R/INPUT/gen_init+bnd_cond/biomass_init/make_bioinit.r
In manfredo89/ED2io: Manages Input/Output for Ecosystem Demography 2 Model
#==========================================================================================#
#==========================================================================================#
# This code creates a PSS/CSS file for a tree survey conducted at the Tapajos #
# National Forest near Santarem in Brazil, in 2000. The original data set can be found at #
# #
# Menton, M., M. Figueira, C.A.D. de Sousa, S.D. Miller, H.R. da Rocha, and M.L. Goulden. #
# 2011. LBA-ECO CD-04 Biomass Survey, km 83 Tower Site, Tapajos National Forest, #
# Brazil. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National #
# Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. #
# doi:10.3334/ORNLDAAC/990 #
# #
# Do not distribute this data. Also, anyone intending to use this data other than for #
# this training MUST comply with LBA data policy. Please visit the ORNL site to learn #
# more: http://dx.doi.org/10.3334/ORNLDAAC/990 #
#------------------------------------------------------------------------------------------#
#----- Leave this as the first command, this will reset your R session. -------------------#
rm(list=ls())
graphics.off()
#------------------------------------------------------------------------------------------#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
# THE FOLLOWING BLOCK ALLOWS YOU TO CONTROL MOST SETTINGS FOR DATA ANALYSIS. #
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#------------------------------------------------------------------------------------------#
# Set paths. #
#------------------------------------------------------------------------------------------#
here = getwd() # Current path
outpath = "/Users/manfredo/Desktop/Rstuff/gen_init+bnd_cond/biomass_init/output"
# Path where the output should be written
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Location of interest. #
#------------------------------------------------------------------------------------------#
place = "Paracou"
iata = "liana_redux"
identity = "default"
lon = -52.000
lat = 5.000
year.out = 2000
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Plot information. #
#------------------------------------------------------------------------------------------#
subplot.area = 70*70 # Area of each subplot (all trees) [m2]
allplot.area = 100*100 # Area of each plot (all large trees) [m2]
nplots = 1 # Number of plots
min.dbh.subplot = 5. # Minimum DBH in the sub-sample
min.dbh.allplot = 35. # Minimum DBH for all plot
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Soil carbon information. These numbers came from a previous simulation, but you can #
# use site level data instead. #
#------------------------------------------------------------------------------------------#
fast.soil.c = 0.1495 # Litter layer [kgC/m2]
struct.soil.c = 6.126 # Structural soil carbon [kgC/m2]
struct.soil.l = 6.126 # Structural soil lignin [kgC/m2]
slow.soil.c = 4.546 # Slow soil carbon [kgC/m2]
min.soil.n = 0.639 # Mineralised soil nitrogen [kgN/m2]
fast.soil.n = 0.00348 # Fast soil nitrogen [kgN/m2]
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# CHANGES BEYOND THIS POINT ARE FOR ADJUSTING THE INPUT FILE ONLY. #
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Load some useful functions. #
#------------------------------------------------------------------------------------------#
source(file.path(here,"allometry.r"))
#------------------------------------------------------------------------------------------#
#----- Create output directory if it doesn't exist. ---------------------------------------#
outplace = file.path(outpath,place)
if (! file.exists(outpath )) dir.create(outpath )
if (! file.exists(outplace)) dir.create(outplace)
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Read the data set with the tree inventory. #
#------------------------------------------------------------------------------------------#
census.input = file.path(here,"paracou_manfredo_redux.csv")
cat(" + Reading in the data set (",basename(census.input),")...","\n",sep="")
census = read.csv(census.input,header=TRUE,stringsAsFactors=FALSE)
ncohorts = nrow(census)
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Find the plant functional type. We use wood density to determine the break points. #
#------------------------------------------------------------------------------------------#
pft.idx = c(2,3,4)
pft.mid.rho = c(0.53,0.71,0.90)
npft = length(pft.mid.rho)
pft.brks = c(-Inf,0.5*(pft.mid.rho[-1]+pft.mid.rho[-npft]),Inf)
pft.cut = as.numeric(cut(census$wood.dens,pft.brks))
census$pft = ifelse (census$scientific == "Liana", 17, pft.idx[pft.cut])
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Find the demographic density of the individuals. #
#------------------------------------------------------------------------------------------#
census$n = with(census, ifelse(dbh < min.dbh.allplot,1/subplot.area,1/allplot.area))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Estimate biomass and leaf area index. #
#------------------------------------------------------------------------------------------#
census$height = with(census,dbh2h (dbh=dbh,ipft=pft))
census$balive = with(census,dbh2ba (dbh=dbh,ipft=pft))
census$bdead = with(census,dbh2bd (dbh=dbh,ipft=pft))
census$lai = with(census,dbh2lai(dbh=dbh,nplant=n,ipft=pft))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Organise the dat using the tags, blocks, and coordinates. #
#------------------------------------------------------------------------------------------#
o = order(census$plots,-census$dbh,census$tag)
census = census[o,]
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# File names for output. #
#------------------------------------------------------------------------------------------#
outprefix = paste(iata,"_",identity,".lat",sprintf("%.3f",lat),"lon",sprintf("%.3f",lon)
,sep="")
pssfile = file.path(outplace,paste(outprefix,"pss",sep="."))
cssfile = file.path(outplace,paste(outprefix,"css",sep="."))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Format the PSS/CSS files. #
#------------------------------------------------------------------------------------------#
cat (" + Creating PSS/CSS file...","\n")
#---------------------------------------------------------------------------------------#
# Output data frame. #
#---------------------------------------------------------------------------------------#
outcohorts = data.frame( time = sprintf("%4.4i" , rep(year.out,times=ncohorts))
, patch = sprintf("0x%3.3X", census$plot )
, cohort = sprintf("0x%3.3X", census$tag )
, dbh = sprintf("%9.3f" , census$dbh )
, hite = sprintf("%9.3f" , census$height )
, pft = sprintf("%5i" , census$pft )
, n = sprintf("%15.8f" , census$n )
, bdead = sprintf("%9.3f" , census$bdead )
, balive = sprintf("%9.3f" , census$balive )
, lai = sprintf("%10.4f" , census$lai )
)#end data.frame
#---------------------------------------------------------------------------------------#
#----- Write the cohort file. ----------------------------------------------------------#
dummy = write.table(x=outcohorts,file=cssfile,append=FALSE,quote=FALSE,sep=" "
,row.names=FALSE,col.names=TRUE)
#---------------------------------------------------------------------------------------#
#---------------------------------------------------------------------------------------#
# Format the output so the table is legible. #
#---------------------------------------------------------------------------------------#
npatches = length(unique(census$plots))
outpatches = list( time = sprintf("%4.4i" ,rep(year.out ,times=npatches))
, patch = sprintf("0x%3.3X",unique(census$plots))
, trk = sprintf("%5i" ,rep(2 ,times=npatches))
, age = sprintf("%6.1f" ,rep(0 ,times=npatches))
, area = sprintf("%9.7f" ,rep(1/npatches ,times=npatches))
, water = sprintf("%5i" ,rep(0 ,times=npatches))
, fsc = sprintf("%10.5f" ,rep(fast.soil.c ,times=npatches))
, stsc = sprintf("%10.5f" ,rep(struct.soil.c,times=npatches))
, stsl = sprintf("%10.5f" ,rep(struct.soil.l,times=npatches))
, ssc = sprintf("%10.5f" ,rep(slow.soil.c ,times=npatches))
, lai = sprintf("%10.5f" ,with(census,tapply(lai,plots,sum)))
, msn = sprintf("%10.5f" ,rep(min.soil.n ,times=npatches))
, fsn = sprintf("%10.5f" ,rep(fast.soil.n ,times=npatches))
, nep = sprintf("%10.5f" ,rep(0 ,times=npatches))
, gpp = sprintf("%10.5f" ,rep(0 ,times=npatches))
, rh = sprintf("%10.5f" ,rep(0 ,times=npatches))
)#end data.frame
#---------------------------------------------------------------------------------------#
#----- Write the patch file. -----------------------------------------------------------#
dummy = write.table(x=outpatches,file=pssfile,append=FALSE,quote=FALSE,sep=" "
,row.names=FALSE,col.names=TRUE)
#---------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
manfredo89/ED2io documentation built on May 21, 2019, 11:24 a.m.
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#==========================================================================================#
#==========================================================================================#
# This code creates a PSS/CSS file for a tree survey conducted at the Tapajos #
# National Forest near Santarem in Brazil, in 2000. The original data set can be found at #
# #
# Menton, M., M. Figueira, C.A.D. de Sousa, S.D. Miller, H.R. da Rocha, and M.L. Goulden. #
# 2011. LBA-ECO CD-04 Biomass Survey, km 83 Tower Site, Tapajos National Forest, #
# Brazil. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National #
# Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. #
# doi:10.3334/ORNLDAAC/990 #
# #
# Do not distribute this data. Also, anyone intending to use this data other than for #
# this training MUST comply with LBA data policy. Please visit the ORNL site to learn #
# more: http://dx.doi.org/10.3334/ORNLDAAC/990 #
#------------------------------------------------------------------------------------------#
#----- Leave this as the first command, this will reset your R session. -------------------#
rm(list=ls())
graphics.off()
#------------------------------------------------------------------------------------------#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
# THE FOLLOWING BLOCK ALLOWS YOU TO CONTROL MOST SETTINGS FOR DATA ANALYSIS. #
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#==========================================================================================#
#------------------------------------------------------------------------------------------#
# Set paths. #
#------------------------------------------------------------------------------------------#
here = getwd() # Current path
outpath = "/Users/manfredo/Desktop/Rstuff/gen_init+bnd_cond/biomass_init/output"
# Path where the output should be written
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Location of interest. #
#------------------------------------------------------------------------------------------#
place = "Paracou"
iata = "liana_redux"
identity = "default"
lon = -52.000
lat = 5.000
year.out = 2000
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Plot information. #
#------------------------------------------------------------------------------------------#
subplot.area = 70*70 # Area of each subplot (all trees) [m2]
allplot.area = 100*100 # Area of each plot (all large trees) [m2]
nplots = 1 # Number of plots
min.dbh.subplot = 5. # Minimum DBH in the sub-sample
min.dbh.allplot = 35. # Minimum DBH for all plot
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Soil carbon information. These numbers came from a previous simulation, but you can #
# use site level data instead. #
#------------------------------------------------------------------------------------------#
fast.soil.c = 0.1495 # Litter layer [kgC/m2]
struct.soil.c = 6.126 # Structural soil carbon [kgC/m2]
struct.soil.l = 6.126 # Structural soil lignin [kgC/m2]
slow.soil.c = 4.546 # Slow soil carbon [kgC/m2]
min.soil.n = 0.639 # Mineralised soil nitrogen [kgN/m2]
fast.soil.n = 0.00348 # Fast soil nitrogen [kgN/m2]
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# CHANGES BEYOND THIS POINT ARE FOR ADJUSTING THE INPUT FILE ONLY. #
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Load some useful functions. #
#------------------------------------------------------------------------------------------#
source(file.path(here,"allometry.r"))
#------------------------------------------------------------------------------------------#
#----- Create output directory if it doesn't exist. ---------------------------------------#
outplace = file.path(outpath,place)
if (! file.exists(outpath )) dir.create(outpath )
if (! file.exists(outplace)) dir.create(outplace)
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Read the data set with the tree inventory. #
#------------------------------------------------------------------------------------------#
census.input = file.path(here,"paracou_manfredo_redux.csv")
cat(" + Reading in the data set (",basename(census.input),")...","\n",sep="")
census = read.csv(census.input,header=TRUE,stringsAsFactors=FALSE)
ncohorts = nrow(census)
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Find the plant functional type. We use wood density to determine the break points. #
#------------------------------------------------------------------------------------------#
pft.idx = c(2,3,4)
pft.mid.rho = c(0.53,0.71,0.90)
npft = length(pft.mid.rho)
pft.brks = c(-Inf,0.5*(pft.mid.rho[-1]+pft.mid.rho[-npft]),Inf)
pft.cut = as.numeric(cut(census$wood.dens,pft.brks))
census$pft = ifelse (census$scientific == "Liana", 17, pft.idx[pft.cut])
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Find the demographic density of the individuals. #
#------------------------------------------------------------------------------------------#
census$n = with(census, ifelse(dbh < min.dbh.allplot,1/subplot.area,1/allplot.area))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Estimate biomass and leaf area index. #
#------------------------------------------------------------------------------------------#
census$height = with(census,dbh2h (dbh=dbh,ipft=pft))
census$balive = with(census,dbh2ba (dbh=dbh,ipft=pft))
census$bdead = with(census,dbh2bd (dbh=dbh,ipft=pft))
census$lai = with(census,dbh2lai(dbh=dbh,nplant=n,ipft=pft))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Organise the dat using the tags, blocks, and coordinates. #
#------------------------------------------------------------------------------------------#
o = order(census$plots,-census$dbh,census$tag)
census = census[o,]
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# File names for output. #
#------------------------------------------------------------------------------------------#
outprefix = paste(iata,"_",identity,".lat",sprintf("%.3f",lat),"lon",sprintf("%.3f",lon)
,sep="")
pssfile = file.path(outplace,paste(outprefix,"pss",sep="."))
cssfile = file.path(outplace,paste(outprefix,"css",sep="."))
#------------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
# Format the PSS/CSS files. #
#------------------------------------------------------------------------------------------#
cat (" + Creating PSS/CSS file...","\n")
#---------------------------------------------------------------------------------------#
# Output data frame. #
#---------------------------------------------------------------------------------------#
outcohorts = data.frame( time = sprintf("%4.4i" , rep(year.out,times=ncohorts))
, patch = sprintf("0x%3.3X", census$plot )
, cohort = sprintf("0x%3.3X", census$tag )
, dbh = sprintf("%9.3f" , census$dbh )
, hite = sprintf("%9.3f" , census$height )
, pft = sprintf("%5i" , census$pft )
, n = sprintf("%15.8f" , census$n )
, bdead = sprintf("%9.3f" , census$bdead )
, balive = sprintf("%9.3f" , census$balive )
, lai = sprintf("%10.4f" , census$lai )
)#end data.frame
#---------------------------------------------------------------------------------------#
#----- Write the cohort file. ----------------------------------------------------------#
dummy = write.table(x=outcohorts,file=cssfile,append=FALSE,quote=FALSE,sep=" "
,row.names=FALSE,col.names=TRUE)
#---------------------------------------------------------------------------------------#
#---------------------------------------------------------------------------------------#
# Format the output so the table is legible. #
#---------------------------------------------------------------------------------------#
npatches = length(unique(census$plots))
outpatches = list( time = sprintf("%4.4i" ,rep(year.out ,times=npatches))
, patch = sprintf("0x%3.3X",unique(census$plots))
, trk = sprintf("%5i" ,rep(2 ,times=npatches))
, age = sprintf("%6.1f" ,rep(0 ,times=npatches))
, area = sprintf("%9.7f" ,rep(1/npatches ,times=npatches))
, water = sprintf("%5i" ,rep(0 ,times=npatches))
, fsc = sprintf("%10.5f" ,rep(fast.soil.c ,times=npatches))
, stsc = sprintf("%10.5f" ,rep(struct.soil.c,times=npatches))
, stsl = sprintf("%10.5f" ,rep(struct.soil.l,times=npatches))
, ssc = sprintf("%10.5f" ,rep(slow.soil.c ,times=npatches))
, lai = sprintf("%10.5f" ,with(census,tapply(lai,plots,sum)))
, msn = sprintf("%10.5f" ,rep(min.soil.n ,times=npatches))
, fsn = sprintf("%10.5f" ,rep(fast.soil.n ,times=npatches))
, nep = sprintf("%10.5f" ,rep(0 ,times=npatches))
, gpp = sprintf("%10.5f" ,rep(0 ,times=npatches))
, rh = sprintf("%10.5f" ,rep(0 ,times=npatches))
)#end data.frame
#---------------------------------------------------------------------------------------#
#----- Write the patch file. -----------------------------------------------------------#
dummy = write.table(x=outpatches,file=pssfile,append=FALSE,quote=FALSE,sep=" "
,row.names=FALSE,col.names=TRUE)
#---------------------------------------------------------------------------------------#
#------------------------------------------------------------------------------------------#
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