In gopalpenny/fao56:

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

Download FAO climate data

The goal of this vignette is to access and download FAO climate data, then prepare that data to calculate ETo. The methods used in this package fao56 are pulled from the FAO 56 report on evapotranspiration. Many of the functions correspond directly to an equation in that report.

library(fao56)
library(dplyr)

Let's say we want climate data for Kuala Lumpur, which has the coordinates 3.1390, 101.6869. We first use get_aquastat_climate() to visit the those coordinates and seek to download climate data. On the page, click the download CSV button. It also helps to note the elevation, which is 49 m in this case.

get_aquastat_climate(lat = 3.1390, lon = 101.6869)

Next, move the file to a convenient locaiton. This package already has the file installed under extdata. It can be accessed by finding the location using system.file(). The file can then be read using read_et0_csv()

kl_path <- system.file("extdata","ET0_3_14N_101_69N.csv", package = "fao56")
kl_climate <- read_et0_csv(kl_path)
kl_climate

Prepare the data for FAO penman monteith

The FAO Penman-Monteith equation requires specific variables to calculate ETo. The function gen_ETo_vars allows calculation of these variables and the ability to select particular ones. It also calculates ETo in the field ETo_calc.

z = 49
lat = 3.139
col_select <- c("Rn_MJ_per_day", "G_MJ_per_day", "Tmp_Mean_degC", "Wind_2m_m_per_s", "es_kPa", "ea_kPa", "ETo_calc", "ETo_mm_per_d")
ETo_df <- gen_ETo_vars(kl_climate, lat = lat, z = z, col_select = col_select) 
ETo_df

There may be small discrepancies between ETo_calc and ETo_mm_per_d. These should be small, on the order of 1%.

ETo_df %>% mutate(pct_error = round(100 * (ETo_calc - ETo_mm_per_d) / ETo_mm_per_d, 1)) %>%
  dplyr::select(ETo_calc, ETo_mm_per_d, pct_error)

gopalpenny/fao56 documentation built on Feb. 14, 2022, 6:01 a.m.