Nothing
evapoRe"
In evapoRe: Evapotranspiration R Recipes
```{=html}
------------------------------------------------------------------------
```r
knitr::opts_chunk$set(
echo = TRUE,
eval = TRUE,
fig.width = 7,
warning = FALSE,
message = FALSE
)
library(evapoRe)
library(kableExtra)
data('gldas_clsm_global_ts')
data('gldas_clsm_subset_ts')
data('gldas_clsm_esp_ts')
data('pet_oudin_global_ts')
data('pet_oudin_subset_ts')
data('pet_oudin_esp_ts')
The evapoRe package developed as a complementary toolbox to the
pRecipe package [@vargas-godoy2023precipitation], available at
[https://CRAN.R-project.org/package=pRecipe]. evapoRe facilitates
the download, exploration, visualization, and analysis of
evapotranspiration (ET) data. Additionally, evapoRe offers the
functionality to calculate various Potential EvapoTranspiration (PET)
methods.
Before We Start
Like many other R packages, evapoRe has some system requirements:
Data
evapoRe database hosts 13 different ET data sets; three
satellite-based, five reanalysis, and five hydrological model products.
Their native specifications, as well as links to their providers, and
their respective references are detailed in the following subsections.
We have already homogenized, compacted to a single file, and stored them
in a Zenodo repository under
the following naming convention:
<data set>_<variable>_<units>_<coverage>_<start date>_<end date>_<resolution>_<time step>.nc
The evapoRe data collection was homogenized to these specifications:
<variable> = evapotranspiration (e)<units> = millimeters (mm)<resolution> = 0.25°
E.g., ERA5 [@hersbach_era5_2020] would be:
era5_e_mm_global_195901_202112_025_monthly.nc
Satellite-Based Products
tibble::tribble(
~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference,
"GLEAM V3.7b", "0.25°", "", "x", "", "Monthly", "1980/01-2021/12", "[Download](https://www.gleam.eu/)", "@martens_gleam_2017",
"BESS V2.0", "0.05°", "", "x", "", "Monthly", "1982/01-2019/12", "[Download](https://www.environment.snu.ac.kr/bessv2)", "@li2023bessv2",
"ETMonitor", "1$km$", "", "x", "", "Daily", "2000/06-2019/12", "[Download](https://data.casearth.cn/en/sdo/detail/63291c7e08415d54af833fe5)", "@zheng2022ETMonitor"
) |>
kbl(align = 'lcccccccr') |>
kable_styling("striped") |>
add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |>
unclass() |> cat()
Reanalysis Products
tibble::tribble(
~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference,
"ERA5-Land", "0.1°", "", "x", "", "Monthly", "1960/01-2022/12", "[Download](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form)", "@munoz-sabater_era5-land_2021",
"ERA5", "0.25°", "", "x", "", "Monthly", "1959/01-2021/12", "[Download](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview)", "@hersbach_era5_2020",
"JRA-55", "1.25°", "", "x", "", "Monthly", "1958/01-2021/12", "[Download](https://rda.ucar.edu/datasets/ds628.1/dataaccess/)", "@kobayashi_jra-55_2015",
"MERRA-2", "0.5° x 0.625°", "", "x", "", "Monthly", "1980/01-2023/01", "[Download](https://disc.gsfc.nasa.gov/datasets?page=1&project=MERRA-2)", "@gelaro_modern-era_2017",
"CAMELE", "0.25°", "", "x", "", "Monthly", "1980/01-2022/12", "[Download](https://zenodo.org/records/8047038)", "@li2023camele"
) |>
kbl(align = 'lcccccccr') |>
kable_styling("striped") |>
add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |>
unclass() |> cat()
Hydrological Models
tibble::tribble(
~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference,
"FLDAS", "0.1°", "", "x", "", "Monthly", "1982/01-2022/12", "[Download](https://ldas.gsfc.nasa.gov/fldas/fldas-data-download)", "@mcnally_land_2017",
"GLDAS CLSM V2.1", "1°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004",
"GLDAS NOAH V2.1", "0.25°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004 and @beaudoing_gldas_2020",
"GLDAS VIC V2.1", "1°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004",
"TerraClimate", "4$km$", "", "x", "", "Monthly", "1958/01-2021/12", "[Download](https://www.climatologylab.org/terraclimate.html)", "@abatzoglou_terraclimate_2018"
) |>
kbl(align = 'lcccccccr') |>
kable_styling("striped") |>
add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |>
unclass() |> cat()
Demo
In this introductory demo we will first download the GLDAS-CLSM data
set. We will then subset the downloaded data over Mediterranean region
for the 2001-2010 period, and crop it to the national scale for Spain.
In paralel, we will estimate potential evapotranspiration over the same
domain and the same record length. In the next step, we will generate
time series for our data sets and conclude with the visualization of our
data.
Installation
devtools::install_github("AkbarR1184/evapoRe") #latest dev version
install.packages('evapoRe') #latest CRAN release
library(evapoRe)
Download
Downloading the entire data collection or only a few data sets is quite
straightforward. You just call the download_data function, which has
four arguments data_name, path, domain, and time_res.
- data_name is set to "all" by default, but you can specify the
names of your data sets of interest only.
- path can be set to ".". I.e., the current working directory. By
replacing it for [your_project_folder], the downloaded files will be
stored in [your_project_folder] instead.
- domain is set to "raw" by default, but you can specify the domain
of your interest only. E.g., "ocean" for ocean only data sets (For
availability please check the [Data] section).
- time_res is set to "monthly" by default, but if you prefer you can
also download annual data with "yearly".
Let's download the GLDAS CLSM data set and inspect its content with
infoNC:
download_data(data_name = 'gldas-clsm', path = ".")
gldas_clsm_global <- raster::brick('gldas-clsm_e_mm_land_200001_202211_025_monthly.nc')
infoNC(gldas_clsm_global)
[1] "class : RasterBrick "
[2] "dimensions : 720, 1440, 1036800, 275 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 "
[6] "source : gldas-clsm_e_mm_land_200001_202211_025_monthly.nc "
[7] "names : X2000.01.01, X2000.02.01, X2000.03.01, X2000.04.01, X2000.05.01, X2000.06.01, X2000.07.01, X2000.08.01, X2000.09.01, X2000.10.01, X2000.11.01, X2000.12.01, X2001.01.01, X2001.02.01, X2001.03.01, ... "
[8] "Date : 2000-01-01, 2022-11-01 (min, max)"
[9] "varname : e "
Processing
Once we have downloaded our database, we can start processing the data
with:
crop_data to crop the data using a shapefile.fldmean to generate a time series by taking the area weighted
average over each timestep.remap to go from the native resolution (0.25°) to coarser ones
(e.g., 0.5°, 1°, 1.5°, ...).subset_data to subset the data in time and/or space.yearstat to aggregate the data from monthly into annual.
Subset
To subset our data to a desired region and period of interest, we use
the subset_data function, which has three arguments x, box, and
yrs.
- x Raster* object or a data.table or a filename (character).
- box is the bounding box of the region of interest with the
coordinates in degrees in the form (xmin, xmax, ymin, ymax).
- yrs is the period of interest with years in the form (start_year,
end_year).
Let's subset the GLDAS CLSM data set over Mediterranean region
(-10,40,30,45) for the 2001-2010 period, and inspect its content with
infoNC:
gldas_clsm_subset <- subset_data(gldas_clsm_global,box = c(-10,40,30,45) ,yrs = c(2001, 2010))
infoNC(gldas_clsm_subset)
[1] "class : RasterBrick "
[2] "dimensions : 60, 200, 12000, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -10, 40, 30, 45 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... "
[8] "min values : 0.85979986, 1.62062681, 1.42477119, 0.76781327, 0.75662607, 0.34450921, 0.25072542, 0.15768366, 0.13057871, 0.05979802, 0.11780920, -0.69875073, 0.36552662, 0.70131457, 0.63548779, ... "
[9] "max values : 80.61240, 89.56071, 101.81876, 143.45859, 158.37830, 202.83186, 192.55907, 190.07066, 111.40405, 116.93645, 67.32398, 48.42713, 74.23843, 59.85103, 88.96181, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
Crop
To further crop our data to a desired polygon other than a rectangle, we
use the crop_data function, which has two arguments x, and y.
- x Raster* object or a data.table or a *.nc filename (character).
- y is a ".shp" filename (character).
Let's crop our GLDAS CLSM subset to cover only Spain with the respective
shape
file,
and inspect its content with infoNC:
gldas_clsm_esp <- crop_data(gldas_clsm_subset, "gadm41_ESP_0.shp")
infoNC(gldas_clsm_esp)
[1] "class : RasterBrick "
[2] "dimensions : 56, 58, 3248, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -10, 4.5, 30, 44 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... "
[8] "min values : 7.216680, 18.606867, 32.398956, 37.939827, 39.484840, 29.796391, 15.073787, 17.676109, 15.789503, 26.564753, 13.147447, 9.846310, 8.794820, 14.355796, 26.288857, ... "
[9] "max values : 80.61240, 89.56071, 101.81876, 139.86717, 151.03282, 197.47284, 146.44232, 145.36212, 111.40405, 116.93645, 67.32398, 48.42713, 74.23843, 58.79382, 88.13857, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
PET calculation
First we need to download temperature data, available at: Zenodo
repository:
NOTE: Temperature data available at the moment is limited to
monthly. The data sets are TerraClimate, MSWX, and CRU and for brevity
We will only estimate PET over the 2001 to 2010 period using MSWX
dataset.
we use the download_t_data function, which has five arguments
data_name, variable, path, time_res, and domain.
- data_name is the dataset name you can specify the names of your
data sets of interest.
- variable is the variable name in which
t2m,tmin, and tmax
stand for average temperature, minimum temperature, and maximum
temperature.
- path can be set to ".". I.e., the current working directory. By
replacing it for [your_project_folder], the downloaded files will be
stored in [your_project_folder] instead.
- domain is set to "raw" by default, but you can specify the domain
of your interest only. E.g., "ocean" for ocean only data sets (For
availability please check the [Data] section).
- time_res is set to "monthly" by default, but if you prefer you can
also download annual data with "yearly".
download_t_data(data_name ="mswx", variable = "t2m", path = ".")
This will download temperature data in following naming convention e.g.,
mswx_t2m_degC_land_197901_202308_025_monthly.nc
As stated above we will work only with the 2001-2010 period. Since
evapoRe makes all of pRecipe functions available we can load and
subset the data as follows:
t2m_global <- raster::brick("mswx_t2m_degC_land_197901_202308_025_monthly.nc") %>%
subset_data(yrs = c(2001, 2010))
infoNC(t2m_global)
[1] "class : RasterBrick "
[2] "dimensions : 720, 1440, 1036800, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... "
[8] "min values : -49.867680, -45.244373, -42.529930, -33.551258, -22.829224, -15.122508, -13.402536, -14.673846, -18.428692, -27.443539, -35.674980, -44.693199, -46.778694, -47.197075, -42.277370, ... "
[9] "max values : 35.00875, 33.52000, 33.29000, 35.04688, 38.26308, 39.35505, 40.33314, 39.98806, 36.99562, 33.00374, 32.52186, 33.59248, 34.46944, 33.25062, 33.71313, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
The pet function estimates PET using a method of choice from the
following available options:
- bc for Blaney and Criddle [@blaney1952determining].
- ha for Hamon [@hamon1961estimating]
- jh for Jensen and Haise [@jensen1963estimating]
- mb for McGuinness and Bordne [@mcguinness1972comparison]
- od for Oudin [@oudin2005potential]
- th for Thornthwaite [@thornthwaite1948approach]
The pet function has two arguments x and method.
- x is a RasterBrick object with average temperature data.
- method a character string indicating the method to be used.
Let's calculate PET using the Oudin formulation. Then, same as GLDAS
CLSM we can subset it over Mediterranean region and Spain, and inspect
its content with infoNC:
NOTE: pet output is [mm/day], in order to get values in [mm] for a
1 to 1 comparison we use muldpm function.
pet_oudin_global <- pet(t2m_global, method = "od") %>% muldpm
infoNC(pet_oudin_global)
[1] "class : RasterBrick "
[2] "dimensions : 720, 1440, 1036800, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... "
[8] "min values : 0.000000e+00, 0.000000e+00, 8.728488e-04, 8.322404e-04, 3.890790e-04, 0.000000e+00, 0.000000e+00, 2.140520e-04, 8.102036e-04, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.431775e-04, ... "
[9] "max values : 219.5310, 176.8754, 184.4747, 189.0052, 222.4694, 224.2932, 233.6676, 220.5922, 187.6766, 182.0405, 189.9230, 206.4958, 214.1489, 176.4865, 184.7495, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
pet_oudin_subset <- subset_data(pet_oudin_global, box = c(-10,40,30,45))
infoNC(pet_oudin_subset)
[1] "class : RasterBrick "
[2] "dimensions : 64, 104, 6656, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -10, 40, 30, 45 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... "
[8] "min values : 0.099306993, 0.740073629, 13.313356668, 11.852477789, 47.733557582, 62.935860157, 79.710862637, 77.624826193, 33.284636736, 28.931746244, 4.929369092, 0.039311446, 0.030302684, 2.928590268, 8.578593999, ... "
[9] "max values : 54.77562, 62.65787, 115.40463, 131.55265, 177.77098, 204.70669, 222.70252, 200.23511, 165.45423, 117.08133, 69.63583, 55.89678, 58.23283, 65.31674, 103.43404, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
pet_oudin_esp <- crop_data(pet_oudin_subset, "gadm41_ESP_0.shp")
infoNC(pet_oudin_esp)
[1] "class : RasterBrick "
[2] "dimensions : 56, 58, 3248, 120 (nrow, ncol, ncell, nlayers)"
[3] "resolution : 0.25, 0.25 (x, y)"
[4] "extent : -10, 4.5, 30, 44 (xmin, xmax, ymin, ymax)"
[5] "crs : +proj=longlat +datum=WGS84 +no_defs "
[6] "source : memory"
[7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... "
[8] "min values : 3.5873966, 3.6652387, 23.3621691, 26.7197371, 54.5738134, 83.2697010, 93.0016851, 91.4133866, 49.0935838, 34.9640317, 5.8351220, 0.5116812, 4.7784175, 7.1974645, 19.2179436, ... "
[9] "max values : 40.66180, 48.18053, 80.11776, 100.17784, 125.64010, 160.57205, 165.80086, 155.79110, 111.13910, 80.95558, 44.86891, 37.12437, 40.82948, 48.49249, 74.67580, ... "
[10] "time : 2001-01-01, 2010-12-01 (min, max)"
Generate Time series
Time series for global ET products
To make a time series out of our data, we use the fldmean function,
which has one argument x.
- x Raster* object or a data.table or a *.nc filename (character).
Let's generate the time series for our three different GLDAS CLSM data
sets (Global, Mediterranean region, and Spain), and inspect its first 12
rows:
gldas_clsm_global_ts <- fldmean(gldas_clsm_global)
head(gldas_clsm_global_ts, 12)
date value
1: 2000-01-01 42.63418
2: 2000-02-01 40.28064
3: 2000-03-01 46.65724
4: 2000-04-01 49.73078
5: 2000-05-01 61.78450
6: 2000-06-01 71.51643
7: 2000-07-01 78.34947
8: 2000-08-01 68.59857
9: 2000-09-01 52.40877
10: 2000-10-01 45.95624
11: 2000-11-01 40.95821
12: 2000-12-01 41.50710
gldas_clsm_subset_ts <- fldmean(gldas_clsm_subset)
head(gldas_clsm_subset_ts, 12)
date value
1: 2001-01-01 14.47589
2: 2001-02-01 19.65537
3: 2001-03-01 38.58488
4: 2001-04-01 45.47299
5: 2001-05-01 57.83225
6: 2001-06-01 63.57403
7: 2001-07-01 51.30824
8: 2001-08-01 41.88030
9: 2001-09-01 29.30722
10: 2001-10-01 24.02233
11: 2001-11-01 16.56476
12: 2001-12-01 12.67189
gldas_clsm_esp_ts <- fldmean(gldas_clsm_esp)
head(gldas_clsm_esp_ts, 12)
date value
1: 2001-01-01 17.99823
2: 2001-02-01 31.41443
3: 2001-03-01 57.23334
4: 2001-04-01 84.13048
5: 2001-05-01 95.06479
6: 2001-06-01 118.33516
7: 2001-07-01 87.58777
8: 2001-08-01 74.37666
9: 2001-09-01 45.09689
10: 2001-10-01 43.91893
11: 2001-11-01 25.11206
12: 2001-12-01 16.99089
Time series for calculated PET
Let's generate the time series for our three different PET calculated by
Oudin method (Global, Mediterranean region, and Spain), and inspect its
first 12 rows:
pet_oudin_global_ts <- fldmean(pet_oudin_global)
head(pet_oudin_global_ts, 12)
date value
1: 2001-01-01 90.97581
2: 2001-02-01 90.72542
3: 2001-03-01 100.12134
4: 2001-04-01 96.08822
5: 2001-05-01 105.25369
6: 2001-06-01 110.88759
7: 2001-07-01 119.98619
8: 2001-08-01 112.29808
9: 2001-09-01 94.00018
10: 2001-10-01 89.70338
11: 2001-11-01 82.71571
12: 2001-12-01 90.02744
pet_oudin_subset_ts <- fldmean(pet_oudin_subset)
head(pet_oudin_subset_ts, 12)
date value
1: 2001-01-01 28.41624
2: 2001-02-01 34.31941
3: 2001-03-01 70.77386
4: 2001-04-01 85.68093
5: 2001-05-01 119.92428
6: 2001-06-01 146.10311
7: 2001-07-01 161.36373
8: 2001-08-01 147.05941
9: 2001-09-01 105.36592
10: 2001-10-01 73.91439
11: 2001-11-01 37.36657
12: 2001-12-01 24.99642
pet_oudin_esp_ts <- fldmean(pet_oudin_esp)
head(pet_oudin_esp_ts, 12)
date value
1: 2001-01-01 23.33118
2: 2001-02-01 28.85419
3: 2001-03-01 57.39954
4: 2001-04-01 71.95438
5: 2001-05-01 101.37500
6: 2001-06-01 134.48663
7: 2001-07-01 139.09158
8: 2001-08-01 131.25821
9: 2001-09-01 87.49866
10: 2001-10-01 59.00385
11: 2001-11-01 25.24541
12: 2001-12-01 16.16446
Visualize
Either after we have processed our data as required or right after
downloaded, we have six different options to visualize our data for more
information refer to visualisation section of
pRecipe:
Maps
To see a map of any data set raw or processed, we use plot_map which
takes only one layer of the RasterBrick as input.
plot_map(gldas_clsm_global[[18]])
plot_map(pet_oudin_global[[6]])
{width="6cm"}
{width="6cm"}
plot_map(gldas_clsm_subset[[6]])
plot_map(pet_oudin_subset[[6]])
{width="6cm"}
{width="6cm"}
plot_map(gldas_clsm_esp[[6]])
plot_map(pet_oudin_esp[[6]])
{width="6cm"}
{width="6cm"}
Time Series Visuals
To draw a time series generated by fldmean, we use any of the options
below, which takes only a fldmean ".csv" generated file.
Lineplots
Plotting globals
p01 <- plot_line(gldas_clsm_global_ts, var = "Evapotranspiration")
p02 <- plot_line(pet_oudin_global_ts, var = "Potential Evapotranspiration")
ggpubr::ggarrange(p01, p02, ncol = 1)
{width="14cm"}
Plotting subsets
p01 <- plot_line(gldas_clsm_subset_ts, var = "ET")
p02 <- plot_line(pet_oudin_subset_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting Spain
p01 <- plot_line(gldas_clsm_esp_ts, var = "ET")
p02 <- plot_line(pet_oudin_esp_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Heatmap
Plotting globals
plot_heatmap(gldas_clsm_global_ts)
{width="14cm"}
plot_heatmap(pet_oudin_global_ts)
{width="12cm"}
Plotting subsets
p01 <- plot_heatmap(gldas_clsm_subset_ts)
p02 <- plot_heatmap(pet_oudin_subset_ts)
ggpubr::ggarrange(p01, p02, ncol = 2, common.legend = TRUE, legend = "right")
{width="16cm"}
Plotting Spain
p01 <- plot_heatmap(gldas_clsm_esp_ts)
p02 <- plot_heatmap(pet_oudin_esp_ts)
ggpubr::ggarrange(p01, p02, ncol = 2, common.legend = TRUE, legend = "right")
{width="16cm"}
Boxplot
Plotting globals
p01 <- plot_box(gldas_clsm_global_ts, var = "ET")
p02 <- plot_box(pet_oudin_global_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting subsets
p01 <- plot_box(gldas_clsm_subset_ts, var = "ET")
p02 <- plot_box(pet_oudin_subset_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting Spain
p01 <- plot_box(gldas_clsm_esp_ts, var = "ET" )
p02 <- plot_box(pet_oudin_esp_ts, var = "PET" )
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Density plots
Plotting globals
p01 <- plot_density(gldas_clsm_global_ts, var = "ET")
p02 <- plot_density(pet_oudin_global_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Plotting subsets
p01 <- plot_density(gldas_clsm_subset_ts, var = "ET")
p02 <- plot_density(pet_oudin_subset_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Plotting Spain
p01 <- plot_density(gldas_clsm_esp_ts, var = "ET")
p02 <- plot_density(pet_oudin_esp_ts, var = "PET")
ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Summary
NOTE: For good aesthetics we recommend saving plot_summary with
ggsave(<filename>, <plot>, width = 16.3, height = 15.03).
plot_summary(gldas_clsm_global_ts, var = "Evapotranspiration")
#plot_summary(gldas_clsm_subset_ts, var = "Evapotranspiration")
#plot_summary(gldas_clsm_esp_ts, var = "Evapotranspiration")
#plot_summary(pet_oudin_global_ts, var = "Potential Evapotranspiration")
#plot_summary(pet_oudin_subset_ts)
#plot_summary(pet_oudin_esp_ts)
{width="14cm"}
Coming Soon
We will introduce significant enhancements to ET database and PET
calculation methods. This expansion builds upon our existing
temperature-based approach and incorporates a radiation-based PET
calculation methods, along with an expanded range of temperature-based
methods. Our aim is to provide users with a more comprehensive and
accurate estimation of ET and PET, catering to a broader range of
applications and requirements.
References
Try the evapoRe package in your browser
Any scripts or data that you put into this service are public.
evapoRe documentation built on May 29, 2024, 3:09 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
```{=html}
------------------------------------------------------------------------
```r
knitr::opts_chunk$set(
echo = TRUE,
eval = TRUE,
fig.width = 7,
warning = FALSE,
message = FALSE
)
library(evapoRe)
library(kableExtra)
data('gldas_clsm_global_ts')
data('gldas_clsm_subset_ts')
data('gldas_clsm_esp_ts')
data('pet_oudin_global_ts')
data('pet_oudin_subset_ts')
data('pet_oudin_esp_ts')
The evapoRe package developed as a complementary toolbox to the
pRecipe package [@vargas-godoy2023precipitation], available at
[https://CRAN.R-project.org/package=pRecipe]. evapoRe facilitates
the download, exploration, visualization, and analysis of
evapotranspiration (ET) data. Additionally, evapoRe offers the
functionality to calculate various Potential EvapoTranspiration (PET)
methods.
Before We Start
Like many other R packages, evapoRe has some system requirements:
Data
evapoRe database hosts 13 different ET data sets; three
satellite-based, five reanalysis, and five hydrological model products.
Their native specifications, as well as links to their providers, and
their respective references are detailed in the following subsections.
We have already homogenized, compacted to a single file, and stored them
in a Zenodo repository under
the following naming convention:
<data set>_<variable>_<units>_<coverage>_<start date>_<end date>_<resolution>_<time step>.nc
The evapoRe data collection was homogenized to these specifications:
<variable>= evapotranspiration (e)<units>= millimeters (mm)<resolution>= 0.25°
E.g., ERA5 [@hersbach_era5_2020] would be:
era5_e_mm_global_195901_202112_025_monthly.nc
Satellite-Based Products
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "GLEAM V3.7b", "0.25°", "", "x", "", "Monthly", "1980/01-2021/12", "[Download](https://www.gleam.eu/)", "@martens_gleam_2017", "BESS V2.0", "0.05°", "", "x", "", "Monthly", "1982/01-2019/12", "[Download](https://www.environment.snu.ac.kr/bessv2)", "@li2023bessv2", "ETMonitor", "1$km$", "", "x", "", "Daily", "2000/06-2019/12", "[Download](https://data.casearth.cn/en/sdo/detail/63291c7e08415d54af833fe5)", "@zheng2022ETMonitor" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
Reanalysis Products
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "ERA5-Land", "0.1°", "", "x", "", "Monthly", "1960/01-2022/12", "[Download](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=form)", "@munoz-sabater_era5-land_2021", "ERA5", "0.25°", "", "x", "", "Monthly", "1959/01-2021/12", "[Download](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview)", "@hersbach_era5_2020", "JRA-55", "1.25°", "", "x", "", "Monthly", "1958/01-2021/12", "[Download](https://rda.ucar.edu/datasets/ds628.1/dataaccess/)", "@kobayashi_jra-55_2015", "MERRA-2", "0.5° x 0.625°", "", "x", "", "Monthly", "1980/01-2023/01", "[Download](https://disc.gsfc.nasa.gov/datasets?page=1&project=MERRA-2)", "@gelaro_modern-era_2017", "CAMELE", "0.25°", "", "x", "", "Monthly", "1980/01-2022/12", "[Download](https://zenodo.org/records/8047038)", "@li2023camele" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
Hydrological Models
tibble::tribble( ~"Data Set", ~"Spatial Resolution", ~Global, ~Land, ~Ocean, ~"Temporal Resolution", ~"Record Length", ~"Get Data", ~Reference, "FLDAS", "0.1°", "", "x", "", "Monthly", "1982/01-2022/12", "[Download](https://ldas.gsfc.nasa.gov/fldas/fldas-data-download)", "@mcnally_land_2017", "GLDAS CLSM V2.1", "1°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004", "GLDAS NOAH V2.1", "0.25°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004 and @beaudoing_gldas_2020", "GLDAS VIC V2.1", "1°", "", "x", "", "Monthly", "2000/01-2022/11", "[Download](https://ldas.gsfc.nasa.gov/gldas/gldas-get-data)", "@rodell_global_2004", "TerraClimate", "4$km$", "", "x", "", "Monthly", "1958/01-2021/12", "[Download](https://www.climatologylab.org/terraclimate.html)", "@abatzoglou_terraclimate_2018" ) |> kbl(align = 'lcccccccr') |> kable_styling("striped") |> add_header_above(c(" " = 1, " " = 1, "Spatial Coverage" = 3, " " = 1, " " = 1, " " = 1, " " = 1)) |> unclass() |> cat()
Demo
In this introductory demo we will first download the GLDAS-CLSM data set. We will then subset the downloaded data over Mediterranean region for the 2001-2010 period, and crop it to the national scale for Spain. In paralel, we will estimate potential evapotranspiration over the same domain and the same record length. In the next step, we will generate time series for our data sets and conclude with the visualization of our data.
Installation
devtools::install_github("AkbarR1184/evapoRe") #latest dev version install.packages('evapoRe') #latest CRAN release library(evapoRe)
Download
Downloading the entire data collection or only a few data sets is quite
straightforward. You just call the download_data function, which has
four arguments data_name, path, domain, and time_res.
- data_name is set to "all" by default, but you can specify the names of your data sets of interest only.
- path can be set to ".". I.e., the current working directory. By replacing it for [your_project_folder], the downloaded files will be stored in [your_project_folder] instead.
- domain is set to "raw" by default, but you can specify the domain of your interest only. E.g., "ocean" for ocean only data sets (For availability please check the [Data] section).
- time_res is set to "monthly" by default, but if you prefer you can also download annual data with "yearly".
Let's download the GLDAS CLSM data set and inspect its content with
infoNC:
download_data(data_name = 'gldas-clsm', path = ".") gldas_clsm_global <- raster::brick('gldas-clsm_e_mm_land_200001_202211_025_monthly.nc') infoNC(gldas_clsm_global)
[1] "class : RasterBrick " [2] "dimensions : 720, 1440, 1036800, 275 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 " [6] "source : gldas-clsm_e_mm_land_200001_202211_025_monthly.nc " [7] "names : X2000.01.01, X2000.02.01, X2000.03.01, X2000.04.01, X2000.05.01, X2000.06.01, X2000.07.01, X2000.08.01, X2000.09.01, X2000.10.01, X2000.11.01, X2000.12.01, X2001.01.01, X2001.02.01, X2001.03.01, ... " [8] "Date : 2000-01-01, 2022-11-01 (min, max)" [9] "varname : e "
Processing
Once we have downloaded our database, we can start processing the data with:
crop_datato crop the data using a shapefile.fldmeanto generate a time series by taking the area weighted average over each timestep.remapto go from the native resolution (0.25°) to coarser ones (e.g., 0.5°, 1°, 1.5°, ...).subset_datato subset the data in time and/or space.yearstatto aggregate the data from monthly into annual.
Subset
To subset our data to a desired region and period of interest, we use
the subset_data function, which has three arguments x, box, and
yrs.
- x Raster* object or a data.table or a filename (character).
- box is the bounding box of the region of interest with the coordinates in degrees in the form (xmin, xmax, ymin, ymax).
- yrs is the period of interest with years in the form (start_year, end_year).
Let's subset the GLDAS CLSM data set over Mediterranean region
(-10,40,30,45) for the 2001-2010 period, and inspect its content with
infoNC:
gldas_clsm_subset <- subset_data(gldas_clsm_global,box = c(-10,40,30,45) ,yrs = c(2001, 2010)) infoNC(gldas_clsm_subset)
[1] "class : RasterBrick " [2] "dimensions : 60, 200, 12000, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -10, 40, 30, 45 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... " [8] "min values : 0.85979986, 1.62062681, 1.42477119, 0.76781327, 0.75662607, 0.34450921, 0.25072542, 0.15768366, 0.13057871, 0.05979802, 0.11780920, -0.69875073, 0.36552662, 0.70131457, 0.63548779, ... " [9] "max values : 80.61240, 89.56071, 101.81876, 143.45859, 158.37830, 202.83186, 192.55907, 190.07066, 111.40405, 116.93645, 67.32398, 48.42713, 74.23843, 59.85103, 88.96181, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
Crop
To further crop our data to a desired polygon other than a rectangle, we
use the crop_data function, which has two arguments x, and y.
- x Raster* object or a data.table or a *.nc filename (character).
- y is a ".shp" filename (character).
Let's crop our GLDAS CLSM subset to cover only Spain with the respective
shape
file,
and inspect its content with infoNC:
gldas_clsm_esp <- crop_data(gldas_clsm_subset, "gadm41_ESP_0.shp") infoNC(gldas_clsm_esp)
[1] "class : RasterBrick " [2] "dimensions : 56, 58, 3248, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -10, 4.5, 30, 44 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... " [8] "min values : 7.216680, 18.606867, 32.398956, 37.939827, 39.484840, 29.796391, 15.073787, 17.676109, 15.789503, 26.564753, 13.147447, 9.846310, 8.794820, 14.355796, 26.288857, ... " [9] "max values : 80.61240, 89.56071, 101.81876, 139.86717, 151.03282, 197.47284, 146.44232, 145.36212, 111.40405, 116.93645, 67.32398, 48.42713, 74.23843, 58.79382, 88.13857, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
PET calculation
First we need to download temperature data, available at: Zenodo repository:
NOTE: Temperature data available at the moment is limited to monthly. The data sets are TerraClimate, MSWX, and CRU and for brevity We will only estimate PET over the 2001 to 2010 period using MSWX dataset.
we use the download_t_data function, which has five arguments
data_name, variable, path, time_res, and domain.
- data_name is the dataset name you can specify the names of your data sets of interest.
- variable is the variable name in which
t2m,tmin, andtmaxstand for average temperature, minimum temperature, and maximum temperature. - path can be set to ".". I.e., the current working directory. By replacing it for [your_project_folder], the downloaded files will be stored in [your_project_folder] instead.
- domain is set to "raw" by default, but you can specify the domain of your interest only. E.g., "ocean" for ocean only data sets (For availability please check the [Data] section).
- time_res is set to "monthly" by default, but if you prefer you can also download annual data with "yearly".
download_t_data(data_name ="mswx", variable = "t2m", path = ".")
This will download temperature data in following naming convention e.g.,
mswx_t2m_degC_land_197901_202308_025_monthly.nc
As stated above we will work only with the 2001-2010 period. Since
evapoRe makes all of pRecipe functions available we can load and
subset the data as follows:
t2m_global <- raster::brick("mswx_t2m_degC_land_197901_202308_025_monthly.nc") %>% subset_data(yrs = c(2001, 2010)) infoNC(t2m_global)
[1] "class : RasterBrick " [2] "dimensions : 720, 1440, 1036800, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : X2001.01.01, X2001.02.01, X2001.03.01, X2001.04.01, X2001.05.01, X2001.06.01, X2001.07.01, X2001.08.01, X2001.09.01, X2001.10.01, X2001.11.01, X2001.12.01, X2002.01.01, X2002.02.01, X2002.03.01, ... " [8] "min values : -49.867680, -45.244373, -42.529930, -33.551258, -22.829224, -15.122508, -13.402536, -14.673846, -18.428692, -27.443539, -35.674980, -44.693199, -46.778694, -47.197075, -42.277370, ... " [9] "max values : 35.00875, 33.52000, 33.29000, 35.04688, 38.26308, 39.35505, 40.33314, 39.98806, 36.99562, 33.00374, 32.52186, 33.59248, 34.46944, 33.25062, 33.71313, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
The pet function estimates PET using a method of choice from the
following available options:
- bc for Blaney and Criddle [@blaney1952determining].
- ha for Hamon [@hamon1961estimating]
- jh for Jensen and Haise [@jensen1963estimating]
- mb for McGuinness and Bordne [@mcguinness1972comparison]
- od for Oudin [@oudin2005potential]
- th for Thornthwaite [@thornthwaite1948approach]
The pet function has two arguments x and method.
- x is a RasterBrick object with average temperature data.
- method a character string indicating the method to be used.
Let's calculate PET using the Oudin formulation. Then, same as GLDAS
CLSM we can subset it over Mediterranean region and Spain, and inspect
its content with infoNC:
NOTE: pet output is [mm/day], in order to get values in [mm] for a
1 to 1 comparison we use muldpm function.
pet_oudin_global <- pet(t2m_global, method = "od") %>% muldpm infoNC(pet_oudin_global)
[1] "class : RasterBrick " [2] "dimensions : 720, 1440, 1036800, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -180, 180, -90, 90 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... " [8] "min values : 0.000000e+00, 0.000000e+00, 8.728488e-04, 8.322404e-04, 3.890790e-04, 0.000000e+00, 0.000000e+00, 2.140520e-04, 8.102036e-04, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 1.431775e-04, ... " [9] "max values : 219.5310, 176.8754, 184.4747, 189.0052, 222.4694, 224.2932, 233.6676, 220.5922, 187.6766, 182.0405, 189.9230, 206.4958, 214.1489, 176.4865, 184.7495, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
pet_oudin_subset <- subset_data(pet_oudin_global, box = c(-10,40,30,45)) infoNC(pet_oudin_subset)
[1] "class : RasterBrick " [2] "dimensions : 64, 104, 6656, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -10, 40, 30, 45 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... " [8] "min values : 0.099306993, 0.740073629, 13.313356668, 11.852477789, 47.733557582, 62.935860157, 79.710862637, 77.624826193, 33.284636736, 28.931746244, 4.929369092, 0.039311446, 0.030302684, 2.928590268, 8.578593999, ... " [9] "max values : 54.77562, 62.65787, 115.40463, 131.55265, 177.77098, 204.70669, 222.70252, 200.23511, 165.45423, 117.08133, 69.63583, 55.89678, 58.23283, 65.31674, 103.43404, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
pet_oudin_esp <- crop_data(pet_oudin_subset, "gadm41_ESP_0.shp") infoNC(pet_oudin_esp)
[1] "class : RasterBrick " [2] "dimensions : 56, 58, 3248, 120 (nrow, ncol, ncell, nlayers)" [3] "resolution : 0.25, 0.25 (x, y)" [4] "extent : -10, 4.5, 30, 44 (xmin, xmax, ymin, ymax)" [5] "crs : +proj=longlat +datum=WGS84 +no_defs " [6] "source : memory" [7] "names : layer.1, layer.2, layer.3, layer.4, layer.5, layer.6, layer.7, layer.8, layer.9, layer.10, layer.11, layer.12, layer.13, layer.14, layer.15, ... " [8] "min values : 3.5873966, 3.6652387, 23.3621691, 26.7197371, 54.5738134, 83.2697010, 93.0016851, 91.4133866, 49.0935838, 34.9640317, 5.8351220, 0.5116812, 4.7784175, 7.1974645, 19.2179436, ... " [9] "max values : 40.66180, 48.18053, 80.11776, 100.17784, 125.64010, 160.57205, 165.80086, 155.79110, 111.13910, 80.95558, 44.86891, 37.12437, 40.82948, 48.49249, 74.67580, ... " [10] "time : 2001-01-01, 2010-12-01 (min, max)"
Generate Time series
Time series for global ET products
To make a time series out of our data, we use the fldmean function,
which has one argument x.
- x Raster* object or a data.table or a *.nc filename (character).
Let's generate the time series for our three different GLDAS CLSM data sets (Global, Mediterranean region, and Spain), and inspect its first 12 rows:
gldas_clsm_global_ts <- fldmean(gldas_clsm_global) head(gldas_clsm_global_ts, 12)
date value 1: 2000-01-01 42.63418 2: 2000-02-01 40.28064 3: 2000-03-01 46.65724 4: 2000-04-01 49.73078 5: 2000-05-01 61.78450 6: 2000-06-01 71.51643 7: 2000-07-01 78.34947 8: 2000-08-01 68.59857 9: 2000-09-01 52.40877 10: 2000-10-01 45.95624 11: 2000-11-01 40.95821 12: 2000-12-01 41.50710
gldas_clsm_subset_ts <- fldmean(gldas_clsm_subset) head(gldas_clsm_subset_ts, 12)
date value 1: 2001-01-01 14.47589 2: 2001-02-01 19.65537 3: 2001-03-01 38.58488 4: 2001-04-01 45.47299 5: 2001-05-01 57.83225 6: 2001-06-01 63.57403 7: 2001-07-01 51.30824 8: 2001-08-01 41.88030 9: 2001-09-01 29.30722 10: 2001-10-01 24.02233 11: 2001-11-01 16.56476 12: 2001-12-01 12.67189
gldas_clsm_esp_ts <- fldmean(gldas_clsm_esp) head(gldas_clsm_esp_ts, 12)
date value 1: 2001-01-01 17.99823 2: 2001-02-01 31.41443 3: 2001-03-01 57.23334 4: 2001-04-01 84.13048 5: 2001-05-01 95.06479 6: 2001-06-01 118.33516 7: 2001-07-01 87.58777 8: 2001-08-01 74.37666 9: 2001-09-01 45.09689 10: 2001-10-01 43.91893 11: 2001-11-01 25.11206 12: 2001-12-01 16.99089
Time series for calculated PET
Let's generate the time series for our three different PET calculated by Oudin method (Global, Mediterranean region, and Spain), and inspect its first 12 rows:
pet_oudin_global_ts <- fldmean(pet_oudin_global) head(pet_oudin_global_ts, 12)
date value 1: 2001-01-01 90.97581 2: 2001-02-01 90.72542 3: 2001-03-01 100.12134 4: 2001-04-01 96.08822 5: 2001-05-01 105.25369 6: 2001-06-01 110.88759 7: 2001-07-01 119.98619 8: 2001-08-01 112.29808 9: 2001-09-01 94.00018 10: 2001-10-01 89.70338 11: 2001-11-01 82.71571 12: 2001-12-01 90.02744
pet_oudin_subset_ts <- fldmean(pet_oudin_subset) head(pet_oudin_subset_ts, 12)
date value 1: 2001-01-01 28.41624 2: 2001-02-01 34.31941 3: 2001-03-01 70.77386 4: 2001-04-01 85.68093 5: 2001-05-01 119.92428 6: 2001-06-01 146.10311 7: 2001-07-01 161.36373 8: 2001-08-01 147.05941 9: 2001-09-01 105.36592 10: 2001-10-01 73.91439 11: 2001-11-01 37.36657 12: 2001-12-01 24.99642
pet_oudin_esp_ts <- fldmean(pet_oudin_esp) head(pet_oudin_esp_ts, 12)
date value 1: 2001-01-01 23.33118 2: 2001-02-01 28.85419 3: 2001-03-01 57.39954 4: 2001-04-01 71.95438 5: 2001-05-01 101.37500 6: 2001-06-01 134.48663 7: 2001-07-01 139.09158 8: 2001-08-01 131.25821 9: 2001-09-01 87.49866 10: 2001-10-01 59.00385 11: 2001-11-01 25.24541 12: 2001-12-01 16.16446
Visualize
Either after we have processed our data as required or right after downloaded, we have six different options to visualize our data for more information refer to visualisation section of pRecipe:
Maps
To see a map of any data set raw or processed, we use plot_map which
takes only one layer of the RasterBrick as input.
plot_map(gldas_clsm_global[[18]]) plot_map(pet_oudin_global[[6]])
{width="6cm"}{width="6cm"}
plot_map(gldas_clsm_subset[[6]]) plot_map(pet_oudin_subset[[6]])
{width="6cm"}{width="6cm"}
plot_map(gldas_clsm_esp[[6]]) plot_map(pet_oudin_esp[[6]])
{width="6cm"}{width="6cm"}
Time Series Visuals
To draw a time series generated by fldmean, we use any of the options
below, which takes only a fldmean ".csv" generated file.
Lineplots
Plotting globals
p01 <- plot_line(gldas_clsm_global_ts, var = "Evapotranspiration") p02 <- plot_line(pet_oudin_global_ts, var = "Potential Evapotranspiration") ggpubr::ggarrange(p01, p02, ncol = 1)
{width="14cm"}
Plotting subsets
p01 <- plot_line(gldas_clsm_subset_ts, var = "ET") p02 <- plot_line(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting Spain
p01 <- plot_line(gldas_clsm_esp_ts, var = "ET") p02 <- plot_line(pet_oudin_esp_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Heatmap
Plotting globals
plot_heatmap(gldas_clsm_global_ts)
{width="14cm"}
plot_heatmap(pet_oudin_global_ts)
{width="12cm"}
Plotting subsets
p01 <- plot_heatmap(gldas_clsm_subset_ts) p02 <- plot_heatmap(pet_oudin_subset_ts) ggpubr::ggarrange(p01, p02, ncol = 2, common.legend = TRUE, legend = "right")
{width="16cm"}
Plotting Spain
p01 <- plot_heatmap(gldas_clsm_esp_ts) p02 <- plot_heatmap(pet_oudin_esp_ts) ggpubr::ggarrange(p01, p02, ncol = 2, common.legend = TRUE, legend = "right")
{width="16cm"}
Boxplot
Plotting globals
p01 <- plot_box(gldas_clsm_global_ts, var = "ET") p02 <- plot_box(pet_oudin_global_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting subsets
p01 <- plot_box(gldas_clsm_subset_ts, var = "ET") p02 <- plot_box(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Plotting Spain
p01 <- plot_box(gldas_clsm_esp_ts, var = "ET" ) p02 <- plot_box(pet_oudin_esp_ts, var = "PET" ) ggpubr::ggarrange(p01, p02, ncol = 2)
{width="16cm"}
Density plots
Plotting globals
p01 <- plot_density(gldas_clsm_global_ts, var = "ET") p02 <- plot_density(pet_oudin_global_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Plotting subsets
p01 <- plot_density(gldas_clsm_subset_ts, var = "ET") p02 <- plot_density(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Plotting Spain
p01 <- plot_density(gldas_clsm_esp_ts, var = "ET") p02 <- plot_density(pet_oudin_esp_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2)
{width="14cm"}
Summary
NOTE: For good aesthetics we recommend saving plot_summary with
ggsave(<filename>, <plot>, width = 16.3, height = 15.03).
plot_summary(gldas_clsm_global_ts, var = "Evapotranspiration") #plot_summary(gldas_clsm_subset_ts, var = "Evapotranspiration") #plot_summary(gldas_clsm_esp_ts, var = "Evapotranspiration") #plot_summary(pet_oudin_global_ts, var = "Potential Evapotranspiration") #plot_summary(pet_oudin_subset_ts) #plot_summary(pet_oudin_esp_ts)
{width="14cm"}
Coming Soon
We will introduce significant enhancements to ET database and PET calculation methods. This expansion builds upon our existing temperature-based approach and incorporates a radiation-based PET calculation methods, along with an expanded range of temperature-based methods. Our aim is to provide users with a more comprehensive and accurate estimation of ET and PET, catering to a broader range of applications and requirements.
References
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