TsEvaNs {RtsEva}

TsEvaNs Function

Description

This function performs non-stationary extreme value analysis (EVA) on a time series data.

Usage

TsEvaNs(
  timeAndSeries,
  timeWindow,
  transfType = "trendPeaks",
  minPeakDistanceInDays = 10,
  seasonalityVar = NA,
  minEventsPerYear = -1,
  gevMaxima = "annual",
  ciPercentile = 90,
  gevType = "GEV",
  evdType = c("GEV", "GPD"),
  tail = "high",
  epy = -1,
  lowdt = 7,
  trans = NULL
)

Arguments

timeAndSeries

A data frame containing the timestamp and corresponding series data.

timeWindow

The time window for analysis.

transfType

The transformation type for non-stationary EVA. It can be one of the following:

trend

Long-term variations of the timeseries

seasonal

Long-term and seasonal variations of extremes

trendCIPercentile

Long-term variations of extremes using a specified percentile

trendPeaks

Long-term variations of the peaks

minPeakDistanceInDays

The minimum peak distance in days.

seasonalityVar

A logical value indicating whether to consider seasonality in the analysis.

minEventsPerYear

The minimum number of events per year.

gevMaxima

The type of maxima for the GEV distribution (annual or monthly, default is annual).

ciPercentile

The percentile value for confidence intervals.

gevType

The type of GEV distribution (GEV or GPD).

evdType

The type of extreme value distribution (GEV or GPD).

tail

The mode of the analysis (e.g., high for flood peaks or low for drought peaks).

epy

The average number of events per year, can be specified by the user or automatically set according to the tail selected.

lowdt

The temporal resolultion used for low values. default is 7 days.

trans

The transformation used to fit the EVD. Can be:

ori

use of original data

rev

Reversing the data (used for low extremes)

inv

inversing the data (used for low extreme, can lead to unstabilities)

lninv

log of inverse the data (used for low extreme, under development)

Details

The function takes a time series data and performs non-stationary EVA using various transformation types and parameters depending on the input data provided. Results are returned as a list containing the non-stationary EVA parameters and the transformed data for stationary EVA and can be used as input for further analysis. In particular for the following function

Value

A list containing the results of the non-stationary EVA. Containing the following components:

nonStationaryEvaParams

The estimated parameters for non-stationary EVA. Parameters include GEV and GPD parameters for each timestep, confidence intervals, and other statistical measures

stationaryTransformData

The transformed data for stationary EVA. Includes the stationary series, trend, and standard deviation series

References

Mentaschi, L., Vousdoukas, M., Voukouvalas, E., Sartini, L., Feyen, L., Besio, G., and Alfieri, L. (2016). The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis. Hydrology and Earth System Sciences, 20(9), 3527-3547. doi:10.5194/hess-20-3527-2016.

Examples

# Example usage of TsEvaNs function
timeAndSeries <- ArdecheStMartin
#go from six-hourly values to daily max
timeAndSeries <- max_daily_value(timeAndSeries)
#keep only the 30 last years
yrs <- as.integer(format(timeAndSeries$date, "%Y"))
tokeep <- which(yrs>=1990)
timeAndSeries <- timeAndSeries[tokeep,]
timeWindow <- 10*365 # 10 years
result <- TsEvaNs(timeAndSeries, timeWindow,
transfType = 'trendPeaks',tail = 'high')