R/ThreepParallelModel.R

In SoilR: Models of Soil Organic Matter Decomposition

#' Implementation of a three pool model with parallel structure
#' 
#' The function creates a model for three 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 3 containing the decomposition rates for the 3
#' pools.
#' @param C0 A vector of length 3 containing the initial amount of carbon for
#' the 3 pools.
#' @param In A scalar or a data.frame object specifying the amount of litter
#' inputs by time.
#' @param gam1 A scalar representing the partitioning coefficient, i.e. the
#' proportion from the total amount of inputs that goes to pool 1.
#' @param gam2 A scalar representing the partitioning coefficient, i.e. the
#' proportion from the total amount of inputs that goes to pool 2.
#' @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 Logical that forces the Model to be created even if the chect
#' suggest problems.
#' @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=ThreepParallelModel(t,ks=c(k1=0.5,k2=0.2,k3=0.1),
#' C0=c(c10=100, c20=150,c30=50),In=20,gam1=0.7,gam2=0.1,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)
#' lines(t,Ct[,3],col=3)
#' legend("topright",c("Total C","C in pool 1", "C in pool 2","C in pool 3"),
#' lty=c(1,1,1,1),col=c(1,2,4,3),lwd=c(2,1,1,1),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)
#' lines(t,Rt[,3],col=3)
#' legend("topright",c("Total C release","C release from pool 1",
#' "C release from pool 2","C release from pool 3"),
#' lty=c(1,1,1,1),col=c(1,2,4,3),lwd=c(2,1,1,1),bty="n")
ThreepParallelModel <- function 
      (
       t,	
       ks,  
       C0,	
       In,     
       gam1,   
       gam2,   
       xi=1,   
       solver=deSolve.lsoda.wrapper, 	
       pass=FALSE 
      )
      {
        t_start=min(t)
        t_end=max(t)
        if(length(ks)!=3) stop("ks must be of length = 3")
        if(length(C0)!=3) stop("the vector with initial conditions must be of length = 3")
        if((gam1+gam2)^2 > 1) stop("The sum of the partitioning coefficients gam is outside the interval [0,1]")
        if(gam1 < 0 | gam2 < 0) stop("Partitioning coefficients gam must be positive")
        if(length(In)==1) inputrates_tm=BoundInFluxes(
            function(t){matrix(nrow=3,ncol=1,c(gam1*In,gam2*In,(1-gam1-gam2)*In))},
            t_start,
            t_end
        )
        if(inherits(In, "data.frame")){
         x=In[,1]  
         y=In[,2]  
         inputrate=function(t0){as.numeric(spline(x,y,xout=t0)[2])}
         inputrates_tm=BoundInFluxes(
            function(t){matrix(nrow=3,ncol=1,c(gam1*inputrate(t),gam2*inputrate(t),(1-gam1-gam2)*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_tm=TimeMap(
        function(times){fX(t)*(-1*abs(ks))},
        min(t),
        max(t)
        )  
     res=ParallelModel(t,coeffs_tm,startvalues=C0,inputrates_tm,solver,pass=pass)
}