chirps (version 0.1.4)

precip_indices: Compute precipitation indices over a time series.

Description

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<U+2013>174. CRC Press.

Examples

# NOT RUN {
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)
# }