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forward_run_v2.R
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# Setup ----
library(SoilR)
library(raster)
library(rgdal)
library(soilassessment)
library(sf)
source('C:/Users/stephen.roecker/OneDrive - USDA/projects/gsp-gsocseq/code/functions.R')
setwd("D:/geodata/project_data/gsp-gsocseq/CONUS")
# Load inputs ----
# forward stack
fr_df <- readRDS(file = "CONUS_fr_df.RDS")
# warm up
wu_df <- readRDS(file = "rothC_r_wu_final_analytical.rds")
wu_df$x <- NULL
wu_df$y <- NULL
# combine
fr_df <- merge(fr_df, wu_df, by = "cell", all.y = TRUE, sort = FALSE)
fr_df <-fr_df[complete.cases(fr_df), ]
# test2 <- readOGR(dsn = "FOWARD_County_AOI.shp")
# fr_df <- fr_df[fr_df$cell %in% test2$cell, ]
# fr_df <- fr_df[order(fr_df$cell), ]
# set.seed(42)
# fr_df <- fr_df[sample(1:nrow(fr_df), size = 50000), ]
# fr_df <- fr_df[order(fr_df$cell), ]
# apply increase ----
lu_cl <- c(2, 12, 3, 5, 6, 8, 13)
fr_df <- within(fr_df, {
CinputFORWARD_low <- ifelse(LU %in% lu_cl, CinputFORWARD.r * 1.05, CinputFORWARD.r)
CinputFORWARD_med <- ifelse(LU %in% lu_cl, CinputFORWARD.r * 1.10, CinputFORWARD.r)
CinputFORWARD_high <- ifelse(LU %in% lu_cl, CinputFORWARD.r * 1.20, CinputFORWARD.r)
# CinputFORWARD_med_min <- ifelse(LU %in% lu_cl, CinputFORWARD.min * 1.05 - 0.15, CinputFORWARD.min)
# CinputFORWARD_med_max <- ifelse(LU %in% lu_cl, CinputFORWARD.max * 1.20 + 0.15, CinputFORWARD.max)
})
saveRDS(fr_df, file = "fr_df_Cinputs.rds")
# Extract variables
pClay_r <- fr_df$CLAY
TEMP <- fr_df[grepl("TEMP_", names(fr_df))]
PREC <- fr_df[grepl("PREC_", names(fr_df))]
PET <- fr_df[grepl("PET_", names(fr_df))]
COV <- fr_df[grepl("COV_", names(fr_df))]
LU <- fr_df$LU
# Moisture effects per month ----
fW_r <- fW(pClay_r , PREC, PET, COV, s_thk = 30, pE = 1)
fW_min <- fW(pClay_r * 0.9, PREC * 0.95, PET, COV, s_thk = 30, pE = 1)
fW_max <- fW(pClay_r * 1.1, PREC * 1.05, PET, COV, s_thk = 30, pE = 1)
# clus <- makeCluster(16)
# clusterExport(clus, list("PREC", "PET", "COV", "pClay_r", "fw1func", "clamp"))
# test <- parLapply(clus, 1:nrow(PREC), function(i) {
# fw1func(
# P = unlist(PREC[i, ]),
# E = unlist(PET[i, ]),
# S.Thick = 30,
# pClay = pClay_r[i],
# pE = 1,
# bare = unlist(COV[i, ] > 0.8)
# )$b
# })
# stopCluster(clus)
# test <- do.call("rbind", test)
#
# all(fW_r == as.data.frame(test))
# summary(fW_r - test)
# Temperature effects per month ----
fT_r <- as.data.frame(lapply(TEMP, function(x) {
temp <- fT.RothC(x)
# temp <- ifelse(is.na(temp), 0, temp)
}))
fT_min <- as.data.frame(lapply(TEMP * 1.02, function(x) {
temp <- fT.RothC(x)
# temp <- ifelse(is.na(temp), 0, temp)
}))
fT_max <- as.data.frame(lapply(TEMP * 0.98, function(x) {
temp <- fT.RothC(x)
# temp <- ifelse(is.na(temp), 0, temp)
}))
# Vegetation Cover effects ----
fC <- COV
# Paddy fields coefficent ----
# fPR = 0.4 if the target point is class = 13 , else fPR=1
# From Shirato and Yukozawa 2004
fPR <- (LU == 13) * 0.4 + (LU != 13) * 1
# Set the factors frame for Model calculations ----
xi_r <- fT_r
xi_min <- fT_min
xi_max <- fT_max
for (i in 1:ncol(fT_r)) {
xi_r[, i] <- fT_r[, i] * fW_r[, i] * fC[, i] * fPR
}
for (i in 1:ncol(fT_min)) {
xi_min[, i] <- fT_min[, i] * fW_min[, i] * fC[, i] * fPR
}
for (i in 1:ncol(fT_max)) {
xi_max[, i] <- fT_max[, i] * fW_max[, i] * fC[, i] * fPR
}
saveRDS(xi_r, "fr_effcts_r.rds")
saveRDS(xi_min, "fr_effcts_min.rds")
saveRDS(xi_max, "fr_effcts_max.rds")
# RUN THE MODEL from soilassessment ----
## load inputs ----
years <- seq(1 / 12, 20, by = 1 / 12)
fr_df <- readRDS("fr_df_Cinputs.rds")
fr_df <- fr_df[!grepl("TEMP_|PREC_|PET_|COV_", names(fr_df))]
fr_df$CLAY_min <- fr_df$CLAY * 0.9
fr_df$CLAY_max <- fr_df$CLAY * 1.1
xi_r <- readRDS("fr_effcts_r.rds")
xi_min <- readRDS("fr_effcts_min.rds")
xi_max <- readRDS("fr_effcts_max.rds")
# Roth C soilassesment in parallel
library(parallel)
## Cinput bau ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_r", "years", "carbonTurnover"))
Sys.time()
rothC_bau <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.r[i], fr_df$RPM_wu.r[i], fr_df$BIO_wu.r[i], fr_df$HUM_wu.r[i], fr_df$IOM_wu.r[i]),
In = fr_df$CinputFORWARD.r[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY[i],
effcts = data.frame(years, unlist(xi_r[i, ])),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_bau, file = "rothC_fr_bau.rds")
stopCluster(clus)
## Cinput bau min ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_min", "years", "carbonTurnover"))
Sys.time()
rothC_fr_bau_min <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.min[i], fr_df$RPM_wu.min[i], fr_df$BIO_wu.min[i], fr_df$HUM_wu.min[i], fr_df$IOM_wu.min[i]),
In = fr_df$CinputFORWARD.min[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY_min[i],
effcts = data.frame(years, rep(unlist(xi_min[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_bau_min, file = "rothC_fr_bau_min.rds")
stopCluster(clus)
## Cinput bau max ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_max", "years", "carbonTurnover"))
Sys.time()
rothC_fr_bau_max <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.max[i], fr_df$RPM_wu.max[i], fr_df$BIO_wu.max[i], fr_df$HUM_wu.max[i], fr_df$IOM_wu.max[i]),
In = fr_df$CinputFORWARD.max[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY_max[i],
effcts = data.frame(years, rep(unlist(xi_max[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_bau_max, file = "rothC_fr_bau_max.rds")
stopCluster(clus)
## Cinput low ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_r", "years", "carbonTurnover"))
Sys.time()
rothC_fr_low <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.r[i], fr_df$RPM_wu.r[i], fr_df$BIO_wu.r[i], fr_df$HUM_wu.r[i], fr_df$IOM_wu.r[i]),
In = fr_df$CinputFORWARD_low[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY[i],
effcts = data.frame(years, rep(unlist(xi_r[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_low, file = "rothC_fr_low.rds")
stopCluster(clus)
## Cinput med ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_r", "years", "carbonTurnover"))
Sys.time()
rothC_fr_med <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.r[i], fr_df$RPM_wu.r[i], fr_df$BIO_wu.r[i], fr_df$HUM_wu.r[i], fr_df$IOM_wu.r[i]),
In = fr_df$CinputFORWARD_med[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY[i],
effcts = data.frame(years, rep(unlist(xi_r[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_med, file = "rothC_fr_med.rds")
stopCluster(clus)
## Cinput high ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_r", "years", "carbonTurnover"))
Sys.time()
rothC_fr_high <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.r[i], fr_df$RPM_wu.r[i], fr_df$BIO_wu.r[i], fr_df$HUM_wu.r[i], fr_df$IOM_wu.r[i]),
In = fr_df$CinputFORWARD_high[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY[i],
effcts = data.frame(years, rep(unlist(xi_r[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_high, file = "rothC_fr_high.rds")
stopCluster(clus)
## Cinput med min ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_min", "years", "carbonTurnover"))
Sys.time()
rothC_fr_medmin <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.min[i], fr_df$RPM_wu.min[i], fr_df$BIO_wu.min[i], fr_df$HUM_wu.min[i], fr_df$IOM_wu.min[i]),
In = fr_df$CinputFORWARD_med_min[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY_min[i],
effcts = data.frame(years, rep(unlist(xi_min[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_medmin, file = "rothC_fr_medmin.rds")
stopCluster(clus)
## Cinput med max ----
clus <- makeCluster(16)
clusterExport(clus, list("fr_df", "xi_max", "years", "carbonTurnover"))
Sys.time()
rothC_fr_medmax <- parLapply(clus, 1:nrow(fr_df), function(i) {
temp <- carbonTurnover(
tt = years,
C0 = c(fr_df$DPM_wu.max[i], fr_df$RPM_wu.max[i], fr_df$BIO_wu.max[i], fr_df$HUM_wu.max[i], fr_df$IOM_wu.max[i]),
In = fr_df$CinputFORWARD_med_max[i],
Dr = fr_df$DR[i],
clay = fr_df$CLAY_max[i],
effcts = data.frame(years, rep(unlist(xi_max[i, 1:12]), length.out = length(years))),
solver = "euler"
)
fp <- tail(temp, 1)
})
Sys.time()
saveRDS(rothC_fr_medmax, file = "rothC_fr_medmax.rds")
stopCluster(clus)
# Load rothC outputs ----
fr_df <- readRDS("fr_df_Cinputs.rds")
vars <- c(
bau = "rothC_fr_bau.rds"
# baumin = "rothC_fr_bau_min.rds",
# baumax = "rothC_fr_bau_max.rds",
# low = "rothC_fr_low.rds",
# med = "rothC_fr_med.rds",
# high = "rothC_fr_high.rds"
# medmin = "rothC_fr_medmin.rds",
# medmax = "rothC_fr_medmax.rds"
)
roth_l <- lapply(1:length(vars), function(x) {
cat("reading", vars[x], "\n")
temp <- cbind(
source = names(vars)[x],
cell = fr_df$cell,
as.data.frame(do.call("rbind", readRDS(vars[x])))
)
return(temp)
})
rc_fr_all <- do.call("rbind", roth_l)
idx <- 4:8
rc_fr_all$f_t <- rowSums(rc_fr_all[idx])
names(rc_fr_all)[idx] <- c("DPM_fr", "RPM_fr", "BIO_fr", "HUM_fr", "IOM_fr")
# rc_fr_all <- reshape(rc_fr_all, direction = "wide",
# idvar = "cell",
# timevar = "source",
# v.names = names(rc_fr_all[c(3:8)])
# )
library(data.table)
rc_fr_all <- data.table::dcast(
as.data.table(rc_fr_all),
cell + time ~ source,
value.var = c("DPM_fr", "RPM_fr", "BIO_fr", "HUM_fr", "IOM_fr", "f_t"),
sep = "."
)
rc_fr_all <- as.data.frame(rc_fr_all)
vars <- c("aoi", "x", "y", "cell", "SOC", "CLAY", "LU", "SOC_t0.r",
# "SOC_t0.min", "SOC_t0.max",
"CinputFORWARD.r"
# "CinputFORWARD.min", "CinputFORWARD.max"
)
# rc_fr_all <- rc_fr_all[order(rc_fr_all$cell), ]
# fr_df <- fr_df[order(fr_df$cell), ]
# all(fr_df$cell == rc_fr_all$cell)
# rc_fr_all$cell <- NULL
# rc_fr_final <- cbind(fr_df[vars], rc_fr_all)
rc_fr_final <- merge(fr_df[vars], rc_fr_all, by = "cell", all.x = TRUE, sort = FALSE)
saveRDS(rc_fr_final, file = "rothC_fr_final_analytical.rds")
# inspect outputs ----
test <- readRDS("rothC_r_wu_final.rds")
set.seed(42)
idx <- sample(1:nrow(test), size = 20000)
test <- test[idx, ]
test <- cbind(fr_df, fW_r)
rc_sf <- st_as_sf(
test,
coords = c("x", "y"),
crs = 4326
)
names(rc_sf) <- gsub("\\.", "_", names(rc_sf))
write_sf(rc_sf, dsn = "test.gpkg", driver = "GPKG", overwrite = TRUE)