, 6, 10, 14, 18, 22, 26, 30, 34, 38, 42,
rep(c(46, 3, 7, 11, 15, 19, 23, 27, 31, 35, 39, 43), 9),
rep(c(46, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44), 9)
),
12, 28), widths=c(1.2, rep(1, 27)), heights=c(0.2, rep(1, 10)))
x_axis_lab <- c(15,46,74,105,135,166,196,227,258,288,319,349)
x_axis_tic <- c( 46,74,105,135,166,196,227,258,288,319,349)-15
sing_snow("WFJ", do_head = T)
sing_snow("ARO")
sing_snow("SIA")
sing_snow("GRC")
sing_snow("DAV")
sing_snow("ANT")
sing_snow("SMM")
sing_snow("ABO")
sing_snow("DIS")
sing_snow("ELM")
sing_snow("SMA")
#Station names
cex_header <- 1.4
par(mar = c(0,0,0,0))
plot(1:100, 1:100, axes = F, type = "n", xlab = "", ylab = "")
mtext("WFJ", side = 2, line = -1.8, cex = cex_header, adj = 0.958, outer = T)
mtext("2691 m", side = 2, line = -3.5, cex = cex_header, adj = 0.967, outer = T)
mtext("ARO", side = 2, line = -1.8, cex = cex_header, adj = 0.865, outer = T)
mtext("1878 m", side = 2, line = -3.5, cex = cex_header, adj = 0.871, outer = T)
mtext("SIA", side = 2, line = -1.8, cex = cex_header, adj = 0.771, outer = T)
mtext("1804 m", side = 2, line = -3.5, cex = cex_header, adj = 0.776, outer = T)
mtext("GRC", side = 2, line = -1.8, cex = cex_header, adj = 0.679, outer = T)
mtext("1606 m", side = 2, line = -3.5, cex = cex_header, adj = 0.682, outer = T)
mtext("DAV", side = 2, line = -1.8, cex = cex_header, adj = 0.588, outer = T)
mtext("1594 m", side = 2, line = -3.5, cex = cex_header, adj = 0.588, outer = T)
mtext("ANT", side = 2, line = -1.8, cex = cex_header, adj = 0.494, outer = T)
mtext("1438 m", side = 2, line = -3.5, cex = cex_header, adj = 0.493, outer = T)
mtext("SMM", side = 2, line = -1.8, cex = cex_header, adj = 0.400, outer = T)
mtext("1386 m", side = 2, line = -3.5, cex = cex_header, adj = 0.397, outer = T)
mtext("ABO", side = 2, line = -1.8, cex = cex_header, adj = 0.305, outer = T)
mtext("1322 m", side = 2, line = -3.5, cex = cex_header, adj = 0.303, outer = T)
mtext("DIS", side = 2, line = -1.8, cex = cex_header, adj = 0.214, outer = T)
mtext("1197 m", side = 2, line = -3.5, cex = cex_header, adj = 0.209, outer = T)
mtext("ELM", side = 2, line = -1.8, cex = cex_header, adj = 0.120, outer = T)
mtext("957 m", side = 2, line = -3.5, cex = cex_header, adj = 0.117, outer = T)
mtext("SMA", side = 2, line = -1.8, cex = cex_header, adj = 0.031, outer = T)
mtext("555 m", side = 2, line = -3.5, cex = cex_header, adj = 0.027, outer = T)
#Analytical method
plot(1:100, 1:100, axes = F, type = "n", xlab = "", ylab = "")
# mtext("a) SD [cm]",
# side = 3, line = -2.35, cex = cex_header+0.2, adj = 0.17)
# mtext("b) AMR [cm/day]",
# side = 3, line = -2.35, cex = cex_header+0.2, adj = 0.53)
# mtext("c) Sub-AMR [cm/day]",
# side = 3, line = -2.35, cex = cex_header+0.2, adj = 0.90)
# mtext("1959-1988",
# side = 3, line = -1.60, cex = cex_header-0.1, adj = 0.07)
# mtext("1989-2018",
# side = 3, line = -2.95, cex = cex_header-0.1, adj = 0.07)
# col_1 <- "steelblue4"
# col_2 <- "darkgoldenrod4"
# points(3.2, 10, pch = 19, col = col_2)
# points(3.2, 65, pch = 19, col = col_1)
dev.off()
#disc_perc----
grdc_data_base <- read_grdc(paste0(grdc_dir, "6935051_Q_Day.Cmd.txt"))
grdc_data_unte <- read_grdc(paste0(grdc_dir, "6935300_Q_Day.Cmd.txt"))
grdc_data_reki <- read_grdc(paste0(grdc_dir, "6935054_Q_Day.Cmd.txt"))
grdc_data_brug <- read_grdc(paste0(grdc_dir, "6935301_Q_Day.Cmd.txt"))
grdc_data_neuh <- read_grdc(paste0(grdc_dir, "6935055_Q_Day.Cmd.txt"))
grdc_data_bern <- read_grdc(paste0(grdc_dir, "6935020_Q_Day.Cmd.txt"))
grdc_data_diep <- read_grdc(paste0(grdc_dir, "6935500_Q_Day.Cmd.txt"))
# pdf(paste0(base_dir,"R/figs_exp/disc_perc.pdf"), width = 12, height = 15)
# tiff(paste0(base_dir,"R/figs_exp/disc_perc.tiff"), width = 12, height = 15,
# units = "in", res = 300)
png(paste0(base_dir,"R/figs_exp/disc_perc.png"), width = 12, height = 15,
units = "in", res = 300)
layout(matrix(c(32, rep(29, 18),
32, rep(1, 8), 2, rep(3, 8), 4,
32, rep(30, 18),
32, rep(5, 8), 6, rep(7, 8), 8,
32, rep(9, 8), 10, rep(11, 8), 12,
32, rep(13, 8), 14, rep(15, 8), 16,
32, rep(31, 18),
32, rep(17, 8), 18, rep(19, 8), 20,
32, rep(21, 8), 22, rep(23, 8), 24,
32, rep(25, 8), 26, rep(27, 8), 28),
10, 19, byrow = T), widths=c(0.8, rep(1, 18)), heights=c(0.20, 1, 0.15, 1, 1, 1, 0.15, 1, 1, 1))
# layout.show(n = 32)
perce_plot <- function(data_in, date_in, main_in = "", year_1, year_2, year_3, year_4,
do_ylab = T, do_unit = T, pos_unit = 1.0){
data_day <- ord_day(data_in = data_in,
date = date_in,
start_y = year_1,
end_y = year_4)
#Mean seasonal cycles
ind_break_1 <- (year_1 - year_1) + 1
ind_break_2 <- (year_2 - year_1) + 1
ind_break_3 <- (year_3 - year_1) + 1
ind_break_4 <- (year_4 - year_1) + 0
#Calculation percentile graph
jan_cols <- 1:31
feb_cols <- 32:59
mar_cols <- 60:90
apr_cols <- 91:120
may_cols <- 121:151
jun_cols <- 152:181
jul_cols <- 182:212
aug_cols <- 213:243
sep_cols <- 244:273
oct_cols <- 274:304
nov_cols <- 305:334
dec_cols <- 335:365
month_cols <- list(oct_cols, nov_cols, dec_cols, jan_cols, feb_cols, mar_cols, apr_cols, may_cols, jun_cols, jul_cols, aug_cols, sep_cols)
f_quants_all <- function(data_in){
probs <- seq(0.01, 0.99, 0.01)
quants_all <- quantile(data_in, probs = probs, type = 8, na.rm = T)
return(quants_all)
}
qmon_1 <- matrix(NA, ncol = 12, nrow = length(seq(0.01, 0.99, 0.01)))
qmon_2 <- matrix(NA, ncol = 12, nrow = length(seq(0.01, 0.99, 0.01)))
for(i in 1:12){
qmon_1[ , i] <- f_quants_all(data_day[ind_break_1:ind_break_2, month_cols[[i]]])
qmon_2[ , i] <- f_quants_all(data_day[ind_break_3:ind_break_4, month_cols[[i]]])
}
qdif <- qmon_2 - qmon_1
#Percentile graph
x_axis_lab <- 1:12
x_axis_tic <- (1:13)-0.5
n_max <- round(abs(max_na(qdif[, ])) / (max_na(qdif[, ]) + abs(min_na(qdif[, ]))), digits = 2) * 200
n_min <- 200 - n_max
# n_max <- 100
# n_min <- 100
# cols_min <- colorRampPalette(c(viridis::viridis(9, direction = 1)[c(1,1,2,3,4)], "grey98"))(n_min)
# cols_max <- colorRampPalette(c("grey98", "gold3", "orangered4", "firebrick4", "firebrick4", "darkred"))(n_max)
cols_min <- colorRampPalette(c("darkred", "firebrick4", "orangered4", "gold3", "grey98"))(n_min)
cols_max <- colorRampPalette(c("grey98", "lightcyan3", viridis::viridis(9, direction = 1)[c(4,3,2,1)]))(n_max)
# my_col <- colorRampPalette(c(cols_min, cols_max))(200)
# my_bre <- seq(min_na(qdif[, ]), max_na(qdif[, ]), length.out = 201)
my_bre <- c(seq(-max_na(abs(qdif)), 0, length.out = n_min),
seq(0, max_na(abs(qdif)), length.out = n_max+1))
my_col <- colorRampPalette(c(cols_min, cols_max))(length(my_bre)-1)
# plot(1:n_max, 1:n_max, pch = 19, cex = 2, col = cols_max)
# plot(1:n_min, 1:n_min, pch = 19, cex = 2, col = cols_min)
par(mar = c(1.6, 3.5, 1.5, 0.2))
par(family = "serif")
image(x = 1:12,
y = seq(0.01, 0.99, 0.01),
z = t(qdif), col = my_col, breaks = my_bre,
ylab = "", xlab = "", axes = F)
axis(1, at = x_axis_tic, c("","","","","","","","","","","","",""), tick = TRUE,
col = "black", col.axis = "black", tck = -0.05)#plot ticks
axis(1, at = x_axis_lab, c("O", "N", "D", "J","F","M","A","M","J","J","A","S"), tick = FALSE,
col="black", col.axis="black", mgp=c(3, 0.30, 0), cex.axis = 1.5)#plot labels
axis(2, mgp=c(3, 0.25, 0), tck = -0.025, cex.axis = 1.5)
if(do_ylab){
mtext("Prob. level", side = 2, line = 1.7, cex = 1.25)
}
mtext(main_in, side = 3, line = 0.25, cex = 1.5, adj = 0.0)
if(do_unit){
mtext(expression(paste("[m"^"3", " s"^"-1", "]")), side = 3, line = 0.2, cex = 1.2, adj = pos_unit)
}
box()
par(mar = c(1.6, 0.2, 1.5, 3.5))
alptempr::image_scale(as.matrix(qdif), col = my_col, breaks = my_bre, horiz=F, ylab="", xlab="", yaxt="n", axes=F)
axis(4, mgp=c(3, 0.45, 0), tck = -0.1, cex.axis = 1.5)
box()
}
perce_plot(data_in = grdc_data_base$value,
date_in = grdc_data_base$date,
main_in = "",
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_base$value,
date_in = grdc_data_base$date,
main_in = "",
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_unte$value,
date_in = grdc_data_unte$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_unte$value,
date_in = grdc_data_unte$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_brug$value,
date_in = grdc_data_brug$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_brug$value,
date_in = grdc_data_brug$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_bern$value,
date_in = grdc_data_bern$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_bern$value,
date_in = grdc_data_bern$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_reki$value,
date_in = grdc_data_reki$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_reki$value,
date_in = grdc_data_reki$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_neuh$value,
date_in = grdc_data_neuh$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_neuh$value,
date_in = grdc_data_neuh$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
perce_plot(data_in = grdc_data_diep$value,
date_in = grdc_data_diep$date,
main_in = "", do_unit = F,
year_1 = 1951, year_2 = 1982, year_3 = 1983, year_4 = 2014)
perce_plot(data_in = grdc_data_diep$value,
date_in = grdc_data_diep$date,
main_in = "", do_ylab = F, do_unit = F,
year_1 = 1919, year_2 = 1967, year_3 = 1968, year_4 = 2016)
#Time frames
par(mar = c(0,0,0,0))
plot(1:10, 1:10, axes = F, type = "n", ylab = "", xlab = "")
mtext("a) 1951-2014", side = 3, line = -2.8, adj = 0.20, cex = 1.6)
mtext("b) 1919-2016", side = 3, line = -2.8, adj = 0.78, cex = 1.6)
#Aare branch
par(mar = c(0,0,0,0))
plot(1:10, 1:10, axes = F, type = "n", ylab = "", xlab = "")
mtext("Aare branch", side = 3, line = -3.0, adj = 0.50, cex = 1.6)
#Rhine branch
par(mar = c(0,0,0,0))
plot(1:10, 1:10, axes = F, type = "n", ylab = "", xlab = "")
mtext("Rhine branch", side = 3, line = -3.0, adj = 0.50, cex = 1.6)
#Gauges
cex_header <- 1.6
par(mar = c(0,0,0,0))
plot(1:100, 1:100, axes = F, type = "n", xlab = "", ylab = "")
mtext("1. Basel", side = 2, line = -2.8, cex = cex_header, adj = 0.929, outer = T)
mtext("2. Untersiggenthal", side = 2, line = -2.8, cex = cex_header, adj = 0.789, outer = T)
mtext("3. Brugg", side = 2, line = -2.8, cex = cex_header, adj = 0.627, outer = T)
mtext("4. Bern", side = 2, line = -2.8, cex = cex_header, adj = 0.490, outer = T)
mtext("5. Rekingen", side = 2, line = -2.8, cex = cex_header, adj = 0.315, outer = T)
mtext("6. Neuhausen", side = 2, line = -2.8, cex = cex_header, adj = 0.170, outer = T)
mtext("7. Diepoldsau", side = 2, line = -2.8, cex = cex_header, adj = 0.015, outer = T)
dev.off()
#disc_rast----
# pdf(paste0(base_dir,"R/figs_exp/disc_rast.pdf"), width = 12, height = 8)
# tiff(paste0(base_dir,"R/figs_exp/disc_rast.tiff"), width = 12, height = 8,
# units = "in", res = 300)
png(paste0(base_dir,"R/figs_exp/disc_rast.png"), width = 12, height = 8,
units = "in", res = 300)
layout(matrix(c(rep(c(15, 3, 7, 11), 4),
rep(c(1, 3, 7, 11), 3),
rep(c(1, 4, 8, 12), 1),
rep(c(1, 5, 9, 13), 3),
rep(c(2, 5, 9, 13), 1),
rep(c(16, 5, 9, 13), 3),
rep(c(16, 6, 10, 14), 1)
),
4, 16), widths=c(), heights=c())
sta_yea_cla <- 1919
yea_cla_1 <- sta_yea_cla
yea_cla_2 <- 1967
yea_cla_3 <- 1968
yea_cla_4 <- 2016
raster_plot <- function(data_in, date_in, main_in = "", year_1 = yea_cla_1, year_2 = yea_cla_4,
quant_break = 0.50){
data_day <- ord_day(data_in = data_in,
date = date_in,
start_y = year_1,
end_y = year_2,
break_day = 274)
#Preparation Raster graph