| runoffratio {hydromad} | R Documentation |
Simple time-varying runoff proportion. Rainfall is scaled by the runoff coefficient estimated in a moving window. This SMA uses streamflow data, so can not be used for prediction.
runoffratio.sim(DATA, width = 30, kernel = 2, sides = 2, rrthresh = 0,
qlag = 0, scale = 1, return_state = FALSE)
DATA |
time-series-like object with columns |
width |
width of the time window (in time steps) in which to estimate the runoff coefficient. |
kernel |
type of window used to estimate the runoff coefficient: 1 is rectangular, 2 is triangular-weighted, 3 is Gaussian-like. |
sides |
2 for time-centered estimates, 1 for estimates using data backward in time only. |
rrthresh |
a theshold value of the runoff ratio, below which there is no effective rainfall. |
qlag |
number of time steps to lag the streamflow (relative to rainfall) before estimating the runoff coefficient. |
scale |
constant multiplier of the result, for mass balance.
If this parameter is set to |
return_state |
ignored. |
the simulated effective rainfall, a time series of the same length as the input series.
Felix Andrews felix@nfrac.org
hydromad(sma = "runoffratio") to work with models as objects (recommended).
## view default parameter ranges:
str(hydromad.options("runoffratio"))
data(HydroTestData)
mod0 <- hydromad(HydroTestData, sma = "runoffratio", routing = "expuh")
mod0
## simulate with some arbitrary parameter values
mod1 <- update(mod0, width = 30, rrthresh = 0.2, tau_s = 10)
## plot results with state variables
testQ <- predict(mod1, return_state = TRUE)
xyplot(cbind(HydroTestData[,1:2], runoffratio = testQ))
## show effect of increase/decrease in each parameter
parRanges <- list(width = c(10, 180), qlag = c(-30, 30),
rrthresh = c(0,0.5))
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") +
layer(panel.lines(fitted(mod1), col = "grey", lwd = 2))