| dbm {hydromad} | R Documentation |
Typical initial model used in Data-Based Mechanistic modelling. Rainfall is scaled by corresponding streamflow values raised to a power. This SMA uses streamflow data, so can not be used for prediction.
dbm.sim(DATA, power, qlag = 0, scale = 1, return_state = FALSE)
DATA |
time-series-like object with columns |
power |
power to apply to streamflow values. |
qlag |
number of time steps to lag the streamflow (relative to rainfall) before multiplication. |
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 = "dbm") to work with models as objects (recommended).
## view default parameter ranges:
str(hydromad.options("dbm"))
data(HydroTestData)
mod0 <- hydromad(HydroTestData, sma = "dbm", routing = "expuh")
mod0
## simulate with some arbitrary parameter values
mod1 <- update(mod0, power = 0.5, qlag = 0, tau_s = 10)
xyplot(cbind(HydroTestData, dbm.Q = predict(mod1)))
## show effect of increase/decrease in each parameter
parRanges <- list(power = c(0.01, 0.9), qlag = c(-1, 2))
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))