Compute precipitation indices over a time series.

Compute precipitation indices over a time series.

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.
https://doi.org/10.1029/2005JD006119

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

# \donttest{
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)
#> Getting your request...

# take the indices for the entire period
precip_indices(dt, timeseries = FALSE)
#>        id       date    lon   lat  index  value
#>     <int>     <date>  <dbl> <dbl>  <chr>  <dbl>
#> 1:      1 2017-12-15 -55.03 -2.81   MLDS   5.00
#> 2:      1 2017-12-15 -55.03 -2.81   MLWS   2.00
#> 3:      1 2017-12-15 -55.03 -2.81  R10mm   3.00
#> 4:      1 2017-12-15 -55.03 -2.81  R20mm   1.00
#> 5:      1 2017-12-15 -55.03 -2.81 Rx1day  37.69
#> ---                                            
#> 16:     2 2017-12-15 -54.99 -2.88 Rx5day  75.08
#> 17:     2 2017-12-15 -54.99 -2.88   R95p  37.54
#> 18:     2 2017-12-15 -54.99 -2.88   R99p  37.54
#> 19:     2 2017-12-15 -54.99 -2.88 Rtotal 128.86
#> 20:     2 2017-12-15 -54.99 -2.88   SDII  18.41

# take the indices for periods of 7 days
precip_indices(dt, timeseries = TRUE, intervals = 7)
#>        id       date    lon   lat  index value
#>     <int>     <date>  <dbl> <dbl>  <chr> <dbl>
#> 1:      1 2017-12-15 -55.03 -2.81   MLDS  3.00
#> 2:      1 2017-12-15 -55.03 -2.81   MLWS  2.00
#> 3:      1 2017-12-15 -55.03 -2.81  R10mm  2.00
#> 4:      1 2017-12-15 -55.03 -2.81  R20mm  0.00
#> 5:      1 2017-12-15 -55.03 -2.81 Rx1day 13.69
#> ---                                           
#> 36:     2 2017-12-22 -54.99 -2.88 Rx5day 65.70
#> 37:     2 2017-12-22 -54.99 -2.88   R95p 37.54
#> 38:     2 2017-12-22 -54.99 -2.88   R99p 37.54
#> 39:     2 2017-12-22 -54.99 -2.88 Rtotal 65.70
#> 40:     2 2017-12-22 -54.99 -2.88   SDII 21.90

# }