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| Original file line number | Diff line number | Diff line change |
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| Type: Package | ||
| Package: mrmagpie | ||
| Title: madrat based MAgPIE Input Data Library | ||
| Version: 1.35.7 | ||
| Date: 2023-07-06 | ||
| Version: 1.36.0 | ||
| Date: 2023-08-10 | ||
| Authors@R: c( | ||
| person("Kristine", "Karstens", , "[email protected]", role = c("aut", "cre")), | ||
| person("Jan Philipp", "Dietrich", , "[email protected]", role = "aut"), | ||
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| #' @title calcPeatland2 | ||
| #' @description This function calculates degraded and intact peatland area at cell level. | ||
| #' The function takes degraded and intact peatland area from the Global Peatland Database 2022 (GPD2022) at the | ||
| #' national level and downscales the peatland area to grid cell level using gridded peatland area | ||
| #' from the Global Peatland Map 2.0 (GPM2) | ||
| #' The data has been provided by Alexandra Barthelmes. | ||
| #' | ||
| #' @return magpie object in cellular resolution | ||
| #' @author Florian Humpenoeder | ||
| #' | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' calcOutput("Peatland2", aggregate = FALSE) | ||
| #' } | ||
| #' | ||
| #' @importFrom madrat toolAggregate | ||
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| calcPeatland2 <- function() { | ||
| # Country-level data on intact and degraded peatland from Global Peatland Database for 2022 (GPD2022) | ||
| gpd2022 <- readSource("GPD2022", convert = TRUE) | ||
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| # Global Peatland Map 2.0; peatland location and extent; but no information on status peatlands (intact / degraded) | ||
| gpm2 <- readSource("GPM2", convert = "onlycorrect") | ||
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| # Dissag. GPD2022 from country to cell with GPM2 as weight | ||
| map <- mrcommons::toolGetMappingCoord2Country(pretty = TRUE) | ||
| outCell <- toolAggregate(x = mrcommons::toolIso2CellCountries(gpd2022, cells = "lpjcell"), rel = map, | ||
| weight = gpm2, dim = 1, from = "iso", to = "coords") | ||
| names(dimnames(outCell)) <- c("coords", "t", "d3") | ||
| dimnames(outCell) <- list("x.y.iso" = paste(map$coords, map$iso, sep = "."), "t" = NULL, "d3" = getNames(outCell)) | ||
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| description <- "Intact and degraded peatland area (Mha) by land-use type, based GPD 2022 and GPM2.0" | ||
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| return(list( | ||
| x = outCell, | ||
| weight = NULL, | ||
| unit = "Mha", | ||
| description = description, | ||
| isocountries = FALSE)) | ||
| } |
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| #' @title convertGPD2022 | ||
| #' @description convert GPD2022 | ||
| #' @param x magpie object provided by the read function | ||
| #' @return List of magpie objects with results on iso level, weight, unit and description. | ||
| #' @author Florian Humpenoeder | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' readSource("GPD2022", convert = TRUE) | ||
| #' } | ||
| #' | ||
| convertGPD2022 <- function(x) { | ||
| # convert to Mha | ||
| x <- x / 1000 | ||
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| # create output object for filling | ||
| out <- new.magpie(getCells(x), NULL, c("intact", "crop", "past", "forestry", "peatExtract"), fill = 0) | ||
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| ## Merge deeply drained (DD) and shallow drained (SD) peatlands for composite agriculture variables | ||
| ag <- setNames(dimSums(x[, , c("agricultureDD", "agricultureSD")], dim = 3), "agriculture") | ||
| # use croplandDD and grasslandDD as weight | ||
| w <- x[, , c("croplandDD", "grasslandDD")] | ||
| # assume 0.5 as weight in case of no information | ||
| w[dimSums(w, dim = 3) == 0] <- 0.5 | ||
| # names | ||
| getNames(w) <- c("crop", "past") | ||
| # create the mapping | ||
| from <- c("agriculture", | ||
| "agriculture") | ||
| to <- c("crop", | ||
| "past") | ||
| map <- data.frame(from, to) | ||
| # do the disaggregation into cropland and grassland | ||
| ag2 <- toolAggregate(ag, map, from = "from", to = "to", dim = 3, weight = w) | ||
| # add the disaggregated categories to the existing categories | ||
| out[, , "crop"] <- setNames(x[, , "croplandDD", ], "crop") + ag2[, , "crop"] | ||
| out[, , "past"] <- setNames(x[, , "grasslandDD", ], "past") + ag2[, , "past"] | ||
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| ## Merge deeply drained (DD) and shallow drained (SD) peatlands used for forestry | ||
| out[, , "forestry"] <- dimSums(x[, , c("forestryDD", "forestrySD")], dim = 3) | ||
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| ## peatExtract | ||
| out[, , "peatExtract"] <- x[, , "peatExtract"] | ||
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| ## calculate intact peatland area | ||
| out[, , "intact"] <- collapseNames(x[, , c("peatlandAreaTotal")]) - dimSums(out, dim = 3) | ||
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| # dimnames | ||
| names(dimnames(out)) <- c("iso", "t", "d3") | ||
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| # area cannot be smaller than 0 | ||
| out[out < 0] <- 0 | ||
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| # fill missing countries | ||
| out <- toolCountryFill(out, fill = 0) | ||
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| return(out) | ||
| } |
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| #' @title correctGPM2 | ||
| #' @description correct peatland area | ||
| #' @param x magpie object provided by the read function | ||
| #' @return List of magpie objects with results on cellular level, weight, unit and description. | ||
| #' @author Florian Humpenoeder | ||
| #' @examples | ||
| #' | ||
| #' \dontrun{ | ||
| #' readSource("GPM2", convert="onlycorrect") | ||
| #' } | ||
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| correctGPM2 <- function(x) { | ||
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| x[is.na(x)] <- 0 | ||
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| return(x) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,30 @@ | ||
| #' @title readGPD2022 | ||
| #' @description read x | ||
| #' Data from the Global Peatland Database provided by Alexandra Barthelmes. | ||
| #' The original xls file has been clean-up manually (country names). Turkey had | ||
| #' two identical entries in the original xls file. | ||
| #' Sources: | ||
| #' "Inventory Reports and National Communications UNFCC 2014", | ||
| #' "soil and peatland science", | ||
| #' "European Mires Book" , | ||
| #' "own estimates (incl. GIS data)", | ||
| #' @return List of magpie objects with results on cellular level, weight, unit and description. | ||
| #' @author Florian Humpenoeder | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' readSource("x", convert = "onlycorrect") | ||
| #' } | ||
| #' @importFrom readxl read_xls | ||
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| readGPD2022 <- function() { | ||
| # read-in xls file Global Peatland Database | ||
| x <- read_xls("GPD2022.xls") | ||
| x <- x[x$ISO3 != "NA", ] | ||
| x <- x[, names(x)[-2]] | ||
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| # convert to magclass object | ||
| x <- as.magpie(x, spatial = 1) | ||
| x[is.na(x)] <- 0 | ||
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| return(x) | ||
| } |
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| #' @title readGPM2 | ||
| #' @description read peatland area from GPM2 | ||
| #' @return List of magpie objects with results on cellular level, weight, unit and description. | ||
| #' @author Florian Humpenoeder | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' readSource("GPM2", convert = "onlycorrect") | ||
| #' } | ||
| #' @importFrom magclass as.magpie | ||
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| readGPM2 <- function() { | ||
| # read-in file | ||
| r <- terra::rast("peatMAY22_1x1_mw_RUS30.tif") | ||
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| # 0.5 deg raster object | ||
| r05 <- terra::rast(res = 0.5) | ||
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| # project r to 0.5 deg raster | ||
| r2 <- terra::project(r, r05, method = "sum") | ||
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| # get cell area | ||
| a <- terra::cellSize(r2[[1]], unit = "ha", mask = TRUE) * 1e-6 | ||
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| # get spatial mapping | ||
| map <- mrcommons::toolGetMappingCoord2Country(pretty = TRUE) | ||
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| # transform raster to magpie object | ||
| x <- as.magpie(terra::extract(a, map[c("lon", "lat")])[, -1], spatial = 1) | ||
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| # set dimension names | ||
| dimnames(x) <- list("coords" = map$coords, "t" = NULL, "d3" = NULL) | ||
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| return(x) | ||
| } |
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