diff --git a/Analysis/Code/P1_SILO_Batch_Download.py b/Analysis/Code/P1_SILO_Batch_Download.py
index 4bc5450..dfb5379 100644
--- a/Analysis/Code/P1_SILO_Batch_Download.py
+++ b/Analysis/Code/P1_SILO_Batch_Download.py
@@ -20,14 +20,12 @@ from matplotlib.backends.backend_pdf import PdfPages
 from ggplot import *
 matplotlib.style.use('ggplot')
 import csv
-from datetime import datetime
-
 # import own modules
 # Set working direcotry (where postprocessed NARClIM data is located)
 os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
-import climdata_fcts as fct
+#import climdata_fcts as fct
 import silo as sil
-
+#==========================================================#
 
 #==========================================================#
 # Set working direcotry (where postprocessed NARClIM data is located)
@@ -39,9 +37,13 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
 #set input parameters
 Case_Study_Name = 'CASESTUDY2' 
 Casestudy2_csv_path =  "Data/NARCLIM_Site_CSVs/CASESTUDY2/CASESTDUY2_NARCLIM_Point_Sites.csv"
-Silo_variables = ['daily_rain', "max_temp", "min_temp", 'et_short_crop']      
+Silo_variables = ['daily_rain', "max_temp", "min_temp", 'et_short_crop', 'evap_syn'] 
+Location  = 'Catchment'  
+startdate= '19600101'
+enddate= '20180101' 
 #==========================================================#
 
+
 #==========================================================#
 #set directory path for output files
 output_directory = 'Data/SILO/' + Case_Study_Name + '/'
@@ -52,19 +54,25 @@ if not os.path.exists(output_directory):
     print("output directory folder didn't exist and was generated")
     print('-------------------------------------------')
 #==========================================================#  
+ 
     
-
+    
+#==========================================================# 
 #read the CSV to extract the lat long and case stuy sites
 with open(Casestudy2_csv_path, mode='r') as infile:
     reader = csv.reader(infile)
     next(reader, None) 
     with open('coors_new.csv', mode='w') as outfile:
         writer = csv.writer(outfile)
-        mydict = dict((rows[0],[rows[1],rows[2]]) for rows in reader)
-        
+        if Location == 'Estuary':
+            mydict = dict((rows[0],[rows[1],rows[2]]) for rows in reader)
+        if Location == 'Ocean':
+            mydict = dict((rows[0],[rows[3],rows[4]]) for rows in reader)
+        if Location == 'Catchment':
+            mydict = dict((rows[0],[rows[5],rows[6]]) for rows in reader)
+            
 for Estuary in mydict:
-    print Estuary
-    print str(mydict[Estuary][0])
+    print Estuary, mydict[Estuary][0], mydict[Estuary][1]
     
     #==========================================================#
     #set directory path for output files
@@ -77,11 +85,10 @@ for Estuary in mydict:
         print('-------------------------------------------')
     #==========================================================#
     
-    output_csv = output_directory_internal + 'SILO_climdata_' + Estuary +'_' + mydict[Estuary][0] + '_' +  mydict[Estuary][1] + '.csv'
-    silo_df = sil.pointdata(Silo_variables, 'Okl9EDxgS2uzjLWtVNIBM5YqwvVcCxOmpd3nCzJh','19900101','20100101',
+    output_csv = output_directory_internal + 'SILO_climdata_' + Estuary +'_'+ Location +'_' + mydict[Estuary][0] + '_' +  mydict[Estuary][1] + '2.csv'
+    silo_df = sil.pointdata(Silo_variables, 'Okl9EDxgS2uzjLWtVNIBM5YqwvVcCxOmpd3nCzJh',startdate, enddate, 
                             None, mydict[Estuary][0], mydict[Estuary][1], True, output_csv)
-
-
+#==========================================================# 
 
 
 
diff --git a/Analysis/Code/climdata_fcts.pyc b/Analysis/Code/climdata_fcts.pyc
new file mode 100644
index 0000000..d6785bb
Binary files /dev/null and b/Analysis/Code/climdata_fcts.pyc differ
diff --git a/Analysis/Code/ggplot_time_series_with_trends.R b/Analysis/Code/ggplot_time_series_with_trends.R
new file mode 100644
index 0000000..986a51e
--- /dev/null
+++ b/Analysis/Code/ggplot_time_series_with_trends.R
@@ -0,0 +1,220 @@
+#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/")
+######################
+
+######################
+#Set inputs
+######################
+Case.Study <- "CASESTUDY2"
+Estuary <- "HUNTER"
+Climvar <- 'tasmean'
+ggplotGAM.k <- 14
+######################
+
+
+######################
+#Set input file paths
+######################
+AirT_CSV_Path <- "./Data/SILO/CASESTUDY2/HUNTER/SILO_climdata_HUNTER_Catchment_-32.162479_150.5335812.csv"
+RivT_CSV_Path <- "./Data/River_Gauge_Data/HUNTER_210064_20180615/210064_Temp.csv"
+SST_CSV_Path <- './Data/NARCLIM_Site_CSVs/CASESTUDY2/HUNTER_32.751_151.690/sstmean_NNRP_HUNTER_33.034_152.156_NARCliM_summary.csv'
+######################
+
+
+######################
+#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 )$coefficients[2,1] * 356
+############
+AirT.full.df$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] * 356
+############tasmean
+
+
+############River temp
+#Load a daily (no gaps) time series as a time serie baseline for other time series used here
+RivT.df <- data.frame(read.csv(RivT_CSV_Path))
+RivT.full.TS <- zoo(RivT.df$Temp, order.by= as.Date(RivT.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
+RivT.TS <- window(RivT.full.TS, start=as.Date("1995-01-01"), end=as.Date("2018-01-01"))
+RivT.full.df <- data.frame(RivT.TS)   ### This is only done because 
+RivT.df <- data.frame(RivT.TS)
+colnames(RivT.df) <- 'rivTmean'
+colnames(RivT.full.df) <- 'rivTmean'
+############
+RivT.df$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] * 356
+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 )$coefficients[2,1] * 356
+############River temp
+
+############ SST
+#Load a daily (no gaps) time series as a time serie baseline for other time series used here
+SST.df <- data.frame(read.csv(SST_CSV_Path))
+SST.full.TS <- zoo(SST.df$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.df <- data.frame(SST.full.TS)
+SST.df <- data.frame(SST.TS)
+str(SST.df)
+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)$coefficients[2,1] * 356
+SST.df$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] * 356
+############ SST
+
+
+
+######################
+#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, " - Linear and smooth trend 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")
+
+#Riv temp Full period 
+p1riv <- ggplot(RivT.full.df, aes(y=rivTmean, x=index(RivT.TS))) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - Linear and smooth trend in river temperature (GAUGE) lin trend was ",
+                round(RivT.full.lintrend,3), ' C°/year with p=', 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")
+
+#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, " - Linear and smooth trend in sea surface temperature (NNRP) lin trend was ",
+                round(SST.full.lintrend,3), ' C°/year with p=', 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")
+
+#export to png
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/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/', Case.Study, '/', Estuary, '/Trends_rivTmean_full_period_', Sys.Date(),".png", sep="")
+png(file = png.name, width = 10.5, height = 7, units='in', res=500)
+grid.arrange(p1riv,ncol=1)
+dev.off()
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_SSTmean_full_period_', Sys.Date(),".png", sep="")
+png(file = png.name, width = 10.5, height = 7, units='in', res=500)
+grid.arrange(p1sst,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, 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')
+
+#Air temp 
+p1air <- ggplot(combined.df, aes(y=tasmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - Linear and smooth trend in catchment airT (SILO) lin 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("Time")
+
+#Riv temp 
+p1riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - Linear and smooth trend in river temperature (GAUGE) lin 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("Time")
+
+#Sea temp 
+p1sst <- ggplot(combined.df, aes(y=SSTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - 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")
+
+#export to png
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_tasmean_1990-present_', 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/', Case.Study, '/', Estuary, '/Trends_rivTmean_1990-present_', Sys.Date(),".png", sep="")
+png(file = png.name, width = 10.5, height = 7, units='in', res=500)
+grid.arrange(p1riv,ncol=1)
+dev.off()
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_SSTmean_1990-present_', Sys.Date(),".png", sep="")
+png(file = png.name, width = 10.5, height = 7, units='in', res=500)
+grid.arrange(p1sst,ncol=1)
+dev.off()
+
+#export an overview plot as png
+gA <- ggplotGrob(p1air)
+gB <- ggplotGrob(p1riv)
+gC <- ggplotGrob(p1sst)
+gA$widths <- gB$widths
+gC$widths <- gB$widths
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_Summary_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()
+
+
+
+
+
+
+
+
+
diff --git a/Analysis/Code/ggplot_time_series_with_trends_sea_levle.R b/Analysis/Code/ggplot_time_series_with_trends_sea_levle.R
new file mode 100644
index 0000000..58eee02
--- /dev/null
+++ b/Analysis/Code/ggplot_time_series_with_trends_sea_levle.R
@@ -0,0 +1,90 @@
+#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/")
+######################
+
+######################
+#Set inputs
+######################
+Case.Study <- "CASESTUDY2"
+Estuary <- "HUNTER"
+Climvar <- 'tasmean'
+ggplotGAM.k <- 7
+######################
+
+
+######################
+#Set input file paths
+######################
+AirT_CSV_Path <- "./Data/Ocean_Data/BOM_monthly_SL_Hunter_Newcastle.txt"
+
+
+
+
+
+
+dat = readLines(AirT_CSV_Path)
+dat = as.data.frame(do.call(rbind, strsplit(dat, split=" {2,10}")), stringsAsFactors=FALSE)
+colnames(dat) <-dat[3,]
+
+dat2 = dat[-c(1:3), ] 
+dat2 = dat2[-(720:726),]
+dat2 = dat2[-(1:108),]
+dat2 = dat2[,-1]
+
+dat2$Date <-  as.yearmon(dat2[,1], "%m %Y")
+
+SeaLev.df <- dat2
+head(SeaLev.df)
+SeaLev.df$MSL <- as.numeric(SeaLev.df$Mean)
+SeaLev.df$Julday1 <- seq(1,length(SeaLev.df[,1]),1)
+linear.trend.model_EC_all <- lm(MSL ~ Julday1, SeaLev.df)
+SeaLev.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] 
+SeaLev.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 12
+
+######################
+#Plot
+######################
+
+##################################### Full Time Period
+p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
+                round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.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=4), se=T, size=0.5, col="red") +
+  ylab("Monthly Mean Sea Level [m]") + xlab("Time")
+
+#export to png
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_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()
+
+#multiple smooths
+p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) + 
+  ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
+                round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.pvalNCV_ECall,10), sep=" ")) +
+  theme(plot.title=element_text(face="bold", size=9)) +
+  stat_smooth(method=gam, formula=y~s(x,  k=13), se=T, size=0.5, col="red") +
+  #stat_smooth(method=gam, formula=y~s(x,  k=8), se=T, size=0.5, cor="blue") +
+  stat_smooth(method=gam, formula=y~s(x,  k=5), se=T, size=0.5, col="green") +
+  ylab("Monthly Mean Sea Level [m]") + xlab("Time")
+
+#export to png
+png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_MultiGAM_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()