VSEM {BayesianTools}R Documentation

Very simple ecosystem model

Description

A very simple ecosystem model, based on three carbon pools and a basic LUE model

Usage

VSEM(pars = c(KEXT = 0.5, LAR = 1.5, LUE = 0.002, GAMMA = 0.4, tauV =
  1440, tauS = 27370, tauR = 1440, Av = 0.5, Cv = 3, Cs = 15, Cr = 3), PAR,
  C = TRUE)

Arguments

pars

a parameter vector with parameters and initial states

PAR

Forcing, photosynthetically active radiation (PAR) MJ /m2 /day

C

switch to choose whether to use the C or R version of the model. C is much faster.

Details

This Very Simple Ecosystem Model (VSEM) is a 'toy' model designed to be very simple but yet bear some resemblance to deterministic processed based ecosystem models (PBMs) that are commonly used in forest modelling.

The model determines the accumulation of carbon in the plant and soil from the growth of the plant via photosynthesis and senescence to the soil which respires carbon back to the atmosphere.

The model calculates Gross Primary Productivity (GPP) using a very simple light-use efficiency (LUE) formulation multiplied by light interception. Light interception is calculated via Beer's law with a constant light extinction coefficient operating on Leaf Area Index (LAI).

A parameter (GAMMA) determines the fraction of GPP that is autotrophic respiration. The Net Primary Productivity (NPP) is then allocated to above and below-ground vegetation via a fixed allocation fraction. Carbon is lost from the plant pools to a single soil pool via fixed turnover rates. Heterotropic respiration in the soil is determined via a soil turnover rate.

The model equations are

– Photosynthesis

LAI = LAR*Cv

GPP = PAR * LUE * (1 - \exp^{(-KEXT * LAI)})

NPP = (1-GAMMA) * GPP

– State equations

dCv/dt = Av * NPP - Cv/tauV

dCr/dt = (1.0-Av) * NPP - Cr/tauR

dCs/dt = Cr/tauR + Cv/tauV - Cs/tauS

The model time-step is daily.

– VSEM inputs:

PAR Photosynthetically active radiation (PAR) MJ /m2 /day

– VSEM parameters:

KEXT Light extinction coefficient m2 ground area / m2 leaf area

LAR Leaf area ratio m2 leaf area / kg aboveground vegetation

LUE Light-Use Efficiency (kg C MJ-1 PAR)

GAMMA Autotrophic respiration as a fraction of GPP

tauV Longevity of aboveground vegetation days

tauR Longevity of belowground vegetation days

tauS Residence time of soil organic matter d

– VSEM states:

Cv Above-ground vegetation pool kg C / m2

Cr Below-ground vegetation pool kg C / m2

Cs Carbon in organic matter kg C / m2

– VSEM fluxes:

G Gross Primary Productivity kg C /m2 /day

NPP Net Primary Productivity kg C /m2 /day

NEE Net Ecosystem Exchange kg C /m2 /day

Value

a matrix with colums NEE, CV, CR and CS units and explanations see details

Author(s)

David Cameron, R and C implementation by Florian Hartig

See Also

VSEMgetDefaults, VSEMcreatePAR, , VSEMcreateLikelihood

Examples


  
## This example shows how to run and calibrate the VSEM model 

library(BayesianTools)

# Create input data for the model
PAR <- VSEMcreatePAR(1:1000)
plot(PAR, main = "PAR (driving the model)", xlab = "Day")

# load reference parameter definition (upper, lower prior)
refPars <- VSEMgetDefaults()
# this adds one additional parameter for the likelihood standard deviation (see below)
refPars[12,] <- c(2, 0.1, 4) 
rownames(refPars)[12] <- "error-sd"
head(refPars)

# create some simulated test data 
# generally recommended to start with simulated data before moving to real data
referenceData <- VSEM(refPars$best[1:11], PAR) # model predictions with reference parameters  
referenceData[,1] = 1000 * referenceData[,1] 
# this adds the error - needs to conform to the error definition in the likelihood
obs <- referenceData + rnorm(length(referenceData), sd = refPars$best[12])
oldpar <- par(mfrow = c(2,2))
for (i in 1:4) plotTimeSeries(observed = obs[,i], 
                              predicted = referenceData[,i], main = colnames(referenceData)[i])

# Best to program in a way that we can choose easily which parameters to calibrate
parSel = c(1:6, 12)

# here is the likelihood 
likelihood <- function(par, sum = TRUE){
  # set parameters that are not calibrated on default values 
  x = refPars$best
  x[parSel] = par
  predicted <- VSEM(x[1:11], PAR) # replace here VSEM with your model 
  predicted[,1] = 1000 * predicted[,1] # this is just rescaling
  diff <- c(predicted[,1:4] - obs[,1:4]) # difference betweeno observed and predicted
  # univariate normal likelihood. Note that there is a parameter involved here that is fit
  llValues <- dnorm(diff, sd = x[12], log = TRUE)  
  if (sum == FALSE) return(llValues)
  else return(sum(llValues))
}

# optional, you can also directly provide lower, upper in the createBayesianSetup, see help
prior <- createUniformPrior(lower = refPars$lower[parSel], 
                            upper = refPars$upper[parSel], best = refPars$best[parSel])

bayesianSetup <- createBayesianSetup(likelihood, prior, names = rownames(refPars)[parSel])

# settings for the sampler, iterations should be increased for real applicatoin
settings <- list(iterations = 2000, nrChains = 2)

out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "DEzs", settings = settings)

## Not run: 

plot(out)
summary(out)
marginalPlot(out)
gelmanDiagnostics(out) # should be below 1.05 for all parameters to demonstrate convergence 

# Posterior predictive simulations

# Create a prediction function
createPredictions <- function(par){
  # set the parameters that are not calibrated on default values 
  x = refPars$best
  x[parSel] = par
  predicted <- VSEM(x[1:11], PAR) # replace here VSEM with your model 
  return(predicted[,1] * 1000)
}

# Create an error function
createError <- function(mean, par){
  return(rnorm(length(mean), mean = mean, sd = par[7]))
}

# plot prior predictive distribution and prior predictive simulations
plotTimeSeriesResults(sampler = out, model = createPredictions, observed = obs[,1],
                      error = createError, prior = TRUE, main = "Prior predictive")

# plot posterior predictive distribution and posterior predictive simulations
plotTimeSeriesResults(sampler = out, model = createPredictions, observed = obs[,1],
                      error = createError, main = "Posterior predictive")


########################################################
# Demonstrating the updating of the prior from old posterior
# Note that it is usually more exact to rerun the MCMC 
# with all (old and new) data, instead of updating the prior
# because likely some information is lost when approximating the
# Posterior by a multivariate normal 

settings <- list(iterations = 5000, nrChains = 2)

out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "DEzs", settings = settings)

plot(out)
correlationPlot(out, start = 1000)

newPrior = createPriorDensity(out, method = "multivariate",
                              eps = 1e-10,
                              lower = refPars$lower[parSel],
                              upper = refPars$upper[parSel], start= 1000)

bayesianSetup <- createBayesianSetup(likelihood = likelihood,
                                     prior = newPrior,
                                     names = rownames(refPars)[parSel] )

# check boundaries are correct set
bayesianSetup$prior$sampler() < refPars$lower[parSel]
bayesianSetup$prior$sampler() > refPars$upper[parSel]

# check prior looks similar to posterior
x = bayesianSetup$prior$sampler(2000)
correlationPlot(x, thin = F)

out <- runMCMC(bayesianSetup = bayesianSetup, sampler = "DEzs", settings = settings)

plot(out)
correlationPlot(out)

plotTimeSeriesResults(sampler = out,
                      model = createPredictions,
                      observed = obs[,1],
                      error = createError,
                      prior = F, main = "Posterior predictive")

plotTimeSeriesResults(sampler = out,
                      model = createPredictions,
                      observed = obs[,1],
                      error = createError,
                      prior = T, main = "Prior predictive")





## End(Not run)

par(oldpar)

[Package BayesianTools version 0.1.7 Index]