rai: Calculate Rainfall Anomaly Index (RAI)

In flogrc/piflow: Precipitation and discharge analysis tools with zoo vector

Description

Calculate RAI and the drought specifications with the length, the drought type and the intensity

Usage

rai(monthly_data, time_step = 12)

Arguments

monthly_data	[zoo] rainfall monthly data in zoo class with date in %Y-%m-%d
time_step	[numeric] default = 12, time step to sum monthly precipitation (1, 3, 6, 9, 12, 24 and 48)

Value

List that contains

• $rai [zoo] zoo with the rai values with date in %Y-%m-%d

• $length_zoo [zoo] zoo with the length of drought with date in %Y-%m-%d [day]

• $drought_type [zoo] zoo with the type of the period for each month

• $drought_number [data.frame] dataframe with the number of different period by type:

  • Extwet (rai > 3)
    

  • Verywet (2.99 > rai > 2)
    

  • Wet (1.99 > rai > 1)
    

  • Normal (0.99 > rai > -0.99)
    

  • Dry (-1 > rai > -1.99)
    

  • VeryDry (-2 > rai > -2.99)
    

  • ExtDry (-3 > rai)

Author(s)

Florine Garcia ([e-mail])

Pierre L'Hermite ([e-mail])

References

Van Rooy, M. P. (1965). A rainfall anomaly index independent of time and space. Notos, 14, 43–48.

See Also

plot_trend plot the index

Examples

## Loading data
data("dailyPrec_bv1")

## Index
result <- rai(dailyPrec, time_step = 12)

## Plot index
plot_trend(result$rai, trend = TRUE, data_kind = "RAI",
name = PluvioData$PluvioName, axis_name_x = "Date",
axis_name_y = RAI [], midvalue = 0)

flogrc/piflow documentation built on May 24, 2019, 7:34 a.m.