BioCro 0.93

Simple function to illustrate soil water content effect on 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.

Value

A list with components:

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.

Details

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

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')))

See also

wsRcoef