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Package: mrcommons | ||
Type: Package | ||
Title: MadRat commons Input Data Library | ||
Version: 1.33.4 | ||
Date: 2023-08-10 | ||
Version: 1.34.0 | ||
Date: 2023-09-06 | ||
Authors@R: c(person("Benjamin Leon", "Bodirsky", email = "[email protected]", role = "aut"), | ||
person("Kristine", "Karstens", role = "aut"), | ||
person("Lavinia", "Baumstark", role = "aut"), | ||
|
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#' @title downloadNitrogenBoundariesGridded | ||
#' @description Download he grid-level regional nitrogen boundary datasets from Schulte-Uebbing et al. (2022) | ||
#' @author Michael S. Crawford | ||
#' | ||
#' @seealso [downloadSource()] | ||
#' @examples \dontrun{ | ||
#' a <- downloadSource("NitrogenBoundariesGridded") | ||
#' } | ||
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downloadNitrogenBoundariesGridded <- function() { | ||
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baseURL <- "https://zenodo.org/record/6395016" | ||
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# download datasets | ||
dataURL <- paste0(baseURL, "/files/Global_critical_N_surpluses_and_N_inputs_and_their_exceedances.7z?download=1") | ||
archiveName <- "critical_N_surpluses_inputs_exceedances.7z" | ||
download.file(dataURL, destfile = archiveName) | ||
system(paste0("7z e ", archiveName)) | ||
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# download readme | ||
readmeURL <- paste0(baseURL, "/files/README.xlsx?download=1") | ||
download.file(readmeURL, "README.xlsx") | ||
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# permissions | ||
files <- list.files(".", all.files = TRUE, full.names = TRUE, recursive = TRUE) | ||
Sys.chmod(paths = files, mode = "664", use_umask = FALSE) | ||
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# nolint start | ||
return(list( | ||
url = baseURL, | ||
title = "Global spatially explicit critical nitrogen surpluses and critical nitrogen inputs, and their exceedances", | ||
doi = "https://doi.org/10.1038/s41586-022-05158-2", | ||
revision = "1.0", | ||
description = "Input datafiles: Contains complete set of input files used in the calculations of global, spatially | ||
explicit critical nitrogen surpluses and critical nitrogen inputs. All input files are output from the IMAGE-GNM model. | ||
For further information on IMAGE-GNM, see: Beusen, A. H. W., Van Beek, L. P. H., Bouwman, A. F., Mogollon, J. M., | ||
& Middelburg, J. J. (2015). Coupling global models for hydrology and nutrient loading to simulate nitrogen and | ||
phosphorus retention in surface water - Description of IMAGE-GNM and analysis of performance. Geoscientific Model | ||
Development, 8(12), 4045:4067. https://doi.org/10.5194/gmd-8-4045-2015. Output datafiles: Selection of output datafiles, | ||
supporting results presented in the paper. For more information, see file README.xlsx.", | ||
unit = "various, see README.xlsx", | ||
release_date = "2022-03-29", | ||
license = "Creative Commons Attribution 4.0 International", | ||
author = list("L.F. Schulte-Uebbing", | ||
"A.H.W. Beusen", | ||
"A.F. Bouwman", | ||
"W. de Vries") | ||
)) | ||
# nolint end | ||
} |
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#' @title readNitrogenBoundariesGridded | ||
#' @description Read the grid-level regional nitrogen boundary datasets from Schulte-Uebbing et al. (2022). | ||
#' For the moment, this only uses the critical N surplus in all agricultural land (arable + intensively | ||
#' managed grassland) in view of all thresholds simultaneously, measured in kg N per ha per yr. | ||
#' @author Michael S. Crawford | ||
#' | ||
#' @return A MAgPIE object with the critical nitrogen surplus in kg N per ha per yr in 2010 | ||
#' | ||
#' @seealso [readSource()] | ||
#' @examples \dontrun{ | ||
#' a <- readSource("NitrogenBoundariesGridded") | ||
#' } | ||
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readNitrogenBoundariesGridded <- function() { | ||
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lon <- seq(-179.750, 179.750, by = 0.50) | ||
lat <- seq(89.750, -89.750, by = -0.50) | ||
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d <- read.table("nsur_crit_mi_all_ph.asc", | ||
col.names = lon, | ||
header = FALSE, skip = 6, check.names = FALSE) | ||
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d <- as.data.frame(d) %>% | ||
dplyr::mutate(lat = lat) %>% | ||
tidyr::pivot_longer(cols = -"lat", names_to = "lon", values_to = "critNitrSurplus") %>% | ||
dplyr::mutate(lon = as.numeric(lon)) | ||
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d$critNitrSurplus <- replace(d$critNitrSurplus, d$critNitrSurplus == -9999, NA) | ||
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# Filter to use MAgPIE's celliso format | ||
mapping <- toolGetMapping(name = "CountryToCellMapping.rds", where = "mrcommons") %>% # nolint: object_usage_linter. | ||
dplyr::select("celliso", "lon", "lat") | ||
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d <- dplyr::inner_join(d, mapping, by = c("lat", "lon")) %>% | ||
dplyr::mutate(year = "y2010") %>% | ||
dplyr::select("celliso", "year", "critNitrSurplus") | ||
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mag <- as.magpie(d, tidy = TRUE, filter = FALSE) | ||
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return(mag) | ||
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} |
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