R/calcLandEmissions.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
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 moinput 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
#' @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,
mapping="mappingCEDS59toMAgPIE.csv",
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[,,"n2o_n"]=ceds2[,,"n2o_n"]*44/28
ceds2[,,"nh3_n"]=ceds2[,,"nh3_n"]*17/14
ceds2[,,"no2_n"]=ceds2[,,"no2_n"]*46/14
ceds2[,,"no3_n"]=ceds2[,,"no3_n"]*62/14
#ceds2[,,"co2_c"]=ceds2[,,"co2_c"]*44/12
map <- c(#co2_c="co2",
#n2o_n="n2o",
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
sector_sum <- dimSums(ceds,dim=3.1)
agri_sum <- dimSums(ceds[,,"Agriculture",pmatch=TRUE],dim=3.1)
burn_sum <- dimSums(ceds[,,"Biomass Burning",pmatch=TRUE],dim=3.1)
# getNames(sector_sum) <- paste0(".",getNames(sector_sum))
sector_sum <- add_dimension(sector_sum,dim=3.1,add="sector",nm="")
getNames(agri_sum) <- paste0("+|Agriculture.",getNames(agri_sum))
getNames(burn_sum) <- paste0("+|Biomass Burning.",getNames(burn_sum))
ceds <- mbind(ceds,agri_sum,burn_sum,sector_sum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","historical.ceds.Emissions|\\2|Land|\\1 (Mt \\2/yr)",getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region","year","scenario","model","variable")
ceds <- ceds[,getYears(ceds)>"y1960",]
out<-ceds
} else if (datasource%in%c("Nsurplus","Nsurplus2")){
out<-calcOutput("EmisNitrogenPast",method=datasource,aggregate = FALSE)
out<-add_columns(out,addnm="n2o_n",dim = 3.2)
out[,,"n2o_n"]<-dimSums(out[,,c("n2o_n_direct","n2o_n_indirect")],dim=3.2)
out<-out[,,c("n2o_n_direct","n2o_n_indirect"),invert=TRUE]
out<-out[,,c("accumulation","n2_n"),invert=TRUE]
out<-add_columns(out,addnm = c("soils","agri"))
out[,,"soils"]<-dimSums(out[,,c("cropland_soils","pasture_soils")],dim=3.1)
out[,,"agri"]<-dimSums(out[,,c("soils","awms")],dim=3.1)
map <- toolGetMapping(type="sectoral",name="mappingIPCCtoMAgPIE.csv")
out<-out[,,getNames(out,dim=1)[getNames(out,dim=1)%in%map[,1]]]
out<-groupAggregate(data=out,dim=3.1,query = map, from="IPCC", to="MAgPIE")
out<-out[,,sort(getNames(out,dim=1))]
#adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out<-out[,,c("n2o_n","nh3_n","no2_n","no3_n")]
out[,,"n2o_n"]=out[,,"n2o_n"]*44/28
out[,,"nh3_n"]=out[,,"nh3_n"]*17/14
out[,,"no2_n"]=out[,,"no2_n"]*46/14
out[,,"no3_n"]=out[,,"no3_n"]*62/14
map <- c(n2o_n="n2o",nh3_n="nh3",no2_n="no2",no3_n="no3")
getNames(out,dim=2) <- map[getNames(out,dim=2)]
getNames(out,dim=2) <- reportingnames(getNames(out,dim=2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","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%in%c("combined_CEDS_PRIMAPhist")){
# WORK IN PROGRESS!!!
inventory<-calcOutput("EmissionInventory",
datasource=datasource,
mapping=NULL,
to="PRIMAPhist",
aggregate = FALSE)
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
inventory<-ceds[,,c("nh3_n","no2_n","no3_n")]
#ceds2[,,"n2o_n"]=ceds2[,,"n2o_n"]*44/28
ceds2[,,"nh3_n"]=ceds2[,,"nh3_n"]*17/14
ceds2[,,"no2_n"]=ceds2[,,"no2_n"]*46/14
ceds2[,,"no3_n"]=ceds2[,,"no3_n"]*62/14
#ceds2[,,"co2_c"]=ceds2[,,"co2_c"]*44/12
map <- c(#co2_c="co2",
#n2o_n="n2o",
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
sector_sum <- dimSums(ceds,dim=3.1)
getNames(sector_sum) <- paste0("Agriculture.",getNames(sector_sum))
ceds <- mbind(ceds,sector_sum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","historical.ceds.Emissions|\\2|\\1 (Mt \\2/yr)",getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region","year","scenario","model","variable")
ceds <- ceds[,getYears(ceds)>"y1960",]
out<-ceds
} else if (datasource=="IPCC") {
out <- calcOutput("EmisNitrogenPast",method=datasource,aggregate = FALSE)
out <- add_columns(out,addnm="n2o_n",dim = 3.2)
out[,,"n2o_n"] <- dimSums(out[,,c("n2o_n_direct","n2o_n_indirect")],dim=3.2)
out <- add_columns(out,addnm = c("soils","agri"))
out[,,"soils"] <- dimSums(out[,,c("inorg_fert","man_crop","resid","som","rice","pasture_soils")],dim=3.1)
out[,,"agri"] <- dimSums(out[,,c("soils","awms")],dim=3.1)
map <- toolGetMapping(type="sectoral",name="mappingIPCCtoMAgPIE.csv")
out <- out[,,getNames(out,dim=1)[getNames(out,dim=1)%in%map[,1]]]
out <- groupAggregate(data=out,dim=3.1,query = map, from=datasource, to="MAgPIE")
out <- out[,,sort(getNames(out,dim=1))]
#adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[,,c("n2o_n","nh3_n","no2_n","no3_n")]
out[,,"n2o_n"] <- out[,,"n2o_n"]*44/28
out[,,"nh3_n"] <- out[,,"nh3_n"]*17/14
out[,,"no2_n"] <- out[,,"no2_n"]*46/14
out[,,"no3_n"] <- out[,,"no3_n"]*62/14
map <- c(n2o_n="n2o",nh3_n="nh3",no2_n="no2",no3_n="no3")
getNames(out,dim=2) <- map[getNames(out,dim=2)]
getNames(out,dim=2) <- reportingnames(getNames(out,dim=2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","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=="FAO_EmisLUC") {
co2 <- readSource("FAO",subtype="EmisLuTotal")
co2 <- co2[,,"1707|Land Use total + (Total).Net_emissions_removal_(CO2)_(Gigagrams)"]/1000 #convert from Gigagrams to Mt
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") {
#The global warming potential (GWP) with a time horizon of 100 years based on the IPCC Fourth Assessment Report (AR4) (2007) is used to convert N2O and CH4 to CO2-eq terms. Consequently, GWP of 25 and 298 were used for CH4 and N2O, respectively.
total <- readSource("FAO",subtype="EmisAgTotal")
mapping=toolMappingFile(type = "sectoral",name = "FAOitems_emissions.csv",readcsv = T)
#n2o
n2o <- toolAggregate(total[,,"Emissions_(CO2eq)_from_N2O_(Gigagrams)"],rel = mapping,from = "fao",to = "magpie_n2o",dim = 3.1,partrel = TRUE)
n2o<-n2o[,,"",invert=T]
n2o<-n2o/1000/298
n2o<-collapseNames(n2o)
#ch4
ch4 <- toolAggregate(total[,,"Emissions_(CO2eq)_from_CH4_(Gigagrams)"],rel = mapping,from = "fao",to = "magpie_ch4",dim = 3.1,partrel = TRUE)
ch4<-ch4[,,"",invert=T]
ch4<-ch4/1000/25
ch4<-collapseNames(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=datasource)
names(dimnames(out))[3] <- "scenario.model.variable"
out[is.na(out)] <- 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)
names_x <- getNames(out)
names(names_x) <- NULL
getNames(out) <- paste0("historical.PRIMAPhist.",names_x)
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/moinput documentation built on June 9, 2020, 12:23 p.m.
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Defines functions 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 moinput 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
#' @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,
mapping="mappingCEDS59toMAgPIE.csv",
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[,,"n2o_n"]=ceds2[,,"n2o_n"]*44/28
ceds2[,,"nh3_n"]=ceds2[,,"nh3_n"]*17/14
ceds2[,,"no2_n"]=ceds2[,,"no2_n"]*46/14
ceds2[,,"no3_n"]=ceds2[,,"no3_n"]*62/14
#ceds2[,,"co2_c"]=ceds2[,,"co2_c"]*44/12
map <- c(#co2_c="co2",
#n2o_n="n2o",
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
sector_sum <- dimSums(ceds,dim=3.1)
agri_sum <- dimSums(ceds[,,"Agriculture",pmatch=TRUE],dim=3.1)
burn_sum <- dimSums(ceds[,,"Biomass Burning",pmatch=TRUE],dim=3.1)
# getNames(sector_sum) <- paste0(".",getNames(sector_sum))
sector_sum <- add_dimension(sector_sum,dim=3.1,add="sector",nm="")
getNames(agri_sum) <- paste0("+|Agriculture.",getNames(agri_sum))
getNames(burn_sum) <- paste0("+|Biomass Burning.",getNames(burn_sum))
ceds <- mbind(ceds,agri_sum,burn_sum,sector_sum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","historical.ceds.Emissions|\\2|Land|\\1 (Mt \\2/yr)",getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region","year","scenario","model","variable")
ceds <- ceds[,getYears(ceds)>"y1960",]
out<-ceds
} else if (datasource%in%c("Nsurplus","Nsurplus2")){
out<-calcOutput("EmisNitrogenPast",method=datasource,aggregate = FALSE)
out<-add_columns(out,addnm="n2o_n",dim = 3.2)
out[,,"n2o_n"]<-dimSums(out[,,c("n2o_n_direct","n2o_n_indirect")],dim=3.2)
out<-out[,,c("n2o_n_direct","n2o_n_indirect"),invert=TRUE]
out<-out[,,c("accumulation","n2_n"),invert=TRUE]
out<-add_columns(out,addnm = c("soils","agri"))
out[,,"soils"]<-dimSums(out[,,c("cropland_soils","pasture_soils")],dim=3.1)
out[,,"agri"]<-dimSums(out[,,c("soils","awms")],dim=3.1)
map <- toolGetMapping(type="sectoral",name="mappingIPCCtoMAgPIE.csv")
out<-out[,,getNames(out,dim=1)[getNames(out,dim=1)%in%map[,1]]]
out<-groupAggregate(data=out,dim=3.1,query = map, from="IPCC", to="MAgPIE")
out<-out[,,sort(getNames(out,dim=1))]
#adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out<-out[,,c("n2o_n","nh3_n","no2_n","no3_n")]
out[,,"n2o_n"]=out[,,"n2o_n"]*44/28
out[,,"nh3_n"]=out[,,"nh3_n"]*17/14
out[,,"no2_n"]=out[,,"no2_n"]*46/14
out[,,"no3_n"]=out[,,"no3_n"]*62/14
map <- c(n2o_n="n2o",nh3_n="nh3",no2_n="no2",no3_n="no3")
getNames(out,dim=2) <- map[getNames(out,dim=2)]
getNames(out,dim=2) <- reportingnames(getNames(out,dim=2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","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%in%c("combined_CEDS_PRIMAPhist")){
# WORK IN PROGRESS!!!
inventory<-calcOutput("EmissionInventory",
datasource=datasource,
mapping=NULL,
to="PRIMAPhist",
aggregate = FALSE)
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
inventory<-ceds[,,c("nh3_n","no2_n","no3_n")]
#ceds2[,,"n2o_n"]=ceds2[,,"n2o_n"]*44/28
ceds2[,,"nh3_n"]=ceds2[,,"nh3_n"]*17/14
ceds2[,,"no2_n"]=ceds2[,,"no2_n"]*46/14
ceds2[,,"no3_n"]=ceds2[,,"no3_n"]*62/14
#ceds2[,,"co2_c"]=ceds2[,,"co2_c"]*44/12
map <- c(#co2_c="co2",
#n2o_n="n2o",
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
sector_sum <- dimSums(ceds,dim=3.1)
getNames(sector_sum) <- paste0("Agriculture.",getNames(sector_sum))
ceds <- mbind(ceds,sector_sum)
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","historical.ceds.Emissions|\\2|\\1 (Mt \\2/yr)",getNames(ceds))
# Add full scenario name
getNames(ceds) <- tmp
getSets(ceds) <- c("region","year","scenario","model","variable")
ceds <- ceds[,getYears(ceds)>"y1960",]
out<-ceds
} else if (datasource=="IPCC") {
out <- calcOutput("EmisNitrogenPast",method=datasource,aggregate = FALSE)
out <- add_columns(out,addnm="n2o_n",dim = 3.2)
out[,,"n2o_n"] <- dimSums(out[,,c("n2o_n_direct","n2o_n_indirect")],dim=3.2)
out <- add_columns(out,addnm = c("soils","agri"))
out[,,"soils"] <- dimSums(out[,,c("inorg_fert","man_crop","resid","som","rice","pasture_soils")],dim=3.1)
out[,,"agri"] <- dimSums(out[,,c("soils","awms")],dim=3.1)
map <- toolGetMapping(type="sectoral",name="mappingIPCCtoMAgPIE.csv")
out <- out[,,getNames(out,dim=1)[getNames(out,dim=1)%in%map[,1]]]
out <- groupAggregate(data=out,dim=3.1,query = map, from=datasource, to="MAgPIE")
out <- out[,,sort(getNames(out,dim=1))]
#adjust units
# add CO2-C, N2O-N, NH3-N and NO2-N (better standard units)
out <- out[,,c("n2o_n","nh3_n","no2_n","no3_n")]
out[,,"n2o_n"] <- out[,,"n2o_n"]*44/28
out[,,"nh3_n"] <- out[,,"nh3_n"]*17/14
out[,,"no2_n"] <- out[,,"no2_n"]*46/14
out[,,"no3_n"] <- out[,,"no3_n"]*62/14
map <- c(n2o_n="n2o",nh3_n="nh3",no2_n="no2",no3_n="no3")
getNames(out,dim=2) <- map[getNames(out,dim=2)]
getNames(out,dim=2) <- reportingnames(getNames(out,dim=2))
# change order, add "Emissions|": Waste.SO2.harm -> Emissions|SO2|Waste|harm
tmp <- gsub("^([^\\.]*)\\.(.*$)","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=="FAO_EmisLUC") {
co2 <- readSource("FAO",subtype="EmisLuTotal")
co2 <- co2[,,"1707|Land Use total + (Total).Net_emissions_removal_(CO2)_(Gigagrams)"]/1000 #convert from Gigagrams to Mt
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") {
#The global warming potential (GWP) with a time horizon of 100 years based on the IPCC Fourth Assessment Report (AR4) (2007) is used to convert N2O and CH4 to CO2-eq terms. Consequently, GWP of 25 and 298 were used for CH4 and N2O, respectively.
total <- readSource("FAO",subtype="EmisAgTotal")
mapping=toolMappingFile(type = "sectoral",name = "FAOitems_emissions.csv",readcsv = T)
#n2o
n2o <- toolAggregate(total[,,"Emissions_(CO2eq)_from_N2O_(Gigagrams)"],rel = mapping,from = "fao",to = "magpie_n2o",dim = 3.1,partrel = TRUE)
n2o<-n2o[,,"",invert=T]
n2o<-n2o/1000/298
n2o<-collapseNames(n2o)
#ch4
ch4 <- toolAggregate(total[,,"Emissions_(CO2eq)_from_CH4_(Gigagrams)"],rel = mapping,from = "fao",to = "magpie_ch4",dim = 3.1,partrel = TRUE)
ch4<-ch4[,,"",invert=T]
ch4<-ch4/1000/25
ch4<-collapseNames(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=datasource)
names(dimnames(out))[3] <- "scenario.model.variable"
out[is.na(out)] <- 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)
names_x <- getNames(out)
names(names_x) <- NULL
getNames(out) <- paste0("historical.PRIMAPhist.",names_x)
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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