This R package builds netCDF files of the Bureau of Meteorology Australian Water Availability Project daily national climate grids and allows efficient extraction of daily, weekly, monthly, quarterly or annual catchment average precipitation, Tmin, Tmax, vapour pressure, solar radiation and then estimation of various measures of potential evaporation.
The package development was funded by the Victorian Government The Department of Environment, Land, Water and Planning Climate and Water Initiate (https://www.water.vic.gov.au/climate-change/research/vicwaci).
For details of the appraoch see the paper "Peterson, Tim J, Wasko, C, Saft, M, Peel, MC. AWAPer: An R package for area weighted catchment daily meteorological data anywhere within Australia. Hydrological Processes. 2020; 34: 1301– 1306. https://doi.org/10.1002/hyp.13637".
For package details see the PDF manual https://github.com/peterson-tim-j/AWAPer/blob/master/AWAPer.pdf. Alternatively see the lastest CRAN published version at https://CRAN.R-project.org/package=AWAPer
Package vignettes can also be found using the R-command:
browseVignettes("AWAPer")Using the default compression settings, each meteorlogical variable requires ~5GB of hard-drive storage for the full record (1900 to 2019). Additionally, the netCDF files should be stored locally, and not over a network, to minimise the time for data extraction. Below are details of the system requirements, how to install AWAPer and the following examples:
- Building and updating the required netCDF files (
)
- Extract and check daily point precipitation data against rain gauge observations (
)
- Extract daily areal weighted precipitation and calculate two measures of ET (
)
- Calculate all measures of ET possible with AWAPer (
)
- Extract and map maximum daily temperature for all of Australia (
)
- Extract monthly areal weighted total precipitation at two catchments and plot the totals and the spatial variability (
)
- Extract and map the monthly total precipitation at two catchments (
)
On Windows OS only the program "7z" is required to uzip the ".Z" compressed grid files. Follow the steps below to download and install 7z.
- Download and intall 7z from https://www.7-zip.org/download.html
- Click "Search Windows", search "Edit environmental variables for your account" and click on it.
- In the "User variables" window, select the "Path", and click "Edit...".
- In the "Edit environmental variable" window, click "New".
- Paste the path to the 7zip application folder, and click OK.
- Restart Windows.
- Check the setup by opening the "Command Prompt" and enter the command "7z". If 7z is correctly setup, output details such as the version, descriptions of commands, etc should be shown.')
The package is available from the R library (i.e. CRAN) at https://cran.r-project.org/web/packages/AWAPer/index.html. To install the package from CRAN use the R command:
install.packages('AWAPer')Alternatively, to install the latest version:
- Download the latest "tar.gz" (e.g. AWAPer_0.1.4.tar.gz) file from https://github.com/peterson-tim-j/AWAPer/releases.
- Open R.
- Install the netCDF package using the following command:
install.packages("ncdf4"). Importantly this step may require installation of netCDF software outside of R. Please read the output R console messages carefully. - Install the remaining required packages using the R command:
install.packages(c("utils", "sp", "raster", "chron", "maptools", "Evapotranspiration","devtools","zoo", "methods", "xts")) - Load the required packages using the R command: `library(c('Evapotranspiration', 'ncdf4', 'utils', 'raster', 'chron', 'maptools', 'sp', 'zoo', 'methods', 'xts')
- Install the AWAPer package using the following example R command (NOTE: use the full file path to the AWAPer folder). For example on a PC
install.packages("C:\MY_FOLDER\AWAPer\AWAPer_0.1.4.tar.gz", repos = NULL, type = "source")and for Macinstall.packages(“~/Users/MyFolder/AWAPer/AWAPer_0.1.4.tar.gz", repos = NULL, type = "source")
This example shows the steps required to build the two netCDF files, and then update the data to yesterday.
# Set dates for building netCDFs and extracting data from 15 to 5 days ago.
startDate = as.Date(Sys.Date()-15,"%Y-%m-%d")
endDate = as.Date(Sys.Date()-5,"%Y-%m-%d")
# Set names for netCDF files (in the system temp. directory).
ncdfFilename = tempfile(fileext='.nc')
ncdfSolarFilename = tempfile(fileext='.nc')
# Build netCDF grids for all met. data but only over the defined time period.
file.names = makeNetCDF_file(ncdfFilename=ncdfFilename,
ncdfSolarFilename=ncdfSolarFilename,
updateFrom=startDate, updateTo=endDate)
# Now, to demonstrate updating the netCDF grids to one dat ago, rerun with
# the same file names but updateFrom=NA.
file.names = makeNetCDF_file(ncdfFilename=ncdfFilename,
ncdfSolarFilename=ncdfSolarFilename,
updateFrom=NA) This example was developed by Ms Xinyang Fan (Uni. Melbourne) and shows how to extract point estimates of daily precipitation at four goundwater bore locations and at one rainfall gauge. The extracted data is then plotted. The rain gauge is also compared against the observed rain gauge. The latter shows that the results are unbiased, but minor differences do exist due to AWAP data having a 5x5 km grid-cell resolution. The plots below show (1) the locattions of the five sites (2) bar graphs of the daily precip. and (3) plots of the observed vs AWAPer estimated precip. at the rainfall gauge.
Importantly, this example downloads the Australia 9-second DEM. This is ~3GB.
# load all necessary packages
library(AWAPer)
# Make the netCDF files of only AWAP precipitation data.
fnames = makeNetCDF_file(ncdfFilename ='AWAP_demo.nc',
updateFrom=as.Date("2010-08-01","%Y-%m-%d"),
updateTo=as.Date("2010-10-01","%Y-%m-%d"),
urlTmin=NA, urlTmax=NA, urlVprp=NA, urlSolarrad=NA)
# Download and import the DEM if it's not in the working directory
if (!file.exists('DEM.RData')) {
DEM_9s = getDEM()
save(DEM_9s,file="DEM.RData" )
} else {
load('DEM.RData')
}
# Set coordinates to four bores locations and one rainfall gauge.
coordinates.data = data.frame( ID =c('Bore-70015656','Bore-50038039','Bore-20057861','Bore-10084446','Rain-63005'),
Longitude = c(131.33588, 113.066933, 143.118263, 153.551875, 149.5559),
Latitude = c(-12.660622, -25.860046, -38.668506,-28.517974,-33.4289))
# Convert the points to a spatial object
sp::coordinates(coordinates.data) <- ~Longitude + Latitude
# Set projection to GDA94
sp::proj4string(coordinates.data) = '+proj=longlat +ellps=GRS80'
# Plot coordinates on top of DEM 9s
raster::plot(DEM_9s)
sp::plot(coordinates.data, add =T)
with(coordinates.data, text(sp::coordinates(coordinates.data)[,1],sp::coordinates(coordinates.data)[,2],
labels = coordinates.data$ID, pos = 1))
# Extract time-series of daily precip data at all five sites
climateData.data = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
extractFrom=as.Date("2010-08-01","%Y-%m-%d"),
extractTo=as.Date("2010-10-01","%Y-%m-%d"),
catchments=coordinates.data,
getTmin=F, getTmax=F, getVprp=F, getSolarrad=F, getET=F)
# Plot the daily rainfall at each site
par.default = par()
par(mfrow=c(5,1), mar = c(4,5,3,0))
for (i in 1:nrow(coordinates.data)){
filt = climateData.data$CatchmentID.ID == coordinates.data$ID[i]
data2plot = climateData.data$precip_mm[filt]
names(data2plot) = paste(climateData.data$day[filt],'/',climateData.data$month[filt],sep='')
barplot(data2plot, main=coordinates.data$ID[i], ylab='Precip [mm/d]', xlab='Date [day/month]')
}
# The following is hard-coded observed rainfall for gauge 63005
obsPrecip <- data.frame(
year= c(2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010,
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010,
2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010),
month = c(8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10),
day = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 1),
precip_mm = c(0.6, 5.2, 0.8, 0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 15.8, 15.6, 7.6, 0.7, 0.4, 1.4, 1.0, 0.0, 0.0, 30.4, 1.0, 0.0,
0.0, 5.0, 2.2, 0.3, 0.8, 13.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.5, 13.8, 15.2, 0.4, 0.2, 0.0, 0.0, 12.4, 0.9,
0.0, 0.0, 0.1, 13.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0))
# Plot the observed vs AWAPer rainfall at gauge ID 63005
par(mfrow=c(1,2), mar = c(4,5,3,2))
filt2 = climateData.data$CatchmentID.ID=='Rain-63005'
plot(obsPrecip$precip_mm,climateData.data$precip_mm[filt2],
xlim = c(0,35),ylim = c(0,35),
main='Obs. vs. AWAPer precip. at 63005',
xlab='Obs. precip [mm/d]', ylab='AWAPer Precip [mm/d]')
abline(0,1, col='grey', lty=2)
# Plot the cumulative observed vs AWAPer rainfall at gauge ID 63005
plot(cumsum(obsPrecip$precip_mm),cumsum(climateData.data$precip_mm[filt2]),
xlim = c(0,175),ylim = c(0,175),
main='Cumulative obs. vs. AWAPer precip. at 63005',
xlab='Obs. precip. [mm]', ylab='AWAPer precip. [mm]', type='l')
abline(0,1, col='grey', lty=2)
# Rest plotting parameters
par(par.default)This example calculates the catchment weighted precipitation at Bet Bet Creek (Victoria, Australia), the spatial standard deviation in precipitation and two measures of potential evapotranspiration. The example was developed by Dr Conrad Wasko. Below is a plot of the output.
# Make the netCDF files of AWAP data
makeNetCDF_file(ncdfFilename ='AWAP_demo.nc',ncdfSolarFilename='AWAP_solar_demo.nc',
updateFrom=as.Date("2010-1-1","%Y-%m-%d"),updateTo=as.Date("2011-12-1","%Y-%m-%d"))
# Download and import the DEM
DEM_9s = getDEM()
# Load example catchment boundaries.
data("catchments")
# Load the ET constants
data(constants,package='Evapotranspiration')
# Extract catchment average data for Bet Bet Creek with
# the Jensen Haise estimate of potential ET.
climateData.ET.JensenHaise.var = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2010-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-31","%Y-%m-%d"), catchments=catchments[2,],
DEM=DEM_9s, ET.function='ET.JensenHaise',
ET.timestep='daily', ET.constants=constants);
# Extract catchment average data for Bet Bet Creek with
# the Mortons CRAE estimate of potential ET.
climateData.ET.MortonCRAE.var = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2010-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-31","%Y-%m-%d"), catchments=catchments[2,],
DEM=DEM_9s, ET.function='ET.MortonCRAE',
ET.timestep='monthly', ET.constants=constants);
# Set up start and end date indices for plotting
srt.date = as.Date("2010-06-01","%Y-%m-%d")
end.date = as.Date("2010-10-01","%Y-%m-%d")
# Convert year, month and day columns from extractions to a date.
climateData.ET.JensenHaise.var.date = as.Date(paste0(climateData.ET.JensenHaise.var$catchmentTemporal.mean$year, "-",
climateData.ET.JensenHaise.var$catchmentTemporal.mean$month, "-",
climateData.ET.JensenHaise.var$catchmentTemporal.mean$day))
climateData.ET.MortonCRAE.var.date = as.Date(paste0(climateData.ET.MortonCRAE.var$catchmentTemporal.mean$year, "-",
climateData.ET.MortonCRAE.var$catchmentTemporal.mean$month, "-",
climateData.ET.MortonCRAE.var$catchmentTemporal.mean$day))
i1.s = match(srt.date, climateData.ET.JensenHaise.var.date)
i1.e = match(end.date, climateData.ET.JensenHaise.var.date)
i2.s = match(srt.date, climateData.ET.MortonCRAE.var.date)
i2.e = match(end.date, climateData.ET.MortonCRAE.var.date)
# Plot rainfall and standard deviation against observations
# ---------------------------------------------------------
max.y = max(climateData.ET.JensenHaise.var$catchmentTemporal.mean$precip_mm[i1.s:i1.e] +
sqrt(climateData.ET.JensenHaise.var$catchmentSpatial.var$precip_mm[i1.s:i1.e]))
# Change the plot margins
par(mar = c(5, 7.5, 4, 2.7) + 0.1)
# Rainfall
plot(climateData.ET.JensenHaise.var.date[i1.s:i1.e],
climateData.ET.JensenHaise.var$catchmentTemporal.mean$precip_mm[i1.s:i1.e],
type = "h", col = "#e31a1c", lwd = 3, mgp = c(2, 0.5, 0), ylim = c(0, 80),
ylab = "", xlab = "2010", xaxs = "i", yaxt = "n", bty = "l", yaxs = "i")
axis(side = 2, mgp = c(2, 0.5, 0), line = 0.5, at = seq(from = 0, to = 80, by = 20),
labels = c("0", "20", "40", "60", "80mm"), col = "#e31a1c", col.axis = "#e31a1c")
# Standard deviation
for (i in 1:length(climateData.ET.JensenHaise.var.date[i1.s:i1.e])) {
x.plot = rep(climateData.ET.JensenHaise.var.date[i1.s:i1.e][i], 2)
y.plot = c(climateData.ET.JensenHaise.var$catchmentTemporal.mean$precip_mm[i1.s:i1.e][i] +
sqrt(climateData.ET.JensenHaise.var$catchmentSpatial.var$precip_mm[i1.s:i1.e][i]),
climateData.ET.JensenHaise.var$catchmentTemporal.mean$precip_mm[i1.s:i1.e][i] -
sqrt(climateData.ET.JensenHaise.var$catchmentSpatial.var$precip_mm[i1.s:i1.e][i]))
lines(x.plot, y.plot, col = "black", lwd = 1.2)
}
# Plot evap data.
par(new = TRUE)
plot(climateData.ET.JensenHaise.var.date[i1.s:i1.e], climateData.ET.JensenHaise.var$catchmentTemporal.mean$ET_mm[i1.s:i1.e], col = "#bc80bd", lwd = 2, ylab = "",
ylim = c(0, 4), lty = 1, xlab = "", xaxs = "i", yaxt = "n", xaxt = "n", type = "l", bty = "n", yaxs = "i")
axis(side = 2, line = 2.3, mgp = c(2, 0.5, 0), labels = c("0", "1", "2", "3", "4mm"), at = seq(from = 0, to = 4, by = 1), col = "#bc80bd", col.axis = "#bc80bd")
lines(climateData.ET.MortonCRAE.var.date[i2.s:i2.e], climateData.ET.MortonCRAE.var$catchmentTemporal.mean$ET_mm[i2.s:i2.e], col = "#bc80bd", lwd = 2, lty = 2)
# Legend
legend("topleft", cex = 0.8, lwd = 2, bty = "n", inset = c(0.01, -0.01),
lty = c(1, 1, 2), pch = c(NA, NA, NA),
col = c("#e31a1c", "#bc80bd", "#bc80bd"),
legend = c("Precipitation (bars +/- one standard dev.)", "Jensen-Haise PET", "Morton CRAE PET"), xpd = NA)This example calculates and plot various estimates of evaportrnspiration using the netCDF data form the previous example. To do this, the Austrlia 9 second DEM is downloaded and two catchment boundaries are loaded from the package. The figure below shows the output plot from the example. It shows 10 different esitmates of area weighted evapotranspiration from 1/1/2010 to 31/12/2010 at catchment 407214 (Victoria, Australia).
# Set working directory.
setwd('~/')`
# Make two netCDF files of AWAP data.
makeNetCDF_file(ncdfFilename='AWAP_demo.nc', ncdfSolarFilename='AWAP_solar_demo.nc',
updateFrom='2010-1-1',updateTo='2011-12-1')
# Download and import the DEM
DEM_9s = getDEM()
# Load example cacthment boundaries.
data("catchments")
# get ET constants
data(constants,package='Evapotranspiration')
# Extract data and esitmate various types of ET and estimate the spatial variance
# using the interquartile range.
#----------------------------------------------
climateData.ET.HargreavesSamani = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.HargreavesSamani',
ET.timestep = 'daily', ET.constants= constants);
climateData.ET.JensenHaise = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.JensenHaise',
ET.timestep = 'daily', ET.constants= constants);
climateData.ET.Makkink = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc',extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"),catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.Makkink'
ET.timestep = 'daily', ET.constants= constants);
climateData.ET.McGuinnessBordne = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"),catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.McGuinnessBordne',
ET.timestep = 'daily', ET.constants= constants);
climateData.ET.MortonCRAE = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRAE',
ET.timestep = 'monthly', ET.constants= constants);
climateData.ET.MortonCRAE.potentialET = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"),catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRAE',
ET.timestep = 'monthly', ET.Mortons.est='potential ET', ET.constants= constants);
climateData.ET.MortonCRAE.wetarealET = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRAE',
ET.timestep = 'monthly', ET.Mortons.est='wet areal ET', ET.constants= constants);
climateData.ET.MortonCRAE.actualarealET = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc',extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRAE',
ET.timestep = 'monthly', ET.Mortons.est='actual areal ET', ET.constants= constants);
climateData.ET.MortonCRWE = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"),catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRWE',
ET.timestep = 'monthly', ET.constants= constants);
climateData.ET.MortonCRWE.shallowLake = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"), catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.MortonCRWE',
ET.timestep = 'monthly', ET.Mortons.est = 'shallow lake ET', ET.constants= constants);
climateData.ET.Turc = extractCatchmentData(ncdfFilename='AWAP_demo.nc',
ncdfSolarFilename='AWAP_solar_demo.nc', extractFrom=as.Date("2009-1-1","%Y-%m-%d"),
extractTo=as.Date("2010-12-1","%Y-%m-%d"),catchments=catchments,
spatial.function.name='IQR',DEM=DEM, ET.function='ET.Turc',
ET.timestep = 'daily', ET.constants= constants);
# Plot the ET estimates for one of the catchmnts.
#----------------------------------------
filt = climateData.ET.HargreavesSamani$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.HargreavesSamani$catchmentTemporal.mean$year,
climateData.ET.HargreavesSamani$catchmentTemporal.mean$month,
climateData.ET.HargreavesSamani$catchmentTemporal.mean$day)
plot(d[filt], climateData.ET.HargreavesSamani$catchmentTemporal.mean$ET_mm[filt],
col='black',lty=1, xlim = c(ISOdate(2010,1,1), ISOdate(2010,12,1)), ylim=c(0, 30),type='l', ylab='ET [mm/d]',xlab='Date')
filt = climateData.ET.JensenHaise$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.JensenHaise$catchmentTemporal.mean$year,
climateData.ET.JensenHaise$catchmentTemporal.mean$month, climateData.ET.JensenHaise$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.JensenHaise$catchmentTemporal.mean$ET_mm[filt], col='red',lty=1)
filt = climateData.ET.Makkink$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.Makkink$catchmentTemporal.mean$year, climateData.ET.Makkink$catchmentTemporal.mean$month,
climateData.ET.Makkink$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.Makkink$catchmentTemporal.mean$ET_mm[filt], col='green',lty=1)
filt = climateData.ET.McGuinnessBordne$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.McGuinnessBordne$catchmentTemporal.mean$year, climateData.ET.McGuinnessBordne$catchmentTemporal.mean$month,
climateData.ET.McGuinnessBordne$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.McGuinnessBordne$catchmentTemporal.mean$ET_mm[filt], col='blue',lty=1)
filt = climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$year,
climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$month, climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$ET_mm[filt], col='black',lty=2)
filt = climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$year,
climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$month, climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$ET_mm[filt], col='red',lty=2)
filt = climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$year,
climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$month,
climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$ET_mm[filt], col='green',lty=2)
filt = climateData.ET.MortonCRWE$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.MortonCRWE$catchmentTemporal.mean$year, climateData.ET.MortonCRWE$catchmentTemporal.mean$month,
climateData.ET.MortonCRWE$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.MortonCRWE$catchmentTemporal.mean$ET_mm[filt], col='blue',lty=2)
filt = climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$year,
climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$month, climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$ET_mm[filt], col='black',lty=3)
filt = climateData.ET.Turc$catchmentTemporal.mean$CatchID==407214
d = ISOdate(climateData.ET.Turc$catchmentTemporal.mean$year, climateData.ET.Turc$catchmentTemporal.mean$month,
climateData.ET.Turc$catchmentTemporal.mean$day)
lines(d[filt], climateData.ET.Turc$catchmentTemporal.mean$ET_mm[filt], col='red',lty=3)
legend(x='topright', legend=c(
'Hargreaves Samani (ref. crop)', 'Jensen Haise (PET)', 'Makkink (ref. crop)', 'McGuinness Bordne (PET)', 'Morton CRAE (PET)',
'Morton CRAE (wet areal ET)', 'Morton CRAE (actual areal ET)', 'Morton CRWE (PET)', 'Morton CRWE (shallowLake)', 'Turc (ref. crop, non-humid'),
lty = c(1,1,1,1,2,2,2,2,3,3), col=c('black','red','green','blue','black','red','green','blue','black','red')
)This example helps to illustrate how the netcdf file created can be interrogated to produce a map for daily maximum tempeature.
# Load the package
library(AWAPer)
library(ncdf4)
library(maps)
library(mapdata)
# Download the AWAP data to an NC file
makeNetCDF_file(ncdfFilename = "AWAP_demo.nc", ncdfSolarFilename="AWAP_solar_demo.nc",
updateFrom = as.Date("2017-12-14","%Y-%m-%d"),updateTo = as.Date("2017-12-15","%Y-%m-%d"))
# Connect to the NC file and list what is inside
ncfile = nc_open("AWAP_demo.nc")
names(ncfile$dim) # "Long" "Lat" "time"
names(ncfile$var) # "tmin" "tmax" "vprp" "precip"
# Get netcdf geometry
lon = ncvar_get(ncfile, "Long")
lat = ncvar_get(ncfile, "Lat")
# Get times
print(ncfile$dim$time$units)
time = ncvar_get(ncfile, "time")
date = as.Date("1900-01-01") + time
# Time point to extract
tar.date = as.Date("2017-12-15")
tar.indx = match(tar.date, date)
# Extract an image/slice
im.tmax = ncvar_get(ncfile, varid = "tmax", start = c(1, 1, tar.indx), count = c(-1, -1, 1)) # matrix
# Close the connection
nc_close(ncfile)
# Plot Tmax
col.vec = rev(heat.colors(10))
plot(NULL, xlim = c(110, 160), ylim = c(-45, -5), xlab = expression(degree*Longitude), ylab = expression(degree*Latitude),
xaxs = "i", yaxs = "i", mgp = c(2, 0.6, 0))
image(lon, lat, im.tmax, zlim = c(0, 50), col = col.vec, add = TRUE)
# Remove data outisde coastal boundaries
outline = map("worldHires", regions = c("Australia"), exact = TRUE, plot = FALSE) # returns a list of x/y coords
xbox = c(111, 156.2)
ybox = c(-39.5, -9)
subset = !is.na(outline$x)
polypath(c(outline$x[subset], NA, c(xbox, rev(xbox))),
c(outline$y[subset], NA, rep(ybox, each = 2)),
col = "white", rule = "evenodd", border = "white")
outline = map("worldHires", regions = c("Australia:Tasmania"), exact = TRUE, plot = FALSE) # returns a list of x/y coords
xbox = c(111, 156.2)
ybox = c(-44.9, -39.5)
subset = !is.na(outline$x)
polypath(c(outline$x[subset], NA, c(xbox, rev(xbox))),
c(outline$y[subset], NA, rep(ybox, each = 2)),
col = "white", rule = "evenodd", border = "white")
# Add legend
for (i in 1:length(col.vec)) {
rect(115+(i-1)*2, -42, 117+(i-1)*2, -42+1.4, col = col.vec[i], border = NULL)
}
text(118.0, -38.5, expression("Tmax ("*degree*"C)"), pos = 1, cex = 0.7)
text(115, -41.3, "0", pos = 1, cex = 0.7)
text(125, -41.3, "25", pos = 1, cex = 0.7)
text(135, -41.3, "50", pos = 1, cex = 0.7)
# Add bounding boxes to show AWAP data boundary
rect(min(lon), min(lat), max(lon), max(lat))
text(118.5, -7.5, "AWAP boundary", pos = 1, cex = 0.8)This example illustrates how to extract data at a monthly time step. Note, extracting data other than at a daily time step requires version 0.1.4 of AWAPer, which is available from https://github.com/peterson-tim-j/AWAPer/releases/tag/1.4. The image below shows the derived monthly total precipitation and the monthly spatial standard deviation.
# Load required librraies
x = c('Evapotranspiration', 'ncdf4', 'utils', 'raster', 'chron', 'maptools', 'sp', 'zoo', 'methods', 'xts')
lapply(x, library, character.only = TRUE)
# Set dates for building netCDFs and extracting data.
startDate = as.Date("2000-01-01","%Y-%m-%d")
endDate = as.Date("2000-12-31","%Y-%m-%d")
# Set names for netCDF files.
ncdfFilename = 'AWAPer_demo.nc'
# Build netCDF grid of ONLY precipitation and over a defined time period.
file.names = makeNetCDF_file(ncdfFilename=ncdfFilename,
updateFrom=startDate, updateTo=endDate,
urlTmin=NA, urlTmax=NA, urlVprp = NA, urlSolarrad = NA)
# Load the two example cacthment boundaries.
data("catchments")
# Extract the monthly total areal average precipitation for the two catchments..
monthlySumPrecip = extractCatchmentData(ncdfFilename=file.names$ncdfFilename,
ncdfSolarFilename=file.names$ncdfSolarFilename,
extractFrom=startDate, extractTo=endDate,
catchments=catchments,
getTmin = F, getTmax = F, getVprp = F, getSolarrad = F, getET = F,
temporal.timestep = 'monthly', temporal.function.name = 'sum',
spatial.function.name='var')
# Extract the monthly precip. sum data and spatial variance for each of the catchments.
filt = monthlySumPrecip$catchmentTemporal.sum$CatchID == 407214
monthlySumPrecip.sum.407214 = monthlySumPrecip$catchmentTemporal.sum[filt,]
monthlySumPrecip.var.407214 = monthlySumPrecip$catchmentSpatial[filt,]
filt = monthlySumPrecip$catchmentTemporal.sum$CatchID == 407220
monthlySumPrecip.sum.407220 = monthlySumPrecip$catchmentTemporal.sum[filt,]
monthlySumPrecip.var.407220 = monthlySumPrecip$catchmentSpatial[filt,]
# Create data time series
dates2plot = ISOdate(monthlySumPrecip.sum.407220$year, monthlySumPrecip.sum.407220$month, monthlySumPrecip.sum.407220$day)
# Plot monthly sum and spatial variance in the monthly sum.
par(mfrow=c(1,2))
plot(dates2plot, monthlySumPrecip.sum.407214$precip_mm, type='l', col='red', xlab='Month', ylab='Monthly precip. [mm/month]')
lines(dates2plot, monthlySumPrecip.sum.407220$precip_mm, col='blue')
legend('topleft',legend=c('Cathcment 407214','Cathcment 407220'),col=c('red','blue'), lty=c(1,1))
plot(dates2plot, sqrt(monthlySumPrecip.var.407214$precip_mm), type='l', col='red', xlab='Month', ylab='Monthly precip. spatial standard deviation [mm/month]')
lines(dates2plot, sqrt(monthlySumPrecip.var.407220$precip_mm), col='blue')This example illustrates how to map the monthly total precipitation across two catchments. Note, mapping data other than at a daily time step requires version 0.1.41 of AWAPer, which is available from https://github.com/peterson-tim-j/AWAPer/releases/tag/1.41. The image below shows maps of the monthly total precipitation..
# Set dates for building netCDFs and extracting data.
startDate = as.Date("2000-01-01","%Y-%m-%d")
endDate = as.Date("2000-02-28","%Y-%m-%d")
# Set names for netCDF files.
ncdfFilename = 'AWAPer_demo.nc'
ncdfSolarFilename = 'AWAPer_solar_demo.nc'
# Remove any existing netCDF demo files.
if (file.exists(ncdfFilename))
is.removed = file.remove(ncdfFilename)
if (file.exists(ncdfSolarFilename))
is.removed = file.remove(ncdfSolarFilename)
# Build netCDF grids and over a defined time period.
file.names = makeNetCDF_file(ncdfFilename=ncdfFilename,
ncdfSolarFilename=ncdfSolarFilename,
updateFrom=startDate, updateTo=endDate)
# Load example cacthment boundaries.
data("catchments")
# Extract the monthly total precipitation.
monthlyPrecipData = extractCatchmentData(ncdfFilename=ncdfFilename,
ncdfSolarFilename=ncdfSolarFilename,
extractFrom=startDate, extractTo=endDate,
catchments=catchments,
getTmin = F, getTmax = F, getVprp = F, getSolarrad = F, getET = F,spatial.function.name = '',
temporal.timestep = 'monthly', temporal.function.name = 'sum')
# Map the monthly total repcip and overlay the catchment boundary.
v = list("sp.polygons", catchments, col = "red",first=FALSE)
spplot(monthlyPrecipData,2:ncol(monthlyPrecipData), sp.layout = list(v))