CC_Est_NARC/Analysis/Code/ggplot_time_series_with_trends.R

#R code for creating ggplots of time series with smooth (GAM) and linear term


######################
#Import Libraries and set working directory
######################
library(zoo)
library(hydroTSM) #you need to install these packages first before you can load them here
library(lubridate)
library(mgcv)
library(ggplot2)
library(gridExtra)
library(scales)
options(scipen=999)
setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/")
######################



GRACE.monthly.av.df <- data.frame(read.csv("./Data/GRACE/Zunyi/CSV/With_border_pixels/MDB.monthly.TWSA.Mean.2002-2017 with border pixels.csv"))
GRACE.monthly.std.df <- data.frame(read.csv("./Data/GRACE/Zunyi/CSV/With_border_pixels/MDB.monthly.TWSA.Stdv.2002-2017 with border pixels.csv"))
















######################
#Set inputs
######################
Case.Study <- "CASESTUDY2"
Estuary <- "HUNTER"
Riv.Gauge.loc <- "GRETA"     #GRETA 
Est.Gauge.loc <-  "RAYMONDTERRACE"   #"CORAKI"          #"RAYMONDTERRACE" # "HEXHAMBRIDGE"
logtransformFlow <- TRUE
ggplotGAM.k <- 15
rivTempGAM.k <- 20
Version <- 'V3'

Fontsize <- 12
######################

######################
Output.Directory <- paste('./Output/CASESTUDY2_V4/', Estuary,'/Recent_Trends/', sep="")
if (file.exists(Output.Directory)){
  print('output folder already existed and was not created again')
  } else {
  dir.create(file.path(Output.Directory))
  print('output folder did not exist and was created')
  }
Output.Directory <- paste('./Output/CASESTUDY2_V4/', Estuary,'/Recent_Trends/Riv_', Riv.Gauge.loc,'_Est_',Est.Gauge.loc,'_GAMk', ggplotGAM.k, 'd/', sep="")
if (file.exists(Output.Directory)){
  print('output folder already existed and was not created again')
} else {
  dir.create(file.path(Output.Directory))
  print('output folder did not exist and was created')
}
######################

######################
#Set input file paths
######################

pattern = paste('SILO_climdata_', Estuary,'_Catchment*', sep="")
AirT_CSV_Path <- list.files(paste("./Data/SILO/",Case.Study, '/',sep=""), pattern, full.names=T, recursive=T)

pattern = paste(Estuary,'@', Riv.Gauge.loc, '.*.ALL.csv', sep="")
RivT_CSV_Path <- list.files("./Data/River_Gauge_Data/", pattern, full.names=T)

pattern = paste(Estuary,'@', Est.Gauge.loc, '.*.ALL.csv', sep="")
EstT_CSV_Path <- list.files("./Data/River_Gauge_Data/", pattern, full.names=T)

pattern = paste('sstmean_NNRP_', Estuary,'*', sep="")
SST_CSV_Path <- list.files(paste("./Data/NARCLIM_Site_CSVs/",Case.Study, '/', sep=""), pattern, full.names=T, recursive=T)
######################


######################
#Analyse
######################

############tasmean
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
AirT.df <- data.frame(read.csv(AirT_CSV_Path))
AirT.full.TS <- zoo((AirT.df$max_temp_Celsius + AirT.df$max_temp_Celsius)/2, order.by= as.Date(AirT.df[,"date"], format = "%Y-%m-%d")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
AirT.TS <- window(AirT.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))
AirT.full.df <- data.frame(AirT.full.TS)
AirT.df <- data.frame(AirT.TS)
colnames(AirT.df) <- 'tasmean'
colnames(AirT.full.df) <- 'tasmean'
############
AirT.df$Julday1 <- seq(1,length(AirT.df[,1]),1)
linear.trend.model_EC_all <- lm(tasmean ~ Julday1, AirT.df)
AirT.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
AirT.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
############
AirT.full.df$Julday1 <- seq(1,length(AirT.full.df[,1]),1)
linear.trend.model_EC_all <- lm(tasmean ~ Julday1, AirT.full.df)
AirT.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
AirT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
############tasmean


############River temp
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
#Here we use the raw DPI CSV format that comes with a bunch of metadata
RivT.df <- data.frame(read.csv(RivT_CSV_Path))
char.df <- data.frame(lapply(RivT.df[2,], as.character), stringsAsFactors=FALSE)
#dat <- data.frame(lapply(RivT.df[(4:nrow(RivT.df)),(2:ncol(RivT.df))], as.numeric), stringsAsFactors=FALSE)
#dat <- RivT.df.num[!is.na(as.numeric(as.character(RivT.df.num))),]
dat <- RivT.df[(4:nrow(RivT.df)),]
colnames(dat) <- lapply(RivT.df[2,], as.character)
dat$Date <- gsub(x=dat$Date,pattern="00:00:00",replacement="",fixed=T)
colnames(dat) <- gsub(x=colnames(dat),pattern="Water Temp(C)",replacement="Temp",fixed=T)
RivT.df <- dat
rm(dat,char.df)
RivT.full.TS <- zoo(as.numeric(as.character(RivT.df$Temp)), order.by= as.Date(RivT.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
RivT.full.TS <- window(RivT.full.TS, start=as.Date("1995-07-01"), end=as.Date("2018-01-01"))
RivT.full.TS <- na.approx(RivT.full.TS)
RivT.TS <- RivT.full.TS
RivT.full.df <- data.frame(RivT.TS)   ### This is only done because 
RivT.df <- data.frame(RivT.TS)
colnames(RivT.df) <- 'rivTmean'
colnames(RivT.full.df) <- 'rivTmean'
############
RivT.df$Julday1 <- seq(1,length(RivT.df[,1]),1)
linear.trend.model_EC_all <- lm(rivTmean ~ Julday1, RivT.df)
RivT.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
RivT.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
RivT.full.df$Julday1 <- seq(1,length(RivT.full.df[,1]),1)
linear.trend.model_EC_all <- lm(rivTmean ~ Julday1, RivT.full.df)
RivT.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
RivT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
############River temp

#export interpolated data as csv
#csv.name <- "C:/Users/z5025317/OneDrive - UNSW/CC_Estuaries_CASESTUDY2/Data/River_Gauge_Data/HUNTER@Greta_210064_Temp_interpolated.csv"
#write.csv(RivT.df, csv.name)



############River flow
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
#Here we use the raw DPI CSV format that comes with a bunch of metadata
RivQ.df <- data.frame(read.csv(RivT_CSV_Path))
char.df <- data.frame(lapply(RivQ.df[2,], as.character), stringsAsFactors=FALSE)
dat <- RivQ.df[(4:nrow(RivQ.df)),]
colnames(dat) <- lapply(RivQ.df[2,], as.character)
dat$Date <- gsub(x=dat$Date,pattern="00:00:00",replacement="",fixed=T)
colnames(dat) <- gsub(x=colnames(dat), pattern="Discharge (ML/d)",replacement="Flow",fixed=T)
RivQ.df <- dat
rm(dat,char.df)
#RivQ.full.TS <- zoo(log10((as.numeric(as.character(RivQ.df$Flow))) +1) , order.by= as.Date(RivQ.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
RivQ.full.TS <- zoo((as.numeric(as.character(RivQ.df$Flow)))/86.4, order.by= as.Date(RivQ.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
RivQ.TS <- window(RivQ.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))
#RivQ.full.TS <- window(RivQ.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-06-01"))
RivQ.full.df <- data.frame(RivQ.full.TS)   ### This is only done because 
RivQ.df <- data.frame(RivQ.TS)
colnames(RivQ.df) <- 'RivQmean'
colnames(RivQ.full.df) <- 'RivQmean'
############trends
RivQ.df$Julday1 <- seq(1,length(RivQ.df[,1]),1)
linear.trend.model_EC_all <- lm(RivQmean ~ Julday1, RivQ.df)
RivQ.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
RivQ.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
RivQ.full.df$Julday1 <- seq(1,length(RivQ.full.df[,1]),1)
linear.trend.model_EC_all <- lm(RivQmean ~ Julday1, RivQ.full.df)
RivQ.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
RivQ.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
############River Flow


############ SST
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
SST.df <- data.frame(read.csv(SST_CSV_Path))
SST.full.TS <- zoo(SST.df$NNRP_R1_1950-273.15, order.by= as.Date(SST.df[,"X"], format = "%Y-%m-%d")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
SST.TS <- window(SST.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))

#SST.full.TS <- window(SST.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-06-01"))

SST.full.df <- data.frame(SST.full.TS)
SST.df <- data.frame(SST.TS)
colnames(SST.df) <- 'SSTmean'
colnames(SST.full.df) <- 'SSTmean'
############
SST.full.df$Julday1 <- seq(1,length(SST.full.df[,1]),1)
linear.trend.model_EC_all <- lm(SSTmean ~ Julday1, SST.full.df)
SST.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all)$coefficients[2,4] 
SST.full.lintrend <- summary(linear.trend.model_EC_all)$coefficients[2,1] * 365
SST.df$Julday1 <- seq(1,length(SST.df[,1]),1)
linear.trend.model_EC_all2 <- lm(SSTmean ~ Julday1, SST.df)
SST.pvalNCV_ECall <- summary(linear.trend.model_EC_all2)$coefficients[2,4] 
SST.lintrend <- summary(linear.trend.model_EC_all2)$coefficients[2,1] * 365
############ SST


############Estuary temp
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
#Here we use the raw DPI CSV format that comes with a bunch of metadata
EstT.df <- data.frame(read.csv(EstT_CSV_Path))
char.df <- data.frame(lapply(EstT.df[2,], as.character), stringsAsFactors=FALSE)
#dat <- data.frame(lapply(EstT.df[(4:nrow(EstT.df)),(2:ncol(EstT.df))], as.numeric), stringsAsFactors=FALSE)

#dat <- EstT.df.num[!is.na(as.numeric(as.character(EstT.df.num))),]
dat <- EstT.df[(4:nrow(EstT.df)),]

colnames(dat) <- lapply(EstT.df[2,], as.character)
dat$Date <- gsub(x=dat$Date,pattern="00:00:00",replacement="",fixed=T)
colnames(dat) <- gsub(x=colnames(dat),pattern="Water Temp(C)",replacement="Temp",fixed=T)
EstT.df <- dat
rm(dat,char.df)
#replace negative values with NA
EstT.df$Temp <- replace(EstT.df$Temp, which(as.numeric(as.character(EstT.df$Temp)) < 11), NA)
EstT.full.TS <- zoo(as.numeric(as.character(EstT.df$Temp)), order.by= as.Date(EstT.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
EstT.TS <- window(EstT.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-06-01"))
EstT.full.TS <- EstT.TS
EstT.full.df <- data.frame(EstT.TS)   ### This is only done because of poor data at beginning
EstT.df <- data.frame(EstT.TS)
colnames(EstT.df) <- 'EstTmean'
colnames(EstT.full.df) <- 'EstTmean'
############
EstT.df$Julday1 <- seq(1,length(EstT.df[,1]),1)
linear.trend.model_EC_all <- lm(EstTmean ~ Julday1, EstT.df)
EstT.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
EstT.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
EstT.full.df$Julday1 <- seq(1,length(EstT.full.df[,1]),1)
linear.trend.model_EC_all <- lm(EstTmean ~ Julday1, EstT.full.df)
EstT.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
EstT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
############Est temp


######################
#Plot
######################

##################################### Full Time Period
#Air temp Full period 
p1air <- ggplot(AirT.full.df, aes(y=tasmean, x=index(AirT.full.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary," Catchment Centroid - Linear and smooth trends in catchment airT (SILO) lin trend was ",
                round(AirT.full.lintrend,3), ' C<>/year with p=', round(AirT.full.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Air Temperature [C<>]") + xlab("Time")

p1riv <- ggplot(RivT.full.df, aes(y=rivTmean, x=index(RivT.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary, " - Linear and smooth trends in gauged river temperature (@", Riv.Gauge.loc  ,") - Linear trend was ",
                round(RivT.full.lintrend,3), 'C<EFBFBD>/year with p=', sprintf("%.5f",round(RivT.full.pvalNCV_ECall,10)), sep="")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("River Temperature [C<>]") + xlab("Time") + xlab(NULL) +  
  theme(axis.text=element_text(size=Fontsize)) +
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))


if(logtransformFlow ==TRUE){
p1rivQ <- ggplot(RivQ.full.df, aes(y=log10(RivQmean+2), x=index(RivQ.full.TS))) + geom_line(alpha=0.5) + 
    ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) lin trend was ",
                  round(RivQ.full.lintrend,3), ' ML/day /year with p=', round(RivQ.full.pvalNCV_ECall,10), sep=" ")) +
    theme(plot.title=element_text(face="bold", size=9)) +
    geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
    stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
    ylab(expression(paste("log10(River Flow [ML/day] + 2)", sep=""))) + xlab("Time") +
    theme(axis.text=element_text(size=Fontsize)) +
    theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))
  } else {
  p1rivQ <- ggplot(RivQ.full.df, aes(y=RivQmean, x=index(RivQ.full.TS))) + geom_line(alpha=0.5) + 
    ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) lin trend was ",
                  round(RivQ.full.lintrend,3), ' ML/day with p=', round(RivQ.full.pvalNCV_ECall,10), sep=" ")) +
    theme(plot.title=element_text(face="bold", size=9)) +
    geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
    stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
    ylab(expression(paste("River Flow [ML/day]", sep=""))) + xlab("Time") +
    theme(axis.text=element_text(size=Fontsize)) +    
    theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))
    }


#Sea temp Full period 
p1sst <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary, " offshore - Linear and smooth trends in sea surface temperature (NNRP NARCLIM reanalysis) linear trend was ",
                round(SST.full.lintrend,3), 'C<EFBFBD>/year with p=', sprintf("%.5f",round(SST.full.pvalNCV_ECall,10)), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Sea Surface Temperature [C<>]") + xlab("Time")+ xlab(NULL) +
  theme(axis.text=element_text(size=Fontsize)) +
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

p1Est <- ggplot(EstT.full.df, aes(y=EstTmean, x=index(EstT.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary,'@', Est.Gauge.loc, " - Linear and smooth trend in Estuary temperature (GAUGE) lin trend was ",
                round(EstT.full.lintrend,3), 'C<EFBFBD>/year with p=', round(EstT.full.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Estuary Temperature [C<>]") + xlab("Time")+
  theme(axis.text=element_text(size=Fontsize)) +
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

p1Est2 <- ggplot(EstT.full.df, aes(y=EstTmean, x=index(EstT.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary,'@', Est.Gauge.loc, " - Linear and smooth trend in Estuary temperature (GAUGE) lin trend was ",
                round(EstT.full.lintrend,3), 'C<EFBFBD>/year with p=', round(EstT.full.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  #stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  binomial_smooth(formula = y ~ splines::ns(x, 2)) +
  ylab("Estuary Temperature [C<>]") + xlab("Time")+
  theme(axis.text=element_text(size=Fontsize)) +
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

png.name <- paste(Output.Directory, Estuary, '@', Est.Gauge.loc, '_Trends_estTmean_full_period_', Sys.Date(),"nosmoothb3.png", sep="")
png(file = png.name, width = 10.5, height = 4, units='in', res=500)
grid.arrange(p1Est2,ncol=1)
dev.off()


gA1 <- ggplotGrob(p1riv)
gA2 <- ggplotGrob(p1sst)
gA1$widths <- gA2$widths

#export to png
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_tasmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1air,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_rivTmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1riv,p1riv,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_rivTmean_SSTmean_full_period4_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA1,gA2,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_RivQmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1rivQ,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SSTmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1sst,p1sst,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Est.Gauge.loc, '_Trends_estTmean_full_period_', Sys.Date(),"b.png", sep="")
png(file = png.name, width = 10.5, height = 4, units='in', res=500)
grid.arrange(p1Est,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Est.Gauge.loc, '_Trends_estTmean_full_period_', Sys.Date(),"nosmoothb.png", sep="")
png(file = png.name, width = 10.5, height = 4, units='in', res=500)
grid.arrange(p1Est2,ncol=1)
dev.off()

##################################### Full Time Period


######################### 1990-present
combined.TS <- window(merge(AirT.full.TS, window(RivT.full.TS, start=as.Date("1995-01-01"), end=end(RivT.full.TS)), SST.full.TS,EstT.full.TS,RivQ.full.TS,  all=T), start=as.Date("1990-01-01"), end=end(AirT.full.TS))
combined.df <- data.frame(combined.TS)
colnames(combined.df) <- c('tasmean','rivTmean', 'SSTmean', 'EstTmean', 'rivQmean')

#Air temp 
p2air <- ggplot(combined.df, aes(y=tasmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary," Catchment centroid - Linear and smooth trend in catchment airT (SILO) linear trend was ",
                round(AirT.lintrend,3), ' C<>/year with p=', round(AirT.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Air Temperature [C<>]") + xlab(NULL)+
  theme(axis.text=element_text(size=Fontsize)) + 
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

#Riv temp 
p2riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river temperature (GAUGE) linear trend was ",
                round(RivT.lintrend,3), ' C<>/year with p=', round(RivT.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("River Temperature [C<>]") + xlab(NULL) +  
  theme(axis.text=element_text(size=Fontsize)) + 
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

#Riv flow
if(logtransformFlow ==TRUE){
  p2rivQ <- ggplot(combined.df, aes(y=log10(rivQmean+2), x=index(combined.TS))) + geom_line(alpha=0.5) + 
    ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) linear trend was ",
                  round(RivQ.full.lintrend,3),  'cubic-meter/year with p=', round(RivQ.full.pvalNCV_ECall,10), sep=" ")) +
    theme(plot.title=element_text(face="bold", size=9)) +
    geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
    stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
    ylab(expression(paste("log10(River flow [m"^"3" *"/s] + 2)", sep=""))) + xlab(NULL)+
    theme(axis.text=element_text(size=Fontsize)) + 
    theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))
  } else {
  p2rivQ <- ggplot(combined.df, aes(y=rivQmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
    ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) linear trend was ",
                  round(RivT.lintrend,3), 'cubic-meter/year with p=', round(RivT.pvalNCV_ECall,10), sep=" ")) +
    theme(plot.title=element_text(face="bold", size=9)) +
    geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
    stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
    ylab(expression(paste("River flow [m"^"3" *"/s]", sep=""))) + xlab(NULL)+
    theme(axis.text=element_text(size=Fontsize)) + 
    theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))
    }

#Sea temp 
p2sst <- ggplot(combined.df, aes(y=SSTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary,"NNRP(NARCLIM reanalysis) - Linear and smooth trend in sea surface temperature (NNRP) lin trend was ",
                round(SST.lintrend,3), ' C<>/year with p=', round(SST.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Sea Surface Temperature [C<>]") + xlab("Time")+
  theme(axis.text=element_text(size=Fontsize)) + 
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

#sst temp 
p2Est <- ggplot(combined.df, aes(y=EstTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
  ggtitle(paste(Estuary,'@', Est.Gauge.loc, " - Linear and smooth trend in Estuary temperature (GAUGE) linear trend was ",
                round(EstT.lintrend,3), ' C<>/year with p=', round(EstT.pvalNCV_ECall,10), sep=" ")) +
  theme(plot.title=element_text(face="bold", size=9)) +
  geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
  #stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
  ylab("Estuary Temperature [C<>]") + xlab("Time")+
  theme(axis.text=element_text(size=Fontsize)) +  
  theme(panel.grid.major.x = element_blank() ,panel.grid.minor.x = element_blank(), panel.grid.major.y = element_line( size=.1, color="white" ))

#export to png
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_tasmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p2air,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_rivTmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p2riv,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_rivQmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p2rivQ,ncol=1)
dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SSTmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p2sst,ncol=1)
dev.off()

#export an overview plot as png
gA <- ggplotGrob(p2air)
gB <- ggplotGrob(p2riv)
gC <- ggplotGrob(p2sst)
gD <- ggplotGrob(p2Est)
gE <- ggplotGrob(p2rivQ)
gA$widths <- gE$widths
gC$widths <- gE$widths
gD$widths <- gE$widths
gB$widths <- gE$widths

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SST_RivT_AirT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA,gB ,gC,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SST_RivT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gB,gC, ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_AirT_RivT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA,gB, ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_RivQ_RivT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gB ,gE, ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SST_RivT_EstT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gB,gD,gC,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_RivQ_RivT_EstT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gE,gB,gD,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_AirT_RivT_RivQ_1990-present_', Sys.Date(),"b.png", sep="")
png(file = png.name, width = 10.5, height = 11, units='in', res=500)
grid.arrange(gB, gA,gE,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_AirT_RivT_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA,gB,ncol=1)
dev.off()

png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_RivT_RivQ_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gB,gE,ncol=1)
dev.off()

lm_eqn <- function(df){
  m <- lm(y ~ x, df);
  eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2, 
                   list(a = format(coef(m)[1], digits = 2), 
                        b = format(coef(m)[2], digits = 2), 
                        r2 = format(summary(m)$r.squared, digits = 3)))
  as.character(as.expression(eq));  
}

Modeling <- 'yes'
if (Modeling == 'yes'){
  source("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code/FUN_do_ggplot_Scatterplot_2vars.R")
  AirT.full.TS.Monthly <- aggregate(AirT.full.TS, as.yearmon(time(AirT.full.TS)),FUN = mean)
  RivT.full.TS.Monthly <- aggregate(RivT.full.TS, as.yearmon(time(RivT.full.TS)),FUN = mean)
  RivQ.full.TS.Monthly <- aggregate(RivQ.full.TS, as.yearmon(time(RivQ.full.TS)),FUN = mean)
  
  if(logtransformFlow ==TRUE){
    RivQ.full.TS.Monthly <- log10(RivQ.full.TS.Monthly +2) 
  }
  All.TS.zoo <- merge(AirT.full.TS.Monthly, RivT.full.TS.Monthly ,RivQ.full.TS.Monthly , all=F)
  AirvsRivT <- Generate.ggplot.scatterplot(All.TS.zoo[,2], All.TS.zoo[,1],"River temp. [<5B>C]" , "Air temp. [<5B>C]")
  if(logtransformFlow ==TRUE){
    QvsRivT <- Generate.ggplot.scatterplot(All.TS.zoo[,2], All.TS.zoo[,3], "River temp. [<5B>C]",  expression(paste("log10(River flow [m"^"3" *"/s] + 2)", sep="")))
  } else {
    QvsRivT <- Generate.ggplot.scatterplot(All.TS.zoo[,2], All.TS.zoo[,3], "River temp. [<5B>C]",  expression(paste("River flow [m"^"3" *"/s]", sep="")))
    
  }
  #export an overview plot as png
  gA <- ggplotGrob(AirvsRivT)
  gB <- ggplotGrob(QvsRivT)
  gB$widths <- gA$widths

  
  png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Scatterplots_of_RivT_vs_predictors_1990-present_', Sys.Date(),"f.png", sep="")
  png(file = png.name, width = 8, height = 4, units='in', res=500)
  grid.arrange(gA,gB , ncol=2)
  dev.off()
  
}