R/TwopParallelModel.R

In SoilR: Models of Soil Organic Matter Decomposition

#' Implementation of a linear two pool model with parallel structure
#' 
#' This function creates a model for two independent (parallel) pools.  It is a
#' wrapper for the more general function \code{\link{ParallelModel}} that can
#' handle an arbitrary number of pools.
#' 
#' 
#' @param t A vector containing the points in time where the solution is
#' sought.
#' @param ks A vector of length 2 containing the decomposition rates for the 2
#' pools.
#' @param C0 A vector of length 2 containing the initial amount of carbon for
#' the 2 pools.
#' @param In A scalar or a data.frame object specifying the amount of litter
#' inputs by time.
#' @param gam A scalar representing the partitioning coefficient, i.e. the
#' proportion from the total amount of inputs that goes to pool 1.
#' @param xi A scalar or a data.frame specifying the external (environmental
#' and/or edaphic) effects on decomposition rates.
#' @param solver A function that solves the system of ODEs. This can be
#' \code{\link{euler}} or \code{\link{deSolve.lsoda.wrapper}} or any other user
#' provided function with the same interface.
#' @param pass Forces the constructor to create the model even if it is invalid
#' @return A Model Object that can be further queried
#' @seealso There are other \code{\link{predefinedModels}} and also more
#' general functions like \code{\link{Model}}.
#' @references Sierra, C.A., M. Mueller, S.E. Trumbore. 2012. Models of soil
#' organic matter decomposition: the SoilR package version 1.0. Geoscientific
#' Model Development 5, 1045-1060.
#' @examples
#' t_start=0 
#' t_end=10 
#' tn=50
#' timestep=(t_end-t_start)/tn 
#' t=seq(t_start,t_end,timestep) 
#' Ex=TwopParallelModel(t,ks=c(k1=0.5,k2=0.2),C0=c(c10=100, c20=150),In=10,gam=0.7,xi=0.5)
#' Ct=getC(Ex)
#' plot(t,rowSums(Ct),type="l",lwd=2,
#' ylab="Carbon stocks (arbitrary units)",xlab="Time",ylim=c(0,sum(Ct[1,]))) 
#' lines(t,Ct[,1],col=2)
#' lines(t,Ct[,2],col=4)
#' legend("topright",c("Total C","C in pool 1", "C in pool 2"),
#' lty=c(1,1,1),col=c(1,2,4),lwd=c(2,1,1),bty="n")
#' 
#' Rt=getReleaseFlux(Ex)
#' plot(t,rowSums(Rt),type="l",ylab="Carbon released (arbitrary units)",
#' xlab="Time",lwd=2,ylim=c(0,sum(Rt[1,]))) 
#' lines(t,Rt[,1],col=2)
#' lines(t,Rt[,2],col=4) 
#' legend("topleft",c("Total C release","C release from pool 1", "C release from pool 2"),
#' lty=c(1,1,1),col=c(1,2,4),lwd=c(2,1,1),bty="n")
TwopParallelModel<- function 
     (t,			
     ks,	
     C0,	
     In,     
     gam,  
     xi=1,   
     solver=deSolve.lsoda.wrapper, 
     pass=FALSE  
    )	
    { 
      t_start=min(t)
      t_stop=max(t)
      if(length(ks)!=2) stop("ks must be of length = 2")
      if(length(C0)!=2) stop("the vector with initial conditions must be of length = 2")
      if(gam > 1 | gam < 0) stop("The the partitioning coefficient gam is outside the interval [0,1]")
      if(length(In)==1) inputrates=BoundInFluxes(
        function(t){matrix(nrow=2,ncol=1,c(gam*In,(1-gam)*In))},
        t_start,
        t_stop
      )
      if(inherits(In, "data.frame")){
         x=In[,1]  
         y=In[,2]  
         inputrate=function(t0){as.numeric(spline(x,y,xout=t0)[2])}
         inputrates=BoundInFluxes(
            function(t){
                matrix(nrow=2,ncol=1,
                    c(
                        gam*inputrate(t),
                        (1-gam)*inputrate(t)
                    )
                )
            },
            min(x),
            max(x)
         )   
        }
      if(length(xi)==1) fX=function(t){xi}
      if(inherits(xi, "data.frame")){
      X=xi[,1]
      Y=xi[,2]
      fX=function(t){as.numeric(spline(X,Y,xout=t)[2])}
      }
      coeffs=TimeMap(
        function(t){fX(t)*(-1*abs(ks))},
        t_start,
        t_stop
      )
      obj=ParallelModel(t,coeffs,startvalues=C0,inputrates,solver,pass=pass)
}