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awaper's Introduction

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AWAPer - an R-package for catchment-weighted climate data anywhere in Australia

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:

  1. Building and updating the required netCDF files (see here)
  2. Extract and check daily point precipitation data against rain gauge observations (see here)
  3. Extract daily areal weighted precipitation and calculate two measures of ET (see here)
  4. Calculate all measures of ET possible with AWAPer (see here)
  5. Extract and map maximum daily temperature for all of Australia (see here)
  6. Extract monthly areal weighted total precipitation at two catchments and plot the totals and the spatial variability (see here)
  7. Extract and map the monthly total precipitation at two catchments (see here)

System Requiements

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.

  1. Download and intall 7z from https://www.7-zip.org/download.html
  2. Click "Search Windows", search "Edit environmental variables for your account" and click on it.
  3. In the "User variables" window, select the "Path", and click "Edit...".
  4. In the "Edit environmental variable" window, click "New".
  5. Paste the path to the 7zip application folder, and click OK.
  6. Restart Windows.
  7. 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.')

Getting Started

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:

  1. Download the latest "tar.gz" (e.g. AWAPer_0.1.4.tar.gz) file from https://github.com/peterson-tim-j/AWAPer/releases.
  2. Open R.
  3. 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.
  4. Install the remaining required packages using the R command: install.packages(c("utils", "sp", "raster", "chron", "maptools", "Evapotranspiration","devtools","zoo", "methods", "xts"))
  5. Load the required packages using the R command: `library(c('Evapotranspiration', 'ncdf4', 'utils', 'raster', 'chron', 'maptools', 'sp', 'zoo', 'methods', 'xts')
  6. 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 Mac install.packages(“~/Users/MyFolder/AWAPer/AWAPer_0.1.4.tar.gz", repos = NULL, type = "source")

Example 1. Build and then update netCDF files with the latest data

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)

Example 2. Extract point precip. data and check with osberved data.

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.

Example 2. Site locations

Example 2. Daily precip at sites

Example 3. Obs. vs Est Precip

# 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)

Example 3. Calculate precip and evapotranspiration

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.

Example 3. Calculate precip and evapotranspiration

# 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)

Example 4. Calculate evapotranspiration

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).

Example 4. ET plot

# 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')
  )

Example 5. Map of Australia

This example helps to illustrate how the netcdf file created can be interrogated to produce a map for daily maximum tempeature.

Example 5. Map of Australia

# 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)

Example 6. Extract monthly precipitation

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.

example6

# 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')

Example 7: Map monthly precipitation

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..

Monthly total precip.

# 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))

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awaper's Issues

Running makeNetCDF_file() in Windows

Thanks so much for this code. Using the netCDF format is incredibly efficient and I am slowly getting my head how to work with it.

Listed below are a couple of quirks that I ran into when running makeNetCDF_file() in a windows environment, using R version 3.4.4:

  • Windows doesn't have a linux "znew" equivalent: I got around this by replacing:
    system(paste('znew -f ',destFile));
    destFile = file.path(workingFolder,paste('precip.',filedate_str,'.grid.gz',sep=''))
    raw<-textConnection(readLines(a<-gzfile(destFile)));
    with
    system(paste('7z e -aoa -bso0 ',destFile));
    destFile = file.path(workingFolder,paste('precip.',filedate_str,'.grid',sep=''))
    raw<-textConnection(readLines(a<-file(destFile)));
    for some reason which I don't understand, it helps to then close both "raw" and "a" connections. Note the flags in 7zip are "e" for extract, "-aoa" to overwrite existing files, and "-bso0" to stop printing successful run messages.

  • For some reason, in download.url the flag "mode = "wb" is needed. Also when the URL doesn't exist, the code spits out an error and refuses to continue. My not very elegant solution is below. Note that I had to change the initialization of the didFail flag and also the destFile name from the original code as I am reading in the ascii grid, and not the .gz file as in the original code:
    didFail_precip=0
    if (!is.na(urlPrecip)) {
    url = paste(urlPrecip,datestring, datestring,'.grid.Z',sep='')
    destFile_precip = file.path(workingFolder,paste('precip.',datestring,'.grid.Z',sep=''))
    didFail_precip = tryCatch({download.file(url,destFile_precip, quiet = T, mode = "wb")},error = function(cond) {return(TRUE)})
    if (didFail_precip==0) {
    system(paste('7z e -aoa -bso0 ',destFile_precip));
    destFile_precip = gsub('.Z', '', destFile_precip)
    }
    }

In windows a .Z file remains after downloading even if keepFiles = FALSE

In download.ASCII.file.R, if the OS is not windows des.file.name is renamed (and then subsequently deleted in MakeNetCDF_file.R).

des.file.name = gsub('.Z', '.gz', des.file.name)

But if the OS is windows then there is an additional manipulation using 7zip and the result is a .grid.Z file that is not deleted.

For consistency after Line 22 the .Z file needs to be deleted in download.ASCII.file.R by inserting the second line of code as below:

exitMessage = system(paste0('7z e -aoa -bso0 "',des.file.name, '"'),intern = T)
file.remove(des.file.name)

MonthlyAnalysis branch required "CatchID" as a field to extract spatial data

Hello Tim,

At line 905 in "extractCatchmentData" the code is:
catchmentAvgTmp = data.frame(CatchmentID=rep(catchments$CatchID[i],nGridCells))

This means the user must specify CatchID as a field in their shapefile - either this needs to be specified in the user manual or maybe the following can be used instead:

catchmentAvgTmp = data.frame(CatchmentID=rep(catchments@data[i,1],nGridCells))

I wanted to check with you first before making any changes as I am still getting comfortable with shapefiles in R.

Error when observation data is missing

Dear Tim,

AWAPer stopped working when there was a missing data point in the input data-frame used for the calculation of ET. I fixed that changing the following line in "extractCatchmentData":

576 - missing_method= NULL, abnormal_method='DoY average', message = "no")

576 - missing_method='DoY average', abnormal_method='DoY average', message = "no")

Error reading the DEM ascii file in getDEM

In getDEM, the variable DEMfilename.2unzip is "GA_9second_v3\ESRI\dem-9s.asc" but unzipping at Line 59 creates dem-9s.asc in the working directory. Hence the following line that is executed at Line 62 should be changed from:

DEM <- sp::read.asciigrid(DEMfilename.2unzip,colname='DEM', proj4string = sp::CRS("+proj=longlat +ellps=GRS80"))

to

DEM <- sp::read.asciigrid(basename(DEMfilename.2unzip),colname='DEM', proj4string = sp::CRS("+proj=longlat +ellps=GRS80"))

in order to remove the current error

In file(fname, "r") :
cannot open file 'GA_9second_v3\ESRI\dem-9s.asc': No such file or directory

getDEM leaves large file behind

minor issue: on Mac, getDEM leaves a large file behind (~1.7GB) named "GA_9second_v3\ESRI\dem-9s.asc"

Mac OSX 10.14.6
Rstudio 1.2.5001
R version 3.6.1
AWAPer 3 June 2019

Issues with solar radiation

First of all nice package.

I've encountered an issue with the solar radiation netCDF file. I used makeNetCDF_file to download data from 1990 to 2018 and it seems from 2010-11-01 no rad data is saved. I looked at the downloaded grid files and they don't seem to have a problem.

Here is a plot for mean solar radiation for Australia from 1990-2018 showing when the issue occurs:
MeanRadPlot

It seems that dates from 2010-11-01 have fill values.

Here is a reproducible example:

library(AWAPer)
library(lubridate)
library(ncdf4)

setwd('~/Data/AWAPer/')

# Make a new netCDF file
makeNetCDF_file(updateFrom = as.Date('2010-10-27'),
                updateTo   = as.Date('2010-11-03'),
                keepFiles  = TRUE)

# Open solar file
nc = nc_open('AWAP_solar.nc')

solar = nc$var$solarrad
varsize = solar$varsize
ndims = solar$ndims
nt = varsize[ndims]

time = ncvar_get(nc, 'time')
origin = as_date('1990-01-01')
delta = as.integer(as_date('2010-11-01') - origin)
indx = delta + 1
print(origin + time[indx])

start = c(1, 1, indx)
count = varsize
count[ndims] = 1

slice = ncvar_get(nc, varid = 'solarrad', start=start, count=count)

image(slice)

nc_close(nc)

# Look at downloaded grid file
library(raster)
R.utils::gunzip('solarrad20101101.grid.gz')
r = raster('solarrad20101101.grid')
plot(r)
# No problems

# precip/tmax etc is unaffected
nc = nc_open('AWAP.nc')

precip = nc$var$precip
varsize = precip$varsize
ndims = precip$ndims
nt = varsize[ndims]

time = ncvar_get(nc, 'time')
origin = as_date('1900-01-01')
delta = as.integer(as_date('2010-11-01') - origin)
indx = delta + 1
print(origin + time[indx])

start = c(1, 1, indx)
count = varsize
count[ndims] = 1

slice = ncvar_get(nc, varid = 'precip', start=start, count=count)

image(slice)

nc_close(nc)

Extraction of point data

When extracting at a set of points (instead of catchment polygons) the data for the first set of coordinates is returned for each subsequent set of coordinates.

I am not sure where this error is originating but my guess is it might be line 263:

catchment.lookup = cbind(rep(1,length(catchments)),rep(1,length(catchments)));

The indices returned I think should increment by 1 with increasing row number, but currently they are all one. I think all the data is extracted from the netcdf fine, but then at line 412:

ind = catchment.lookup[i,1]:catchment.lookup[i,2]

The data returned is always for the first set of coordinates.

I am not sure as I haven't tested this but these are my thoughts at the moment.

Download link from BOM seems to have been blocked

Hi Tim et al. We have been using AWAPer with success, but now the BOM link seems to be blocked:

Example code

library(AWAPer)

setwd('S:/PRJ-MuttamaCreek/AWAP')

makeNetCDF_file(ncdfFilename = 'AWAP_2000_2020.nc',
ncdfSolarFilename = 'AWAP_2000_2020_solar.nc',
updateFrom = as.Date("2000-01-01"),
updateTo = as.Date('2020-12-31'))

Starting to build both netCDF files.
... Testing downloading of AWAP precip. grid
... Getting grid gemoetry from file.
Error in readLines(a <- file(des.file.name)) : cannot open the connection
In addition: Warning messages:
1: In utils::download.file(url, des.file.name, quiet = T, mode = "wb") :
cannot open URL 'http://www.bom.gov.au/web03/ncc/www/awap/rainfall/totals/daily/grid/0.05/history/nat/2000010120000101.grid.Z': HTTP status was '403 Forbidden'
2: In readLines(a <- file(des.file.name)) :
cannot open file 'S:/PRJ-MuttamaCreek/AWAP/precip.20000101.grid': No such file or directory

Contact BOM?

No error check for getSolarrad=F but when getET=T

When the extraction comment has getSolarrad=F but when getET=T the following error occurs. A check on getSolarrad=T is required.

climateData = extractCatchmentData(ncdfFilename = '/home/tim/Documents/Research/DataSets/AWAPer/AWAP.nc',

  •                                ncdfSolarFilename =  '/home/tim/Documents/Research/DataSets/AWAPer/AWAP_solar.nc',

  •                                extractFrom='1950-1-1',extractTo = as.Date(Sys.Date()-60, "%Y-%m-%d"),

  •                                getTmin=T, getTmax=T,getVprp=T, getSolarrad=F,getET=T,catchments=coordinates.data,

  •                                DEM=DEM_9s,ET.constants=constants);

WARNING: The projection string of the catchment boundaries does not appear to be +proj=longlat +ellps=GRS80. Attempting to transform coordinates...
Extraction data summary:
NetCDF non-solar radiation climate data exists from 1900-01-01 to 2020-10-23
Data will be extracted from 1950-01-01 to 2020-08-25 at 6 catchments
Starting data extraction:
... Building catchment weights:
... Starting to extract data across all catchments:
... Extracting data for time point 1825 of 25805
... Extracting data for time point 3650 of 25805
... Extracting data for time point 5475 of 25805
... Extracting data for time point 7300 of 25805
... Extracting data for time point 9125 of 25805
... Extracting data for time point 10950 of 25805
... Extracting data for time point 12775 of 25805
... Extracting data for time point 14600 of 25805
... Extracting data for time point 16425 of 25805
... Extracting data for time point 18250 of 25805
... Extracting data for time point 20075 of 25805
... Extracting data for time point 21900 of 25805
... Extracting data for time point 23725 of 25805
... Extracting data for time point 25550 of 25805
... Calculating catchment weighted daily data.
... Calculating PET for grid cell 1 of 1
Error in extractCatchmentData(ncdfFilename = "/home/tim/Documents/Research/DataSets/AWAPer/AWAP.nc", :
object 'DEMpoints' not found
In addition: There were 50 or more warnings (use warnings() to see the first 50)

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