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#Sep 2018 version of the NARCLIM suite of codes for OEH tech guide

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tinoheimhuber 6 years ago
parent 0a8d32769e
commit 0cd9a32a19
11 changed files with 469 additions and 128 deletions
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13
Analysis/Code/NARCLIM_Download/R_Preparing_BASH_script_for_NARCLIM_batch_download.R
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@ -17,12 +17,13 @@
# 'wssmean' Surface wind speed standard_name: air_velocity units: m s-1 # 'wssmean' Surface wind speed standard_name: air_velocity units: m s-1
# 'sstmean' Sea surface temperatuer daily mean # 'sstmean' Sea surface temperatuer daily mean
Clim_Var <- 'rldsmean' Clim_Var <- 'evspsblmean'
Datatype <- 'T_GCMS' #T_GCMS for GCM forcing, T_NNRP for reanalysis (only 1950-2009) Datatype <- 'T_GCMS' #T_GCMS for GCM forcing, T_NNRP for reanalysis (only 1950-2009)
Biasboolean <- 'False' #use bias corrected data? Biasboolean <- 'False' #use bias corrected data?
Location <- 'catchment' #location of coordinate
Directory <- 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs/CASESTUDY2/' Directory <- 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs/CASESTUDY2/'
Filename <- 'CASESTDUY2_NARCLIM_Point_Sites.csv' Filename <- 'CASESTUDY2_NARCLIM_Point_Sites.csv'
#Load CSV with location names and lat lon coordinates #Load CSV with location names and lat lon coordinates
Location.df <- data.frame(read.csv(paste(Directory, Filename, sep=""), header=T, fileEncoding="UTF-8-BOM")) Location.df <- data.frame(read.csv(paste(Directory, Filename, sep=""), header=T, fileEncoding="UTF-8-BOM"))
@ -34,7 +35,7 @@ text1 <- c(paste("Datatype='",Datatype,"'", sep=""),
paste("Bias_corrected='",Biasboolean,"'", sep=""), paste("ClimVarName='",Clim_Var,"'", sep="")) paste("Bias_corrected='",Biasboolean,"'", sep=""), paste("ClimVarName='",Clim_Var,"'", sep=""))
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text1) Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text1)
for (i in 1:(length(Location.df$Name))){ for (i in 1:(length(Location.df$Name))){
name<-as.character(Location.df$Name[i]) name <-as.character(Location.df$Name[i])
#name<-gsub('([[:punct:]])|\\s+','_',name) #name<-gsub('([[:punct:]])|\\s+','_',name)
# if(i<10){ # if(i<10){
# name<-paste('Catchment_0', as.character(i), sep="") # name<-paste('Catchment_0', as.character(i), sep="")
@ -46,6 +47,10 @@ for (i in 1:(length(Location.df$Name))){
if (Clim_Var == 'sstmean'){ if (Clim_Var == 'sstmean'){
latitude=round(as.numeric(Location.df$Ocean_Lat[i]),3) latitude=round(as.numeric(Location.df$Ocean_Lat[i]),3)
longitude=round(as.numeric(Location.df$Ocean_Long[i]),3) longitude=round(as.numeric(Location.df$Ocean_Long[i]),3)
}
if (Location == 'catchment'){
latitude=round(as.numeric(Location.df$Catch_Lat[i]),3)
longitude=round(as.numeric(Location.df$Catch_Lon[i]),3)
} }
text <- c(paste("latitude=",latitude,"", sep=""), paste("longitude=",longitude,"", sep=""), text <- c(paste("latitude=",latitude,"", sep=""), paste("longitude=",longitude,"", sep=""),
paste("name='",name,"'", sep=""), paste("name='",name,"'", sep=""),
@ -56,7 +61,7 @@ P1_NARCliM_NC_to_CSV_CCRC_SS.py \\
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text) Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text)
if(i==10|i==20|i==31|i==length(Location.df$Name)){ if(i==10|i==20|i==31|i==length(Location.df$Name)){
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, " ") Vector.for.command.line.txt <- c(Vector.for.command.line.txt, " ")
text.file.name <- paste(Directory ,'/',Clim_Var, "_", Datatype, "_", Biasboolean,substring(as.character(i), 1,1), "_", ".txt", sep="") text.file.name <- paste(Directory ,'/',Clim_Var, "_", Datatype, "_", Biasboolean,substring(as.character(i), 1,1), "_",Location, ".txt", sep="")
#open and fill text file #open and fill text file
fileConn <- file(text.file.name) fileConn <- file(text.file.name)
writeLines(Vector.for.command.line.txt, fileConn) writeLines(Vector.for.command.line.txt, fileConn)

81
Analysis/Code/NARCLIM_Download/R_Preparing_BASH_script_for_NARCLIM_batch_download_GAB.R
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@ -0,0 +1,81 @@
#code for preparing a text file with BASH code for batch download of NARCLIM data for the HUNTER WQ modeling of
#future climate scenarios
#NARCLIM Variables
#evspsblmean water_evaporation flux (actual ET) long_name: Surface evaporation standard_name: water_evaporation_flux units: kg m-2 s-1
#tasmean mean near surface temperature
#pracc precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip)
Clim_Var <- 'sstmean'
Datatype <- 'T_NNRP' #T_GCMS for GCM forcing, T_NNRP for reanalysis (only 1950-2009)
Biasboolean <- 'False' #use bias corrected data? True or False python boolean
Directory <- 'C:/Users/z5025317/OneDrive - UNSW/Hunter_CC_Modeling/07_Modelling/01_Input/BCGeneration/catchments/'
Filename <- 'Catchment_Prev_Hunter_Model_Centroids_VH_WGS84_attribute_Table.csv'
Directory <- 'C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs/CASESTUDY_GAB/'
Filename <- 'CASESTUDY2_GAB_NARCLIM_Point_Sites_V4.csv'
#Load CSV with location names and lat lon coordinates
Location.df <- data.frame(read.csv(paste(Directory, Filename, sep=""), header=T))
for(type in c('min','max')){
#create empty vector for storing the command line text and open file
Vector.for.command.line.txt <- c()
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, "module load python")
text1 <- c(paste("Datatype='",Datatype,"'", sep=""),
paste("Bias_corrected='",Biasboolean,"'", sep=""), paste("ClimVarName='",Clim_Var,"'", sep=""))
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text1)
for (i in 1:(length(Location.df[,1]))){
#for (i in c(1,5,10)){ if only subset of catchments is needed like for Met file.
#name<-as.character(Location.df$Name[i])
#name<-gsub('([[:punct:]])|\\s+','_',name)
if(type == 'min'){
#latlongs <- as.character(Location.df[,9])
name <- as.character(Location.df[,1][i])
latitude <- as.character(Location.df[,2][i])
longitude <- as.character(Location.df[,3][i])
}else if(type == 'max'){
name <- as.character(Location.df[,6][i])
latitude<-as.character(Location.df[,7][i])
longitude<-as.character(Location.df[,8][i])
}
# print(latlongs[i])
# names <- as.character(Location.df[,17])
# if(i<10){
# name<-paste('Plant_0', as.character(i), sep="")
# }else{
# name<-paste('Plant_', as.character(i), sep="")
# }
#print(name)
#print(names[i])
# latmin <- substr(latlongs[i], 5, 10)
# lonmin <- substr(latlongs[i], 18, 23)
# latmax <- substr(latlongs[i], 32, 37)
# lonmax <- substr(latlongs[i], 45, 50)
# latitude=latmax
# longitude=lonmax
text <- c(paste("latitude=",latitude,"", sep=""), paste("longitude=",longitude,"", sep=""),
paste("name='",name,"'", sep=""),
"python /srv/ccrc/data02/z5025317/Code_execution/\\
P1_NARCliM_NC_to_CSV_CCRC_SS.py \\
--lat $latitude --lon $longitude --varName $ClimVarName --domain 'd01' --timestep \\
'DAY' --LocationName $name --Datatype $Datatype --BiasBool $Bias_corrected")
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text)
if(i==10|i==20|i==30|i==40|i==45){
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, " ")
text.file.name <- paste('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/NARCLIM_Site_CSVs/CASESTUDY_GAB/DataDownloadCCRC/',Clim_Var, "_", Datatype, "_", Biasboolean,as.character(i), type, ".txt", sep="")
#open and fill text file
fileConn <- file(text.file.name)
writeLines(Vector.for.command.line.txt, fileConn)
close(fileConn)
Vector.for.command.line.txt <- c()
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, "module load python")
text1 <- c(paste("Datatype='",Datatype,"'", sep=""),
paste("Bias_corrected='",Biasboolean,"'", sep=""), paste("ClimVarName='",Clim_Var,"'", sep=""))
Vector.for.command.line.txt <- c(Vector.for.command.line.txt, text1)
}
}
}

123
Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots.py
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@ -1,4 +1,4 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
#==========================================================# #==========================================================#
#Last Updated - March 2018 #Last Updated - March 2018
#@author: z5025317 Valentin Heimhuber #@author: z5025317 Valentin Heimhuber
@ -34,34 +34,38 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
#loop through case study estuaries #loop through case study estuaries
Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE'] Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE']
Estuaries = ['HUNTER'] Estuaries = ['NADGEE','HUNTER', 'RICHMOND']
for Est in Estuaries: for Est in Estuaries:
#==========================================================# #==========================================================#
#set input parameters #set input parameters
#==========================================================# #==========================================================#
Case_Study_Name = 'CASESTUDY2' Case_Study_Name = 'CASESTUDY2'
Casestudy2_csv_path = "Data/NARCLIM_Site_CSVs/CASESTUDY2/CASESTDUY2_NARCLIM_Point_Sites.csv" Version = "V4"
OutVersion = "V4"
Casestudy2_csv_path = 'Data/NARCLIM_Site_CSVs/' + Case_Study_Name + '/' + Case_Study_Name + '_NARCLIM_Point_Sites.csv'
#Estuary = 'HUNTER' # 'Belongil' #Estuary = 'HUNTER' # 'Belongil'
Estuary = Est # 'Belongil' Estuary = Est # 'Belongil'
print Estuary print Estuary
Base_period_start = '1970-01-01' #Start of interval for base period of climate variability Base_period_start = '1950-01-01' #Start of interval for base period of climate variability
Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period) Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period)
Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting
Clim_var_type = 'pracc' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder Clim_var_type = 'pracc' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
Stats = 'days_h_30' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum Stats = 'days_h_20' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum 'days_h_30'
PD_Datasource = 'Station' #Source for present day climate data (historic time series) can be either: 'Station' or 'SILO' PD_Datasource = 'SILO' #Source for present day climate data (historic time series) can be either: 'Station' or 'SILO'
SILO_Clim_var = ['max_temp'] #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions SILO_Clim_var = ['daily_rain'] #select the SILO clim variable to be used for base period. - see silo.py for detailed descriptions
force_sea_levels = 'no' #use fort denison sea levels to run the statistics
Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean
Main_Plot = 'yes' Main_Plot = 'yes'
presentdaybar = False #include a bar for present day variability in the plots? presentdaybar = False #include a bar for present day variability in the plots?
present_day_plot = 'no' #save a time series of present day present_day_plot = 'no' #save a time series of present day
Allin1_delta_plot = 'yes' Allin1_delta_plot = 'yes'
Median_markers = 'no' #plot the median horizontal lines on top of the delta range in the allin1 plot? Deltaxmin, Deltaymin = -20,40 #y limits for 0 baseline delta plots
Figure_headings = 'no' Median_markers = 'yes' #plot the median horizontal lines on top of the delta range in the allin1 plot?
Version = "V1" Figure_headings = 'yes'
ALPHA_figs = 0 #Set alpha of figure background (0 makes everything around the plot panel transparent) ALPHA_figs = 0 #Set alpha of figure background (0 makes everything around the plot panel transparent)
font = {'family' : 'sans-serif', font = {'family' : 'sans-serif',
'weight' : 'normal', 'weight' : 'normal',
@ -72,7 +76,7 @@ for Est in Estuaries:
#==========================================================# #==========================================================#
#set directory path for output files #set directory path for output files
output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/' + '/Clim_Deviation_Plots/' output_directory = 'Output/' + Case_Study_Name + '_' + OutVersion + '/' + Estuary + '/' + '/Clim_Deviation_Plots/'
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted' #output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(output_directory): if not os.path.exists(output_directory):
os.makedirs(output_directory) os.makedirs(output_directory)
@ -86,7 +90,7 @@ for Est in Estuaries:
#==========================================================# #==========================================================#
#read CSV file #read CSV file
#==========================================================# #==========================================================#
Clim_Var_CSVs = glob.glob('./Output/' + Case_Study_Name + '/' + Estuary + '/' + Estuary + '_' + Clim_var_type + '_' + Stats + '*') Clim_Var_CSVs = glob.glob('./Output/' + Case_Study_Name + '_' + Version + '/' + Estuary + '/' + Estuary + '_' + Clim_var_type + '_' + Stats + '*')
clim_var_csv_path = Clim_Var_CSVs[0] clim_var_csv_path = Clim_Var_CSVs[0]
Filename = os.path.basename(os.path.normpath(clim_var_csv_path)) Filename = os.path.basename(os.path.normpath(clim_var_csv_path))
Clim_var_type = Filename.split('_', 2)[1] Clim_var_type = Filename.split('_', 2)[1]
@ -117,6 +121,17 @@ for Est in Estuaries:
if PD_Datasource == 'Station': if PD_Datasource == 'Station':
Present_day_df,minplotDelta, maxplotDelta = fct.import_present_day_climdata_csv(Estuary, Clim_var_type) Present_day_df,minplotDelta, maxplotDelta = fct.import_present_day_climdata_csv(Estuary, Clim_var_type)
if force_sea_levels == 'yes':
Estuary_Folder = "C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/Ocean_Data/Tide_Levels"
Present_day_df = pd.read_csv(Estuary_Folder + '/Fort_Denison_Daily_1900-2011.csv' , parse_dates=True, index_col=0)
Present_day_df = pd.read_csv(Estuary_Folder + '/Fort_Denison_Daily_1900-2011.csv' , parse_dates=True, index_col=0)
Present_day_df = Present_day_df.interpolate()
Present_day_df = pd.DataFrame(Present_day_df.loc[(Present_day_df.index >= Base_period_start) & (Present_day_df.index <= Base_period_end)])
[minplotDelta, maxplotDelta]=[1, 1]
df = pd.read_csv(Estuary_Folder + '/Fort_Denison_Sub_Daily_1900-2011.csv', parse_dates=[['Date', 'Time']])
df.index = pd.to_datetime(df.Date, format = 'dd/mm/Y') + pd.to_timedelta(df.Hour, unit='h')
if PD_Datasource == 'SILO': if PD_Datasource == 'SILO':
#read the CSV to extract the lat long and case stuy sites #read the CSV to extract the lat long and case stuy sites
with open(Casestudy2_csv_path, mode='r') as infile: with open(Casestudy2_csv_path, mode='r') as infile:
@ -162,7 +177,7 @@ for Est in Estuaries:
#substract a constant from all values to convert from kelvin to celcius (temp) #substract a constant from all values to convert from kelvin to celcius (temp)
if Clim_var_type == 'sstmean': if Clim_var_type == 'sstmean':
Present_day_df = Present_day_df.iloc[:,0:(len(Present_day_df)-1)]-273.15 #Present_day_df = Present_day_df.iloc[:,0:(len(Present_day_df)-1)]-273.15
Present_day_df.index = Present_day_df.index.to_period('D') Present_day_df.index = Present_day_df.index.to_period('D')
#==========================================================# #==========================================================#
#create seasonal sums etc. #create seasonal sums etc.
@ -172,7 +187,12 @@ for Est in Estuaries:
Present_day_df_annual = Present_day_df_annual.replace(0, np.nan) Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
Fdf_Seas_means = Present_day_df.resample('Q-NOV').max() #seasonal means Fdf_Seas_means = Present_day_df.resample('Q-NOV').max() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
if(Stats[:4] =='days'): elif Stats == 'maxmonthly':
Present_day_df_annual = Present_day_df.resample('M').max().resample('A').mean()
Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
Fdf_Seas_means = Present_day_df.resample('M').max().resample('Q-NOV').mean() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
elif(Stats[:4] =='days'):
Threshold = int(Stats[-2:]) Threshold = int(Stats[-2:])
agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()), agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
Present_day_df_annual = Present_day_df.resample('A').agg(agg) Present_day_df_annual = Present_day_df.resample('A').agg(agg)
@ -191,7 +211,30 @@ for Est in Estuaries:
Present_day_df_weekly = Present_day_df.resample('W').mean() Present_day_df_weekly = Present_day_df.resample('W').mean()
Fdf_Seas_means = Present_day_df.resample('Q-NOV').mean() #seasonal means Fdf_Seas_means = Present_day_df.resample('Q-NOV').mean() #seasonal means
#==========================================================# #==========================================================#
#==========================================================#
#calculate linear trend in present day series
#==========================================================#
if Stats == 'maxdaily' or Stats[:4] =='days' or Stats == 'maxmonthly':
coefficients, residuals, _, _, _ = np.polyfit(range(len(Present_day_df_annual.index)),Present_day_df_annual.values,1,full=True)
#mse = residuals[0]/(len(selected.index))
#nrmse = np.sqrt(mse)/(selected.max() - selected.min())
trend = coefficients[0]
print('Slope ' + str(coefficients[0]))
print(Base_period_start[:4]+ '-' + Base_period_end[:4] + ' trend was: ' + str(np.round(trend,4)) + ' units/year')
elif Stats == 'mean':
coefficients, residuals, _, _, _ = np.polyfit(range(len(Present_day_df.index)),Present_day_df.values,1,full=True)
#mse = residuals[0]/(len(selected.index))
#nrmse = np.sqrt(mse)/(selected.max() - selected.min())
if force_sea_levels == 'yes':
trend = coefficients[0]*12
else:
trend = coefficients[0]*365
print('Slope ' + str(coefficients[0]))
print(Base_period_start[:4]+ '-' + Base_period_end[:4] + ' trend was: ' + str(np.round(trend,5)) + ' units/year')
#==========================================================#
#==========================================================# #==========================================================#
#Loop through annual and seasons and create a deviation plot for each. #Loop through annual and seasons and create a deviation plot for each.
@ -224,7 +267,8 @@ for Est in Estuaries:
if temp == 'SON': if temp == 'SON':
Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4] Mean_df = Fdf_Seas_means[Fdf_Seas_means.index.quarter==4]
Column_names = ['Spring_near', 'Spring_far'] Column_names = ['Spring_near', 'Spring_far']
Present_Day_ref_df = Mean_df Present_Day_ref_df = Mean_df
#Subset to present day variability period #Subset to present day variability period
Present_Day_ref_df = pd.DataFrame(Present_Day_ref_df.loc[(Present_Day_ref_df.index >= Base_period_start) & (Present_Day_ref_df.index <= Base_period_end)]) Present_Day_ref_df = pd.DataFrame(Present_Day_ref_df.loc[(Present_Day_ref_df.index >= Base_period_start) & (Present_Day_ref_df.index <= Base_period_end)])
#Subset to present day variability delta base period (this is the statistical baseline used for present day conditions) #Subset to present day variability delta base period (this is the statistical baseline used for present day conditions)
@ -284,8 +328,12 @@ for Est in Estuaries:
Plot_in_df3['near future'] = df.iloc[1,0] Plot_in_df3['near future'] = df.iloc[1,0]
Plot_in_df3['far future'] = df.iloc[0,0] Plot_in_df3['far future'] = df.iloc[0,0]
Plot_in_df3 = Plot_in_df3.transpose() Plot_in_df3 = Plot_in_df3.transpose()
plt.plot(Plot_in_df3['Med'], linestyle="", markersize=45, # plt.plot(Plot_in_df3['Med'], linestyle="", markersize=45,
marker="_", color='darkblue', label="Median") # marker="_", color='red', label="Median")
#Plot_in_df3['Med']
#Plot_in_df3['Med'].values
plt.plot(np.arange(2),Plot_in_df3['Med'].values[::-1], linestyle="", markersize=45,
marker="_", color='red', label="Median")
z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5) z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5) z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
@ -296,10 +344,10 @@ for Est in Estuaries:
z.set_zorder(-1) z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5) z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
#plt.ylim(xmin, xmax) plt.ylim(xmin, xmax)
plt.ylim(10, 140) #plt.ylim(10, 140)
if Figure_headings == 'yes': if Figure_headings == 'yes':
plt.title(Clim_var_type + ' ' + temp ) plt.title(Clim_var_type + ' ' + temp)
ax.grid(False) ax.grid(False)
for tick in ax.get_xticklabels(): for tick in ax.get_xticklabels():
tick.set_rotation(0) tick.set_rotation(0)
@ -321,17 +369,20 @@ for Est in Estuaries:
z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5) z = plt.axhline(float(Present_Day_Mean), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
#fig.patch.set_facecolor('deepskyblue') #fig.patch.set_facecolor('deepskyblue')
plt.ylim(0, 40)
fig.tight_layout() fig.tight_layout()
fig.patch.set_alpha(ALPHA_figs) fig.patch.set_alpha(ALPHA_figs)
if Figure_headings == 'yes': if Figure_headings == 'yes':
plt.title(Clim_var_type + ' ' + Stats + ' ' + temp +' present day') #plt.title(Clim_var_type)
#plt.ylim(xmin, xmax) #plt.title(Clim_var_type + ' ' + Stats + ' ' + Base_period_start[:4] + '-' + Base_period_end[:4] + ' trend:' + str(np.round(trend,4)) + '/year')
plt.title(Base_period_start[:4] + '-' + Base_period_end[:4] + ' trend:' + str(np.round(trend,4)) + '/year')
plt.ylim(xmin, xmax)
#plt.ylim(0, 40)
ax.grid(False) ax.grid(False)
#if temp == 'MAM': #if temp == 'MAM':
i=i+4 i=i+4
else: else:
i=i+1 i=i+1
#create a data frame that contains all of the delta bars #create a data frame that contains all of the delta bars
Plot_in_df_tp.index = Column_names Plot_in_df_tp.index = Column_names
Plot_in_df_tp['-2std'] = Plot_in_df_tp['-2std'] - Present_Day_Mean Plot_in_df_tp['-2std'] = Plot_in_df_tp['-2std'] - Present_Day_Mean
@ -349,11 +400,12 @@ for Est in Estuaries:
else: else:
Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax) Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax)
if Median_markers == 'yes': if Median_markers == 'yes':
plt.plot(Plot_in_df_tp.index, Plot_in_df_tp['Median'], linestyle="", markersize=13, plt.plot(np.arange(2),Plot_in_df_tp['Median'].values , linestyle="", markersize=45,
marker="_", color='darkblue', label="Median") marker="_", color='red', label="Median")
z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5) z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
plt.ylim(-1, 20) plt.ylim(Deltaxmin, Deltaymin)
#plt.ylim(xmin, xmax)
if Figure_headings == 'yes': if Figure_headings == 'yes':
plt.title(Clim_var_type + ' All Deltas ' + Stats) plt.title(Clim_var_type + ' All Deltas ' + Stats)
#ax.grid(False) #ax.grid(False)
@ -366,7 +418,7 @@ for Est in Estuaries:
#plt.show() #plt.show()
if Main_Plot == 'yes': if Main_Plot == 'yes':
if presentdaybar == False: if presentdaybar == False:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + Version + '_' + '_NPDB.png' out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + '_' + Base_period_start + '_' + Base_period_end + '_' + Version + '_' + ' trend_' + str(np.round(trend[0],4)) + '.png'
else: else:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + Version + '_' + '2.png' out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + PD_Datasource + '_' + SILO_Clim_var[0] + '_' + Version + '_' + '2.png'
out_path = output_directory + '/' + out_file_name out_path = output_directory + '/' + out_file_name
@ -387,17 +439,21 @@ for Est in Estuaries:
else: else:
Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax) Plot_in_df_tp.plot.bar(stacked=True, color=uni_colors, edgecolor='none', legend=False, width=0.5,ax=ax)
if Median_markers == 'yes': if Median_markers == 'yes':
plt.plot(Plot_in_df_tp.index, Plot_in_df_tp['Median'], linestyle="", markersize=13, # plt.bar(Plot_in_df_tp.index, Plot_in_df_tp['Median'], linestyle="", markersize=13,
marker="_", color='darkblue', label="Median") # marker="_", color='red', label="Median")
plt.plot(np.arange(10), Plot_in_df_tp['Median'], linestyle="", markersize=13,
marker="_", color='red', label="Median")
z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5) z = plt.axhline(float(0), linestyle='--', color='red', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
plt.ylim(-5, 10) plt.ylim(Deltaxmin, Deltaymin)
if Figure_headings == 'yes': if Figure_headings == 'yes':
plt.title(Clim_var_type + ' All Delstas ' + Stats) plt.title(Clim_var_type + ' All Delstas ' + Stats)
#ax.grid(False) #ax.grid(False)
ax.xaxis.grid(False) ax.xaxis.grid(False)
for tick in ax.get_xticklabels(): for tick in ax.get_xticklabels():
tick.set_rotation(90) tick.set_rotation(50)
plt.setp(ax.xaxis.get_majorticklabels(), ha='right')
#ax.set_xticklabels(xticklabels, rotation = 45, ha="right")
fig.tight_layout() fig.tight_layout()
fig.patch.set_alpha(ALPHA_figs) fig.patch.set_alpha(ALPHA_figs)
plt.show() plt.show()
@ -450,7 +506,8 @@ for Est in Estuaries:
z.set_zorder(-1) z.set_zorder(-1)
#fig.patch.set_facecolor('deepskyblue') #fig.patch.set_facecolor('deepskyblue')
fig.patch.set_alpha(0) fig.patch.set_alpha(0)
plt.ylim(0, 400) #plt.ylim(0, 400)
plt.ylim(xmin, xmax)
#export plot to png #export plot to png
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + '_' + Version + 'Present_Day_Period.png' out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + '_' + Version + 'Present_Day_Period.png'
out_path = output_directory + '/' + out_file_name out_path = output_directory + '/' + out_file_name

27
Analysis/Code/P1_NARCliM_First_Pass_variab_deviation_plots_nonNARCLIM.py
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@ -1,4 +1,4 @@
# -*- coding: utf-8 -*- # -*- coding: utf-8 -*-
""" """
Created on Thu Jun 28 12:15:17 2018 Created on Thu Jun 28 12:15:17 2018
@ -53,16 +53,16 @@ Estuary = 'HUNTER' # 'Belongil'
Base_period_start = '1970-01-01' #Start of interval for base period of climate variability Base_period_start = '1970-01-01' #Start of interval for base period of climate variability
Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period) Base_period_DELTA_start = '1990-01-01' #Start of interval used for calculating mean and SD for base period (Delta base period)
Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting Base_period_end = '2009-01-01' #use last day that's not included in period as < is used for subsetting
Clim_var_type = 'Acidity' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder Clim_var_type = 'acidity' #Name of climate variable in NARCLIM models '*' will create pdf for all variables in folder
[minplotDelta, maxplotDelta]=[0,1] [minplotDelta, maxplotDelta]=[0.5,1]
#Source for present day climate data (historic time series) can be either: 'Station' or 'SILO' #Source for present day climate data (historic time series) can be either: 'Station' or 'SILO'
Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean Location = 'Estuary' # pick locaiton for extracting the SILO data can be: Estuary, Catchment, or Ocean
presentdaybar = False #include a bar for present day variability in the plots? presentdaybar = False #include a bar for present day variability in the plots?
present_day_plot = 'yes' #save a time series of present day present_day_plot = 'yes' #save a time series of present day
Version = "V1" Version = "V3"
Stats = 'mindaily' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum Stats = 'mean' #'maxdaily' #maximum takes the annual max Precipitation instead of the sum
ALPHA_figs = 1 #Set alpha of figure background (0 makes everything around the plot panel transparent) ALPHA_figs = 1 #Set alpha of figure background (0 makes everything around the plot panel transparent)
#==========================================================# #==========================================================#
@ -70,7 +70,7 @@ ALPHA_figs = 1 #Set alpha of figure background (0 makes everything around the pl
#==========================================================# #==========================================================#
#set directory path for output files #set directory path for output files
output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/' + '/Clim_Deviation_Plots/' output_directory = 'Output/' + Case_Study_Name +'_'+ Version + '/' + Estuary + '/' + '/Clim_Deviation_Plots/'
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted' #output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(output_directory): if not os.path.exists(output_directory):
os.makedirs(output_directory) os.makedirs(output_directory)
@ -116,6 +116,9 @@ Present_day_df= Present_day_df['PH']
if Stats == 'mindaily': if Stats == 'mindaily':
Present_day_df_annual = Present_day_df.resample('A').min() Present_day_df_annual = Present_day_df.resample('A').min()
Present_day_df_annual = Present_day_df_annual.replace(0, np.nan) Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
if Stats == 'minmonthly':
Present_day_df_annual = Present_day_df.resample('M').resample('A').min()
Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
if Stats == 'mean': if Stats == 'mean':
Present_day_df_annual = Present_day_df.resample('A').mean() Present_day_df_annual = Present_day_df.resample('A').mean()
Present_day_df_annual = Present_day_df_annual.replace(0, np.nan) Present_day_df_annual = Present_day_df_annual.replace(0, np.nan)
@ -146,10 +149,10 @@ index=['-2std', '-1std', 'Med', '1std', '2std']
columns = ['near future', 'far future'] columns = ['near future', 'far future']
Plot_in_df2 = pd.DataFrame(index=index, columns =columns ) Plot_in_df2 = pd.DataFrame(index=index, columns =columns )
Plot_in_df2['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near['10th']),np.NaN, float(Ensemble_Delta_df.near['Median']-Ensemble_Delta_df.near['10th']), Plot_in_df2['near future'] = [float(Present_Day_Mean + Ensemble_Delta_df.near['90th']),np.NaN, float(Ensemble_Delta_df.near['Median']-Ensemble_Delta_df.near['90th']),
np.NaN, float(Ensemble_Delta_df.near['90th']-Ensemble_Delta_df.near['Median'])] np.NaN, float(Ensemble_Delta_df.near['10th']-Ensemble_Delta_df.near['Median'])]
Plot_in_df2['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far['10th']),np.NaN, float(Ensemble_Delta_df.far['Median']-Ensemble_Delta_df.far['10th']), Plot_in_df2['far future'] = [float(Present_Day_Mean + Ensemble_Delta_df.far['90th']),np.NaN, float(Ensemble_Delta_df.far['Median']-Ensemble_Delta_df.far['90th']),
np.NaN, float(Ensemble_Delta_df.far['90th']-Ensemble_Delta_df.far['Median'])] np.NaN, float(Ensemble_Delta_df.far['10th']-Ensemble_Delta_df.far['Median'])]
#transpose the data frame #transpose the data frame
Plot_in_df_tp = Plot_in_df2.transpose() Plot_in_df_tp = Plot_in_df2.transpose()
#do the individual plots #do the individual plots
@ -172,7 +175,7 @@ Plot_in_df3['near future'] = df.iloc[1,0]
Plot_in_df3['far future'] = df.iloc[0,0] Plot_in_df3['far future'] = df.iloc[0,0]
Plot_in_df3 = Plot_in_df3.transpose() Plot_in_df3 = Plot_in_df3.transpose()
plt.plot(Plot_in_df3['Med'], linestyle="", markersize=52, plt.plot(Plot_in_df3['Med'], linestyle="", markersize=52,
marker="_", color='darkblue', label="Median") marker="_", color='red', label="Median")
z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5) z = plt.axhline(float(Present_Day_Mean-2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
z.set_zorder(-1) z.set_zorder(-1)
z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5) z = plt.axhline(float(Present_Day_Mean+2*Present_Day_SD), linestyle='-', color='black', alpha=.5)
@ -212,7 +215,7 @@ plt.ylim(xmin, xmax)
ax.grid(False) ax.grid(False)
if presentdaybar == False: if presentdaybar == False:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + Version + '_' + '_NPDB.png' out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_' + Base_period_start + '_' + Base_period_end + Version + '_' + '_NPDB2.png'
out_path = output_directory + '/' + out_file_name out_path = output_directory + '/' + out_file_name
fig.savefig(out_path) fig.savefig(out_path)

3
Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS.py
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@ -1,4 +1,4 @@
# -*- coding: utf-8 -*- `# -*- coding: utf-8 -*-
from netCDF4 import * from netCDF4 import *
import numpy as np import numpy as np
from numpy import * from numpy import *
@ -152,6 +152,7 @@ if Bias_Correction_BOOL == 'True':
#time.sleep(10) #time.sleep(10)
#set up the loop variables for interrogating the entire NARCLIM raw data #set up the loop variables for interrogating the entire NARCLIM raw data
GCMs = ('CCCMA3.1','CSIRO-MK3.0','ECHAM5', 'MIROC3.2', 'NNRP') GCMs = ('CCCMA3.1','CSIRO-MK3.0','ECHAM5', 'MIROC3.2', 'NNRP')
#set up the loop variables for interrogating the entire NARCLIM raw data
# #
#Define empty pandas data frames #Define empty pandas data frames
Full_df = pd.DataFrame() Full_df = pd.DataFrame()

1
Analysis/Code/P1_NARCliM_NC_to_CSV_CCRC_SS_BASH_script_readme.txt
Unescape Escape View File

@ -7,6 +7,7 @@ Variables available from NARCLIM (output):
‘pracc’ precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip) ‘pracc’ precipitation daily precipitation sum (sum of convective prcacc and stratiform prncacc precip)
‘pr1Hmaxtstep’ maximum 1 hour interval rainfall in a one day period ‘pr1Hmaxtstep’ maximum 1 hour interval rainfall in a one day period
'pr1Hmaxtstep' Max. 1-hour time-window moving averaged precipitation rate units: kg m-2 s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second 'pr1Hmaxtstep' Max. 1-hour time-window moving averaged precipitation rate units: kg m-2 s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
'pr30maxtstep' Max. 30min time-window moving averaged precipitation rate units: kg m-2 s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
'wss1Hmaxtstep' Max. 1-hour time-window moving averaged surface wind speed units: m s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second 'wss1Hmaxtstep' Max. 1-hour time-window moving averaged surface wind speed units: m s-1 maximum 1-hour time-window moving averaged values from point values 60.0 second
'wssmax' Surface wind speed standard_name: air_velocity units: m s-1 height: 10 m 'wssmax' Surface wind speed standard_name: air_velocity units: m s-1 height: 10 m
'wssmean' Surface wind speed standard_name: air_velocity units: m s-1 'wssmean' Surface wind speed standard_name: air_velocity units: m s-1

115
Analysis/Code/P1_NARCliM_plots_Windows.py
Unescape Escape View File

@ -25,6 +25,7 @@ matplotlib.style.use('ggplot')
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code') 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 import silo as sil
ALPHA_figs = 0 ALPHA_figs = 0
font = {'family' : 'sans-serif', font = {'family' : 'sans-serif',
'weight' : 'normal', 'weight' : 'normal',
@ -39,10 +40,11 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
#==========================================================# #==========================================================#
#set input parameters #set input parameters
Case_Study_Name = 'CASESTUDY2' Case_Study_Name = 'CASESTUDY2'
Version = 'V5' #V4 is latest for all model runs #V5 is just for the Hunter catchment climatology
Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE'] Estuaries = ['HUNTER', 'RICHMOND', 'NADGEE', 'SHOALHAVEN', 'GEORGES','CATHIE']
Estuaries = ['HUNTER'] Estuaries = ['HUNTER'] #,'HUNTER','RICHMOND']
#Estuaries = ['SHOALHAVEN']
for Est in Estuaries: for Est in Estuaries:
#Estuary = 'HUNTER' # 'Belongil' #Estuary = 'HUNTER' # 'Belongil'
Estuary = Est # 'Belongil' Estuary = Est # 'Belongil'
@ -56,16 +58,17 @@ for Est in Estuaries:
#set input preferences #set input preferences
#==========================================================# #==========================================================#
plot_pdf = 'no' plot_pdf = 'no'
plot_pngs = 'yes' plot_pngs = 'no'
delta_csv = 'yes' delta_csv = 'yes'
Stats = 'days_h_30' # 'maxdaily', 'mean', 'days_h_25' Stats = 'mean' # 'maxdaily', 'mean', 'days_h_25'
Version = 'V1' DoMonthly = True
#==========================================================# Perc_Vs_Abs = 'percent' #'percent' vs. 'absolute' deltas for near and far future
Location = 'Catchment'
#==========================================================#
#==========================================================# #==========================================================#
#set directory path for output files #set directory path for output files
output_directory = 'Output/' + Case_Study_Name + '/' + Estuary output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + Estuary
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted' #output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(output_directory): if not os.path.exists(output_directory):
os.makedirs(output_directory) os.makedirs(output_directory)
@ -74,7 +77,7 @@ for Est in Estuaries:
print('-------------------------------------------') print('-------------------------------------------')
#set directory path for individual png files #set directory path for individual png files
png_output_directory = 'Output/' + Case_Study_Name + '/' + Estuary + '/NARCLIM_Key_Figs' png_output_directory = 'Output/' + Case_Study_Name + '_' + Version + '/' + Estuary + '/NARCLIM_Key_Figs'
#output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted' #output_directory = 'J:/Project wrl2016032/NARCLIM_Raw_Data/Extracted'
if not os.path.exists(png_output_directory): if not os.path.exists(png_output_directory):
os.makedirs(png_output_directory) os.makedirs(png_output_directory)
@ -82,13 +85,13 @@ for Est in Estuaries:
print("output directory folder didn't exist and was generated") print("output directory folder didn't exist and was generated")
print('-------------------------------------------') print('-------------------------------------------')
#==========================================================# #==========================================================#
#==========================================================# #==========================================================#
Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*' ) Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*' )
if Location == 'Catchment':
Estuary_Folder = glob.glob('./Data/NARCLIM_Site_CSVs/'+ Case_Study_Name + '/' + Estuary + '*catch' )
Clim_Var_CSVs = glob.glob(Estuary_Folder[0] + '/' + Clim_var_type + '*') Clim_Var_CSVs = glob.glob(Estuary_Folder[0] + '/' + Clim_var_type + '*')
#==========================================================# #==========================================================#
#==========================================================# #==========================================================#
#read CSV files and start analysis #read CSV files and start analysis
@ -101,7 +104,7 @@ for Est in Estuaries:
Full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True) Full_df = pd.read_csv(clim_var_csv_path, index_col=0, parse_dates = True)
#pandas datestamp index to period (we don't need the 12 pm info in the index (daily periods are enough)) #pandas datestamp index to period (we don't need the 12 pm info in the index (daily periods are enough))
Full_df.index = Full_df.index.to_period('D') Full_df.index = Full_df.index.to_period('D')
Full_df = Full_df.drop(columns=['period']) #Full_df = Full_df.drop(columns=['period'])
Ncols_df = len(Full_df) Ncols_df = len(Full_df)
#check data types of columns #check data types of columns
#Full_df.dtypes #Full_df.dtypes
@ -119,7 +122,7 @@ for Est in Estuaries:
if Clim_var_type in ['pr1Hmaxtstep']: if Clim_var_type in ['pr1Hmaxtstep']:
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60 Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60
if Clim_var_type in ['rsdsmean','rldsmean']: if Clim_var_type in ['rsdsmean','rldsmean']:
Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24/1000000 Full_df = Full_df.iloc[:,0:(Ncols_df-1)]*60*60*24/1000000 #convert to unit used in SILO
Fdf_1900_2080 = Full_df Fdf_1900_2080 = Full_df
#==========================================================# #==========================================================#
@ -133,18 +136,40 @@ for Est in Estuaries:
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan) Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #seasonal means
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
elif(Stats[:4] =='days'):
Threshold = int(Stats[-2:])
#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').agg(agg)
#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
else: else:
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum() Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').sum()
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan) Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').sum() #seasonal means Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').sum() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
Fdf_Monthly_means = Fdf_1900_2080.resample('M').sum() #Monthlyonal means
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
else: else:
if(Stats == 'maxdaily'): if(Stats == 'maxdaily'):
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max() Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').max()
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan) Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').max() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
if(Stats[:4] =='days'): Fdf_Monthly_means = Fdf_1900_2080.resample('M').max() #Monthlyonal means
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
elif(Stats == 'maxmonthly'):
Fdf_1900_2080_annual = Fdf_1900_2080.resample('M').max().resample('A').mean()
Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #seasonal means
Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
Fdf_Monthly_means = Fdf_1900_2080.resample('M').max().resample('Q-NOV').mean() #Monthlyonal means
Fdf_Monthly_means = Fdf_Monthly_means.replace(0, np.nan)
elif(Stats[:4] =='days'):
Threshold = int(Stats[-2:]) Threshold = int(Stats[-2:])
#agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean') #agg = ('abobe_27_count', lambda x: x.gt(27).sum()), ('average', 'mean')
agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()), agg = ('>'+ str(Threshold) + '_count', lambda x: x.gt(Threshold).sum()),
@ -152,9 +177,11 @@ for Est in Estuaries:
#Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan) #Fdf_1900_2080_annual = Fdf_1900_2080_annual.replace(0, np.nan)
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').agg(agg) #seasonal means
#Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan) #Fdf_Seas_means = Fdf_Seas_means.replace(0, np.nan)
Fdf_Monthly_means = Fdf_1900_2080.resample('M').agg(agg)
else: else:
Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean() Fdf_1900_2080_annual = Fdf_1900_2080.resample('A').mean()
Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means Fdf_Seas_means = Fdf_1900_2080.resample('Q-NOV').mean() #seasonal means
Fdf_Monthly_means = Fdf_1900_2080.resample('M').mean() #seasonal means
Fdf_1900_2080_means = Fdf_1900_2080.mean() Fdf_1900_2080_means = Fdf_1900_2080.mean()
#==========================================================# #==========================================================#
@ -179,14 +206,20 @@ for Est in Estuaries:
#==========================================================# #==========================================================#
#Create Deltas of average change for annual and seasonal basis #Create Deltas of average change for annual and seasonal basis
#==========================================================# #==========================================================#
delta_all_df = fct.calculate_deltas_NF_FF2(Fdf_1900_2080_annual, Fdf_Seas_means, Stats) delta_all_df = fct.calculate_deltas_NF_FF2(Fdf_1900_2080_annual, Fdf_Seas_means, Stats, Perc_Vs_Abs)
if DoMonthly ==True:
delta_all_df_monthly = fct.calculate_deltas_monthly(Fdf_Monthly_means, Stats, Perc_Vs_Abs)
delta_all_df_monthly = pd.concat([delta_all_df_monthly.filter(regex= 'near'),delta_all_df_monthly.filter(regex= 'far')], axis=1)[-3:]
#==========================================================# #==========================================================#
if delta_csv == 'yes': if delta_csv == 'yes':
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes.csv' out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes.csv'
out_path = output_directory + '/' + out_file_name out_path = output_directory + '/' + out_file_name
delta_all_df.to_csv(out_path) delta_all_df.to_csv(out_path)
if delta_csv == 'yes' and DoMonthly ==True:
out_file_name = Estuary + '_' + Clim_var_type + '_' + Stats + '_NARCliM_ensemble_changes_monthly.csv'
out_path = output_directory + '/NARCLIM_Changes_Hunter/' + out_file_name
delta_all_df_monthly.to_csv(out_path)
#==========================================================# #==========================================================#
#==========================================================# #==========================================================#
@ -216,6 +249,9 @@ for Est in Estuaries:
'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ] 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' , 'tomato', 'royalblue', 'mediumpurple' ]
plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal'] plotcolours12 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal']
plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue'] plotcolours15 = ['darkolivegreen','turquoise', 'lightgreen', 'darkgreen', 'lightpink','slateblue', 'slategray', 'orange', 'tomato', 'peru', 'navy', 'teal', 'lightgreen','lightpink','slateblue']
color_dict = {'CCCMA3.1_R1': 'darkolivegreen', 'CCCMA3.1_R2': 'turquoise', 'CCCMA3.1_R3': 'lightgreen', 'CSIRO-MK3.0_R2': 'darkgreen',
'CSIRO-MK3.0_R1':'lightpink', 'CSIRO-MK3.0_R3':'slateblue','ECHAM5_R1':'slategray', 'ECHAM5_R2': 'orange', 'ECHAM5_R3': 'tomato',
'MIROC3.2_R1': 'peru', 'MIROC3.2_R2': 'navy' ,'MIROC3.2_R3': 'teal', '10th':'lightgreen','median':'lightpink','90th':'slateblue'}
#==========================================================# #==========================================================#
@ -284,27 +320,38 @@ for Est in Estuaries:
#=========# #=========#
# time series plot annual ALL models # time series plot annual ALL models
#=========# #=========#
png_out_file_name = Clim_var_type + '_' + Stats + '_TimeSeries_AllMods_' + Version + '.png' png_out_file_name = Clim_var_type + '_' + Stats + '_TimeSeries_AllMods_' + Version + '3.png'
png_out_path = png_output_directory + '/' + png_out_file_name png_out_path = png_output_directory + '/' + png_out_file_name
plt.title(Clim_var_type + ' - ' + Stats + ' - Time series - representative models') plt.title(Clim_var_type + ' - ' + Stats + ' - Time series - representative models')
#prepare data #prepare data
Mod_order = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,19,20,21,16,17,18,22,23,24,31,32,33,25,26,27,28,29,30,34,35,36] # Mod_order = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,19,20,21,16,17,18,22,23,24,31,32,33,25,26,27,28,29,30,34,35,36]
test = Fdf_1900_2080_annual # test = Fdf_1900_2080_annual
Mod_Names = test.columns # Mod_Names = test.columns
New_Mod_Name = [] # New_Mod_Name = []
for i in range(0,len(Mod_Names)): # for i in range(0,len(Mod_Names)):
if(Stats[:4] =='days'): # if(Stats[:4] =='days'):
New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i][0]) # New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i][0])
else: # else:
New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i]) # New_Mod_Name.append(str(Mod_order[i]+10) + '_' + Mod_Names[i])
test.columns = New_Mod_Name # test.columns = New_Mod_Name
test_sorted = test.reindex_axis(sorted(test.columns), axis=1) # test_sorted = test.reindex_axis(sorted(test.columns), axis=1)
colnamest = test.columns # colnamest = test.columns
test_sorted.columns = [w[3:-5] for w in colnamest] # test_sorted.columns = [w[3:-5] for w in colnamest]
#plot #plot
fig = plt.figure(figsize=(8,7)) fig = plt.figure(figsize=(8,7))
ax=plt.subplot(2,1,1) ax=plt.subplot(2,1,1)
test_sorted.plot(ax=ax,color = plotcolours36) #test_sorted.plot(ax=ax) #,color = plotcolours36)
Fdf_1900_2080_annual_b = Fdf_1900_2080_annual
New_Mod_Name = []
if(Stats[:4] =='days'):
for each in Fdf_1900_2080_annual_b.columns:
New_Mod_Name.append(each[0][:-5])
else:
for each in Fdf_1900_2080_annual_b.columns:
New_Mod_Name.append(each[:-5])
Fdf_1900_2080_annual_b.columns = New_Mod_Name
Fdf_1900_2080_annual_b.plot(ax=ax, color=[color_dict[x] for x in Fdf_1900_2080_annual_b.columns])
plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2)) plt.legend(loc=9, bbox_to_anchor=(0.5, -0.2))
ax.xaxis.grid(False) ax.xaxis.grid(False)
fig.patch.set_alpha(ALPHA_figs) fig.patch.set_alpha(ALPHA_figs)

65
Analysis/Code/climdata_fcts.py
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@ -71,9 +71,8 @@ def select_min_med_max_dif_model(NARCLIM_df):
return dfall , dfmin, dfmed, dfmax, Min_dif_mod_name,Med_dif_mod_name, Max_dif_mod_name return dfall , dfmin, dfmed, dfmax, Min_dif_mod_name,Med_dif_mod_name, Max_dif_mod_name
def calculate_deltas_NF_FF2(Annual_df, Seasonal_df, Stats): def calculate_deltas_NF_FF2(Annual_df, Seasonal_df, Stats, Perc_vs_Abs):
"""calculates the "deltas" between nearfuture and present day for annual or seasonal climate data in pandas TS format""" """calculates the "deltas" between nearfuture and present day for annual or seasonal climate data in pandas TS format"""
times = ['annual', 'DJF', 'MAM', 'JJA','SON'] times = ['annual', 'DJF', 'MAM', 'JJA','SON']
delta_all_df = pd.DataFrame() delta_all_df = pd.DataFrame()
for temp in times: for temp in times:
@ -106,10 +105,63 @@ def calculate_deltas_NF_FF2(Annual_df, Seasonal_df, Stats):
for unique_model in unique_models: for unique_model in unique_models:
dfdiff = Mean_df.filter(regex= unique_model) dfdiff = Mean_df.filter(regex= unique_model)
type(dfdiff) type(dfdiff)
delta_NF = dfdiff[1] - dfdiff[0] if Perc_vs_Abs == 'absolute':
delta_NF_ensemble.append(delta_NF) delta_NF = dfdiff[1] - dfdiff[0]
delta_FF = dfdiff[2] - dfdiff[1] delta_NF_ensemble.append(delta_NF)
delta_FF_ensemble.append(delta_FF) delta_FF = dfdiff[2] - dfdiff[0]
delta_FF_ensemble.append(delta_FF)
if Perc_vs_Abs == 'percent':
delta_NF = ((dfdiff[1] - dfdiff[0])/dfdiff[0])*100
delta_NF_ensemble.append(delta_NF)
delta_FF = ((dfdiff[2] - dfdiff[0])/dfdiff[0])*100
delta_FF_ensemble.append(delta_FF)
delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
delta_df= pd.concat([delta_df1, delta_df2], axis=1)
#rename columns
delta_df.columns = Column_names
#add a row with medians and 10 and 90th percentiles
delta_df.loc['10th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 10), Column_names[1]:np.percentile(delta_df[Column_names[1]], 10)})
delta_df.loc['median'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 50), Column_names[1]:np.percentile(delta_df[Column_names[1]], 50)})
delta_df.loc['90th'] = pd.Series({Column_names[0]:np.percentile(delta_df[Column_names[0]], 90), Column_names[1]:np.percentile(delta_df[Column_names[1]], 90)})
#append df to overall df
delta_all_df = pd.concat([delta_all_df, delta_df], axis=1)
if(Stats[:4] =='days'):
delta_all_df = delta_all_df .astype(int).fillna(0.0)
return delta_all_df
def calculate_deltas_monthly(Monthly_df, Stats, Perc_vs_Abs):
"""calculates the "deltas" between nearfuture and present day for annual or seasonal climate data in pandas TS format"""
delta_all_df = pd.DataFrame()
for i in range(1, 13, 1):
Mean_df = Monthly_df[Monthly_df.index.month==i].mean()
Column_names = [str(i)+'_near', str(i)+'_far']
if(Stats[:4] =='days'):
models = list(Monthly_df.mean().index.get_level_values(0))
else:
models = list(Monthly_df.mean().index)
newmodel = []
for each in models:
newmodel.append(each[:-5])
unique_models = set(newmodel)
# calculate diff for each unique model
delta_NF_ensemble = []
delta_FF_ensemble = []
for unique_model in unique_models:
dfdiff = Mean_df.filter(regex= unique_model)
type(dfdiff)
if Perc_vs_Abs == 'absolute':
delta_NF = dfdiff[1] - dfdiff[0]
delta_NF_ensemble.append(delta_NF)
delta_FF = dfdiff[2] - dfdiff[0]
delta_FF_ensemble.append(delta_FF)
if Perc_vs_Abs == 'percent':
delta_NF = ((dfdiff[1] - dfdiff[0])/dfdiff[0])*100
delta_NF_ensemble.append(delta_NF)
delta_FF = ((dfdiff[2] - dfdiff[0])/dfdiff[0])*100
delta_FF_ensemble.append(delta_FF)
delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models) delta_df1=pd.DataFrame(delta_NF_ensemble, index=unique_models)
delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models) delta_df2=pd.DataFrame(delta_FF_ensemble, index=unique_models)
@ -126,7 +178,6 @@ def calculate_deltas_NF_FF2(Annual_df, Seasonal_df, Stats):
if(Stats[:4] =='days'): if(Stats[:4] =='days'):
delta_all_df = delta_all_df .astype(int).fillna(0.0) delta_all_df = delta_all_df .astype(int).fillna(0.0)
return delta_all_df return delta_all_df
def import_present_day_climdata_csv(Estuary, Clim_var_type): def import_present_day_climdata_csv(Estuary, Clim_var_type):
""" """

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Analysis/Code/climdata_fcts.pyc
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138
Analysis/Code/ggplot_time_series_with_trends.R
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@ -19,30 +19,39 @@ setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/P
#Set inputs #Set inputs
###################### ######################
Case.Study <- "CASESTUDY2" Case.Study <- "CASESTUDY2"
Estuary <- "RICHMOND" Estuary <- "HUNTER"
Riv.Gauge.loc <- "OAKLANDROAD" #GRETA Riv.Gauge.loc <- "GRETA" #GRETA
Est.Gauge.loc <- "CORAKI" #"RAYMONDTERRACE" # "HEXHAMBRIDGE" Est.Gauge.loc <- "RAYMONDTERRACE" #"CORAKI" #"RAYMONDTERRACE" # "HEXHAMBRIDGE"
logtransformFlow <- TRUE logtransformFlow <- TRUE
ggplotGAM.k <- 15 ggplotGAM.k <- 15
rivTempGAM.k <- 20 rivTempGAM.k <- 20
Version <- 'V2' Version <- 'V3'
Fontsize <- 12
###################### ######################
###################### ######################
Output.Directory <- paste('./Output/', Case.Study, '/', Estuary,'/Recent_Trends/Riv_', Riv.Gauge.loc,'_Est_',Est.Gauge.loc,'_GAMk', ggplotGAM.k, '/', sep="") Output.Directory <- paste('./Output/CASESTUDY2_V4/', Estuary,'/Recent_Trends/', sep="")
if (file.exists(Output.Directory)){ if (file.exists(Output.Directory)){
print('output folder already existed and was not created again') print('output folder already existed and was not created again')
} else { } else {
dir.create(file.path(Output.Directory)) dir.create(file.path(Output.Directory))
print('output folder did not exist and was created') 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, 'b/', 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 #Set input file paths
###################### ######################
pattern = paste('SILO_climdata_', Estuary,'*', sep="") pattern = paste('SILO_climdata_', Estuary,'_Catchment*', sep="")
AirT_CSV_Path <- list.files(paste("./Data/SILO/",Case.Study, '/',sep=""), pattern, full.names=T, recursive=T) 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="") pattern = paste(Estuary,'@', Riv.Gauge.loc, '.*.ALL.csv', sep="")
@ -114,6 +123,12 @@ 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 RivT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 356
############River temp ############River temp
#export interpolated data as csv
#csv.name <- "C:/Users/z5025317/OneDrive - UNSW/CC_Estuaries_CASESTUDY2/Data/River_Gauge_Data/HUNTER@Greta_210064_Temp_interpolated.csv"
#write.csv(RivT.df, csv.name)
############River flow ############River flow
#Load a daily (no gaps) time series as a time serie baseline for other time series used here #Load a daily (no gaps) time series as a time serie baseline for other time series used here
#Here we use the raw DPI CSV format that comes with a bunch of metadata #Here we use the raw DPI CSV format that comes with a bunch of metadata
@ -128,7 +143,8 @@ rm(dat,char.df)
#RivQ.full.TS <- zoo(log10((as.numeric(as.character(RivQ.df$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(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)), 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$Flow)), order.by= as.Date(RivQ.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
RivQ.TS <- window(RivQ.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01")) #RivQ.TS <- window(RivQ.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))
RivQ.full.TS <- window(RivQ.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-06-01"))
RivQ.full.df <- data.frame(RivQ.full.TS) ### This is only done because RivQ.full.df <- data.frame(RivQ.full.TS) ### This is only done because
RivQ.df <- data.frame(RivQ.TS) RivQ.df <- data.frame(RivQ.TS)
colnames(RivQ.df) <- 'RivQmean' colnames(RivQ.df) <- 'RivQmean'
@ -149,7 +165,10 @@ RivQ.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 35
#Load a daily (no gaps) time series as a time serie baseline for other time series used here #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.df <- data.frame(read.csv(SST_CSV_Path))
SST.full.TS <- zoo(SST.df$NNRP_R1_1950-273.15, order.by= as.Date(SST.df[,"X"], format = "%Y-%m-%d")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q SST.full.TS <- zoo(SST.df$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.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.full.df <- data.frame(SST.full.TS)
SST.df <- data.frame(SST.TS) SST.df <- data.frame(SST.TS)
str(SST.df) str(SST.df)
@ -185,8 +204,7 @@ rm(dat,char.df)
#replace negative values with NA #replace negative values with NA
EstT.df$Temp <- replace(EstT.df$Temp, which(as.numeric(as.character(EstT.df$Temp)) < 11), NA) EstT.df$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$Temp)), order.by= as.Date(EstT.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q EstT.full.TS <- zoo(as.numeric(as.character(EstT.df$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
plot(EstT.full.TS) EstT.TS <- window(EstT.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-06-01"))
EstT.TS <- window(EstT.full.TS, start=as.Date("2013-06-01"), end=as.Date("2018-01-01"))
EstT.full.TS <- EstT.TS EstT.full.TS <- EstT.TS
EstT.full.df <- data.frame(EstT.TS) ### This is only done because of poor data at beginning EstT.full.df <- data.frame(EstT.TS) ### This is only done because of poor data at beginning
EstT.df <- data.frame(EstT.TS) EstT.df <- data.frame(EstT.TS)
@ -211,7 +229,7 @@ EstT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 35
##################################### Full Time Period ##################################### Full Time Period
#Air temp Full period #Air temp Full period
p1air <- ggplot(AirT.full.df, aes(y=tasmean, x=index(AirT.full.TS))) + geom_line(alpha=0.5) + 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 trend in catchment airT (SILO) lin trend was ", 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=" ")) + 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)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
@ -219,12 +237,14 @@ p1air <- ggplot(AirT.full.df, aes(y=tasmean, x=index(AirT.full.TS))) + geom_line
ylab("Air Temperature [C°]") + xlab("Time") ylab("Air Temperature [C°]") + xlab("Time")
p1riv <- ggplot(RivT.full.df, aes(y=rivTmean, x=index(RivT.TS))) + geom_line(alpha=0.5) + p1riv <- ggplot(RivT.full.df, aes(y=rivTmean, x=index(RivT.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river temperature (GAUGE) lin trend was ", ggtitle(paste(Estuary, " - Linear and smooth trends in gauged river temperature (@", Riv.Gauge.loc ,") - Linear trend was ",
round(RivT.full.lintrend,3), ' C°/year with p=', round(RivT.full.pvalNCV_ECall,10), sep=" ")) + round(RivT.full.lintrend,3), 'C°/year with p=', sprintf("%.5f",round(RivT.full.pvalNCV_ECall,10)), sep="")) +
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("River Temperature [C°]") + xlab("Time") 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){ if(logtransformFlow ==TRUE){
@ -234,7 +254,9 @@ p1rivQ <- ggplot(RivQ.full.df, aes(y=log10(RivQmean+2), x=index(RivQ.full.TS)))
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + 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") 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 { } else {
p1rivQ <- ggplot(RivQ.full.df, aes(y=RivQmean, x=index(RivQ.full.TS))) + geom_line(alpha=0.5) + 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 ", ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) lin trend was ",
@ -242,28 +264,54 @@ p1rivQ <- ggplot(RivQ.full.df, aes(y=log10(RivQmean+2), x=index(RivQ.full.TS)))
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + 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") 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 #Sea temp Full period
p1sst <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(alpha=0.5) + p1sst <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, "NNRP (NARCLIM reanalysis) - Linear and smooth trend in sea surface temperature (NNRP) lin trend was ", ggtitle(paste(Estuary, " offshore - Linear and smooth trends in sea surface temperature (NNRP NARCLIM reanalysis) linear trend was ",
round(SST.full.lintrend,3), ' C°/year with p=', round(SST.full.pvalNCV_ECall,10), sep=" ")) + round(SST.full.lintrend,3), 'C°/year with p=', sprintf("%.5f",round(SST.full.pvalNCV_ECall,10)), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Sea Surface Temperature [C°]") + xlab("Time") 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) + 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 ", ggtitle(paste(Estuary,'@', Est.Gauge.loc, " - Linear and smooth trend in Estuary temperature (GAUGE) lin trend was ",
round(EstT.full.lintrend,3), ' C°/year with p=', round(EstT.full.pvalNCV_ECall,10), sep=" ")) + round(EstT.full.lintrend,3), 'C°/year with p=', round(EstT.full.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Estuary Temperature [C°]") + xlab("Time") 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°/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 #export to png
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_tasmean_full_period_', Sys.Date(),".png", sep="") 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) png(file = png.name, width = 10.5, height = 7, units='in', res=500)
@ -271,7 +319,11 @@ grid.arrange(p1air,ncol=1)
dev.off() dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_rivTmean_full_period_', Sys.Date(),".png", sep="") 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) png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1riv,ncol=1) 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() dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_RivQmean_full_period_', Sys.Date(),".png", sep="") 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) png(file = png.name, width = 10.5, height = 7, units='in', res=500)
@ -279,12 +331,18 @@ grid.arrange(p1rivQ,ncol=1)
dev.off() dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_SSTmean_full_period_', Sys.Date(),".png", sep="") 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) png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1sst,ncol=1) grid.arrange(p1sst,p1sst,ncol=1)
dev.off() dev.off()
png.name <- paste(Output.Directory, Estuary, '@', Est.Gauge.loc, '_Trends_estTmean_full_period_', Sys.Date(),".png", sep="") 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 = 7, units='in', res=500) png(file = png.name, width = 10.5, height = 4, units='in', res=500)
grid.arrange(p1Est,ncol=1) grid.arrange(p1Est,ncol=1)
dev.off() 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 ##################################### Full Time Period
@ -300,7 +358,9 @@ p2air <- ggplot(combined.df, aes(y=tasmean, x=index(combined.TS))) + geom_line(a
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Air Temperature [C°]") + xlab("Time") ylab("Air 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" ))
#Riv temp #Riv temp
p2riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + p2riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(alpha=0.5) +
@ -309,7 +369,9 @@ p2riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("River Temperature [C°]") + xlab("Time") ylab("River 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" ))
#Riv flow #Riv flow
if(logtransformFlow ==TRUE){ if(logtransformFlow ==TRUE){
@ -319,7 +381,9 @@ if(logtransformFlow ==TRUE){
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + 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") 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 { } else {
p2rivQ <- ggplot(combined.df, aes(y=rivQmean, x=index(combined.TS))) + geom_line(alpha=0.5) + 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) lin trend was ", ggtitle(paste(Estuary,'@', Riv.Gauge.loc, " - Linear and smooth trend in river flow (GAUGE) lin trend was ",
@ -327,7 +391,9 @@ if(logtransformFlow ==TRUE){
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + 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") 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 #Sea temp
@ -337,7 +403,9 @@ p2sst <- ggplot(combined.df, aes(y=SSTmean, x=index(combined.TS))) + geom_line(a
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Sea Surface Temperature [C°]") + xlab("Time") 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 #sst temp
p2Est <- ggplot(combined.df, aes(y=EstTmean, x=index(combined.TS))) + geom_line(alpha=0.5) + p2Est <- ggplot(combined.df, aes(y=EstTmean, x=index(combined.TS))) + geom_line(alpha=0.5) +
@ -346,7 +414,9 @@ p2Est <- ggplot(combined.df, aes(y=EstTmean, x=index(combined.TS))) + geom_line(
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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) + #stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Estuary Temperature [C°]") + xlab("Time") 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 #export to png
png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_tasmean_1990-present_', Sys.Date(),".png", sep="") png.name <- paste(Output.Directory, Estuary, '@', Riv.Gauge.loc, '_Trends_tasmean_1990-present_', Sys.Date(),".png", sep="")
@ -382,6 +452,11 @@ png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA,gB ,gC,ncol=1) grid.arrange(gA,gB ,gC,ncol=1)
dev.off() 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_SST_RivT_EstT_1990-present_', Sys.Date(),".png", sep="") 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) png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gB,gD,gC,ncol=1) grid.arrange(gB,gD,gC,ncol=1)
@ -413,3 +488,4 @@ dev.off()

31
Analysis/Code/ggplot_time_series_with_trends_Waves_SST_OEH.R
Unescape Escape View File

@ -19,7 +19,7 @@ setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/P
#Set inputs #Set inputs
###################### ######################
Case.Study <- "CASESTUDY2" Case.Study <- "CASESTUDY2"
Estuary <- "RICHMOND" Estuary <- "GEORGES"
Climvar <- 'tasmean' Climvar <- 'tasmean'
ggplotGAM.k <- 7 ggplotGAM.k <- 7
###################### ######################
@ -43,7 +43,7 @@ pattern = paste(Estuary, '.*.csv', sep="")
Buoy_CSV_Path <- list.files("./Data/Ocean_Data/Waves_SST/", pattern, full.names=T) Buoy_CSV_Path <- list.files("./Data/Ocean_Data/Waves_SST/", pattern, full.names=T)
Buoy.name <- substr(list.files("./Data/Ocean_Data/Waves_SST/", pattern, full.names=F), nchar(Estuary)+2, nchar(Estuary)+7) Buoy.name <- substr(list.files("./Data/Ocean_Data/Waves_SST/", pattern, full.names=F), nchar(Estuary)+2, nchar(Estuary)+7)
SST.df <- data.frame(read.csv(AirT_CSV_Path, colClasses = "character")) SST.df <- data.frame(read.csv(Buoy_CSV_Path, colClasses = "character"))
colnames(SST.df) <- as.character(SST.df[9,]) colnames(SST.df) <- as.character(SST.df[9,])
SST.df = SST.df[-c(1:9), ] SST.df = SST.df[-c(1:9), ]
@ -95,6 +95,13 @@ SST.full.df$Julday1 <- seq(1,length(SST.full.df[,1]),1)
linear.trend.model_EC_all <- lm(SSTmean ~ Julday1, SST.full.df) linear.trend.model_EC_all <- lm(SSTmean ~ Julday1, SST.full.df)
SST.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4] SST.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
SST.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365 SST.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 365
#export interpolated data as csv
csv.name <- paste("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Data/Ocean_Data/Waves_SST/Processed/",Estuary , "_SST_Daily.csv", sep="")
write.csv(SST.full.df, csv.name)
###################### ######################
@ -109,7 +116,10 @@ p1air <- ggplot(Hsig.full.df, aes(y=Hsigmean, x=index(Hsig.full.TS))) + geom_lin
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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") + stat_smooth(method=gam, formula=y~s(x, k=4), se=T, size=0.5, col="red") +
ylab("Significant Wave Height [m]") + xlab("Time") ylab("Significant Wave Height [m]") + 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 #export to png
png.name <- paste(Output.Directory, '/Trends_Significant_Wave_Height_',Buoy.name, '_full_period_', Sys.Date(),".png", sep="") png.name <- paste(Output.Directory, '/Trends_Significant_Wave_Height_',Buoy.name, '_full_period_', Sys.Date(),".png", sep="")
@ -125,7 +135,10 @@ p1air <- ggplot(Hsig.full.df, aes(y=Hsigmean, x=index(Hsig.full.TS))) + geom_lin
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=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=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") + stat_smooth(method=gam, formula=y~s(x, k=5), se=T, size=0.5, col="green") +
ylab("Significant Wave Height [m]") + xlab("Time") ylab("Significant Wave Height [m]") + 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 #export to png
png.name <- paste(Output.Directory, '/Trends_Significant_Wave_Height_',Buoy.name, '_MultiGAM_full_period_', Sys.Date(),".png", sep="") png.name <- paste(Output.Directory, '/Trends_Significant_Wave_Height_',Buoy.name, '_MultiGAM_full_period_', Sys.Date(),".png", sep="")
@ -140,7 +153,10 @@ p1air <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(a
theme(plot.title=element_text(face="bold", size=9)) + theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) + 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") + stat_smooth(method=gam, formula=y~s(x, k=4), se=T, size=0.5, col="red") +
ylab("Sea Surface Temperature [C°]") + xlab("Time") 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" ))
#export to png #export to png
png.name <- paste(Output.Directory, '/Trends_SST_',Buoy.name, '_full_period_', Sys.Date(),".png", sep="") png.name <- paste(Output.Directory, '/Trends_SST_',Buoy.name, '_full_period_', Sys.Date(),".png", sep="")
@ -156,7 +172,10 @@ p1air <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(a
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=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=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") + stat_smooth(method=gam, formula=y~s(x, k=5), se=T, size=0.5, col="green") +
ylab("Sea Surface Temperature [C°]") + xlab("Time") 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" ))
#export to png #export to png
png.name <- paste(Output.Directory, '/Trends_SST_',Buoy.name, '_MultiGAM_full_period_', Sys.Date(),".png", sep="") png.name <- paste(Output.Directory, '/Trends_SST_',Buoy.name, '_MultiGAM_full_period_', Sys.Date(),".png", sep="")

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