climatrends (version 0.1.0)

ETo: Evapotranspiration

Description

Compute evapotranspiration using the Blaney-Criddle method. A theoretical method used when no measured data on pan evaporation are available locally.

Usage

ETo(object, day.one = NULL, span = 150, lat = NULL, Kc = 1, p = NULL)

Arguments

object

a numeric vector of geographic coordinates (lonlat) or an array with two dimensions containing the temperature data; 1st dimension contains the day temperature and 2nd dimension the night temperature. When lonlat is used, the function makes a call to nasapower::get_power() to fetch and concatenate environmental data from NASA POWER (https://power.larc.nasa.gov/) for the parameters T2M_MAX (Maximum Temperature at 2 m) and T2M_MIN (Minimum Temperature at 2 m)

day.one

a vector of class Date for the starting date to capture the environmental data (YYYY-MM-DD)

span

an integer or a vector with integers for the duration of the timespan to be captured

lat

a vector for the latitude (in Decimal degrees) used to compute mean daily percentage of annual daytime hours based on the latitude and month. See details

Kc

a numeric value for the crop factor for water requirement

p

optional, a numeric value (from 0 to 1) used if lat is not given, representing the mean daily percentage of annual daytime hours for different latitudes

Value

The evapotranspiration in mm/day

Details

When lat is used, it is combined with the month provided in day.one to call for the system data daylight to find the correct value for p which represents the daily percentage of daytime hours in the given month and latitude.

References

Brouwer C. & Heibloem M. (1986). Irrigation water management: Irrigation water needs. Food and Agriculture Organization of The United Nations, Rome, Italy. http://www.fao.org/3/S2022E/s2022e00.htm

See Also

Other climatology functions: GDD(), rainfall(), temperature()

Examples

# NOT RUN {
# Using local sources
data("modis", package = "climatrends")

day <- as.Date("2013-10-28", format = "%Y-%m-%d")

ETo(modis, 
    day.one = day,
    span = 10,
    Kc = 0.92)
    
# }
# NOT RUN {
# Using remote sources 
library("nasapower")

# random geographic locations around bbox(11, 12, 55, 58)
set.seed(123)
lonlat <- data.frame(lon = runif(2, 11, 12),
                     lat = runif(2, 55, 58))

# random dates around 2018-05-15 and 2018-05-20
set.seed(321)
dates <- as.integer(runif(2, 17666, 17670))
dates <- as.Date(dates, origin = "1970-01-01")

# the evapotranspiration in the first 50 days after day.one
ETo(lonlat,
    day.one = dates,
    span = 50,
    lat = lonlat[["lat"]])
# }
# NOT RUN {
# }