R/calcLandEmissions.R
In pik-piam/mrcommons: MadRat commons Input Data Library
Defines functions
calcLandEmissions
Documented in
calcLandEmissions
#' @title calcLandEmissions
#' @description Land emission data. This function was originally called calcValidEmissions and located in
#' mrvalidation.
#'
#' @param datasource The Emission Inventory that shall be used. For futher information, best see function
#' calcEmissionInventory. Options are e.g. CEDS, combined_CEDS_IPCC (including own estimates where available),
#' IPCC(own estimates), Nsurplus (own estimates)
#'
#' @return List of magpie object with results on country level, weight on country level, unit and description.
#' @author Benjamin Leon Bodirsky, Michael S. Crawford
#' @examples
#' \dontrun{
#' calcOutput("LandEmissions")
#' }
#'
#' @importFrom magpiesets reportingnames
#' @importFrom luscale groupAggregate
#'
calcLandEmissions <- function(datasource = "CEDS") {
if (datasource %in% c("CEDS", "combined_CEDS_IPCC", "combined_CEDS_Nsurplus2")) {
ceds <- calcOutput("EmissionInventory",
datasource = datasource,
targetResolution = "magpie",
from = "CEDS59",
to = "MAgPIE",
aggregate = FALSE)
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
ceds2 <- ceds[, , c("nh3_n", "no2_n", "no3_n")]
ceds2[, , "nh3_n"] <- ceds2[, , "nh3_n"] * 17 / 14
ceds2[, , "no2_n"] <- ceds2[, , "no2_n"] * 46 / 14
ceds2[, , "no3_n"] <- ceds2[, , "no3_n"] * 62 / 14
map <- c(nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(ceds2, dim = 2) <- map[getNames(ceds2, dim = 2)]
ceds <- mbind(ceds, ceds2)
# rename emissions according to map
getNames(ceds, dim = 2) <- reportingnames(getNames(ceds, dim = 2))
# add sum over sector for each species
sectorSum <- dimSums(ceds, dim = 3.1)
agriSum <- dimSums(ceds[, , "Agriculture", pmatch = TRUE], dim = 3.1)
burnSum <- dimSums(ceds[, , "Biomass Burning", pmatch = TRUE], dim = 3.1)
sectorSum <- add_dimension(sectorSum, dim = 3.1, add = "sector", nm = "")
getNames(agriSum) <- paste0("+|Agriculture.", getNames(agriSum))
getNames(burnSum) <- paste0("+|Biomass Burning.", getNames(burnSum))
ceds <- mbind(ceds, agriSum, burnSum, sectorSum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "historical.ceds.Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region", "year", "scenario", "model", "variable")
ceds <- ceds[, getYears(ceds) > "y1960", ]
out <- ceds
} else if (datasource %in% c("Nsurplus", "Nsurplus2")) {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
out <- add_columns(out, addnm = "n2o_n", dim = 3.2)
out[, , "n2o_n"] <- dimSums(out[, , c("n2o_n_direct", "n2o_n_indirect")], dim = 3.2)
out <- out[, , c("n2o_n_direct", "n2o_n_indirect"), invert = TRUE]
out <- out[, , c("accumulation", "n2_n"), invert = TRUE]
out <- add_columns(out, addnm = c("soils", "agri"))
out[, , "soils"] <- dimSums(out[, , c("cropland_soils", "pasture_soils")], dim = 3.1)
out[, , "agri"] <- dimSums(out[, , c("soils", "awms")], dim = 3.1)
map <- toolGetMapping(type = "sectoral", name = "mappingIPCCtoMAgPIE.csv", where = "mappingfolder")
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, 1]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = "IPCC", to = "MAgPIE")
out <- out[, , sort(getNames(out, dim = 1))]
# adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[, , c("n2o_n", "nh3_n", "no2_n", "no3_n")]
out[, , "n2o_n"] <- out[, , "n2o_n"] * 44 / 28
out[, , "nh3_n"] <- out[, , "nh3_n"] * 17 / 14
out[, , "no2_n"] <- out[, , "no2_n"] * 46 / 14
out[, , "no3_n"] <- out[, , "no3_n"] * 62 / 14
map <- c(n2o_n = "n2o", nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(out, dim = 2) <- map[getNames(out, dim = 2)]
getNames(out, dim = 2) <- reportingnames(getNames(out, dim = 2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(out))
getNames(out) <- tmp
out <- clean_magpie(out)
# Add full scenario name
names(dimnames(out))[3] <- "variable"
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
} else if (datasource == "combined_CEDS_PRIMAPhist") {
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
ceds2[, , "nh3_n"] <- ceds2[, , "nh3_n"] * 17 / 14
ceds2[, , "no2_n"] <- ceds2[, , "no2_n"] * 46 / 14
ceds2[, , "no3_n"] <- ceds2[, , "no3_n"] * 62 / 14
map <- c(nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(ceds2, dim = 2) <- map[getNames(ceds2, dim = 2)]
ceds <- mbind(ceds, ceds2)
# rename emissions according to map
getNames(ceds, dim = 2) <- reportingnames(getNames(ceds, dim = 2))
# add sum over sector for each species
sectorSum <- dimSums(ceds, dim = 3.1)
getNames(sectorSum) <- paste0("Agriculture.", getNames(sectorSum))
ceds <- mbind(ceds, sectorSum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "historical.ceds.Emissions|\\2|\\1 (Mt \\2/yr)", getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region", "year", "scenario", "model", "variable")
ceds <- ceds[, getYears(ceds) > "y1960", ]
out <- ceds
} else if (datasource == "IPCC") {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
out <- add_columns(out, addnm = "n2o_n", dim = 3.2)
out[, , "n2o_n"] <- dimSums(out[, , c("n2o_n_direct", "n2o_n_indirect")], dim = 3.2)
out <- add_columns(out, addnm = c("soils", "agri"))
out[, , "soils"] <- dimSums(out[, , c("inorg_fert", "man_crop", "resid", "som", "rice", "pasture_soils")],
dim = 3.1)
out[, , "agri"] <- dimSums(out[, , c("soils", "awms")], dim = 3.1)
map <- toolGetMapping(type = "sectoral", name = "mappingIPCCtoMAgPIE.csv", where = "mappingfolder")
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, 1]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = datasource, to = "MAgPIE")
out <- out[, , sort(getNames(out, dim = 1))]
# adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[, , c("n2o_n", "nh3_n", "no2_n", "no3_n")]
out[, , "n2o_n"] <- out[, , "n2o_n"] * 44 / 28
out[, , "nh3_n"] <- out[, , "nh3_n"] * 17 / 14
out[, , "no2_n"] <- out[, , "no2_n"] * 46 / 14
out[, , "no3_n"] <- out[, , "no3_n"] * 62 / 14
map <- c(n2o_n = "n2o", nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(out, dim = 2) <- map[getNames(out, dim = 2)]
getNames(out, dim = 2) <- reportingnames(getNames(out, dim = 2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(out))
getNames(out) <- tmp
out <- clean_magpie(out)
# Add full scenario name
names(dimnames(out))[3] <- "variable"
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
} else if (datasource == "EDGAR_LU") {
co2 <- readSource("EDGAR_LU", subtype = "CO2") * 44 / 12 # convert from C to CO2
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
n2o <- readSource("EDGAR_LU", subtype = "N2O") * 44 / 28 # convert from N to N2O
getNames(n2o) <- "Emissions|N2O|Land|+|Agriculture (Mt N2O/yr)"
ch4 <- readSource("EDGAR_LU", subtype = "CH4")
getNames(ch4) <- "Emissions|CH4|Land|+|Agriculture (Mt CH4/yr)"
out <- mbind(co2, n2o, ch4)
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = "EDGAR_LU")
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "EDGAR6") {
edgar <- calcOutput("EmissionInventory", datasource = "EDGAR6", targetResolution = NULL, aggregate = FALSE)
# Unit conversion when appropriate (e.g. n2o_n to n2o)
.formatToReporting <- function(x, old, new, conversion) {
x[, , old] <- x[, , old] * conversion
getNames(x, dim = 1)[which(getNames(x, dim = 1) == old)] <- new
return(x)
}
edgar <- .formatToReporting(edgar, "n2o_n", "n2o", (44 / 28))
edgar <- .formatToReporting(edgar, "nh3_n", "nh3", (17 / 14))
edgar <- .formatToReporting(edgar, "no2_n", "no2", (46 / 14))
edgar <- .formatToReporting(edgar, "co2_c", "co2", (44 / 12))
# Rename emissions according to their MAgPIE reporting names
sectorMap <- toolGetMapping(type = "sectoral", name = "mappingEDGAR6toMAgPIE.csv", where = "mappingfolder")
edgar <- toolAggregate(x = edgar, rel = sectorMap,
from = "EDGAR6", to = "MAgPIE_reporting",
dim = 3, partrel = FALSE)
# Format for validation .mif
edgar <- add_dimension(edgar, dim = 3.1, add = "scenario", nm = "historical")
edgar <- add_dimension(edgar, dim = 3.2, add = "model", nm = "EDGAR6")
names(dimnames(edgar))[3] <- "scenario.model.variable"
out <- edgar
} else if (datasource == "FAO_EmisLUC") {
co2 <- readSource("FAO", subtype = "EmisLuTotal")
# / 1000 to convert from Gigagrams to Mt
co2 <- co2[, , "1707|Land Use total + (Total).Net_emissions_removal_(CO2)_(Gigagrams)"] / 1000
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
out <- co2
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "FAO_EmisAg") {
total <- readSource("FAO_online", subtype = "EmisAgTotal")
mapping <- toolGetMapping(type = "sectoral", name = "FAO_online_emissionsMapping.csv", where = "mappingfolder")
.calculateEmissions <- function(faoName, magpieName) {
emission <- toolAggregate(total[, , faoName],
rel = mapping, from = "fao", to = magpieName,
dim = 3.1, partrel = TRUE)
emission <- emission[, , "", invert = TRUE]
emission <- emission / 1000 # Gg to Mt X
emission <- collapseDim(emission, dim = 3.2)
return(emission)
}
n2o <- .calculateEmissions("N2O_emissions_(gigagrams)", "magpie_n2o")
ch4 <- .calculateEmissions("CH4_emissions_(gigagrams)", "magpie_ch4")
out <- mbind(n2o, ch4)
out <- mbind(add_dimension(out, dim = 3.1, add = "scenario", nm = "historical"),
add_dimension(out, dim = 3.1, add = "scenario", nm = "projection"))
out[, , "projection"] <- NA
out[, , "projection"][, c("y2030", "y2050"), ] <- collapseNames(out[, , "historical"][, c("y2030", "y2050"), ])
out[, , "historical"][, c("y2030", "y2050"), ] <- NA
out <- add_dimension(out, dim = 3.2, add = "model", nm = "FAO_EmisAg")
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "GFED") {
gfedEmissions <- readSource("GFED")
countryToCell <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mstools")
ch4 <- gfedEmissions[, , "AGRI.ch4"]
ch4 <- toolAggregate(ch4, rel = countryToCell, from = "celliso", to = "iso")
getNames(ch4) <- "Emissions|CH4|Land|Biomass Burning|+|Burning of Crop Residues (Mt CH4/yr)"
no2 <- gfedEmissions[, , "AGRI.no2"]
no2 <- toolAggregate(no2, rel = countryToCell, from = "celliso", to = "iso")
getNames(no2) <- "Emissions|NO2|Land|Biomass Burning|+|Burning of Crop Residues (Mt NO2/yr)"
n2o <- gfedEmissions[, , "AGRI.n2o"]
n2o <- toolAggregate(n2o, rel = countryToCell, from = "celliso", to = "iso")
getNames(n2o) <- "Emissions|N2O|Land|Biomass Burning|+|Burning of Crop Residues (Mt N2O/yr)"
nh3 <- gfedEmissions[, , "AGRI.nh3"]
nh3 <- toolAggregate(nh3, rel = countryToCell, from = "celliso", to = "iso")
getNames(nh3) <- "Emissions|NH3|Land|Biomass Burning|+|Burning of Crop Residues (Mt NH3/yr)"
out <- mbind(ch4, no2, n2o, nh3)
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = "GFED")
names(dimnames(out))[3] <- "scenario.model.variable"
out <- toolCountryFill(out, fill = 0)
} else if (datasource == "PRIMAPhist") {
# The PRIMAP-hist national historical emissions time series (1850-2014)
primap <- readSource("PRIMAPhist", "hist")
n2o <- primap[, , "n2o_n"][, , "CAT4"] / 28 * 44
getNames(n2o) <- "Emissions|N2O|Land|+|Agriculture (Mt N2O/yr)"
ch4 <- primap[, , "ch4"][, , "CAT4"]
getNames(ch4) <- "Emissions|CH4|Land|+|Agriculture (Mt CH4/yr)"
co2 <- primap[, , "co2_c"][, , "CAT4"] / 12 * 44
getNames(co2) <- "Emissions|CO2|Land|+|Agriculture (Mt CO2/yr)"
ag <- mbind(n2o, ch4, co2)
n2o <- primap[, , "n2o_n"][, , "CAT5"] / 28 * 44
getNames(n2o) <- "Emissions|N2O|Land|+|Land-use Change (Mt N2O/yr)"
ch4 <- primap[, , "ch4"][, , "CAT5"]
getNames(ch4) <- "Emissions|CH4|Land|+|Land-use Change (Mt CH4/yr)"
co2 <- primap[, , "co2_c"][, , "CAT5"] / 12 * 44
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
luluc <- mbind(n2o, ch4, co2)
out <- mbind(ag, luluc)
namesX <- getNames(out)
names(namesX) <- NULL
getNames(out) <- paste0("historical.PRIMAPhist.", namesX)
getSets(out) <- c("region", "year", "scenario", "model", "variable")
} else {
stop("datasource unknown")
}
return(list(x = out,
weight = NULL,
unit = "Mt",
description = "historic emissions in 1970-2015. NOx is in NO2 equivalents."))
}
pik-piam/mrcommons documentation built on Dec. 8, 2024, 7:23 a.m.
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Defines functions calcLandEmissions
Documented in calcLandEmissions
#' @title calcLandEmissions
#' @description Land emission data. This function was originally called calcValidEmissions and located in
#' mrvalidation.
#'
#' @param datasource The Emission Inventory that shall be used. For futher information, best see function
#' calcEmissionInventory. Options are e.g. CEDS, combined_CEDS_IPCC (including own estimates where available),
#' IPCC(own estimates), Nsurplus (own estimates)
#'
#' @return List of magpie object with results on country level, weight on country level, unit and description.
#' @author Benjamin Leon Bodirsky, Michael S. Crawford
#' @examples
#' \dontrun{
#' calcOutput("LandEmissions")
#' }
#'
#' @importFrom magpiesets reportingnames
#' @importFrom luscale groupAggregate
#'
calcLandEmissions <- function(datasource = "CEDS") {
if (datasource %in% c("CEDS", "combined_CEDS_IPCC", "combined_CEDS_Nsurplus2")) {
ceds <- calcOutput("EmissionInventory",
datasource = datasource,
targetResolution = "magpie",
from = "CEDS59",
to = "MAgPIE",
aggregate = FALSE)
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
ceds2 <- ceds[, , c("nh3_n", "no2_n", "no3_n")]
ceds2[, , "nh3_n"] <- ceds2[, , "nh3_n"] * 17 / 14
ceds2[, , "no2_n"] <- ceds2[, , "no2_n"] * 46 / 14
ceds2[, , "no3_n"] <- ceds2[, , "no3_n"] * 62 / 14
map <- c(nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(ceds2, dim = 2) <- map[getNames(ceds2, dim = 2)]
ceds <- mbind(ceds, ceds2)
# rename emissions according to map
getNames(ceds, dim = 2) <- reportingnames(getNames(ceds, dim = 2))
# add sum over sector for each species
sectorSum <- dimSums(ceds, dim = 3.1)
agriSum <- dimSums(ceds[, , "Agriculture", pmatch = TRUE], dim = 3.1)
burnSum <- dimSums(ceds[, , "Biomass Burning", pmatch = TRUE], dim = 3.1)
sectorSum <- add_dimension(sectorSum, dim = 3.1, add = "sector", nm = "")
getNames(agriSum) <- paste0("+|Agriculture.", getNames(agriSum))
getNames(burnSum) <- paste0("+|Biomass Burning.", getNames(burnSum))
ceds <- mbind(ceds, agriSum, burnSum, sectorSum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "historical.ceds.Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region", "year", "scenario", "model", "variable")
ceds <- ceds[, getYears(ceds) > "y1960", ]
out <- ceds
} else if (datasource %in% c("Nsurplus", "Nsurplus2")) {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
out <- add_columns(out, addnm = "n2o_n", dim = 3.2)
out[, , "n2o_n"] <- dimSums(out[, , c("n2o_n_direct", "n2o_n_indirect")], dim = 3.2)
out <- out[, , c("n2o_n_direct", "n2o_n_indirect"), invert = TRUE]
out <- out[, , c("accumulation", "n2_n"), invert = TRUE]
out <- add_columns(out, addnm = c("soils", "agri"))
out[, , "soils"] <- dimSums(out[, , c("cropland_soils", "pasture_soils")], dim = 3.1)
out[, , "agri"] <- dimSums(out[, , c("soils", "awms")], dim = 3.1)
map <- toolGetMapping(type = "sectoral", name = "mappingIPCCtoMAgPIE.csv", where = "mappingfolder")
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, 1]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = "IPCC", to = "MAgPIE")
out <- out[, , sort(getNames(out, dim = 1))]
# adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[, , c("n2o_n", "nh3_n", "no2_n", "no3_n")]
out[, , "n2o_n"] <- out[, , "n2o_n"] * 44 / 28
out[, , "nh3_n"] <- out[, , "nh3_n"] * 17 / 14
out[, , "no2_n"] <- out[, , "no2_n"] * 46 / 14
out[, , "no3_n"] <- out[, , "no3_n"] * 62 / 14
map <- c(n2o_n = "n2o", nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(out, dim = 2) <- map[getNames(out, dim = 2)]
getNames(out, dim = 2) <- reportingnames(getNames(out, dim = 2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(out))
getNames(out) <- tmp
out <- clean_magpie(out)
# Add full scenario name
names(dimnames(out))[3] <- "variable"
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
} else if (datasource == "combined_CEDS_PRIMAPhist") {
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
ceds2[, , "nh3_n"] <- ceds2[, , "nh3_n"] * 17 / 14
ceds2[, , "no2_n"] <- ceds2[, , "no2_n"] * 46 / 14
ceds2[, , "no3_n"] <- ceds2[, , "no3_n"] * 62 / 14
map <- c(nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(ceds2, dim = 2) <- map[getNames(ceds2, dim = 2)]
ceds <- mbind(ceds, ceds2)
# rename emissions according to map
getNames(ceds, dim = 2) <- reportingnames(getNames(ceds, dim = 2))
# add sum over sector for each species
sectorSum <- dimSums(ceds, dim = 3.1)
getNames(sectorSum) <- paste0("Agriculture.", getNames(sectorSum))
ceds <- mbind(ceds, sectorSum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "historical.ceds.Emissions|\\2|\\1 (Mt \\2/yr)", getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region", "year", "scenario", "model", "variable")
ceds <- ceds[, getYears(ceds) > "y1960", ]
out <- ceds
} else if (datasource == "IPCC") {
out <- calcOutput("EmisNitrogenPast", method = datasource, aggregate = FALSE)
out <- add_columns(out, addnm = "n2o_n", dim = 3.2)
out[, , "n2o_n"] <- dimSums(out[, , c("n2o_n_direct", "n2o_n_indirect")], dim = 3.2)
out <- add_columns(out, addnm = c("soils", "agri"))
out[, , "soils"] <- dimSums(out[, , c("inorg_fert", "man_crop", "resid", "som", "rice", "pasture_soils")],
dim = 3.1)
out[, , "agri"] <- dimSums(out[, , c("soils", "awms")], dim = 3.1)
map <- toolGetMapping(type = "sectoral", name = "mappingIPCCtoMAgPIE.csv", where = "mappingfolder")
out <- out[, , getNames(out, dim = 1)[getNames(out, dim = 1) %in% map[, 1]]]
out <- groupAggregate(data = out, dim = 3.1, query = map, from = datasource, to = "MAgPIE")
out <- out[, , sort(getNames(out, dim = 1))]
# adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[, , c("n2o_n", "nh3_n", "no2_n", "no3_n")]
out[, , "n2o_n"] <- out[, , "n2o_n"] * 44 / 28
out[, , "nh3_n"] <- out[, , "nh3_n"] * 17 / 14
out[, , "no2_n"] <- out[, , "no2_n"] * 46 / 14
out[, , "no3_n"] <- out[, , "no3_n"] * 62 / 14
map <- c(n2o_n = "n2o", nh3_n = "nh3", no2_n = "no2", no3_n = "no3")
getNames(out, dim = 2) <- map[getNames(out, dim = 2)]
getNames(out, dim = 2) <- reportingnames(getNames(out, dim = 2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)", "Emissions|\\2|Land|\\1 (Mt \\2/yr)", getNames(out))
getNames(out) <- tmp
out <- clean_magpie(out)
# Add full scenario name
names(dimnames(out))[3] <- "variable"
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
} else if (datasource == "EDGAR_LU") {
co2 <- readSource("EDGAR_LU", subtype = "CO2") * 44 / 12 # convert from C to CO2
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
n2o <- readSource("EDGAR_LU", subtype = "N2O") * 44 / 28 # convert from N to N2O
getNames(n2o) <- "Emissions|N2O|Land|+|Agriculture (Mt N2O/yr)"
ch4 <- readSource("EDGAR_LU", subtype = "CH4")
getNames(ch4) <- "Emissions|CH4|Land|+|Agriculture (Mt CH4/yr)"
out <- mbind(co2, n2o, ch4)
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = "EDGAR_LU")
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "EDGAR6") {
edgar <- calcOutput("EmissionInventory", datasource = "EDGAR6", targetResolution = NULL, aggregate = FALSE)
# Unit conversion when appropriate (e.g. n2o_n to n2o)
.formatToReporting <- function(x, old, new, conversion) {
x[, , old] <- x[, , old] * conversion
getNames(x, dim = 1)[which(getNames(x, dim = 1) == old)] <- new
return(x)
}
edgar <- .formatToReporting(edgar, "n2o_n", "n2o", (44 / 28))
edgar <- .formatToReporting(edgar, "nh3_n", "nh3", (17 / 14))
edgar <- .formatToReporting(edgar, "no2_n", "no2", (46 / 14))
edgar <- .formatToReporting(edgar, "co2_c", "co2", (44 / 12))
# Rename emissions according to their MAgPIE reporting names
sectorMap <- toolGetMapping(type = "sectoral", name = "mappingEDGAR6toMAgPIE.csv", where = "mappingfolder")
edgar <- toolAggregate(x = edgar, rel = sectorMap,
from = "EDGAR6", to = "MAgPIE_reporting",
dim = 3, partrel = FALSE)
# Format for validation .mif
edgar <- add_dimension(edgar, dim = 3.1, add = "scenario", nm = "historical")
edgar <- add_dimension(edgar, dim = 3.2, add = "model", nm = "EDGAR6")
names(dimnames(edgar))[3] <- "scenario.model.variable"
out <- edgar
} else if (datasource == "FAO_EmisLUC") {
co2 <- readSource("FAO", subtype = "EmisLuTotal")
# / 1000 to convert from Gigagrams to Mt
co2 <- co2[, , "1707|Land Use total + (Total).Net_emissions_removal_(CO2)_(Gigagrams)"] / 1000
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
out <- co2
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = datasource)
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "FAO_EmisAg") {
total <- readSource("FAO_online", subtype = "EmisAgTotal")
mapping <- toolGetMapping(type = "sectoral", name = "FAO_online_emissionsMapping.csv", where = "mappingfolder")
.calculateEmissions <- function(faoName, magpieName) {
emission <- toolAggregate(total[, , faoName],
rel = mapping, from = "fao", to = magpieName,
dim = 3.1, partrel = TRUE)
emission <- emission[, , "", invert = TRUE]
emission <- emission / 1000 # Gg to Mt X
emission <- collapseDim(emission, dim = 3.2)
return(emission)
}
n2o <- .calculateEmissions("N2O_emissions_(gigagrams)", "magpie_n2o")
ch4 <- .calculateEmissions("CH4_emissions_(gigagrams)", "magpie_ch4")
out <- mbind(n2o, ch4)
out <- mbind(add_dimension(out, dim = 3.1, add = "scenario", nm = "historical"),
add_dimension(out, dim = 3.1, add = "scenario", nm = "projection"))
out[, , "projection"] <- NA
out[, , "projection"][, c("y2030", "y2050"), ] <- collapseNames(out[, , "historical"][, c("y2030", "y2050"), ])
out[, , "historical"][, c("y2030", "y2050"), ] <- NA
out <- add_dimension(out, dim = 3.2, add = "model", nm = "FAO_EmisAg")
names(dimnames(out))[3] <- "scenario.model.variable"
} else if (datasource == "GFED") {
gfedEmissions <- readSource("GFED")
countryToCell <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mstools")
ch4 <- gfedEmissions[, , "AGRI.ch4"]
ch4 <- toolAggregate(ch4, rel = countryToCell, from = "celliso", to = "iso")
getNames(ch4) <- "Emissions|CH4|Land|Biomass Burning|+|Burning of Crop Residues (Mt CH4/yr)"
no2 <- gfedEmissions[, , "AGRI.no2"]
no2 <- toolAggregate(no2, rel = countryToCell, from = "celliso", to = "iso")
getNames(no2) <- "Emissions|NO2|Land|Biomass Burning|+|Burning of Crop Residues (Mt NO2/yr)"
n2o <- gfedEmissions[, , "AGRI.n2o"]
n2o <- toolAggregate(n2o, rel = countryToCell, from = "celliso", to = "iso")
getNames(n2o) <- "Emissions|N2O|Land|Biomass Burning|+|Burning of Crop Residues (Mt N2O/yr)"
nh3 <- gfedEmissions[, , "AGRI.nh3"]
nh3 <- toolAggregate(nh3, rel = countryToCell, from = "celliso", to = "iso")
getNames(nh3) <- "Emissions|NH3|Land|Biomass Burning|+|Burning of Crop Residues (Mt NH3/yr)"
out <- mbind(ch4, no2, n2o, nh3)
out <- add_dimension(out, dim = 3.1, add = "scenario", nm = "historical")
out <- add_dimension(out, dim = 3.2, add = "model", nm = "GFED")
names(dimnames(out))[3] <- "scenario.model.variable"
out <- toolCountryFill(out, fill = 0)
} else if (datasource == "PRIMAPhist") {
# The PRIMAP-hist national historical emissions time series (1850-2014)
primap <- readSource("PRIMAPhist", "hist")
n2o <- primap[, , "n2o_n"][, , "CAT4"] / 28 * 44
getNames(n2o) <- "Emissions|N2O|Land|+|Agriculture (Mt N2O/yr)"
ch4 <- primap[, , "ch4"][, , "CAT4"]
getNames(ch4) <- "Emissions|CH4|Land|+|Agriculture (Mt CH4/yr)"
co2 <- primap[, , "co2_c"][, , "CAT4"] / 12 * 44
getNames(co2) <- "Emissions|CO2|Land|+|Agriculture (Mt CO2/yr)"
ag <- mbind(n2o, ch4, co2)
n2o <- primap[, , "n2o_n"][, , "CAT5"] / 28 * 44
getNames(n2o) <- "Emissions|N2O|Land|+|Land-use Change (Mt N2O/yr)"
ch4 <- primap[, , "ch4"][, , "CAT5"]
getNames(ch4) <- "Emissions|CH4|Land|+|Land-use Change (Mt CH4/yr)"
co2 <- primap[, , "co2_c"][, , "CAT5"] / 12 * 44
getNames(co2) <- "Emissions|CO2|Land|+|Land-use Change (Mt CO2/yr)"
luluc <- mbind(n2o, ch4, co2)
out <- mbind(ag, luluc)
namesX <- getNames(out)
names(namesX) <- NULL
getNames(out) <- paste0("historical.PRIMAPhist.", namesX)
getSets(out) <- c("region", "year", "scenario", "model", "variable")
} else {
stop("datasource unknown")
}
return(list(x = out,
weight = NULL,
unit = "Mt",
description = "historic emissions in 1970-2015. NOx is in NO2 equivalents."))
}
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