Performs the Transformed-Stationary Extreme Values Analysis

Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi available here: < https://github.com/menta78/tsEva>. It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary extreme value Analysis (EVA) as described in Mentaschi et al. (2016) . In synthesis this approach consists in: (i) transforming a non-stationary time series into a stationary one to which the stationary extreme value theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution. 'RtsEva' offers several options for trend estimation (mean, extremes, seasonal) and contains multiple plotting functions displaying different aspects of the non-stationarity of extremes.

RtsEva

[]

This package is an adaptation of the Matalb tsEVA toolbox developed by Lorenzo Mentaschi availaible here: https://github.com/menta78/tsEva

It contains an implementation of the Transformed-Stationary (TS) methodology for non-stationary EVA as described in Mentaschi et al. (2016). In synthesis this approach consists in (i) transforming a non-stationary time series into a stationary one to which the stationary EVA theory can be applied; and (ii) reverse-transforming the result into a non-stationary extreme value distribution.

References

Mentaschi, L., Vousdoukas, M., Voukouvalas, E., Sartini, L., Feyen, L., Besio, G., and Alfieri, L.: The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis, Hydrol. Earth Syst. Sci., 20,3527-3547, doi:10.5194/hess-20-3527-2016, 2016

Installation

You can install the released version of RtsEva from

CRAN with:

install.packages("RtsEva")

Alternatively, you can install the development version of RtsEva from GitHub with:

# install.packages("devtools")
devtools::install_github("Alowis/RtsEva")

Example

This is a basic example which shows you how to solve a common problem:

library(RtsEva)
# Load a time series
timeAndSeries <- ArdecheStMartin
# go from six-hourly values to daily max
timeAndSeries <- max_daily_value(timeAndSeries)

# set a temporal window for the computation of running statistics
timeWindow <- 30*365 # 30 years

# Run the non-stationnary EVA
result <- TsEvaNs(timeAndSeries, timeWindow,
transfType = 'trendPeaks',tail = 'high')

After fitting the non-stationnay EVA, the package offers functions to visualize the plots.

Other resources are available for users to get a grasp of RtsEva and what can be done with it:

Quarto document

A quarto document showing a step-by-step real world application of TSEVA for flood and drought analysis is available here: https://alowis.github.io/RTSEVA_guide/RtsEVA_demo.html