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‎vignettes/D_Catchment_avg_ET_types.Rmd‎

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---
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title: "Extract various measures of evapotranspiration"
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description: >
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This vignette shows how to estimate various measures of the area weighted evapotranspiration.
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Extract various measures of evapotranspiration}
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%\VignetteEngine{knitr::rmarkdown}
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%\usepackage[utf8]{inputenc}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(collapse = T, comment = "#>")
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options(tibble.print_min = 4L, tibble.print_max = 4L)
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```
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```{r setup}
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library(AWAPer, warn.conflicts = FALSE)
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```
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This example calculates and plot various estimates of evaportranspiration. Ten different estimates of area weighted evapotranspiration over one year at catchment 407214 (Victoria, Australia) are derived.
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## Make netCDF files
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Like the other vignettes, the netCDF data grids need to be built.
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First, let's define the start and end dates for data grids and the file names.
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```{r}
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date.from = as.Date("2010-01-01","%Y-%m-%d")
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date.to = as.Date("2010-12-31","%Y-%m-%d")
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ncdfFilename = tempfile(fileext='.nc')
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ncdfSolarFilename = tempfile(fileext='.nc')
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```
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Next, let's make the data grids over this period.
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```{r}
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makeNetCDF_file(ncdfFilename = ncdfFilename,
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ncdfSolarFilename = ncdfSolarFilename,
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updateFrom = date.from,
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updateTo = date.to)
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```
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## Load a catchment boundary
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Now that we have the meteorological data we can begin extracting data for the catchment. Here the catchment boundaries built into the package are used.
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```{r}
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data("catchments")
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```
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## Extract daily precipitation and PET data
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Next, the 11 different measures of evapotranspiration that can be derived from the available gridded data are calculated for 12 months.
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The estimation of ET uses the _evapotranspiration_ package. It requires a set of constants, which are loaded as follows.
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```{r}
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data(constants,package='Evapotranspiration')
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```
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Next, all 11 ET measures are derived. For each measure, only the following commands change: _ET.function_ , _ET.timestep_ and _ET.Mortons.est_ (when Morton's estimate is derived).
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```{R}
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climateData.ET.HargreavesSamani = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.HargreavesSamani',
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ET.timestep = 'daily',
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ET.constants= constants);
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climateData.ET.JensenHaise = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.JensenHaise',
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ET.timestep = 'daily',
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ET.constants= constants);
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climateData.ET.Makkink = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.Makkink',
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ET.timestep = 'daily',
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ET.constants= constants);
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climateData.ET.McGuinnessBordne = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.McGuinnessBordne',
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ET.timestep = 'daily',
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ET.constants= constants);
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climateData.ET.MortonCRAE = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRAE',
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ET.timestep = 'monthly',
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ET.constants= constants);
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climateData.ET.MortonCRAE.potentialET = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRAE',
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ET.timestep = 'monthly',
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ET.Mortons.est='potential ET',
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ET.constants= constants);
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climateData.ET.MortonCRAE.wetarealET = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRAE',
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ET.timestep = 'monthly',
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ET.Mortons.est='wet areal ET',
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ET.constants= constants);
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climateData.ET.MortonCRAE.actualarealET = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRAE',
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ET.timestep = 'monthly',
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ET.Mortons.est='actual areal ET',
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ET.constants= constants);
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climateData.ET.MortonCRWE = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRWE',
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ET.timestep = 'monthly',
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ET.constants= constants);
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climateData.ET.MortonCRWE.shallowLake = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.MortonCRWE',
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ET.timestep = 'monthly',
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ET.Mortons.est = 'shallow lake ET',
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ET.constants= constants);
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climateData.ET.Turc = extractCatchmentData(ncdfFilename= ncdfFilename,
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ncdfSolarFilename= ncdfSolarFilename,
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extractFrom= date.from,
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extractTo= date.to,
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locations=catchments,
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spatial.function.name='IQR',
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ET.function='ET.Turc',
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ET.timestep = 'daily',
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ET.constants= constants);
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```
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Next each estimate is plotted over time.
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```{r}
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#| fig.width = 8,
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#| fig.height = 10
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filt = climateData.ET.HargreavesSamani$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.HargreavesSamani$catchmentTemporal.mean$year,
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climateData.ET.HargreavesSamani$catchmentTemporal.mean$month,
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climateData.ET.HargreavesSamani$catchmentTemporal.mean$day)
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plot(d[filt], climateData.ET.HargreavesSamani$catchmentTemporal.mean$ET_mm[filt],
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col='black',lty=1, xlim = c(ISOdate(2010,1,1), ISOdate(2010,12,1)),
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ylim=c(0, 30),type='l', ylab='ET [mm/d]',xlab='Date')
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filt = climateData.ET.JensenHaise$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.JensenHaise$catchmentTemporal.mean$year,
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climateData.ET.JensenHaise$catchmentTemporal.mean$month,
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climateData.ET.JensenHaise$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.JensenHaise$catchmentTemporal.mean$ET_mm[filt],
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col='red',lty=1)
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filt = climateData.ET.Makkink$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.Makkink$catchmentTemporal.mean$year,
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climateData.ET.Makkink$catchmentTemporal.mean$month,
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climateData.ET.Makkink$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.Makkink$catchmentTemporal.mean$ET_mm[filt],
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col='green',lty=1)
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filt = climateData.ET.McGuinnessBordne$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.McGuinnessBordne$catchmentTemporal.mean$year,
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climateData.ET.McGuinnessBordne$catchmentTemporal.mean$month,
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climateData.ET.McGuinnessBordne$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.McGuinnessBordne$catchmentTemporal.mean$ET_mm[filt],
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col='blue',lty=1)
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filt = climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$year,
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climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$month,
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climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.MortonCRAE.potentialET$catchmentTemporal.mean$ET_mm[filt],
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col='black',lty=2)
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filt = climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$year,
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climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$month,
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climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.MortonCRAE.wetarealET$catchmentTemporal.mean$ET_mm[filt],
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col='red',lty=2)
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filt = climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$year,
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climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$month,
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climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.MortonCRAE.actualarealET$catchmentTemporal.mean$ET_mm[filt],
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col='green',lty=2)
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filt = climateData.ET.MortonCRWE$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.MortonCRWE$catchmentTemporal.mean$year,
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climateData.ET.MortonCRWE$catchmentTemporal.mean$month,
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climateData.ET.MortonCRWE$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.MortonCRWE$catchmentTemporal.mean$ET_mm[filt],
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col='blue',lty=2)
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filt = climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$year,
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climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$month,
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climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.MortonCRWE.shallowLake$catchmentTemporal.mean$ET_mm[filt],
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col='black',lty=3)
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filt = climateData.ET.Turc$catchmentTemporal.mean$CatchID==407214
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d = ISOdate(climateData.ET.Turc$catchmentTemporal.mean$year,
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climateData.ET.Turc$catchmentTemporal.mean$month,
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climateData.ET.Turc$catchmentTemporal.mean$day)
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lines(d[filt], climateData.ET.Turc$catchmentTemporal.mean$ET_mm[filt],
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col='red',lty=3)
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legend(x='topright', legend=c(
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'Hargreaves Samani (ref. crop)',
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'Jensen Haise (PET)',
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'Makkink (ref. crop)',
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'McGuinness Bordne (PET)',
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'Morton CRAE (PET)',
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'Morton CRAE (wet areal ET)',
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'Morton CRAE (actual areal ET)',
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'Morton CRWE (PET)',
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'Morton CRWE (shallowLake)',
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'Turc (ref. crop, non-humid'),
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lty = c(1,1,1,1,2,2,2,2,3,3),
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col=c('black','red','green','blue','black','red','green','blue','black','red')
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)
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```

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