I’m currently endeavoring to generate IDF (intensity-duration-frequency) curves utilizing rainfall data. Up to this point, I’ve succeeded in crafting probability/frequency curves for precipitation using lmom packeage

Would anyone be able to offer assistance or guidance on how to proceed further with this endeavor?

Below, I’ve included a reproducible example of the code for reference:

#Dados
  set.seed(999)
  dates <- seq(as.POSIXct("2000-01-01 00:00:00"),as.POSIXct("2019-12-31 23:00:00"),by = 'day')
  sample.precip <- rgamma(n = length(dates), shape = 0.05, rate = 0.4)
  precip.df <- data.frame(date=dates,RR=sample.precip)
  
  prec.ts=ts(precip.df$RR,frequency=365.25,start=c(2000,1,1))
  
  plot(prec.ts,main="Prec time series", ylab="Precipitação (mm)")
  
  chuva <- sort(precip.df$RR, decreasing = T)
  
  #Probabilidade de ocorrência
  
  df <- data.frame(x = 100/length(chuva)*1:length(chuva),y = chuva)
  
  
  #Ajustando o modelo
  library(lubridate)
  library(lmom)
  
  str(prec.ts)
  
  
  data <- precip.df %>%
    mutate(year = lubridate::year(date)) 
  
  max.by.year<- data %>%
    group_by(year) %>%
    summarize(max_amount = max(RR, na.rm = T))
  
  maximas <- max.by.year$max_amount
  maximas <- maximas[!is.na(maximas)]
  
  
  
  sorted.maximas <- sort(maximas,decreasing=T)
  
  
  p<-(c(1:length(sorted.maximas)))/(length(sorted.maximas)+1)
  
  #Computing the recurrence time
  tr<-1/p
  
  
  fit<-lmom::samlmu(maximas)
  plot(fit)
  
  para<-lmom::pelgum(fit)
  para
  
  #Plotando
  plot(p,sorted.maximas,ylab="Precipitação (mm)",xlab="Probabilidade de observar um evento maior",main="")
  
  #Gumbel fitting
  lines(1-cdfgum(sorted.maximas,para),sorted.maximas,col="blue",lty=2)
  grid()