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spin_up_v2_as.R
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spin_up_v2_as.R
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# SPATIAL SOIL R for VECTORS
###### SPIN UP ################
# MSc Ing Agr Luciano E Di Paolo
# Dr Ing Agr Guillermo E Peralta
###################################
# SOilR from 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(4),
#1045--1060. URL http://www.geosci-model-dev.net/5/1045/2012/gmd-5-1045-2012.html.
#####################################
# Setup ----
library(SoilR)
library(raster)
library(rgdal)
library(soilassessment)
source('C:/Users/stephen.roecker/OneDrive - USDA/projects/gsp-gsocseq/code/functions.R')
# set to aoi
setwd("D:/geodata/project_data/gsp-gsocseq/CONUS")
# Load inputs ----
# Stack_Set_1 is a stack that contains the spatial variables
su_df <- readRDS(file = "CONUS_su_df.RDS")
# su_df <- st_as_sf(
# su_df,
# coords = c("x", "y"),
# crs = 4326
# )
# su_df <- cbind(st_coordinates(su_df), su_df)
# names(su_df)[1:2] <- tolower(names(su_df)[1:2])
# tile <- readRDS("tile_crop.rds")
# su_df <- st_crop(su_df, st_bbox(tile)) %>%
# st_drop_geometry()
# Extract the layers from the Vector
SOC_r <- su_df$SOC
SOC_min <- su_df$SOC * 0.8
SOC_max <- su_df$SOC * 1.2
pClay_r <- su_df$CLAY
pClay_min <- su_df$CLAY * 0.9
pClay_max <- su_df$CLAY * 1.1
DR <- su_df$DR
LU <- su_df$LU
TEMP <- su_df[grepl("^TEMP_", names(su_df))]
PREC <- su_df[grepl("^PREC_", names(su_df))]
PET <- su_df[grepl("^PET_", names(su_df))]
COV <- su_df[grepl("^COV_", names(su_df))]
# 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
# 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)
}))
# 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)
# Vegetation Cover effects ----
fC <- COV
# 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, "su_effcts_r.rds")
saveRDS(xi_min, "su_effcts_min.rds")
saveRDS(xi_max, "su_effcts_max.rds")
# # Roth C outputs
# ro <- list(data.frame(id = NULL, C1 = NULL, C2 = NULL, C3 = NULL, C4 = NULL, C5 = NULL))[rep(1, nrow(ri))]
#
# # RUN THE MODEL from soilassessment
# # Roth C soilassesment
#
# iter <- as.integer(seq(200, nrow(ri), 200))
# for (i in 1:nrow(ri)) {
# if (any(i %in% iter)) cat("iteration", i, as.character(Sys.time()), "\n")
# temp <- carbonTurnover(
# tt = years,
# C0 = c(DPMptf, RPMptf, BIOptf, HUMptf, ri[i, 3]),
# In = Cinputs,
# Dr = ri[i, 4],
# clay = ri[i, 5],
# effcts = data.frame(years, rep(unlist(ri[i, 5:16]), length.out = length(years))),
# solver = "euler"
# )
# ro[[i]] <- c(ri[1], unlist(temp[6000, 2:6]))
# }
# RUN THE MODEL from soilassessment ----
# Roth C soilassesment in parallel
su_df <- readRDS(file = "CONUS_su_df.RDS")
years <- seq(1 / 12, 500, by = 1 / 12)
Cinputs <- 1
DR <- su_df$DR
pClay_r <- su_df$CLAY
pClay_min <- su_df$CLAY * 0.9
pClay_max <- su_df$CLAY * 1.1
SOC_r <- su_df$SOC
SOC_min <- su_df$SOC * 0.8
SOC_max <- su_df$SOC * 1.2
FallIOM_r <- 0.049 * SOC_r^(1.139)
FallIOM_min <- 0.049 * SOC_min^(1.139)
FallIOM_max <- 0.049 * SOC_max^(1.139)
fractI <- cbind(
DPM = DR / (DR + 1),
RPM = 1 - DR / (DR + 1),
HUM = 0
)
xi_r <- readRDS("su_effcts_r.rds")
xi_min <- readRDS("su_effcts_min.rds")
xi_max <- readRDS("su_effcts_max.rds")
xi_r2 <- rowMeans(xi_r)
xi_min2 <- rowMeans(xi_min)
xi_max2 <- rowMeans(xi_max)
idx <- complete.cases(su_df)
sum(!idx)
library(parallel)
clus <- makeCluster(16)
clusterExport(clus, list("SOC_r", "pClay_r", "xi_r2", "fractI", "fget_equilibrium_fractions.RothC_input", "fIOM.Falloon.RothC"))
Sys.time()
rothC_r <- parLapply(clus, 1:nrow(su_df), function(i) {
temp <- fget_equilibrium_fractions.RothC_input(
xi = xi_r2[i],
C.tot = SOC_r[i],
clay = pClay_r[i],
fractI = fractI[i, ]
)
return(temp)
})
Sys.time()
saveRDS(rothC_r, file = "rothC_r_v3_analytical.rds")
stopCluster(clus)
# # UNCERTAINTIES C input equilibrium (MINIMUM)
# # C input equilibrium. (Ceq) ----
clus <- makeCluster(16)
clusterExport(clus, list("SOC_min", "pClay_min", "xi_min2", "fractI", "fget_equilibrium_fractions.RothC_input", "fIOM.Falloon.RothC"))
Sys.time()
rothC_min <- parLapply(clus, 1:nrow(su_df), function(i) {
temp <- fget_equilibrium_fractions.RothC_input(
xi = xi_min2[i],
C.tot = SOC_min[i],
clay = pClay_min[i],
fractI = fractI[i, ]
)
return(temp)
})
Sys.time()
saveRDS(rothC_min, file = "rothC_min_v3_analytical.rds")
stopCluster(clus)
# UNCERTAINTIES C input equilibrium (MAXIMUM)
clus <- makeCluster(16)
clusterExport(clus, list("SOC_max", "pClay_max", "xi_max2", "fractI", "fget_equilibrium_fractions.RothC_input", "fIOM.Falloon.RothC"))
Sys.time()
rothC_max <- parLapply(clus, 1:nrow(su_df), function(i) {
temp <- fget_equilibrium_fractions.RothC_input(
xi = xi_max2[i],
C.tot = SOC_max[i],
clay = pClay_max[i],
fractI = fractI[i, ]
)
return(temp)
})
Sys.time()
saveRDS(rothC_max, file = "rothC_max_v3_analytical.rds")
stopCluster(clus)
# Outputs ----
rothC_r <- as.data.frame(
cbind(su_df[c("aoi", "cell", "x", "y", "LU", "DR")], SOC = SOC_r, FallIOM = FallIOM_r, pClay = pClay_r,
source = "r",
do.call(
"rbind",
readRDS("rothC_r_v3_analytical.rds")
)))
rothC_min <- as.data.frame(
cbind(su_df[c("aoi", "cell", "x", "y", "LU", "DR")], SOC = SOC_min, FallIOM = FallIOM_min, pClay = pClay_min,
source = "min",
do.call(
"rbind",
readRDS("rothC_min_v3_analytical.rds")
)))
rothC_max <- as.data.frame(
cbind(su_df[c("aoi", "cell", "x", "y", "LU", "DR")], SOC = SOC_max, FallIOM = FallIOM_max, pClay = pClay_max,
source = "max",
do.call(
"rbind",
readRDS("rothC_max_v3_analytical.rds")
)))
rothC_df <- rbind(rothC_r, rothC_min, rothC_max)
rothC_df$fract.sum <- rowSums(rothC_df[grepl("^fract", names(rothC_df))])
library(data.table)
rothC_dfw2 <- dcast(
as.data.table(rothC_df),
cell + x + y + aoi + LU + DR ~ source,
value.var = c("SOC", "FallIOM", "pClay", "fract.dpm", "fract.rpm", "fract.bio", "fract.hum", "fract.iom", "Cin", "fract.sum"),
sep = "."
)
# # convert to sf
# rothC_sf <- st_as_sf(
# rothC_dfw,
# coords = c("x", "y"),
# crs = 4326
# )
rc_df <- as.data.frame(rothC_dfw2)
saveRDS(rc_df, file = "su_results_v3_analytical.rds")
# inspect outputs
test <- readRDS("su_results_v3_analytical.rds")
set.seed(42)
idx <- sample(1:nrow(test), size = 20000)
test <- test[idx, ]
rc_sf <- st_as_sf(
test,
coords = c("x", "y"),
crs = 4326
)
rc_sf <- rc_sf[rc_sf$aoi == "AK1", ]
names(rc_sf) <- gsub("\\.", "_", names(rc_sf))
write_sf(rc_sf, dsn = "test.gpkg", driver = "GPKG", overwrite = TRUE)
aoi <- "AK1"
gsoc <- rast(paste0(aoi, "_GSOCmap1.5.0.tif"))
gsoc[!is.na(gsoc)] <- 1
lu <- rast(paste0(aoi, "_glc_shv10_DOM.tif"))
lu <- lu %in% c(2, 3, 5, 12, 13)
gsoc <- gsoc * lu
writeRaster(gsoc, "test_socfracsum.tif", overwrite = TRUE)
gdalUtilities::gdal_rasterize(
src_datasource = "test.gpkg",
a = "SOC_t0_r",
dst_filename = "test_socfracsum.tif",
of = "GTiff",
te = bbox(gsoc),
tr = res(gsoc),
co = c("COMPRESS=DEFLATE"),
a_srs = "EPSG:4326",
a_nodata = -999
)