--- title: "evapoRe" author: "Akbar Rahmati Ziveh, Mijael Rodrigo Vargas Godoy, Vishal Thakur, Yannis Markonis" date: "`r Sys.Date()`" output: rmarkdown::html_vignette: toc: true toc_depth: 4 vignette: > %\VignetteIndexEntry{evapoRe} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console bibliography: evapoRe.bib link-citations: true --- ```{=html} ``` ------------------------------------------------------------------------ ```{r start, include = FALSE} 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 []. `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: - [PROJ](https://proj.org/download.html) - Geospatial Data Abstraction Library [(GDAL)](https://gdal.org/download.html) - Network Common Data Form [(NetCDF)](https://www.unidata.ucar.edu/software/netcdf/) ## 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](https://doi.org/10.5281/zenodo.10011192) under the following naming convention: `_______.nc` The `evapoRe` data collection was homogenized to these specifications: - `` = evapotranspiration (e) - `` = millimeters (mm) - `` = 0.25° E.g., ERA5 [@hersbach_era5_2020] would be: `era5_e_mm_global_195901_202112_025_monthly.nc` ### Satellite-Based Products ```{r satellite, echo=FALSE, results = 'asis'} 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 ```{r reanalysis, echo=FALSE, echo=FALSE, results = 'asis'} 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 ```{r models, echo=FALSE, results = 'asis'} 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 ```{r evapoRe_installation, eval = FALSE} 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`: ```{r download, eval = FALSE} 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`: ```{r gldas-clsm_subset, eval = FALSE} 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](https://geodata.ucdavis.edu/gadm/gadm4.1/shp/gadm41_ESP_shp.zip), and inspect its content with `infoNC`: ```{r gldas-clsm_crop, eval = FALSE} 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](https://doi.org/10.5281/zenodo.10019933): **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". ```{r download_mswx, eval=FALSE} 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: ```{r subset_mswx, eval=FALSE} 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. ```{r pet, eval=FALSE} 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)" ``` ```{r pet_oudin_subset, eval = FALSE} 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)" ``` ```{r pet_oudin_crop, eval = FALSE} 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: ```{r gldas_clsm_global_ts, eval=FALSE} 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 ``` ```{r gldas_clsm_subset_ts, eval=FALSE} 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 ``` ```{r gldas_clsm_esp_ts, eval=FALSE} 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: ```{r pet_oudin_global_ts, eval=FALSE} 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 ``` ```{r pet_oudin_subset_ts, eval=FALSE} 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 ``` ```{r pet_odin_esp_ts, eval=FALSE} 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](https://CRAN.R-project.org/package=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. ```{r map_global, eval = F} plot_map(gldas_clsm_global[[18]]) plot_map(pet_oudin_global[[6]]) ``` ![](plots/map_gldas_global.png){width="6cm"}![](plots/map_pet_oudin_global.png){width="6cm"} ```{r map_subset, eval = FALSE} plot_map(gldas_clsm_subset[[6]]) plot_map(pet_oudin_subset[[6]]) ``` ![](plots/map_gldas_subset.png){width="6cm"}![](plots/map_pet_oudin_subset.png){width="6cm"} ```{r map_esp, eval = FALSE} plot_map(gldas_clsm_esp[[6]]) plot_map(pet_oudin_esp[[6]]) ``` ![](plots/map_gldas_esp.png){width="6cm"}![](plots/map_pet_oudin_esp.png){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 ```{r lines, eval = FALSE} 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) ``` ![](plots/global_ts.png){width="14cm"} ##### Plotting subsets ```{r lines_subset, eval = FALSE} p01 <- plot_line(gldas_clsm_subset_ts, var = "ET") p02 <- plot_line(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/subset_ts.png){width="16cm"} ##### Plotting Spain ```{r lines_esp, eval = FALSE} p01 <- plot_line(gldas_clsm_esp_ts, var = "ET") p02 <- plot_line(pet_oudin_esp_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/esp_ts.png){width="16cm"} #### Heatmap ##### Plotting globals ```{r heatmap_gldas, eval = FALSE} plot_heatmap(gldas_clsm_global_ts) ``` ![](plots/heatmap_gldas_global.png){width="14cm"} ```{r heatmap_oudin, eval = FALSE} plot_heatmap(pet_oudin_global_ts) ``` ![](plots/heatmap_pet_oudin_global.png){width="12cm"} ##### Plotting subsets ```{r heatmap_subset, eval = FALSE} 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") ``` ![](plots/heatmap_subset.png){width="16cm"} ##### Plotting Spain ```{r heatmap_esp, eval = FALSE} 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") ``` ![](plots/heatmap_esp.png){width="16cm"} #### Boxplot ##### Plotting globals ```{r box, eval = FALSE} p01 <- plot_box(gldas_clsm_global_ts, var = "ET") p02 <- plot_box(pet_oudin_global_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/box_global.png){width="16cm"} ##### Plotting subsets ```{r box_subset, eval = FALSE} p01 <- plot_box(gldas_clsm_subset_ts, var = "ET") p02 <- plot_box(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/box_subset.png){width="16cm"} ##### Plotting Spain ```{r box_esp, eval = FALSE} p01 <- plot_box(gldas_clsm_esp_ts, var = "ET" ) p02 <- plot_box(pet_oudin_esp_ts, var = "PET" ) ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/box_esp.png){width="16cm"} #### Density plots ##### Plotting globals ```{r density, eval = FALSE} p01 <- plot_density(gldas_clsm_global_ts, var = "ET") p02 <- plot_density(pet_oudin_global_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/density_global.png){width="14cm"} ##### Plotting subsets ```{r density_subset, eval = FALSE} p01 <- plot_density(gldas_clsm_subset_ts, var = "ET") p02 <- plot_density(pet_oudin_subset_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/density_subset.png){width="14cm"} ##### Plotting Spain ```{r density_esp, eval = FALSE} p01 <- plot_density(gldas_clsm_esp_ts, var = "ET") p02 <- plot_density(pet_oudin_esp_ts, var = "PET") ggpubr::ggarrange(p01, p02, ncol = 2) ``` ![](plots/density_esp.png){width="14cm"} #### Summary **NOTE:** For good aesthetics we recommend saving `plot_summary` with `ggsave(, , width = 16.3, height = 15.03)`. ```{r, eval=FALSE} 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) ``` ![](plots/summary_gldas.png){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