Compute precipitation indices over a time series.

Compute precipitation indices over a time series.

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 dataframe 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)

R99p

total precipitation when rain > 99th percentile (mm)

Rtotal

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

SDII

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

References

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

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.

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, server = "ClimateSERV")

# 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)
}