evapoRe

Akbar Rahmati Ziveh, Mijael Rodrigo Vargas Godoy, Vishal Thakur, Yannis Markonis

2025-04-10

The evapoRe package developed as a complementary toolbox to the pRecipe package (Vargas Godoy and Markonis 2023), 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:

E.g., ERA5 (Hersbach et al. 2020) would be:

era5_e_mm_global_195901_202112_025_monthly.nc

Satellite-Based Products

Spatial Coverage
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 Martens et al. (2017)
BESS V2.0 0.05° x Monthly 1982/01-2019/12 Download B. Li et al. (2023)
ETMonitor 1\(km\) x Daily 2000/06-2019/12 Download Zheng et al. (2022)

Reanalysis Products

Spatial Coverage
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 Muñoz-Sabater et al. (2021)
ERA5 0.25° x Monthly 1959/01-2021/12 Download Hersbach et al. (2020)
JRA-55 1.25° x Monthly 1958/01-2021/12 Download Kobayashi et al. (2015)
MERRA-2 0.5° x 0.625° x Monthly 1980/01-2023/01 Download Gelaro et al. (2017)
CAMELE 0.25° x Monthly 1980/01-2022/12 Download C. Li and al. (2023)

Hydrological Models

Spatial Coverage
Data Set Spatial Resolution Global Land Ocean Temporal Resolution Record Length Get Data Reference
FLDAS 0.1° x Monthly 1982/01-2022/12 Download McNally et al. (2017)
GLDAS CLSM V2.1 1° x Monthly 2000/01-2022/11 Download Rodell et al. (2004)
GLDAS NOAH V2.1 0.25° x Monthly 2000/01-2022/11 Download Rodell et al. (2004) and Beaudoing and Rodell (2020)
GLDAS VIC V2.1 1° x Monthly 2000/01-2022/11 Download Rodell et al. (2004)
TerraClimate 4\(km\) x Monthly 1958/01-2021/12 Download Abatzoglou et al. (2018)

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.

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:

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:

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]]) 

plot_map(gldas_clsm_subset[[6]])
plot_map(pet_oudin_subset[[6]])

plot_map(gldas_clsm_esp[[6]])
plot_map(pet_oudin_esp[[6]])

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)

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)

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)

Heatmap

Plotting globals
plot_heatmap(gldas_clsm_global_ts)

plot_heatmap(pet_oudin_global_ts)

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")

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")

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)

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)

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)

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)

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)

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)

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)

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

Abatzoglou, John T, Solomon Z Dobrowski, Sean A Parks, and Katherine C Hegewisch. 2018. “TerraClimate, a High-Resolution Global Dataset of Monthly Climate and Climatic Water Balance from 1958–2015.” Scientific Data 5 (1): 1–12.
Beaudoing, Hiroko, and Matthew Rodell. 2020. “GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1.” Greenbelt, Maryland, USA: Goddard Earth Sciences Data and Information Services Center (GES DISC). https://doi.org/10.5067/SXAVCZFAQLNO.
Blaney, Harry French. 1952. “Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data.”
Gelaro, Ronald, Will McCarty, Max J. Suárez, Ricardo Todling, Andrea Molod, Lawrence Takacs, Cynthia A. Randles, et al. 2017. “The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2).” Journal of Climate 30 (14): 5419–54. https://doi.org/10.1175/JCLI-D-16-0758.1.
Hamon, W. R. 1961. “Estimating Potential Evapotranspiration.” Journal of the Hydraulics Division 87 (3): 107–20.
Hersbach, Hans, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, et al. 2020. “The ERA5 Global Reanalysis.” Quarterly Journal of the Royal Meteorological Society 146 (730): 1999–2049.
Jensen, M. E., and H. R. Haise. 1963. “Estimating Evapotranspiration from Solar Radiation.” Journal of the Irrigation and Drainage Division 89 (4): 15–41.
Kobayashi, Shinya, Yukinari Ota, Yayoi Harada, Ayataka Ebita, Masami Moriya, Hirokatsu Onoda, Kazutoshi Onogi, et al. 2015. “The JRA-55 Reanalysis: General Specifications and Basic Characteristics.” Journal of the Meteorological Society of Japan. Ser. II 93 (1): 5–48. https://doi.org/10.2151/jmsj.2015-001.
Li, B., Y. Ryu, C. Jiang, B. Dechant, J. Liu, Y. Yan, and X. Li. 2023. “BESSv2.0: A Satellite-Based and Coupled-Process Model for Quantifying Long-Term Global Land–Atmosphere Fluxes.” Remote Sensing of Environment 295: 113696.
Li, Changming, and et al. 2023. “CAMELE: Collocation-Analyzed Multi-Source Ensembled Land Evapotranspiration Data.” Earth System Science Data Discussions 2023: 1–60.
Martens, Brecht, Diego G. Miralles, Hans Lievens, Robin van der Schalie, Richard A. M. de Jeu, Diego Fernández-Prieto, Hylke E. Beck, Wouter A. Dorigo, and Niko E. C. Verhoest. 2017. “GLEAM v3: satellite-based land evaporation and root-zone soil moisture.” Geoscientific Model Development 10 (5): 1903–25. https://doi.org/10.5194/gmd-10-1903-2017.
McGuinness, J. L., and E. F. Bordne. 1972. “A Comparison of Lysimeter-Derived Potential Evapotranspiration with Computed Values.” 1452. US Department of Agriculture.
McNally, Amy, Kristi Arsenault, Sujay Kumar, Shraddhanand Shukla, Pete Peterson, Shugong Wang, Chris Funk, Christa D. Peters-Lidard, and James P. Verdin. 2017. “A Land Data Assimilation System for Sub-Saharan Africa Food and Water Security Applications.” Scientific Data 4 (1): 170012. https://doi.org/10.1038/sdata.2017.12.
Muñoz-Sabater, Joaquín, Gianpaolo Balsamo, Emanuel Dutra, Hans Hersbach, Patricia López, Diego Miralles, Robin van der Schalie, et al. 2021. “ERA5-Land: A state-of-the-art global reanalysis dataset for land applications.” Earth System Science Data 13 (9): 4349–83. https://doi.org/10.5194/essd-13-4349-2021.
Oudin, L., F. Hervieu, C. Michel, C. Perrin, V. Andréassian, F. Anctil, and C. Loumagne. 2005. “Which Potential Evapotranspiration Input for a Lumped Rainfall–Runoff Model?: Part 2–Towards a Simple and Efficient Potential Evapotranspiration Model for Rainfall–Runoff Modelling.” Journal of Hydrology 303 (1-4): 290–306.
Rodell, Matthew, Paul R. Houser, Uli Jambor, Jon Gottschalck, Ken Mitchell, Chuanmin Meng, Kristi Arsenault, et al. 2004. “The Global Land Data Assimilation System.” Bulletin of the American Meteorological Society 85: 381–94. https://doi.org/10.1175/BAMS-85-3-381.
Thornthwaite, C. W. 1948. “An Approach Toward a Rational Classification of Climate.” Geographical Review 38 (1): 55–94.
Vargas Godoy, Mijael Rodrigo, and Yannis Markonis. 2023. “pRecipe: A Global Precipitation Climatology Toolbox and Database.” Environmental Modelling & Software 165: 105711.
Zheng, C., L. Chaolei, L. Jia, and H. Guangcheng. 2022. “Global Land Surface Evapotranspiration Monitoring by ETMonitor Model Driven by Multi-Source Satellite Earth Observations.” Journal of Hydrology 613: 128444.