The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Transformed-stationary EVA workflow example

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.