CVA: Coefficient of variation analysis

In IETD: Inter-Event Time Definition

Description

This function computes the inter-event time definition (IETD) based on the coefficient of variation analysis.

Usage

CVA(Time_series,MaxIETD,xlabel,ylabel)

Arguments

Time_series	A dataframe. The first column contains the time and day of a rainfall pulse and the second one the depth of rainfall in each time step. The date must be as POSIXct class.
MaxIETD	The maximum value of IETD to be analyzed (in hours). Default value 24.
xlabel	Label of the x-axis of the figure IETD vs CV.
ylabel	Label of the y-axis of the figure IETD vs CV.

Details

This method assumes that inter-event times (b) are represented well by a exponential distribution. Since by definition b>= IETD, IETD is computed as the value whose resulting coefficient of variation (CV) of b equal to unity \insertCiteRestrepo-Posada1982,Adams2000IETD. This analysis is done by testing several values of IETD and analyzing the resulting CV. The computed IETD is obtained via interpolation from the figure of IETD vs CV.

Value

A list with a figure of IETD vs CV, a dataframe with the values of that figure, and the computed value of IETD.

Note

To review the concepts of b and IETD, go to the details of drawre function.

Author(s)

Luis F. Duque <[e-mail]> <[e-mail]>

References

\insertAllCited

Examples

CVA (Time_series=hourly_time_series)

Example output

$Figure

$Values
   IETD        CV
1     1 1.7078311
2     2 1.5608263
3     3 1.4593811
4     4 1.3609599
5     5 1.2999098
6     6 1.2565020
7     7 1.2321522
8     8 1.1978118
9     9 1.1577157
10   10 1.1269553
11   11 1.0847750
12   12 1.0631700
13   13 1.0413543
14   14 1.0304247
15   15 1.0250257
16   16 1.0034494
17   17 0.9870803
18   18 0.9592473
19   19 0.9479295
20   20 0.9190785
21   21 0.9072833
22   22 0.8894048
23   23 0.8711700
24   24 0.8650536

$EITD
[1] 16.2

IETD documentation built on March 13, 2020, 1:53 a.m.