armax.inverse.sim {hydromad} | R Documentation |
Invert transfer function models to estimate input series.
armax.inverse.sim(DATA, a_1 = 0, a_2 = 0, a_3 = 0, b_0 = 1, b_1 = 0, b_2 = 0, b_3 = 0, pars = NULL, delay = 0, init = 0, rain.factor = 1.1, rises.only = FALSE, use.Qm = TRUE, use.fft.method = FALSE, constrain.fft = TRUE, mass.balance = use.fft.method, scale.window = NA) expuh.inverse.sim(DATA, delay = 0, tau_s = 0, tau_q = 0, tau_3 = 0, v_s = 1, v_q = NA, v_3 = 0, series = 0, Xs_0 = 0, Xq_0 = 0, X3_0 = 0, pars = NULL, ...)
DATA |
time-series-like object with columns |
delay |
delay (lag time / dead time) in number of time steps. |
Felix Andrews felix@nfrac.org
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armax.inverse.fit
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armax
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expuh
## baseflow filtering using two-store unit hydrograph data(Murrindindi) x <- Murrindindi[1:1000,] ## case 1 (preferred): streamflow + rainfall data constrained ## such that effective rainfall is less than observed rainfall foo <- hydromad(x, sma = "armax.inverse", routing = "armax", rfit = list("inverse", order = c(2,1))) foo xsq <- predict(foo, return_components = TRUE) xyplot(cbind(observed = x$Q, slow_component = xsq$Xs), superpose = TRUE) ## case 2: using streamflow data only, constrained ## to have effective rainfall only when flow is rising foo <- hydromad(x$Q, sma = "armax.inverse", routing = "armax", rfit = list("inverse", order = c(2,1), rises.only = TRUE)) xsq <- predict(foo, return_components = TRUE) xyplot(cbind(observed = x$Q, slow_component = xsq$Xs), superpose = TRUE) ## case 3: using streamflow data only, unconstrained foo <- hydromad(x$Q, sma = "armax.inverse", routing = "armax", rfit = list("inverse", order = c(2,1))) xsq <- predict(foo, return_components = TRUE) xyplot(cbind(observed = x$Q, slow_component = xsq$Xs), superpose = TRUE)