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Paper 2 Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin 19982016 R code.R
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Paper 2 Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin 19982016 R code.R
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# paper 2 PhD code ------------------------------------------------------
##Spatiotemporal Precipitation Variability Modeling in the Blue Nile Basin: 1998-2016 R Code
## Jan 2018
#Install packages------------------------------------------------------
install.packages("plm", "Formula")
install.packages("zoo")
install.packages(c("gstat", "spacetime", "geoR"))
install.packages(c("sp", "xts", "raster", "rgdal", "rgeos", "ggplot2", "plyr"))
install.packages(c("ggmap", "maps", "mapproj", "rasterVis", "maptools", "dismo"))
library(plm)
library(gstat)
library(spacetime)
library(xts)
library(rgdal)
library(rgeos)
library(zoo)
library(plyr)
library(ggplot2)
library(ggmap)
library(maps)
library(mapproj)
library(sp) # classes for spatial data
library(raster) # grids, rasters
library(rasterVis) # raster visualisation
library(maptools)
library(dismo)
#create time object ------------------------------------------------------
time_index <- seq(from = as.POSIXct("1998-01-01", tz = 'UTC'), to = as.POSIXct("2016-12-01", tz = 'UTC'), by = "month")
time_index <- seq(from = as.Date("1998-01-01", tz = 'UTC'), to = as.Date("2016-12-01", tz = 'UTC'), by = "month")
rainSP=subset(TRMMENSO, TRMMENSO$date =="1998-01-01")
# create a grid dataframe------------------------------------------------------
rainSP <- subset(TRMMENSO, TRMMENSO$Year =="1998" & TRMMENSO$Month=="1" )
rainSP <-data.frame(cbind(rainSP$lon, rainSP$lat))
rainSP=rename (rainSP, c("X1"="lon","X2"="lat"))
# Define grid dataframe as spatial------------------------------------------------------
coordinates(rainSP)=~lon+lat
proj4string(rainSP)="+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
str(rainSP)
# make anomaly dataset------------------------------------------------------
TRMMENSO$month <- format(TRMMENSO$date, "%m")
TRMMENSO$rainanom <- TRMMENSO$monthlyrain - ave(TRMMENSO$monthlyrain, TRMMENSO$month, FUN = function(x) mean(x, na.rm = TRUE))
library(reshape2)
TRMMwide=acast(TRMMENSO, date~lon+lat, value.var="anom")
save(TRMMwide, file = "TRMMwide.RData")
save(TRMMENSO, file = "TRMMENSO.RData")
### Defining Spatiotemporal------------------------------------------------------
library(spacetime)
m = stConstruct(TRMMENSO, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
summary(STFDF[,,"monthlyrain"])
STFDF.years = 1998:2016
#STplot yearly------------------------------------------------------
sel = (1:19) * 1
stplot(STFDF[,sel, "monthlyrain"],
STFDF.years[sel],
col.regions=rainbow (n = 100, start = 0, end = .9),
key.space = "right", main="", scales = list(draw = TRUE,
alternating=c(1,2), cex=.8, rot=45), cuts=(breaks=c(0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800)))
# Aggregate by month------------------------------------------------------
library(lubridate)
bymonth <- aggregate(monthlyrain ~Month+lon+lat, data=STFDF,FUN=mean)
bymonth$date=seq(as.Date("2000/1/1"), by = "month", length.out = 12)
summary(bymonth)
# summary statistics by month and year raindall----------------------------------
library(purrr)
bymonth %>% split(.$Month) %>% map(summary)
rain<- subset(TRMMENSO, select = c(Year, monthlyrain, onefourth))
rain %>% split(.$Year) %>% map(summary)
#STplot monthly------------------------------------------------------
m = stConstruct(bymonth, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
STFDF.months = 1:12
#STplot yearly------------------------------------------------------
sel = (1:12) * 1
stplot(STFDF[,sel, "monthlyrain"],
STFDF.months[sel],
col.regions=rainbow (n = 100, start = 0, end = .9),
key.space = "right", main="", scales = list(draw = TRUE,
alternating=c(1, 2), cex=.8, rot=45),
cuts=(breaks=c(0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800)))
# Aggregate by kirmet------------------------------------------------------
kirmet <- subset(TRMMENSO, Month > 5 & Month < 10)
library(spacetime)
m = stConstruct(kirmet, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
summary(STFDF[,,"monthlyrain"])
STFDF.years = 1998:2016
#STplot kirmet yearly------------------------------------------------------
sel = (1:19) * 1
stplot(STFDF[,sel, "monthlyrain"],
STFDF.years[sel],
col.regions=rainbow (n = 100, start = 0, end = .9),
key.space = "right", main="", scales = list(draw = TRUE,
alternating=c(1, 2), cex=.8, rot=45), cuts=(breaks=c(0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800)))
dev.off()
# Aggregate by Belg------------------------------------------------------
belg=subset(TRMMENSO, Month > 2 & Month < 6)
library(spacetime)
m = stConstruct(belg, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
summary(STFDF[,,"monthlyrain"])
STFDF.years = 1998:2016
#STplot Belg yearly------------------------------------------------------
sel = (1:19) * 1
stplot(STFDF[,sel, "monthlyrain"],
STFDF.years[sel],
col.regions=rainbow (n = 100, start = 0, end = .9),
key.space = "right", main="", scales = list(draw = TRUE,
alternating=c(1, 2), cex=.9, rot=45), cuts=(breaks=c(0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800)))
# Aggregate by Bega------------------------------------------------------
bega=subset(TRMMENSO, Month =c(1, 2, 10, 11, 12))
summary(bega)
library(spacetime)
m = stConstruct(bega, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
summary(STFDF[,,"monthlyrain"])
STFDF.years = 1998:2016
#STplot bega yearly------------------------------------------------------
sel = (1:19) * 1
stplot(STFDF[,sel, "monthlyrain"],
STFDF.years[sel],
col.regions=rainbow (n = 100, start = 0, end = .9),
key.space = "right", main="", scales = list(draw = TRUE, alternating=c(1, 2), cex=.8, rot=45), cuts=(breaks=c(0, 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800)))
# Aggregate by spatial ------------------------------------------------------
library(lubridate)
bysptial <- aggregate(monthlyrain ~Month+Year, data=STFDF,FUN=mean)
bysptial$date<- seq(from = as.Date("1998-01-01", tz = 'UTC'), to = as.Date("2016-12-01", tz = 'UTC'), by = "month")
# Merge TS datasets -----------------------------------------------------------
rainglobal=merge(bysptial, global_sst_2016, by = c("Month", "Year"))
rainglobal$date<- seq(from = as.POSIXct("1998-01-01", tz = 'UTC'), to = as.POSIXct("2016-12-01", tz = 'UTC'), by = "month")
global.temp=merge(global_sst_2016, temp, by = c("Month", "Year"))
save(temp,file = "temp.RData")
save(global.temp,file = "globaltemp.RData")
# TS graph of Rainfall ------------------------------------------------------
library(ggplot2)
library(scales)
rain=ggplot(data = bysptial,
aes(x = date,y = monthlyrain, ymin = 0, ymax = monthlyrain)) + geom_linerange(color="blue", size=1)
rain=rain + labs(y = "Average monthly precip. ")+ labs(x = "Date")+ theme(legend.position='none')
rain+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
## define date in global_sst_2016$date as as.POSIXct------------------------------------------------------
global_sst_2016$date=as.POSIXct(global_sst_2016$date, tz="UTC")
class(global_sst_2016$date)
global_sst_2016$date<- seq(from = as.Date("1998-01-01", tz = 'UTC'), to = as.Date("2016-12-01", tz = 'UTC'), by = "month")
save(global_sst_2016, file = "globalsst.RData")
# TS graph of ENSO ------------------------------------------------------
library(ggplot2)
NINO12graph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = nino1.2anom, ymin = 0, ymax = nino1.2anom, colour = ifelse(nino1.2anom <0, "blue", "red")))
NINO12graph=NINO12graph + labs(y = "Niño 1+2")+ labs(x = "Date")+ theme(legend.position='none')
NINO12graph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
NINO3graph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = nino3anom, ymin = 0, ymax = nino3anom, colour = ifelse(nino3anom <0, "blue", "red")))
NINO3graph=NINO3graph + labs(y = "Niño 3")+ labs(x = "Date")+ theme(legend.position='none')
NINO3graph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
NINO4graph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = nino4anom, ymin = 0, ymax = nino4anom, colour = ifelse(nino4anom <0, "blue", "red")))
NINO4graph=NINO4graph + labs(y = "Niño 4")+ labs(x = "Date")+ theme(legend.position='none')
NINO4graph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
NINO34graph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = nino3.4anom, ymin = 0, ymax = nino3.4anom, colour = ifelse(nino3.4anom <0, "blue", "red")))
NINO34graph=NINO34graph + labs(y = "Niño 3.4")+ labs(x = "Date")+ theme(legend.position='none')
NINO34graph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
ammgraph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = amm, ymin = 0, ymax = amm, colour = ifelse(amm <0, "blue", "red")))
ammgraph=ammgraph + labs(y = "AMM")+ labs(x = "Date")+ theme(legend.position='none')
ammgraph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
pdograph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = pdo, ymin = 0, ymax = pdo, colour = ifelse(pdo <0, "blue", "red")))
pdograph=pdograph + labs(y = "PDO")+ labs(x = "Date")+ theme(legend.position='none')
pdograph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
meigraph=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = mei, ymin = 0, ymax = mei, colour = ifelse(mei <0, "blue", "red")))
meigraph=meigraph + labs(y = "MEI")+ labs(x = "Date")+ theme(legend.position='none')
meigraph+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
tsa=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = tsa, ymin = 0, ymax = tsa, colour = ifelse(tsa <0, "blue", "red")))
tsa=tsa + labs(y = "TSA")+ labs(x = "Date")+ theme(legend.position='none')
tsa+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
soi=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = soi, ymin = 0, ymax = soi, colour = ifelse(soi <0, "blue", "red")))
soi=soi + labs(y = "SOI")+ labs(x = "Date")+ theme(legend.position='none')
soi+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
oni=ggplot() + geom_linerange(data = global_sst_2016,
aes(x = date,y = soi, ymin = 0, ymax = oni, colour = ifelse(oni <0, "blue", "red")))
oni=oni + labs(y = "ONI")+ labs(x = "Date")+ theme(legend.position='none')
oni+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
# Graph Global Land Temperature -------
library(ggplot2)
landtemp=ggplot() + geom_linerange(data = global.temp,
aes(x = date,y = temp, ymin = 0, ymax = temp, colour = ifelse(temp <0, "blue", "red")))
landtemp=landtemp + labs(y = "Global land temp.")+ labs(x = "Date")+ theme(legend.position='none')
landtemp+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
# Put ggplot in one page -------
library(gridExtra)
grid.arrange(NINO12graph, NINO34graph, meigraph, ncol=1)
grid.arrange(ammgraph, pdograph, tsa, soi, oni, ncol=1)
# make anomaly dataset -------
TRMMENSO$month <- format(TRMMENSO$date, "%m")
TRMMENSO$anom <- TRMMENSO$monthlyrain - ave(TRMMENSO$monthlyrain, month, FUN = function(x) mean(x, na.rm = TRUE))
library(reshape2)
TRMMwide=acast(TRMMENSO, date~lon+lat, value.var="anom")
## Monthly mean
TRMMENSO$monthlymean= tapply(TRMMENSO$monthlyrain, list(TRMMENSO$Month), mean, na.rm = TRUE)
TRMMENSO$error=TRMMENSO$monthlyrain-TRMMENSO$monthlymean
# EOF/PCA of Precipitation anom -------
library(spacetime)
library(gstat)
m = stConstruct(TRMMENSO, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
raineof= eof(STFDF[,, "anom"], "spatial", scale.=TRUE, center = TRUE)
raineof.PCs = eof(STFDF[,, "anom"], "spatial", scale.=TRUE, center = TRUE, returnEOFs = FALSE)
summary(raineof.PCs)
plot(raineof.PCs)
raineof.PCs = eof(STFDF[,, "anom"], "spatial", scale.=TRUE, center = TRUE, returnEOFs = FALSE)
varimaxpc2=varimax(raineof.PCs$rotation[, 1:2])
newData= scale(TRMMwide)%*%varimaxpc2$loadings
## Map the leading EOF mode
# Figure 5.17: EOF's
spplot(raineof[1], col.regions = bpy.colors(), scales = list(draw=TRUE),
main = "")
spplot(raineof[2], col.regions = bpy.colors(), scales = list(draw=TRUE),
main = "")
spplot(raineof[3], col.regions = bpy.colors(), scales = list(draw=TRUE),
main = "")
library(xts)
eof.t = xts(predict(raineof.PCs), time_index)
plot(eof.t[,1], main = " ")
plot(eof.t[,2], main = "")
# ... and so on.
names(raineof.PCs)
v = raineof.PCs$sdev^2
w=100*cumsum(v[1:228])/sum(v)
plot(100*cumsum(v[1:100])/sum(v),
ylim=c(30, 100), xlim=c(0, 100),ylab = "Percent",
xlab = "EOF", main = "")
library(Hmisc)
minor.tick(nx=10, ny=10, tick.ratio = 10)
plot(raineof.PCs$sdev, xlab = 'Eigenvalue Number', ylab = 'Eigenvalue Size', type = "lines", main = " ")
lines(raineof.PCs$sdev)
screeplot(raineof.PCs, npcs = min(10, length(raineof.PCs$sdev)), type = "lines")
# REOF/RPCA of Precipitation anom -------
library(gstat)
library(sp)
library(spacetime)
m = stConstruct(TRMMENSO, c("lon", "lat"), "date", crs = CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"))
STFDF = as(m, "STFDF")
str(STFDF)
raineof= eof(STFDF[,, "anom"], "spatial", scale.=TRUE, center = TRUE, retx = TRUE, rank. = 3)
raineof.PCs = eof(STFDF[,, "anom"], "spatial", scale.=TRUE, center = TRUE, retx = TRUE, rank. = 3, returnEOFs = FALSE)
summary(raineof.PCs)
plot(raineof.PCs)
## Map the leading EOF mode
# Figure 5.17: EOF's
spplot(raineof[1], col.regions = bpy.colors(), scales = list(draw=TRUE),
main = "")
spplot(raineof[2], col.regions = bpy.colors(), scales = list(draw=TRUE),
main = "")
#Mapping EOF using ggplot2 ----------------------------
raineofdf=as.data.frame(raineof)
fix(raineofdf)
library(ggplot2)
EOF1<- ggplot(raineofdf, aes(lon, lat, z = REOF1), colour = ..level..)
EOF1=EOF1 + geom_raster(aes(fill = REOF1)) +
scale_fill_gradient(high = "midnightblue", low= "dodgerblue", breaks=c(-0.06, -0.04, -0.02, 0, 0.02, 0.04)) +
geom_contour(colour = "white")+ggtitle("") +
labs(x = "Longitude",y = "Latitude")
EOF1+guides(fill = guide_legend(" "))
EOF1
EOF2<- ggplot(raineofdf, aes(lon, lat, z = REOF2), colour = ..level..)
EOF2=EOF2 + geom_raster(aes(fill = REOF2)) +
scale_fill_gradient(high = "dodgerblue3", low= "yellow", breaks=c(-0.06, -0.04, -0.02, 0, 0.02, 0.04)) +
geom_contour(colour = "white")+ggtitle("") +
labs(x = "Longitude",y = "Latitude")
EOF2+guides(fill = guide_legend(" "))
EOF2
EOF3<- ggplot(raineofdf, aes(lon, lat, z = REOF3), colour = ..level..)
EOF3=EOF3 + geom_raster(aes(fill = REOF3)) +
scale_fill_gradient(high = "dodgerblue", low= "yellow", breaks=c(-0.06, -0.04, -0.02, 0, 0.02, 0.04)) +
geom_contour(colour = "white")+ggtitle("") +
labs(x = "Longitude",y = "Latitude")
EOF3+guides(fill = guide_legend(" "))
EOF3
library(xts)
eof.t = xts(predict(raineof.PCs), time_index)
RPC1=plot(eof.t[,1], main = "RPC 1")
RPC1=plot(eof.t[,2], main = "RPC 2")
library(ggplot2)
rainpcdf=as.data.frame(eof.t)
rainpcdf$date <- seq(from = as.Date("1998-01-01", tz = 'UTC'), to = as.Date("2016-12-01", tz = 'UTC'), by = "month")
save(rainpcdf, file = "rainpcdf.csv")
save(rainpcdf, file = "rainpcdf.RData")
RPC1=ggplot() + geom_linerange(data = rainpcdf,
aes(x = date,y = PC1, ymin = 0, ymax = PC1, colour = ifelse(PC1 <0, "blue", "red")))
RPC1=RPC1 + labs(y = "mm")+ labs(x = "Date")+ theme(legend.position='none')+ggtitle("(a)")
RPC1+ scale_x_date(date_breaks = "1 year", date_labels = "%Y")
RPC2=ggplot() + geom_linerange(data = rainpcdf,
aes(x = date,y = PC2, ymin = 0, ymax = PC2, colour = ifelse(PC2 <0, "blue", "red")))
RPC2=RPC2 + labs(y = "mm")+ labs(x = "Date")+ theme(legend.position='none')+ggtitle("(b)")
RPC2+ scale_x_date(date_breaks = "2 year", date_labels = "%Y")
RPC3=ggplot() + geom_linerange(data = rainpcdf,
aes(x = date,y = PC3, ymin = 0, ymax = PC3, colour = ifelse(PC3 <0, "blue", "red")))
RPC3=RPC3 + labs(y = "mm")+ labs(x = "Date")+ theme(legend.position='none')+ggtitle("(c)")
RPC3+ scale_x_date(date_breaks = "3 year", date_labels = "%Y")
# ... and so on.
names(raineof.PCs)
v = raineof.PCs$sdev^2
plot(100*cumsum(v[1:228])/sum(v),
ylim=c(30, 100), ylab = "Percent", xlab = "EOF", main = "")
plot(raineof.PCs$sdev, xlab = 'Eigenvalue Number', ylab = 'Eigenvalue Size', type = "lines", main = " ")
lines(raineof.PCs$sdev)
screeplot(raineof.PCs, npcs = min(10, length(raineof.PCs$sdev)), type = "lines")
# https://www.rdocumentation.org/packages/spacetime/versions/1.1-5/topics/EOF
## https://stats.stackexchange.com/questions/72421/showing-spatial-and-temporal-correlation-on-maps
# https://www.rdocumentation.org/packages/stats/versions/3.4.3/topics/prcomp
# Put REOF/RPC ggplot in one page -------
library(gridExtra)
grid.arrange(EOF1, EOF2, EOF3, RPC1, RPC2,RPC3, ncol=3)
# Merge 2 TS datasets (there is something wrong in this merge)-----------------------------------------------------------
rainglobal$date<- seq(from = as.POSIXct("1998-01-01", tz = 'UTC'), to = as.POSIXct("2016-12-01", tz = 'UTC'), by = "month")
global.temp=merge(global_sst_2016, temp, by = c("Month", "Year"))
global.temp$date <- seq(from = as.Date("1998-01-01", tz = 'UTC'), to = as.Date("2016-12-01", tz = 'UTC'), by = "month")
pc.global.temp=merge(global.temp, rainpcdf, by = "date")
save(global_temp,file = "global_temp.RData")
# Select variables from PC + Global SST Rainfall-------
pc.global.temp.subset <- subset(pc.global.temp, select = c(nino1.2anom, nino3.4anom, amm, pdo, mei, tsa, soi, temp, PC1, PC2, PC3))
#Correlation matrix with significance levels (p-value) -----------
res=cor(pc.global.temp.subset,method="pearson")
round(res, 2)
library("Hmisc")
res2 <- rcorr(as.matrix(pc.global.temp.subset))
res2
#Visualize correlation matrix-----------------
library(corrplot)
corrplot(res, type = "upper", order = "hclust",
tl.col = "black", tl.srt = 45)
# Insignificant correlation are crossed
corrplot(res2$r, type="upper", order="hclust",
p.mat = res2$P, sig.level = 0.05, insig = "pch")
# Positive correlations are displayed in blue and negative correlations in red color. Color intensity and the size of the circle are proportional to the correlation coefficients. In the right side of the correlogram, the legend color shows the correlation coefficients and the corresponding colors.
#Use chart.Correlation()--------------
install.packages("PerformanceAnalytics")
library("PerformanceAnalytics")
chart.Correlation(pc.global.temp.subset, histogram=TRUE, pch=19)
#The distribution of each variable is shown on the diagonal.
#On the bottom of the diagonal : the bivariate scatter plots with a fitted line are displayed
#On the top of the diagonal : the value of the correlation plus the significance level as stars
#Each significance level is associated to a symbol : p-values(0, 0.001, 0.01, 0.05, 0.1, 1) <=> symbols("***", "**", "*", ".", " ")
#beautiful LATEX, HTML and ASCII tables from R statistical output -------------
install.packages("stargazer")
library(stargazer)
# Select variables from Global SST Rainfall-------
global.temp.subset <- subset(global.temp, select =
-c(date, Year, Month, NINO1.2, NINO3, nino3anom, NINO4, nino4anom, NINO3.4))