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ACM_GPP_ET_global_propogation_NUE_half_Tair.r
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###
## Create needed ACM_GPP_ET shared object
# set to the working directory that this script should be called from
setwd("/home/lsmallma/WORK/GREENHOUSE/models/ACM_GPP_ET/") ; wkdir = getwd()
# compile the shared object containing ACM_GPP and ACM_ET
system("gfortran ./src/ACM_GPP_ET.f90 ./src/ACM_GPP_ET_R_interface.f90 -o ./src/acm_gpp_et.so -fPIC -shared")
system("mv ./src/acm_gpp_et.so .")
###
## Borrow met data from an existing CARDAMOM analysis
# set to the cardamom working directory for the moment
setwd("/home/lsmallma/WORK/GREENHOUSE/models/CARDAMOM/")
## Load needed libraries and internal functions
source("./cardamom_functions/load_all_cardamom_functions.r")
# define file name for PROJECT file we will be borrowing from
PROJECTfile=paste("./CARDAMOM_OUTPUTS/DALECN_GSI_BUCKET_MHMCMC/global_1x1_new_acm/infofile.RData",sep="")
load(PROJECTfile)
# this information will be used in loop to create the met inputs needed for the emulator, moving on the next task
cardamom = nc_open("/disk/scratch/local.2/lsmallma/Forest2020/C_cycle_analyses/DALEC_GSI_DFOL_CWD_FR_1_2001_2015_NEE_GPP_Rh_Ra_Bio_lit_cwd_som_timeseries.nc")
lai = ncvar_get(cardamom,"lai_median") ; root = ncvar_get(cardamom,"root_median")
## return back to working directory
setwd(wkdir)
###
## Define our output variables based on the grid of the CARDAMOM analysis we are borrowing
mean_lai = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_root = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
sd_lai = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
sd_root = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_gpp = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_transpiration = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_wetcanopyevap = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_soilevaporation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_rootwatermm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_wue = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_wSWP = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
min_wSWP = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_runoffmm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_drainagemm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_LWP = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_ci = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
# save some mean statistics
mean_temperature = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_radiation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_precipitation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
mean_vpd = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
nos_years = length(as.numeric(PROJECT$start_year):as.numeric(PROJECT$end_year))
mean_annual_temperature = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,nos_years))
mean_annual_radiation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,nos_years))
mean_annual_precipitation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,nos_years))
mean_annual_vpd = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,nos_years))
timeseries_lai = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_root = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_gpp = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_transpiration = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_wetcanopyevap = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_soilevaporation = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_rootwatermm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_WUE = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_wSWP = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_runoffmm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_drainagemm = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_LWP = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
timeseries_ci = array(NA, dim=c(PROJECT$long_dim,PROJECT$lat_dim,length(PROJECT$model$timestep_days)))
# work out area matrix for the pixels in meters
# include adjustment for g-> Tg (*1e-12)
if (PROJECT$grid_type == "UK") {
area=array(PROJECT$resolution**2, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
} else if (PROJECT$grid_type == "wgs84") {
# generate the lat / long grid again
output=generate_wgs84_grid(PROJECT$latitude,PROJECT$longitude,PROJECT$resolution)
# then generate the area estimates for each pixel
area=calc_pixel_area(output$lat,output$long,PROJECT$resolution)
# this output is in vector form and we need matching array shapes so...
area=array(area, dim=c(PROJECT$long_dim,PROJECT$lat_dim))
} else {
stop("valid spatial grid option not selected (UK, or wgs84)")
}
###
## Some ACM_GPP_ET parameters
output_dim=11 ; nofluxes = 8 ; nopools = 1 ; nopars = 4 ; nos_iter = 1
steps_per_year = length(PROJECT$model$timestep_days)/nos_years
# iterative process through the years...
for (n in seq(1,PROJECT$nosites)) {
if (n%%1000 == 0){print(paste("...beginning site:",n," of ",PROJECT$nosites, sep=""))}
# locational information
slot_j=as.numeric(PROJECT$sites[n])/PROJECT$long_dim
slot_i=as.numeric(PROJECT$sites[n])-(floor(slot_j)*PROJECT$long_dim)
if(slot_i == 0) {slot_i = PROJECT$long_dim} ; slot_j=ceiling(slot_j)
# load the met data for each site
drivers=read_binary_file_format(paste(PROJECT$datapath,PROJECT$name,"_",PROJECT$sites[n],".bin",sep=""))
# met note that the dimension here are different to that of drivers$met
met=array(-9999,dim=c(length(PROJECT$model$timestep_days),12))
met[,1] = drivers$met[,1] # day of analysis
met[,2] = drivers$met[,2]+1 # min temperature (oC)
met[,3] = drivers$met[,3]+1 # max temperature (oC)
met[,4] = drivers$met[,4] # SW Radiation (MJ.m-2.day-1)
met[,5] = drivers$met[,5] # CO2 ppm
met[,6] = drivers$met[,6] # day of year
met[,7] = drivers$met[,7] # rainfall (kg.m-2.s-1)
met[,8] = drivers$met[,14] # avg temperature (oC)
met[,9] = drivers$met[,15] # avg wind speed (m.s-1)
met[,10]= drivers$met[,16] # avg VPD (Pa)
# Extract LAI (m2/m2) and root (gC/m2) from CARDAMOM analysis
met[,11]= lai[n,]
met[,12]= root[n,]
# Restrict maximum rainfall to 5000 mm/yr, i.e. 0.0001584404 kgH2O/m2/s
if (mean(met[,7]) > 0.0001584404) {met[,7] = met[,7] / (mean(met[,7])/0.0001584404) }
# Restrict LAI to realistic range
met[which(met[,11] > 8.5),11] = 8.5
# Assuming I have not LAI and root information we will run the analysis
if (length(which(is.na(met[,11]) == TRUE)) == 0 & length(which(is.na(met[,12]) == TRUE)) == 0) {
# parameters
parameters = array(NA, dim=c(nopars,nos_iter))
parameters[1,] = 1.89*0.5 # foliar N (gN.m-2)
parameters[2,] = -9999 # min leaf water potential (MPa)
parameters[3,] = 100 # root biomass needed to reach 50 % depth
parameters[4,] = 2.0 # max root depth (m)
# other inputs
lat = drivers$lat
soil_info=c(drivers$top_sand,drivers$bot_sand,drivers$top_clay,drivers$bot_clay)
if (length(which(met[,11] > 0)) > 0) {
# If the shared object has not been loaded yet do so...
if (is.loaded("racmgppet") == FALSE) { dyn.load("./acm_gpp_et.so") }
tmp=.Fortran("racmgppet",output_dim=as.integer(output_dim),met=as.double(t(met)),pars=as.double(parameters)
,out_var=as.double(array(0,dim=c(nos_iter,(dim(met)[1]),output_dim)))
,lat=as.double(lat),nopars=as.integer(nopars),nomet=as.integer(dim(met)[2])
,nofluxes=as.integer(nofluxes),nopools=as.integer(nopools),nodays=as.integer(dim(met)[1])
,deltat=as.double(array(0,dim=c(as.integer(dim(met)[1])))),nos_iter=as.integer(nos_iter)
,soil_frac_clay=as.double(array(c(soil_info[3],soil_info[3],soil_info[4],soil_info[4]),dim=c(4)))
,soil_frac_sand=as.double(array(c(soil_info[1],soil_info[1],soil_info[2],soil_info[2]),dim=c(4))) )
# extract output from the analysis
output=tmp$out_var ; output=array(output, dim=c(nos_iter,(dim(met)[1]),output_dim))
# If this is the last site in the list best un-load the shared onject now
if (n == PROJECT$sites[length(PROJECT$sites)]) {dyn.unload("./acm_gpp_et.so")}
rm(tmp) ; gc()
} # If have LAI data
# Record mean climate data
mean_temperature[slot_i,slot_j] = mean((met[,2]+met[,3]) * 0.5)
mean_radiation[slot_i,slot_j] = mean(met[,4])
mean_precipitation[slot_i,slot_j] = mean(met[,7])
mean_vpd[slot_i,slot_j] = mean(met[,10])
# Mean annual conditions
a = 1 ; b = steps_per_year
for (y in seq(1,nos_years)) {
mean_annual_temperature[slot_i,slot_j,y] = mean((met[a:b,2]+met[a:b,3]) * 0.5)
mean_annual_radiation[slot_i,slot_j,y] = mean(met[a:b,4])
mean_annual_precipitation[slot_i,slot_j,y] = mean(met[a:b,7])
mean_annual_vpd[slot_i,slot_j,y] = mean(met[a:b,10])
a = a + steps_per_year ; b = b + steps_per_year
}
# assign time series to grid
timeseries_lai[slot_i,slot_j,] = (output[,1:dim(met)[1],1]) # lai (m2/m2)
timeseries_root[slot_i,slot_j,] = root[n,] # root (gC/m2)
timeseries_gpp[slot_i,slot_j,] = (output[,1:dim(met)[1],2]) # GPP (gC.m-2.day-1)
timeseries_transpiration[slot_i,slot_j,] = (output[,1:dim(met)[1],3]) # transpiration (kg.m-2.day-1)
timeseries_wetcanopyevap[slot_i,slot_j,] = (output[,1:dim(met)[1],4]) # wetcanopy evaporation (kg.m-2.day-1)
timeseries_soilevaporation[slot_i,slot_j,] = (output[,1:dim(met)[1],5]) # soil evaporation (kg.m-2.day-1)
timeseries_wSWP[slot_i,slot_j,] = (output[,1:dim(met)[1],6]) # weighted soil water potential (MPa)
timeseries_rootwatermm[slot_i,slot_j,] = (output[,1:dim(met)[1],7]) # water in rooting zone (mm)
timeseries_runoffmm[slot_i,slot_j,] = (output[,1:dim(met)[1],8]) # surface runoff (mm)
timeseries_drainagemm[slot_i,slot_j,] = (output[,1:dim(met)[1],9]) # drainage / underflow from bottom of soil column (mm)
timeseries_LWP[slot_i,slot_j,] = (output[,1:dim(met)[1],10]) # Leaf water potential (MPa)
timeseries_ci[slot_i,slot_j,] = (output[,1:dim(met)[1],11]) # internal CO2 concentration (umol/mol)
# assign timeseries mean values to grid
mean_lai[slot_i,slot_j] = mean(output[,1:dim(met)[1],1]) # lai (m2/m2)
mean_root[slot_i,slot_j] = mean(root[n,]) # root (gC/m2)
sd_lai[slot_i,slot_j] = sd(output[,1:dim(met)[1],1]) # lai (m2/m2)
sd_root[slot_i,slot_j] = sd(root[n,]) # root (gC/m2)
mean_gpp[slot_i,slot_j] = mean(output[,1:dim(met)[1],2]) # GPP (gC.m-2.day-1)
mean_transpiration[slot_i,slot_j] = mean(output[,1:dim(met)[1],3]) # transpiration (kg.m-2.day-1)
mean_wetcanopyevap[slot_i,slot_j] = mean(output[,1:dim(met)[1],4]) # wetcanopy evaporation (kg.m-2.day-1)
mean_soilevaporation[slot_i,slot_j] = mean(output[,1:dim(met)[1],5]) # soil evaporation (kg.m-2.day-1)
mean_wSWP[slot_i,slot_j] = mean(output[,1:dim(met)[1],6]) # weighted soil water potential (MPa)
min_wSWP[slot_i,slot_j] = min(output[,1:dim(met)[1],6]) # weighted soil water potential (MPa)
mean_rootwatermm[slot_i,slot_j] = mean(output[,1:dim(met)[1],7]) # water in rooting zone (mm)
mean_runoffmm[slot_i,slot_j] = mean(output[,1:dim(met)[1],8]) # surface runoff (mm)
mean_drainagemm[slot_i,slot_j] = mean(output[,1:dim(met)[1],9]) # drainage / underflow from bottom of soil column (mm)
mean_LWP[slot_i,slot_j] = mean(output[,1:dim(met)[1],10]) # Leaf water potential
mean_ci[slot_i,slot_j] = mean(output[,1:dim(met)[1],11]) # internal CO2 concentration
} # have got LAI and root infromation
} # site loop
###
## Generate some statistics
###
# Calculate the time series and mean values for water use efficiency (gC/kgH2O)
#mean_wue = mean_gpp/mean_transpiration
#timeseries_WUE = timeseries_gpp/timeseries_transpiration
mean_wue = mean_gpp/(mean_transpiration + mean_wetcanopyevap + mean_soilevaporation)
timeseries_WUE = timeseries_gpp/(timeseries_transpiration + timeseries_wetcanopyevap + timeseries_soilevaporation)
# Global mean GPP (PgC/yr); note 1e-15 is conversion from gC to PgC
global_mean_annual_gpp = sum(mean_gpp*365.25*area*1e-15,na.rm=TRUE)
# Global mean Transpiration (PgH2O/yr); note 1e-12 is conversion from kgH2O to PgH2O
global_mean_annual_transpiration = sum(mean_transpiration*365.25*area*1e-12,na.rm=TRUE)
# Global mean Soil evaporation (PgH2O/yr); note 1e-12 is conversion from kgH2O to PgH2O
global_mean_annual_soilevaporation = sum(mean_soilevaporation*365.25*area*1e-12,na.rm=TRUE)
# Global mean Wet canopy evaporation (PgH2O/yr); note 1e-12 is conversion from kgH2O to PgH2O
global_mean_annual_wetcanopyevap = sum(mean_wetcanopyevap*365.25*area*1e-12,na.rm=TRUE)
# Global mean evapo-transpiration (PgH2O/yr); note 1e-12 is conversion from kgH2O to PgH2O
global_mean_annual_et = sum((mean_wetcanopyevap+mean_transpiration+mean_soilevaporation)*365.25*area*1e-12,na.rm=TRUE)
# Global mean water use efficiency (gC/kgH2O)
global_mean_annual_wue = mean(mean_wue,na.rm=TRUE)
# Global mean weighted soil water potential (MPa)
global_mean_annual_wSWP = mean(mean_wSWP,na.rm=TRUE)
# Global mean water in rooted zone (kgH2O/m2)
global_mean_annual_rootwatermm = mean(mean_rootwatermm,na.rm=TRUE)
# Global mean LAI (m2/m2)
global_mean_annual_lai = mean(mean_lai,na.rm=TRUE)
# Global mean LAI (gC/m2)
global_mean_annual_root = mean(mean_root,na.rm=TRUE)
###
## Save output to files for later use
###
units=c("LAI = m2/m2","Roots = gC/m2","Water use efficiency (WUE) = gC/kgH2O"
,"GPP = gC/m2/day, global_mean_annual_gpp = PgC"
,"All water fluxes = kgH2O/m2/day except global_mean* = PgH2O"
,"mean_rootwatermm = kg/m2","All soil water potentials (SWP) = MPa"
,"mean_temperature = Celcius","mean_radiation = MJ/m2/day"
,"mean_precipitation = kgH2O/m2/s","mean_vpd = kPa")
# Save output for later use
global_output_NUE_half_Tair_plus1 = list( units = units,
area = area,
mean_temperature = mean_temperature,
mean_radiation = mean_radiation,
mean_precipitation = mean_precipitation,
mean_vpd = mean_vpd,
mean_annual_temperature = mean_annual_temperature,
mean_annual_radiation = mean_annual_radiation,
mean_annual_precipitation = mean_annual_precipitation,
mean_annual_vpd = mean_annual_vpd,
global_mean_annual_gpp = global_mean_annual_gpp,
global_mean_annual_transpiration = global_mean_annual_transpiration,
global_mean_annual_soilevaporation = global_mean_annual_soilevaporation,
global_mean_annual_wetcanopyevap = global_mean_annual_wetcanopyevap,
global_mean_annual_et = global_mean_annual_et,
global_mean_annual_wue = global_mean_annual_wue,
global_mean_annual_wSWP = global_mean_annual_wSWP,
global_mean_annual_rootwatermm = global_mean_annual_rootwatermm,
global_mean_annual_lai = global_mean_annual_lai,
global_mean_annual_root = global_mean_annual_root,
mean_lai = mean_lai,
mean_root = mean_root,
sd_lai = sd_lai,
sd_root = sd_root,
mean_gpp = mean_gpp,
mean_transpiration = mean_transpiration,
mean_wetcanopyevap = mean_wetcanopyevap,
mean_soilevaporation = mean_soilevaporation,
mean_rootwatermm = mean_rootwatermm,
mean_wue = mean_wue,
mean_wSWP = mean_wSWP,
min_wSWP = min_wSWP,
mean_runoffmm = mean_runoffmm,
mean_drainagemm = mean_drainagemm,
mean_LWP = mean_LWP,
mean_ci = mean_ci,
timeseries_lai = timeseries_lai,
timeseries_root = timeseries_root,
timeseries_gpp = timeseries_gpp,
timeseries_transpiration = timeseries_transpiration,
timeseries_wetcanopyevap = timeseries_wetcanopyevap,
timeseries_soilevaporation = timeseries_soilevaporation,
timeseries_rootwatermm = timeseries_rootwatermm,
timeseries_WUE = timeseries_WUE,
timeseries_wSWP = timeseries_wSWP,
timeseries_runoffmm = timeseries_runoffmm,
timeseries_drainagemm = timeseries_drainagemm,
timeseries_LWP = timeseries_LWP,
timeseries_ci = timeseries_ci)
# Now save the file
save(global_output_NUE_half_Tair_plus1, file="./outputs/global_1x1_degree_2001_2015_NUE_half_Tair_plus1.RData")
###
## Print some default information to the user
###
print(paste("Global GPP = ",round(global_mean_annual_gpp,digits=1)," PgC",sep=""))
print(paste("Global Transpiration = ",round(global_mean_annual_transpiration,digits=1)," PgH2O",sep=""))
print(paste("Global Soil Evaporation = ",round(global_mean_annual_soilevaporation,digits=1)," PgH2O",sep=""))
print(paste("Global Wet Canopy Evaporation = ",round(global_mean_annual_wetcanopyevap,digits=1)," PgH2O",sep=""))
print(paste("Global ET = ",round(global_mean_annual_et,digits=1)," PgH2O",sep=""))
print(paste("Global WUE = ",round(global_mean_annual_wue,digits=2)," gC/kgH2O",sep=""))
print(paste("Global wSWP = ",round(global_mean_annual_wSWP,digits=1)," MPa",sep=""))
print(paste("Global Water in root zone = ",round(global_mean_annual_rootwatermm,digits=1),sep=""))