sacramento {hydromad} | R Documentation |

Sacramento Soil Moisture Accounting model. Developed by the US National Weather Service.

sacramento.sim(DATA, uztwm, uzfwm, uzk, pctim, adimp, zperc, rexp, lztwm, lzfsm, lzfpm, lzsk, lzpk, pfree, etmult = 1, dt = 1, return_state = FALSE)

`DATA` |
time-series-like object with columns |

`uztwm` |
Upper zone tension water maximum capacity (mm). |

`uzfwm` |
Upper zone free water maximum capacity (mm). |

`uzk` |
Lateral drainage rate of upper zone free water expressed as a fraction of contents per day. |

`pctim` |
The fraction of the catchment which produces impervious runoff during low flow conditions. |

`adimp` |
The additional fraction of the catchment which exhibits impervious characteristics when the catchment's tension water requirements are met. |

`zperc` |
Maximum percolation (from upper zone free water into the lower zone) rate coefficient. |

`rexp` |
An exponent determining the rate of change of the percolation rate with changing lower zone water contents. |

`lztwm` |
Lower zone tension water maximum capacity (mm). |

`lzfsm` |
Lower zone supplemental free water maximum capacity (mm). |

`lzfpm` |
Lower zone primary free water maximum capacity (mm). |

`lzsk` |
Lateral drainage rate of lower zone supplemental free water expressed as a fraction of contents per day. |

`lzpk` |
Lateral drainage rate of lower zone primary free water expressed as a fraction of contents per day. |

`pfree` |
Direct percolation fraction from upper to lower zone free water (the percentage of percolated water which is available to the lower zone free water aquifers before all lower zone tension water deficiencies are satisfied). |

`etmult` |
Multiplier applied to |

`dt` |
Length of each time step in days. |

`return_state` |
Not currently supported. |

This description of the model is given by Burnash (1995):

“The moisture accounting system utilized in the Sacramento Catchment Model is a carefully structured representation of the catchment's soil moisture storage system. It is based on using simple approximations of many of those soil moisture processes which have been reported in the hydrologic literature. The authors have organised these approximations in a manner which would allow the determination of many catchment characteristics from carefully selected portions of the catchment's hydrologic record. Inasmuch as many of the catchment characteristics are related to the soil moisture capabilities of the catchment, an intelligent application of the model start with a good understanding of the three basic types of soil moisture which can potentially influence catchment runoff conditions. These soil moisture types are: (1) Hygroscopic Water, (2) Tension Water and (3) Free Water. ”

[...]

“Streamflow as computed by the Sacramento Catchment Model is the result of processing precipiatation through an algorithm representing the uppermost soil mantle identified as the upper zone and a deeper portion of the soil mantle or lower zone. The algorithm computes runoff in five basic forms. These are (1) direct runoff from permanant and temporary impervious areas, (2) surface runoff due to precipitation occurring at a rate faster than percolation and interflow can take place when both upper zone storages are full, (3) interflow resulting from the lateral drainage of a temporary free water storage, (4) supplemental base flow, and (5) primary base flow.” (Burnash, 1995)

The default parameter ranges were taken from Blasone et. al. (2008).

the simulated effective rainfall (“total channel inflow”), a time series of the same length as the input series.

Felix Andrews felix@nfrac.org

Burnash, R.J.C (1995). The NWS River Forecast System – Catchment Modeling.
In: Vijay P. Singh (ed.), *Computer models of watershed hydrology.*
Revised edition, Highlands Ranch, Colo. : Water Resources Publications, c1995.
http://www.wrpllc.com/books/cmwh.html.

Blasone, R., J.A. Vrugt, H. Madsen, D. Rosbjerg, B.A. Robinson,
G.A. Zyvoloski (2008). Generalized likelihood uncertainty estimation
(GLUE) using adaptive Markov Chain Monte Carlo
sampling. *Advances in Water Resources* 31, pp. 630-648.

`hydromad(sma = "sacramento")`

to work with models as objects (recommended).

## view default parameter ranges: str(hydromad.options("sacramento")) data(HydroTestData) mod0 <- hydromad(HydroTestData, sma = "sacramento") mod0 ## simulate with some arbitrary parameter values set.seed(2) mod1 <- simulate(update(mod0, etmult = 0.01), 1, sampletype = "random")[[1]] testQ <- predict(mod1, return_state = TRUE) xyplot(window(cbind(HydroTestData[,1:2], sacramento = testQ), start = 100)) mod1 ## show effect of increase/decrease in each parameter parRanges <- hydromad.getOption("sacramento") 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), strip = FALSE, strip.left = TRUE, main = "Simple parameter perturbation example") + layer(panel.lines(fitted(mod1), col = "grey", lwd = 2))

[Package *hydromad* version 0.9-18 Index]