Demo of the TSEVA method

Step 1: Argument selection

data=ArdecheStMartin
haz = "drought"
tail="low"
var = "dis"
timeWindow = 365.25*30; #time windows in days on which to compute trends
minPeakDistanceInDays=30 # minimum distance between two events/peaks
lowdt=7 # low flows temporal aggregations

Step 2: Prepare inputs for TSEVA

timeStamps=data$time
dt = difftime(timeStamps[2],timeStamps[1],units="days")
dt= as.numeric(dt)
percentile=95
names(data)=c("date","Qs")

if (haz=="drought"){
  #seasonality divide: frost vs non frost
  if (!exists("trans")){trans="rev"}
  print(paste0(trans," transformation used for low flows"))
  
  #compute 7days moving average
  data$Q7=tsEvaNanRunningMean(data$Qs,7/dt)
  timeAndSeries=data.frame(data$date,data$Q7)

}else if (haz=="flood"){
  percentile=95
  timeAndSeries <- max_daily_value(timeAndSeries)
}

## [1] "rev transformation used for low flows"

names(timeAndSeries)=c("timestamp","dis")
dt1=min(diff(timeAndSeries$timestamp),na.rm=T)
dt=as.numeric(dt1)
tdim=attributes(dt1)$units
if (tdim=="hours") dt=dt/24
if (dt==1){
  timeDays=timeAndSeries$timestamp
}else{
  timeDays=unique(as.Date(timeAndSeries$timestamp))
}

names(timeAndSeries)=c("timestamp","data")
trendtypes=c("trend","trendPeaks","trendCIPercentile")

Choose which transformation to use, here we choose the “trendPeaks” transformation

Step 3: Run TSEVA

Nonstat<-TsEvaNs(timeAndSeries, timeWindow, transfType=trendtypes[2],
                 ciPercentile= 90, minPeakDistanceInDays = minPeakDistanceInDays, tail=tail, lowdt=lowdt,trans=trans)

## 
## evaluating long term variations of the peaks

## no change point

## 
## computing the trend on extremes...

## trend threshold= 0.75

## 
## Executing stationary eva

## 
## max threshold is: 95%

## 
## average number of events per year = 1

## Fitted GPD

## 
## Transforming to non stationary eva ...

Step 4: Plot results

nonStationaryEvaParams=Nonstat[[1]]
stationaryTransformData=Nonstat[[2]]

ExRange= c(min(nonStationaryEvaParams$potObj$parameters$peaks),max(nonStationaryEvaParams$potObj$parameters$peaks))

if (haz=="flood") wr2 <- c(seq(min(ExRange),max(ExRange),length.out=700))
if (haz=="drought") wr2 <- c(seq(1.1*min(ExRange),0.1*max(ExRange),length.out=700))

Plot1= tsEvaPlotGPDImageScFromAnalysisObj(wr2, nonStationaryEvaParams, stationaryTransformData, minYear = '1950',trans=trans)
Plot1

timeIndex=1
Plot2 = tsEvaPlotReturnLevelsGPDFromAnalysisObj(nonStationaryEvaParams, stationaryTransformData, timeIndex, trans=trans,ylabel="Discharge (m3/s)")
Plot2

Plot3 = tsEvaPlotReturnLevelsGEVFromAnalysisObj(nonStationaryEvaParams, stationaryTransformData, timeIndex, trans=trans,ylabel="Discharge (m3/s)")
Plot3

Transformed-stationary EVA workflow example

Demo of the TSEVA method

Step 1: Argument selection

Step 2: Prepare inputs for TSEVA

Step 3: Run TSEVA

Step 4: Plot results