| CVA {IETD} | R Documentation |
Coefficient of variation analysis
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 (Restrepo-Posada and Eagleson 1982; Adams and Papa 2000). 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 <lfduquey@gmail.com> <l.f.duque-yaguache2@newcastle.ac.uk>
References
Adams BJ, Papa F (2000).
Urban Storm water Management Planning with Analytical Probabilistic Models.
John Wiley and Sons, New York, NY.
ISBN 0-471-35641-7.
Restrepo-Posada PJ, Eagleson PS (1982).
“Identification of independent rainstorms.”
Journal of Hydrology.
ISSN 00221694, doi: 10.1016/0022-1694(82)90136-6.
Examples
CVA (Time_series=hourly_time_series)