Runoff as rainfall to a power

Runoff as rainfall to a power. This allows an increasing fraction of runoff to be generated by increasingly intense/large rainfall events (for power > 0). The fraction increases up to a full runoff level at maxP.

intensity.sim(DATA, power, maxP = 500, scale = 1, return_state = FALSE)

Arguments

DATA

time-series-like object with columns P (precipitation) and Q (streamflow).

power

power on rainfall used to estimate effective rainfall.

maxP

level of rainfall at which full runoff occurs (effective rainfall == rainfall).

scale

constant multiplier of the result, for mass balance. If this parameter is set to NA (as it is by default) in hydromad it will be set by mass balance calculation.

return_state

ignored.

Value

the simulated effective rainfall, a time series of the same length as the input series.

See also

hydromad(sma = "intensity") to work with models as objects (recommended).

Author

Felix Andrews felix@nfrac.org

Examples

## view default parameter ranges:
str(hydromad.options("intensity"))
#> List of 1
#>  $ NA: NULL

data(HydroTestData)
mod0 <- hydromad(HydroTestData, sma = "intensity", routing = "expuh")
mod0
#> 
#> Hydromad model with "intensity" SMA and "expuh" routing:
#> Start = 2000-01-01, End = 2000-03-31
#> 
#> SMA Parameters:
#>       lower upper  
#> power     0     2  
#> maxP    100  1000  
#> scale    NA    NA  
#> Routing Parameters:
#> NULL

## simulate with some arbitrary parameter values
mod1 <- update(mod0, power = 1, maxP = 200, tau_s = 10)

## plot results with state variables
testQ <- predict(mod1, return_state = TRUE)
xyplot(cbind(HydroTestData[, 1:2], intensity = testQ))


## show effect of increase/decrease in each parameter
parRanges <- list(power = c(0, 2), maxP = c(100, 500), scale = NA)
parsims <- mapply(
  val = parRanges, nm = names(parRanges),
  FUN = function(val, nm) {
    lopar <- min(val)
    hipar <- max(val)
    names(lopar) <- names(hipar) <- nm
    fitted(runlist(
      decrease = update(mod1, newpars = lopar),
      increase = update(mod1, newpars = hipar)
    ))
  }, SIMPLIFY = FALSE
)

xyplot.list(parsims,
  superpose = TRUE, layout = c(1, NA),
  main = "Simple parameter perturbation example"
) +
  latticeExtra::layer(panel.lines(fitted(mod1), col = "grey", lwd = 2))