wtrstr: Simple function to illustrate soil water content effect on...

In serbinsh/biocro: Simulation of Energycane and Sugarcane

Description

This is a very simple function which implements the 'bucket' model for soil water content and it calculates a coefficient of plant water stress.

Usage

  wtrstr(precipt, evapo, cws, soildepth, fieldc, wiltp,
    phi1 = 0.01, phi2 = 10,
    wsFun = c("linear", "logistic", "exp", "none"))

Arguments

precipt	Precipitation (mm).
evapo	Evaporation (Mg H2O ha-1 hr-1).
cws	current water content (fraction).
soildepth	Soil depth, typically 1m.
fieldc	Field capacity of the soil (fraction).
wiltp	Wilting point of the soil (fraction).
phi1	coefficient which controls the spread of the logistic function.
phi2	coefficient which controls the effect on leaf area expansion.
wsFun	option to control which method is used for the water stress function.

Details

This is a very simple function and the details can be seen in the code.

Value

A list with components:

See Also

wsRcoef

Examples

## Looking at the three possible models for the effect of soil moisture on water
## stress

aws <- seq(0,0.4,0.001)
wats.L <- numeric(length(aws)) # linear
wats.Log <- numeric(length(aws)) # logistic
wats.exp <- numeric(length(aws)) # exp
wats.none <- numeric(length(aws)) # none
for(i in 1:length(aws)){
wats.L[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4)$wsPhoto
wats.Log[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4,wsFun='logistic')$wsPhoto
wats.exp[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4, wsFun='exp')$wsPhoto
wats.none[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4, wsFun='none')$wsPhoto
}

xyplot(wats.L + wats.Log + wats.exp  + wats.none~ aws,
       col=c('blue','green','purple','red'),
       type = 'l',
       xlab='Soil Water',
       ylab='Stress Coefficient',
       key = list(text=list(c('linear','logistic','exp', 'none')),
       col=c('blue','green','purple','red'), lines = TRUE) )

## This function is sensitive to the soil depth parameter

SDepth <- seq(0.05,2,0.05)

wats <- numeric(length(SDepth))

for(i in 1:length(SDepth)){
wats[i] <- wtrstr(1,1,0.3,SDepth[i],0.37,0.2,2e-2,3)$wsPhoto
}

xyplot(wats ~ SDepth, ylab='Water Stress Coef',
       xlab='Soil depth')

## Difference between the effect on assimilation and leaf expansion rate

aws <- seq(0,0.4,0.001)
wats.P <- numeric(length(aws))
wats.L <- numeric(length(aws))
for(i in 1:length(aws)){
wats.P[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4)$wsPhoto
wats.L[i] <- wtrstr(1,1,aws[i],0.5,0.37,0.2,2e-2,4)$wsSpleaf
}

xyplot(wats.P + wats.L ~ aws,
       xlab='Soil Water',
       ylab='Stress Coefficient')


## An example for wsRcoef
## The scale parameter makes a big difference

aws <- seq(0.2,0.4,0.001)
wats.1 <- wsRcoef(aw=aws,fieldc=0.37,wiltp=0.2,phi1=1e-2,phi2=1, wsFun='logistic')$wsPhoto
wats.2 <- wsRcoef(aw=aws,fieldc=0.37,wiltp=0.2,phi1=2e-2,phi2=1, wsFun='logistic')$wsPhoto
wats.3 <- wsRcoef(aw=aws,fieldc=0.37,wiltp=0.2,phi1=3e-2,phi2=1, wsFun='logistic')$wsPhoto

xyplot(wats.1 + wats.2 + wats.3 ~ aws,type='l',
       col=c('blue','red','green'),
       ylab='Water Stress Coef',
       xlab='SoilWater Content',
       key=list(text=list(c('phi1 = 1e-2','phi1 = 2e-2','phi1 = 3e-2')),
         lines=TRUE,col=c('blue','red','green')))

serbinsh/biocro documentation built on May 29, 2019, 6:57 p.m.