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linux6-sens1.R
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linux6-sens1.R
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##### FIRST GROUP OF SENSITIVITY RUNS
# This is to house sensitivity runs of 7 or 8 sites to be run on one core of 6
### LOAD LIBRARIES
library(RCurl)
library(plyr)
library(reshape2)
library(Rcpp)
library(ggplot2)
library(grid)
library(jsonlite)
dodge = position_dodge(width=0.9)
### SET BASE WORKING DIRECTORY
setwd("~/Dropbox/R Projects/")
### LOAD IN FUNCTIONS FROM NUMEROUS FILES
source("RFunctions.R")
source("USDA DAP/Sensitivities/LHC-sens-functions.r")
source("USDA DAP/Sensitivities/LHC-sens-constants.r")
# First Site --------------------------------------------------------------
#====================================================================
#### START MAIN PROGRAM:
#====================================================================
# Settings for the simulation runs:
#====================================================================
path = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WF" # path of the modelfolder
climpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WF" # path of the climate data
soilpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WF" # path of the soil data
modelpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WF" # path of the model
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
start_year = styr
end_year = enyr
growth_fac = 0.45 # this is the growth factor (only necessary to get the yield)
runs = sens.sims
#====================================================================
setwd(path)
# Set seed for reproducible random LHC sample
set.seed(2)
# Set min and max for variable ranges. Ranges are plus and minus 0.2 * value
clay.val = ste.sum.clay
clay.min = clay.val - (clay.val * 0.2)
clay.max = clay.val + (clay.val * 0.2)
silt.val = ste.sum.silt
sand.val = ste.sum.sand
bulk.val = ste.sum.bulk
bulk.min = bulk.val - (bulk.val * 0.2)
bulk.max = bulk.val + (bulk.val * 0.2)
ph.val = ste.sum.ph
ph.min = ph.val - (ph.val * 0.2)
ph.max = ph.val + (ph.val * 0.2)
# Build LHC sample
lcube = randomLHS(runs, 5)
# Set variables names on lcube
colnames(lcube) = c("temp", "prec", "clay", "bulk", "ph")
# Convert soil variables to values to use as run inputs
lcube[, "clay"] = clay.min + ((clay.max - clay.min) * lcube[, "clay"])
lcube[, "bulk"] = bulk.min + ((bulk.max - bulk.min) * lcube[, "bulk"])
lcube[, "ph"] = ph.min + ((ph.max - ph.min) * lcube[, "ph"])
# Reverse sign on random selection of half of the samples for temp and prec
# in order to get plus and minus 1 degree or 1 mm
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"]
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"]
# DayCent precipitation units are cm, so divide prec by 10
lcube[, "prec"] = lcube[, "prec"] / 10
# Soil values in lcube are used as-is. Weather variables are added to data.
# Soil texture inputs must sum to 1. Keep silt constant, and vary sand with clay.
lcube = cbind(lcube, "silt" = silt.val, "sand" = 1 - (lcube[, "clay"] + silt.val))
# change the climate file in folder
climfile = paste("climate_base.txt", sep="")
CLIM = read.table(climfile,
header = FALSE,
sep = "\t",
colClasses = c(rep("integer", 4), rep("numeric", 3), rep("NULL", 2)))
CLIM_BASE = read.table(climfile, header = FALSE, sep="\t", fill=TRUE)
climout = paste(climpath, "climate.wth", sep = "/")
# Write initial climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# change the soil file in folder
soilfile = paste("soilfile_base.txt", sep = "")
SOIL_list = read.table(soilfile)
SOIL = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
SOIL_BASE = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
soilout = paste(soilpath, "soils.in", sep = "/")
# Write initial soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
# Set results file header
header = c("sim",
"NPP",
"NEE",
"YIELD",
"RESP",
"Active",
"Slow",
"Passive",
"SOC",
"N2O",
"NO",
"N2",
"CH4",
"Tmax",
"Tmin",
"prec",
"pH",
"BD",
"clay")
# temporary fix to just rerun global simulation
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
# Set up simulation loop
for (sim_id in testvars) {
if (sim_id == "global") {
cat("Running global Monte Carlo simulation\n")
} else {
cat("Now running Monte Carlo simulations for variable ", sim_id, "\n", sep = "")
}
# Set name for results file
outfile = paste("sensitivity-", sim_id, ".txt", sep = "")
# Open results file and set to write mode
dataout = file(outfile, "w")
# Start writing results to table
write.table(t(header),
dataout,
sep = " ",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
# Start main Monte Carlo simulation loop
for (run_num in 1:runs){
# Set temperature input
if (sim_id == "temp") {
CLIM[, 5] = CLIM_BASE[, 5]
CLIM[, 6] = CLIM_BASE[, 6]
} else {
CLIM[, 5] = CLIM_BASE[, 5] + lcube[run_num, "temp"]
CLIM[, 6] = CLIM_BASE[, 6] + lcube[run_num, "temp"]
}
Tmax = max(CLIM[, 5])
Tmin = min(CLIM[, 6])
# Set precipitation input
if (sim_id == "prec") {
CLIM[, 7] = CLIM_BASE[, 7]
} else {
CLIM[, 7] = CLIM_BASE[ ,7] + lcube[run_num, "prec"]
}
# Convert negative values to zero, for zero precipitation
CLIM[CLIM[, 7] < 0, 7] = 0
prectot = sum(CLIM[, 7])
# Set soil inputs
if (sim_id == "ph") {
SOIL[, 13] = SOIL_BASE[, 13]
} else {
SOIL[, 13] = lcube[run_num, "ph"]
}
ph = SOIL[1, 13]
if (sim_id == "bulk") {
SOIL[, 3] = SOIL_BASE[, 3]
} else {
SOIL[, 3] = lcube[run_num, "bulk"]
}
bulk = SOIL[1, 3]
if (sim_id == "clay") {
SOIL[, 9] = SOIL_BASE[, 9]
SOIL[, 8] = SOIL_BASE[, 8]
} else {
SOIL[, 9] = lcube[run_num, "clay"]
SOIL[, 8] = lcube[run_num, "sand"]
}
clay = SOIL[1, 9]
# Write updated climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# Write updated soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
setwd(modelpath)
system("./ste_sum_wf1.sh")
file_in = "dc_sip.csv"
file_harv = "harvest.csv"
file_ghg = "tgmonth.out"
summary_out = get_data1(path = modelpath,
file = file_in,
growth_fac = growth_fac)
#harvest<-get_harvest(modelpath,file_harv,start_year,end_year)
ghg = get_ghg1(path = modelpath,
file = file_ghg)
#summary<-c(r,summary,harvest,ghg)
summary_out = c(run_num, summary_out, ghg, Tmax, Tmin, prectot, ph, bulk, clay)
# summary_out = c(summary_out, max(T_max), min(T_min), sum(prec), pH[1], BD[1], clay, sand)
write.table(t(summary_out),
dataout,
sep = "\t",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
cat("Run number ", run_num, " of ", runs, "\n",
"Variable ", grep(sim_id, testvars), " of ", length(testvars), "\n",
sep = "")
} # End of Monte Carlo simulation loop
# Close output file
close(dataout)
cat("Successful Monte Carlo simulation!\n")
} # End of simulation loop
# End routine
Sys.sleep(0.5)
time = Sys.time()
setwd("~/Dropbox/R Projects/USDA DAP/Sensitivities/")
write.csv(time, file = "6 cores 1-1.csv")
# Second Site -------------------------------------------------------------
#====================================================================
#### START MAIN PROGRAM:
#====================================================================
# Settings for the simulation runs:
#====================================================================
path = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCF" # path of the modelfolder
climpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCF" # path of the climate data
soilpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCF" # path of the soil data
modelpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCF" # path of the model
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
start_year = styr
end_year = enyr
growth_fac = 0.45 # this is the growth factor (only necessary to get the yield)
runs = sens.sims
#====================================================================
setwd(path)
# Set seed for reproducible random LHC sample
set.seed(2)
# Set min and max for variable ranges. Ranges are plus and minus 0.2 * value
clay.val = ste.sum.clay
clay.min = clay.val - (clay.val * 0.2)
clay.max = clay.val + (clay.val * 0.2)
silt.val = ste.sum.silt
sand.val = ste.sum.sand
bulk.val = ste.sum.bulk
bulk.min = bulk.val - (bulk.val * 0.2)
bulk.max = bulk.val + (bulk.val * 0.2)
ph.val = ste.sum.ph
ph.min = ph.val - (ph.val * 0.2)
ph.max = ph.val + (ph.val * 0.2)
# Build LHC sample
lcube = randomLHS(runs, 5)
# Set variables names on lcube
colnames(lcube) = c("temp", "prec", "clay", "bulk", "ph")
# Convert soil variables to values to use as run inputs
lcube[, "clay"] = clay.min + ((clay.max - clay.min) * lcube[, "clay"])
lcube[, "bulk"] = bulk.min + ((bulk.max - bulk.min) * lcube[, "bulk"])
lcube[, "ph"] = ph.min + ((ph.max - ph.min) * lcube[, "ph"])
# Reverse sign on random selection of half of the samples for temp and prec
# in order to get plus and minus 1 degree or 1 mm
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"]
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"]
# DayCent precipitation units are cm, so divide prec by 10
lcube[, "prec"] = lcube[, "prec"] / 10
# Soil values in lcube are used as-is. Weather variables are added to data.
# Soil texture inputs must sum to 1. Keep silt constant, and vary sand with clay.
lcube = cbind(lcube, "silt" = silt.val, "sand" = 1 - (lcube[, "clay"] + silt.val))
# change the climate file in folder
climfile = paste("climate_base.txt", sep="")
CLIM = read.table(climfile,
header = FALSE,
sep = "\t",
colClasses = c(rep("integer", 4), rep("numeric", 3), rep("NULL", 2)))
CLIM_BASE = read.table(climfile, header = FALSE, sep="\t", fill=TRUE)
climout = paste(climpath, "climate.wth", sep = "/")
# Write initial climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# change the soil file in folder
soilfile = paste("soilfile_base.txt", sep = "")
SOIL_list = read.table(soilfile)
SOIL = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
SOIL_BASE = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
soilout = paste(soilpath, "soils.in", sep = "/")
# Write initial soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
# Set results file header
header = c("sim",
"NPP",
"NEE",
"YIELD",
"RESP",
"Active",
"Slow",
"Passive",
"SOC",
"N2O",
"NO",
"N2",
"CH4",
"Tmax",
"Tmin",
"prec",
"pH",
"BD",
"clay")
# temporary fix to just rerun global simulation
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
# Set up simulation loop
for (sim_id in testvars) {
if (sim_id == "global") {
cat("Running global Monte Carlo simulation\n")
} else {
cat("Now running Monte Carlo simulations for variable ", sim_id, "\n", sep = "")
}
# Set name for results file
outfile = paste("sensitivity-", sim_id, ".txt", sep = "")
# Open results file and set to write mode
dataout = file(outfile, "w")
# Start writing results to table
write.table(t(header),
dataout,
sep = " ",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
# Start main Monte Carlo simulation loop
for (run_num in 1:runs){
# Set temperature input
if (sim_id == "temp") {
CLIM[, 5] = CLIM_BASE[, 5]
CLIM[, 6] = CLIM_BASE[, 6]
} else {
CLIM[, 5] = CLIM_BASE[, 5] + lcube[run_num, "temp"]
CLIM[, 6] = CLIM_BASE[, 6] + lcube[run_num, "temp"]
}
Tmax = max(CLIM[, 5])
Tmin = min(CLIM[, 6])
# Set precipitation input
if (sim_id == "prec") {
CLIM[, 7] = CLIM_BASE[, 7]
} else {
CLIM[, 7] = CLIM_BASE[ ,7] + lcube[run_num, "prec"]
}
# Convert negative values to zero, for zero precipitation
CLIM[CLIM[, 7] < 0, 7] = 0
prectot = sum(CLIM[, 7])
# Set soil inputs
if (sim_id == "ph") {
SOIL[, 13] = SOIL_BASE[, 13]
} else {
SOIL[, 13] = lcube[run_num, "ph"]
}
ph = SOIL[1, 13]
if (sim_id == "bulk") {
SOIL[, 3] = SOIL_BASE[, 3]
} else {
SOIL[, 3] = lcube[run_num, "bulk"]
}
bulk = SOIL[1, 3]
if (sim_id == "clay") {
SOIL[, 9] = SOIL_BASE[, 9]
SOIL[, 8] = SOIL_BASE[, 8]
} else {
SOIL[, 9] = lcube[run_num, "clay"]
SOIL[, 8] = lcube[run_num, "sand"]
}
clay = SOIL[1, 9]
# Write updated climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# Write updated soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
setwd(modelpath)
system("./ste_sum_wcf1.sh")
file_in = "dc_sip.csv"
file_harv = "harvest.csv"
file_ghg = "tgmonth.out"
summary_out = get_data1(path = modelpath,
file = file_in,
growth_fac = growth_fac)
#harvest<-get_harvest(modelpath,file_harv,start_year,end_year)
ghg = get_ghg1(path = modelpath,
file = file_ghg)
#summary<-c(r,summary,harvest,ghg)
summary_out = c(run_num, summary_out, ghg, Tmax, Tmin, prectot, ph, bulk, clay)
# summary_out = c(summary_out, max(T_max), min(T_min), sum(prec), pH[1], BD[1], clay, sand)
write.table(t(summary_out),
dataout,
sep = "\t",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
cat("Run number ", run_num, " of ", runs, "\n",
"Variable ", grep(sim_id, testvars), " of ", length(testvars), "\n",
sep = "")
} # End of Monte Carlo simulation loop
# Close output file
close(dataout)
cat("Successful Monte Carlo simulation!\n")
} # End of simulation loop
# End routine
Sys.sleep(0.5)
time = Sys.time()
setwd("~/Dropbox/R Projects/USDA DAP/Sensitivities/")
write.csv(time, file = "6 cores 1-2.csv")
# Third Site --------------------------------------------------------------
#====================================================================
#### START MAIN PROGRAM:
#====================================================================
# Settings for the simulation runs:
#====================================================================
path = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCMF" # path of the modelfolder
climpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCMF" # path of the climate data
soilpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCMF" # path of the soil data
modelpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_WCMF" # path of the model
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
start_year = styr
end_year = enyr
growth_fac = 0.45 # this is the growth factor (only necessary to get the yield)
runs = sens.sims
#====================================================================
setwd(path)
# Set seed for reproducible random LHC sample
set.seed(2)
# Set min and max for variable ranges. Ranges are plus and minus 0.2 * value
clay.val = ste.sum.clay
clay.min = clay.val - (clay.val * 0.2)
clay.max = clay.val + (clay.val * 0.2)
silt.val = ste.sum.silt
sand.val = ste.sum.sand
bulk.val = ste.sum.bulk
bulk.min = bulk.val - (bulk.val * 0.2)
bulk.max = bulk.val + (bulk.val * 0.2)
ph.val = ste.sum.ph
ph.min = ph.val - (ph.val * 0.2)
ph.max = ph.val + (ph.val * 0.2)
# Build LHC sample
lcube = randomLHS(runs, 5)
# Set variables names on lcube
colnames(lcube) = c("temp", "prec", "clay", "bulk", "ph")
# Convert soil variables to values to use as run inputs
lcube[, "clay"] = clay.min + ((clay.max - clay.min) * lcube[, "clay"])
lcube[, "bulk"] = bulk.min + ((bulk.max - bulk.min) * lcube[, "bulk"])
lcube[, "ph"] = ph.min + ((ph.max - ph.min) * lcube[, "ph"])
# Reverse sign on random selection of half of the samples for temp and prec
# in order to get plus and minus 1 degree or 1 mm
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"]
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"]
# DayCent precipitation units are cm, so divide prec by 10
lcube[, "prec"] = lcube[, "prec"] / 10
# Soil values in lcube are used as-is. Weather variables are added to data.
# Soil texture inputs must sum to 1. Keep silt constant, and vary sand with clay.
lcube = cbind(lcube, "silt" = silt.val, "sand" = 1 - (lcube[, "clay"] + silt.val))
# change the climate file in folder
climfile = paste("climate_base.txt", sep="")
CLIM = read.table(climfile,
header = FALSE,
sep = "\t",
colClasses = c(rep("integer", 4), rep("numeric", 3), rep("NULL", 2)))
CLIM_BASE = read.table(climfile, header = FALSE, sep="\t", fill=TRUE)
climout = paste(climpath, "climate.wth", sep = "/")
# Write initial climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# change the soil file in folder
soilfile = paste("soilfile_base.txt", sep = "")
SOIL_list = read.table(soilfile)
SOIL = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
SOIL_BASE = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
soilout = paste(soilpath, "soils.in", sep = "/")
# Write initial soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
# Set results file header
header = c("sim",
"NPP",
"NEE",
"YIELD",
"RESP",
"Active",
"Slow",
"Passive",
"SOC",
"N2O",
"NO",
"N2",
"CH4",
"Tmax",
"Tmin",
"prec",
"pH",
"BD",
"clay")
# temporary fix to just rerun global simulation
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
# Set up simulation loop
for (sim_id in testvars) {
if (sim_id == "global") {
cat("Running global Monte Carlo simulation\n")
} else {
cat("Now running Monte Carlo simulations for variable ", sim_id, "\n", sep = "")
}
# Set name for results file
outfile = paste("sensitivity-", sim_id, ".txt", sep = "")
# Open results file and set to write mode
dataout = file(outfile, "w")
# Start writing results to table
write.table(t(header),
dataout,
sep = " ",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
# Start main Monte Carlo simulation loop
for (run_num in 1:runs){
# Set temperature input
if (sim_id == "temp") {
CLIM[, 5] = CLIM_BASE[, 5]
CLIM[, 6] = CLIM_BASE[, 6]
} else {
CLIM[, 5] = CLIM_BASE[, 5] + lcube[run_num, "temp"]
CLIM[, 6] = CLIM_BASE[, 6] + lcube[run_num, "temp"]
}
Tmax = max(CLIM[, 5])
Tmin = min(CLIM[, 6])
# Set precipitation input
if (sim_id == "prec") {
CLIM[, 7] = CLIM_BASE[, 7]
} else {
CLIM[, 7] = CLIM_BASE[ ,7] + lcube[run_num, "prec"]
}
# Convert negative values to zero, for zero precipitation
CLIM[CLIM[, 7] < 0, 7] = 0
prectot = sum(CLIM[, 7])
# Set soil inputs
if (sim_id == "ph") {
SOIL[, 13] = SOIL_BASE[, 13]
} else {
SOIL[, 13] = lcube[run_num, "ph"]
}
ph = SOIL[1, 13]
if (sim_id == "bulk") {
SOIL[, 3] = SOIL_BASE[, 3]
} else {
SOIL[, 3] = lcube[run_num, "bulk"]
}
bulk = SOIL[1, 3]
if (sim_id == "clay") {
SOIL[, 9] = SOIL_BASE[, 9]
SOIL[, 8] = SOIL_BASE[, 8]
} else {
SOIL[, 9] = lcube[run_num, "clay"]
SOIL[, 8] = lcube[run_num, "sand"]
}
clay = SOIL[1, 9]
# Write updated climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# Write updated soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
setwd(modelpath)
system("./ste_sum_wcmf1.sh")
file_in = "dc_sip.csv"
file_harv = "harvest.csv"
file_ghg = "tgmonth.out"
summary_out = get_data1(path = modelpath,
file = file_in,
growth_fac = growth_fac)
#harvest<-get_harvest(modelpath,file_harv,start_year,end_year)
ghg = get_ghg1(path = modelpath,
file = file_ghg)
#summary<-c(r,summary,harvest,ghg)
summary_out = c(run_num, summary_out, ghg, Tmax, Tmin, prectot, ph, bulk, clay)
# summary_out = c(summary_out, max(T_max), min(T_min), sum(prec), pH[1], BD[1], clay, sand)
write.table(t(summary_out),
dataout,
sep = "\t",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
cat("Run number ", run_num, " of ", runs, "\n",
"Variable ", grep(sim_id, testvars), " of ", length(testvars), "\n",
sep = "")
} # End of Monte Carlo simulation loop
# Close output file
close(dataout)
cat("Successful Monte Carlo simulation!\n")
} # End of simulation loop
# End routine
Sys.sleep(0.5)
time = Sys.time()
setwd("~/Dropbox/R Projects/USDA DAP/Sensitivities/")
write.csv(time, file = "6 cores 1-3.csv")
# Fourth Site -------------------------------------------------------------
#====================================================================
#### START MAIN PROGRAM:
#====================================================================
# Settings for the simulation runs:
#====================================================================
path = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_OPP" # path of the modelfolder
climpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_OPP" # path of the climate data
soilpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_OPP" # path of the soil data
modelpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_OPP" # path of the model
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
start_year = styr
end_year = enyr
growth_fac = 0.45 # this is the growth factor (only necessary to get the yield)
runs = sens.sims
#====================================================================
setwd(path)
# Set seed for reproducible random LHC sample
set.seed(2)
# Set min and max for variable ranges. Ranges are plus and minus 0.2 * value
clay.val = ste.sum.clay
clay.min = clay.val - (clay.val * 0.2)
clay.max = clay.val + (clay.val * 0.2)
silt.val = ste.sum.silt
sand.val = ste.sum.sand
bulk.val = ste.sum.bulk
bulk.min = bulk.val - (bulk.val * 0.2)
bulk.max = bulk.val + (bulk.val * 0.2)
ph.val = ste.sum.ph
ph.min = ph.val - (ph.val * 0.2)
ph.max = ph.val + (ph.val * 0.2)
# Build LHC sample
lcube = randomLHS(runs, 5)
# Set variables names on lcube
colnames(lcube) = c("temp", "prec", "clay", "bulk", "ph")
# Convert soil variables to values to use as run inputs
lcube[, "clay"] = clay.min + ((clay.max - clay.min) * lcube[, "clay"])
lcube[, "bulk"] = bulk.min + ((bulk.max - bulk.min) * lcube[, "bulk"])
lcube[, "ph"] = ph.min + ((ph.max - ph.min) * lcube[, "ph"])
# Reverse sign on random selection of half of the samples for temp and prec
# in order to get plus and minus 1 degree or 1 mm
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "temp"]
lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"] = 0 - lcube[sample(1:nrow(lcube), nrow(lcube) / 2), "prec"]
# DayCent precipitation units are cm, so divide prec by 10
lcube[, "prec"] = lcube[, "prec"] / 10
# Soil values in lcube are used as-is. Weather variables are added to data.
# Soil texture inputs must sum to 1. Keep silt constant, and vary sand with clay.
lcube = cbind(lcube, "silt" = silt.val, "sand" = 1 - (lcube[, "clay"] + silt.val))
# change the climate file in folder
climfile = paste("climate_base.txt", sep="")
CLIM = read.table(climfile,
header = FALSE,
sep = "\t",
colClasses = c(rep("integer", 4), rep("numeric", 3), rep("NULL", 2)))
CLIM_BASE = read.table(climfile, header = FALSE, sep="\t", fill=TRUE)
climout = paste(climpath, "climate.wth", sep = "/")
# Write initial climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# change the soil file in folder
soilfile = paste("soilfile_base.txt", sep = "")
SOIL_list = read.table(soilfile)
SOIL = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
SOIL_BASE = matrix(as.numeric(unlist(SOIL_list)), nrow = nrow(SOIL_list), ncol = 13)
soilout = paste(soilpath, "soils.in", sep = "/")
# Write initial soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
# Set results file header
header = c("sim",
"NPP",
"NEE",
"YIELD",
"RESP",
"Active",
"Slow",
"Passive",
"SOC",
"N2O",
"NO",
"N2",
"CH4",
"Tmax",
"Tmin",
"prec",
"pH",
"BD",
"clay")
# temporary fix to just rerun global simulation
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
# Set up simulation loop
for (sim_id in testvars) {
if (sim_id == "global") {
cat("Running global Monte Carlo simulation\n")
} else {
cat("Now running Monte Carlo simulations for variable ", sim_id, "\n", sep = "")
}
# Set name for results file
outfile = paste("sensitivity-", sim_id, ".txt", sep = "")
# Open results file and set to write mode
dataout = file(outfile, "w")
# Start writing results to table
write.table(t(header),
dataout,
sep = " ",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
# Start main Monte Carlo simulation loop
for (run_num in 1:runs){
# Set temperature input
if (sim_id == "temp") {
CLIM[, 5] = CLIM_BASE[, 5]
CLIM[, 6] = CLIM_BASE[, 6]
} else {
CLIM[, 5] = CLIM_BASE[, 5] + lcube[run_num, "temp"]
CLIM[, 6] = CLIM_BASE[, 6] + lcube[run_num, "temp"]
}
Tmax = max(CLIM[, 5])
Tmin = min(CLIM[, 6])
# Set precipitation input
if (sim_id == "prec") {
CLIM[, 7] = CLIM_BASE[, 7]
} else {
CLIM[, 7] = CLIM_BASE[ ,7] + lcube[run_num, "prec"]
}
# Convert negative values to zero, for zero precipitation
CLIM[CLIM[, 7] < 0, 7] = 0
prectot = sum(CLIM[, 7])
# Set soil inputs
if (sim_id == "ph") {
SOIL[, 13] = SOIL_BASE[, 13]
} else {
SOIL[, 13] = lcube[run_num, "ph"]
}
ph = SOIL[1, 13]
if (sim_id == "bulk") {
SOIL[, 3] = SOIL_BASE[, 3]
} else {
SOIL[, 3] = lcube[run_num, "bulk"]
}
bulk = SOIL[1, 3]
if (sim_id == "clay") {
SOIL[, 9] = SOIL_BASE[, 9]
SOIL[, 8] = SOIL_BASE[, 8]
} else {
SOIL[, 9] = lcube[run_num, "clay"]
SOIL[, 8] = lcube[run_num, "sand"]
}
clay = SOIL[1, 9]
# Write updated climate input file
write.table(CLIM, climout, row.names = FALSE, col.names = FALSE)
# Write updated soil input file
write.table(SOIL, soilout, row.names = FALSE, col.names = FALSE)
setwd(modelpath)
system("./ste_sum_opp1.sh")
file_in = "dc_sip.csv"
file_harv = "harvest.csv"
file_ghg = "tgmonth.out"
summary_out = get_data1(path = modelpath,
file = file_in,
growth_fac = growth_fac)
#harvest<-get_harvest(modelpath,file_harv,start_year,end_year)
ghg = get_ghg1(path = modelpath,
file = file_ghg)
#summary<-c(r,summary,harvest,ghg)
summary_out = c(run_num, summary_out, ghg, Tmax, Tmin, prectot, ph, bulk, clay)
# summary_out = c(summary_out, max(T_max), min(T_min), sum(prec), pH[1], BD[1], clay, sand)
write.table(t(summary_out),
dataout,
sep = "\t",
append = FALSE,
quote = FALSE,
col.names = FALSE,
row.names = FALSE)
cat("Run number ", run_num, " of ", runs, "\n",
"Variable ", grep(sim_id, testvars), " of ", length(testvars), "\n",
sep = "")
} # End of Monte Carlo simulation loop
# Close output file
close(dataout)
cat("Successful Monte Carlo simulation!\n")
} # End of simulation loop
# End routine
Sys.sleep(0.5)
time = Sys.time()
setwd("~/Dropbox/R Projects/USDA DAP/Sensitivities/")
write.csv(time, file = "6 cores 1-4.csv")
# Fifth Site --------------------------------------------------------------
#====================================================================
#### START MAIN PROGRAM:
#====================================================================
# Settings for the simulation runs:
#====================================================================
path = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_Grass" # path of the modelfolder
climpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_Grass" # path of the climate data
soilpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_Grass" # path of the soil data
modelpath = "/media/sf_M_DRIVE/SterlingSims/Sensitivity Files/LHC_yao/Summit_Grass" # path of the model
testvars = c("global", "temp", "prec", "clay", "bulk", "ph")
start_year = styr
end_year = enyr
growth_fac = 0.45 # this is the growth factor (only necessary to get the yield)
runs = sens.sims
#====================================================================
setwd(path)
# Set seed for reproducible random LHC sample
set.seed(2)
# Set min and max for variable ranges. Ranges are plus and minus 0.2 * value
clay.val = ste.sum.clay
clay.min = clay.val - (clay.val * 0.2)
clay.max = clay.val + (clay.val * 0.2)
silt.val = ste.sum.silt
sand.val = ste.sum.sand
bulk.val = ste.sum.bulk
bulk.min = bulk.val - (bulk.val * 0.2)
bulk.max = bulk.val + (bulk.val * 0.2)
ph.val = ste.sum.ph
ph.min = ph.val - (ph.val * 0.2)