Compute simple precipitation indices over a time series

A simple example of how to compute rainfall indices from the data returned by get_chirps or get_imerg. For analysis, use the climatrends package instead. climatrends::rainfall() computes the same indices with tested definitions and flexible periods per location, and climatrends also has temperature, growing degree days, evapotranspiration and crop sensitive indices. See the examples for how to pass CHIRPS data to it.

Usage

precip_indices(object, timeseries = FALSE, intervals = NULL)

Arguments

object

an object of class chirps as provided by get_chirps

timeseries

logical, FALSE for a single point time series observation or TRUE for a time series based on intervals

intervals

integer no lower than 5, for the days intervals when timeseries = TRUE

Value

A data frame with precipitation indices:

MLDS

maximum length of consecutive dry day, rain < 1 mm (days)

MLWS

maximum length of consecutive wet days, rain >= 1 mm (days)

R10mm

number of heavy precipitation days 10 >= rain < 20 mm (days)

R20mm

number of very heavy precipitation days rain >= 20 (days)

Rx1day

maximum 1-day precipitation (mm)

Rx5day

maximum 5-day precipitation (mm)

R95p

total precipitation when rain > 95th percentile (mm), with the percentile taken over all days in the period, dry days included

R99p

total precipitation when rain > 99th percentile (mm), with the percentile taken over all days in the period, dry days included

Rtotal

total precipitation (mm) in wet days, rain >= 1 (mm)

SDII

simple daily intensity index, total precipitation in wet days divided by the number of wet days (mm/days)

References

Aguilar E., et al. (2005). Journal of Geophysical Research, 110(D23), D23107.

de Sousa K., van Etten J., Neby M., Solberg S. Ø. (2023). Climate variability indices for ecological and crop models in R: the climatrends package. Journal of Open Source Software, 8(85), 4405. doi:10.21105/joss.04405

Kehel Z., et al. (2016). In: Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits (eds Bari A., Damania A. B., Mackay M., Dayanandan S.), pp. 151-174. CRC Press.

See also

climatrends::rainfall()

Examples

if (FALSE) { # interactive()
lonlat = data.frame(lon = c(-55.0281,-54.9857),
                     lat = c(-2.8094, -2.8756))

dates = c("2017-12-15", "2017-12-31")

dt = get_chirps(lonlat, dates)

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)

# recommended, the same data in climatrends
if (requireNamespace("climatrends", quietly = TRUE)) {
  rain = get_chirps(lonlat, dates, as.matrix = TRUE)
  climatrends::rainfall(rain, day.one = dates[1], span = ncol(rain))
}
}