philip: R optimisation of philip infiltration parameters

In gowusu/vadose: The estimation of saturated hydraulic conductivity and soil water retention Curves in the vadose zone

Description

This function can linearly or nonlinearly optimise philip cumulative infiltration (I) or infiltration rate (i) parameters: A and S.

Usage

philip(data = NULL, time, I, S = 0.1, A = 0.1, type = "nonlinear",
  group = NULL)

## Default S3 method:
philip(data = NULL, time, I, S = 0.1, A = 0.1,
  type = "nonlinear", group = NULL)

## S3 method for class 'philip'
predict(object, time = NULL, ...)

## S3 method for class 'philip'
plot(x, xlab = "Time(Minutes)", ylab = "Cumulative (mm)",
  main = NULL, layout = NULL, ...)

## S3 method for class 'philip'
summary(object, ...)

## S3 method for class 'philip'
print(x, ...)

## S3 method for class 'philip'
coef(object, ...)

Arguments

data	dataframe. It can contain data with column names of "time" and "I"
time	character or numeric. The name of time variable in the dataframe. If the "data" parameter contains "time", this will be ignored. The unit must be in seconds.
I	cumulative infiltration (I) or infiltration rate (i) [mm]
S	numeric. sorptivity parameter
A	numeric.It is related Hydraulic Conductivity parameter
type	character. It takes "linear" or "nonlinear" Philip equation
group	character. The name of the group variables if the data is from different areas.
object	Model output object
...	Any other graphical parameter
x	a return object of the function.
xlab	x label of the plot
ylab	y label of the plot
main	Title of the plot
layout	plot layout

Value

• A: opitmised A parameter

• Ks: opitmised saturated hydraulic conductivity=0.5*A [L/T]

• S: optimised sorptivity [LT^-0.5]

• output: output of the group simulation

Author(s)

George Owusu

References

Philip, J. R. (1957). The theory of infiltration:Sorptivity and algebraic infiltration equations. Soil Science, 84, 257-264.

Examples

data=read.csv(system.file("ext","sys","exampleBEST.csv",package="vadose"))
philip1<-philip(data=data,time="time",I="I")
print(gof(philip1))
plot(philip1)
predict(philip1)
coef(philip1)
gof(philip1)

#infiltration rate
data=read.csv(system.file("ext","sys","infiltration2.csv",package="vadose"))
assin_breko<- subset(data, ID=="41A20_1")
philipr<-philip(data=assin_breko,time="minutes",I="cm.hr")

#group simulation
philipg<-philip(data=data,time="minutes",I="cm.hr",group="ID")
coef(philipg)
modprediction<-philip(time=1,S=0.1,A=0.5)

gowusu/vadose documentation built on May 17, 2019, 7:59 a.m.