Package 'mrmagpie'

Title: madrat based MAgPIE Input Data Library
Description: Provides functions for MAgPIE country and cellular input data generation.
Authors: Kristine Karstens [aut, cre], Jan Philipp Dietrich [aut], David Chen [aut], Michael Windisch [aut], Marcos Alves [aut], Felicitas Beier [aut], Alexandre Köberle [aut], Patrick v. Jeetze [aut], Abhijeet Mishra [aut], Florian Humpenoeder [aut], Pascal Sauer [aut]
Maintainer: Kristine Karstens <[email protected]>
License: LGPL-3 | file LICENSE
Version: 1.52.1
Built: 2024-09-12 16:34:17 UTC
Source: https://github.com/pik-piam/mrmagpie

Help Index

mrmagpie: madrat based MAgPIE Input Data Library

Description

Provides functions for MAgPIE country and cellular input data generation.

Author(s)

Maintainer: Kristine Karstens [email protected]

Authors:

See Also

Useful links:

calcAfforestationMask

Description

Afforestation mask for where afforestation possible

Usage

calcAfforestationMask(subtype, cells = "lpjcell")

Arguments

subtype

afforestation mask sub type
cells

"magpiecell" or "lpjcell"

Value

magpie object in cellular resolution

Author(s)

David Chen, Florian Humpenoeder

Examples

## Not run: 
calcOutput("AfforestationMask", subtype = "noboreal", aggregate = FALSE)

## End(Not run)

calcAgeClassDistribution

Description

This function calculates the share of each age class in secondary forests in each MAgPIE simulation cluster based on Global Forest Age Dataset from Poulter et al. 2019

Usage

calcAgeClassDistribution(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cluster resolution

Author(s)

Abhijeet Mishra, Felicitas Beier

Examples

## Not run: 
calcOutput("AgeClassDistribution", aggregate = FALSE)

## End(Not run)

calcAreaActuallyIrrigated

Description

retrieves irrigated crop area from croparea intialization

Usage

calcAreaActuallyIrrigated(aggregationlevel = "iso", selectyears = "y1995")

Arguments

aggregationlevel

default is iso
selectyears

select years

Value

magpie object with results on cellular or iso country level

Author(s)

Felicitas Beier

Examples

## Not run: 
calcOutput("AreaActuallyIrrigated")

## End(Not run)

calcAreaEquippedForIrrigation

Description

Calculates the area equipped for irrigation based on LU2v2 or Mehta data sets. For LUH2v2, it assumes, that all cropland irrigated in the last 20 years at least once is equipped for irrigation. Mehta et al. (2022) directly report Global Area Equipped for Irrigation for the years 1900-2015

Usage

calcAreaEquippedForIrrigation(
  cellular = FALSE,
  cells = "lpjcell",
  selectyears = "past"
)

Arguments

cellular

if TRUE: 0.5 degree resolution returned
cells

number of cells to be returned: magpiecell (59199), lpjcell (67420)
selectyears

default on "past"

Value

List of magpie objects with results on country/cellular level, weight on country level, unit and description.

Author(s)

Benjamin Leon Bodirsky, Kristine Karstens, Felicitas Beier

See Also

[calcLanduseInitialisation()]

Examples

## Not run: 
calcOutput("AreaEquippedForIrrigation", source = "LUH2v2", cellular = TRUE, aggregate = FALSE)

## End(Not run)

calcAvlLandSi

Description

Extracts si0 and nsi0 areas based on Ramankutty dataset

Usage

calcAvlLandSi(cells = "lpjcell")

Arguments

cells

magpiecell (59199 cells) or lpjcell (67420 cells)

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier

Examples

## Not run: 
calcOutput("AvlLandSi", aggregate = FALSE)

## End(Not run)

calcRangeSoilCarbonHist

Description

calculates soil carbon for rangelands

Usage

calcBinnedLsuDensity(
  breaks = c(seq(0, 2, 0.1), 2.25, 2.5),
  labels = c(0, 0.2, 0.2, 0.4, 0.4, 0.6, 0.6, 0.8, 0.8, 1, 1, 1.2, 1.2, 1.4, 1.4, 1.6,
    1.6, 1.8, 1.8, 2, 2, 2.5),
  years = 1995
)

Arguments

breaks

Binning breaks
labels

Binning labels
years

years where data should binned

Value

Magpie object with lsu per cell.

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("BinnedLsuDensity ", breaks, labels, years)

## End(Not run)

calcBphEffect

Description

Biogeophysical temperature change of afforestation (degree C). File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018

Usage

calcBphEffect(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Michael Windisch, Felicitas Beier

Examples

## Not run: 
calcOutput("BphEffect", aggregate = FALSE)

## End(Not run)

calcBphMask

Description

Mask of Datapoints of biogeophysical temperature change of afforestation (degree C) to be used as weight. File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018

Usage

calcBphMask(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Michael Windisch, Felicitas Beier

Examples

## Not run: 
calcOutput("BphMask", aggregate = FALSE)

## End(Not run)

calcBphTCRE

Description

Transient Climate Response to accumulated doubling of CO2. File based on CMIP5 +1perc CO2 per year experiment. To be used in the translation to carbon equivalents of BphEffect

Usage

calcBphTCRE(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Michael Windisch, Felicitas Beier

Examples

## Not run: 
calcOutput("BphTCRE", aggregate = FALSE)

## End(Not run)

calcCarbon

Description

This function extracts carbon densities from LPJ to MAgPIE

Usage

calcCarbon(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  cells = "lpjcell"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different GCM climate scenarios
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

magpie object in cellular resolution

Author(s)

Kristine Karstens, Patrick v. Jeetze

Examples

## Not run: 
calcOutput("Carbon", aggregate = FALSE)

## End(Not run)

calcCarbonTests

Description

This function extracts carbon densities from LPJ to MAgPIE

Usage

calcCarbonTests(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  stage = "raw"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different GCM climate scenarios
stage

Switch for raw data or harmonization

Value

magpie object in cellular resolution

Author(s)

Kristine Karstens, Florian Humpenoeder

Examples

## Not run: 
calcOutput("CarbonTests", aggregate = FALSE)

## End(Not run)

calcCellCountryFraction

Description

cell fraction belonging to a country based on LanduseInitialisation

Usage

calcCellCountryFraction(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

Clustered MAgPIE object on requested resolution

Author(s)

Florian Humpenoeder

Examples

## Not run: 
calcOutput("calcCellCountryFraction", aggregate = FALSE)

## End(Not run)

calcCluster

Description

This function calculates the aggregation mapping for a given cluster methodology

Usage

calcCluster(
  ctype,
  regionscode = madrat::regionscode(),
  seed = 42,
  weight = NULL,
  lpjml = c(natveg = "LPJmL4", crop = "LPJmL5"),
  clusterdata = "yield_airrig"
)

Arguments

ctype

aggregation clustering type, which is a combination of a single letter, indicating the cluster methodology, and a number, indicating the number of resulting clusters. Available methodologies are hierarchical clustering (h), normalized k-means clustering (n), combined hierarchical/normalized k-means clustering (c) and manual setting for clusters per region (m). In the combined clustering hierarchical clustering is used to determine the cluster distribution among regions whereasit is manually set for the m type. Both use normalized k-means for the clustering within a region.
regionscode

regionscode of the regional mapping to be used. Must agree with the regionscode of the mapping mentioned in the madrat config! Can be retrieved via regionscode().
seed

Seed for Random Number Generation. If set to NULL it is chosen automatically, if set to an integer it will always return the same pseudo-random numbers (useful to get identical clusters under identical inputs for n and c clustering)
weight

Should specific regions be resolved with more or less detail? Values > 1 mean higher share, < 1 lower share e.g. cfg$cluster_weight <- c(LAM=2) means that a higher level of detail for region LAM if set to NULL all weights will be assumed to be 1 (examples: c(LAM=1.5,SSA=1.5,OAS=1.5), c(LAM=2,SSA=2,OAS=2))
lpjml

defines LPJmL version for crop/grass and natveg specific inputs
clusterdata

similarity data to be used to determine clusters: yield_airrig (current default) or yield_increment

Value

map from cells to clusters as data.frame

Author(s)

Jan Philipp Dietrich

Examples

## Not run: 
calcOutput("Cluster", ctype = "c200", aggregate = FALSE)

## End(Not run)

calcClusterBase

Description

Reads a series of MAgPIE files and combines them to a matrix which is then used for calculating a clustering.

Usage

calcClusterBase(
  clusterdata = "yield_airrig",
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb")
)

Arguments

clusterdata

similarity data to be used to determine clusters: yield_airrig (current default) or yield_increment
lpjml

defines LPJmL version for crop/grass and natveg specific inputs

Value

A matrix containing the data

Author(s)

Jan Philipp Dietrich, Felicitas Beier

See Also

calcCluster

calcClusterHierarchical

Description

Performs MAgPIE hierarchical clustering and calculates corresponding spam relation matrix

As the creation of a clustering tree is very time consuming the function checks first in the input folder if the corresponding data already exists and if not it stores the tree information in the input folder for later use in the next execution of this function.

Usage

calcClusterHierarchical(
  regionscode,
  ncluster,
  lpjml = c(natveg = "LPJmL4", crop = "LPJmL5"),
  clusterdata = "yield_airrig",
  mode = "h",
  weight = NULL
)

Arguments

regionscode

regionscode of the regional mapping to be used. Must agree with the regionscode of the mapping mentioned in the madrat config! Can be retrieved via regionscode().
ncluster

The desired total number of clusters.
lpjml

defines LPJmL version for crop/grass and natveg specific inputs
clusterdata

similarity data to be used to determine clusters: yield_airrig (current default) or yield_increment
mode

Clustering type. At the moment you can choose between complete linkage clustering (h), single linkage clustering (s) and Ward clustering (w).
weight

named vector with weighting factors for each region for the cluster distribution, e.g. weight=c(AFR=3,EUR=0.5). weight > 1 will grant more cluster to a region and weight < 1 less cluster than by default.

Value

A mapping between regions and clusters

Author(s)

Jan Philipp Dietrich

See Also

calcCluster, calcClusterKMeans

calcClusterKMeans

Description

Performs MAgPIE kmeans clustering and calculates corresponding spam relation matrix

Usage

calcClusterKMeans(
  regionscode,
  ncluster,
  weight = NULL,
  cpr = NULL,
  seed = 42,
  lpjml = c(natveg = "LPJmL4", crop = "LPJmL5"),
  clusterdata = "yield_airrig"
)

Arguments

regionscode

regionscode of the regional mapping to be used. Must agree with the regionscode of the mapping mentioned in the madrat config! Can be retrieved via regionscode().
ncluster

The desired total number of clusters.
weight

named vector with weighting factors for each region for the cluster distribution, e.g. weight=c(AFR=3,EUR=0.5). weight > 1 will grant more cluster to a region and weight < 1 less cluster than by default.
cpr

cells-per-region information as returned by toolClusterPerRegionManual. Weight and ncluster are ignored in case that cpr is provided!
seed

a single value, interpreted as an integer, or NULL, to define seed for random calculations
lpjml

defines LPJmL version for crop/grass and natveg specific inputs
clusterdata

similarity data to be used to determine clusters: yield_airrig (current default) or yield_increment

Value

A mapping between regions and clusters

Author(s)

Jan Philipp Dietrich

See Also

toolClusterPerRegionManual, calcClusterHierarchical

calcClusterTreeHierarchical

Description

calculates hierarchical clustering tree

Usage

calcClusterTreeHierarchical(
  regionscode,
  mode = "h",
  weight = NULL,
  lpjml = c(natveg = "LPJmL4", crop = "LPJmL5"),
  clusterdata = "yield_airrig"
)

Arguments

regionscode

regionscode of the regional mapping to be used. Must agree with the regionscode of the mapping mentioned in the madrat config! Can be retrieved via regionscode().
mode

Clustering type. At the moment you can choose between complete linkage clustering (h), single linkage clustering (s) and Ward clustering (w).
weight

named vector with weighting factors for each region for the cluster distribution, e.g. weight = c(AFR = 3, EUR = 0.5). weight > 1 will grant more cluster to a region and weight < 1 less cluster than by default.
lpjml

defines LPJmL version for crop/grass and natveg specific inputs
clusterdata

similarity data to be used to determine clusters: yield_airrig (current default) or yield_increment

Value

A spam relation matrix

Author(s)

Jan Philipp Dietrich

calcCO2Atmosphere_new

Description

Disaggregate CO2 global atmospheric concentration to cellular level

Usage

calcCO2Atmosphere_new(
  subtype = "ISIMIP3b:ssp126",
  co2Evolution = "rising",
  cells = "lpjcell"
)

Arguments

subtype

specify the version and scenario eg. "ISIMIP3b:ssp126"
co2Evolution

Define 'rising' for rising CO2 according to the climate scenario selected or 'static' for stable CO2 at the last past time step level.
cells

"magpiecell" or "lpjcell"

Value

magpie object in cellular resolution

Author(s)

Marcos Alves, Kristine Karstens

Examples

## Not run: 
calcOutput("CO2Atmosphere_new", aggregate = FALSE, subtype, co2Evolution)

## End(Not run)

calcCollectEnvironmentData_new

Description

Calculate climate, CO2 and soil environmental conditions on cellular level

Usage

calcCollectEnvironmentData_new(
  subtype = "ISIMIP3b:IPSL-CM6A-LR:ssp126:1965-2100",
  sar = 20,
  sel_feat = c("tas", "pr", "lwnet", "rsds", "CO2", "Ks", "Sf", "w_pwp", "w_fc", "w_sat",
    "hsg", "wet")
)

Arguments

subtype

Switch between different climate scenarios (default: "CRU_4") eg. "ISIMIP3b:IPSL-CM6A-LR:ssp126:1965-2100"
sar

Average range for smoothing annual variations
sel_feat

features names to be included in the output file

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("CollectEnvironmentData_new", subtype, sar = 20, sel_feat = "temp")

## End(Not run)

calcCollectSoilCarbonLSU

Description

Calculate soil carbon stocks for different LSU and climate conditions

Usage

calcCollectSoilCarbonLSU(
  lsu_levels = c(seq(0, 2, 0.2), 2.5),
  lpjml = "LPJML5.2_pasture",
  climatemodel = "IPSL_CM6A_LR",
  scenario = "ssp126_co2_limN",
  sar = 20
)

Arguments

lsu_levels

Livestock unit levels in the source folder
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatemodel

Switch between different climate scenarios
scenario

scenario specifications (eg. ssp126_co2_limN)
sar

Average range for smoothing annual variations

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("CollectSoilCarbonLSU", lsu_levels = c(seq(0, 2, 0.2), 2.5), scenario)

## End(Not run)

calcCollectSoilCarbonPastr

Description

calculates soil carbon content for pasture areas

Usage

calcCollectSoilCarbonPastr(
  past_mngmt = "me2",
  lpjml = "lpjml5p2_pasture",
  climatemodel = "IPSL_CM6A_LR",
  scenario = "ssp126_co2_limN",
  sar = 1
)

Arguments

past_mngmt

pasture areas management option
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatemodel

Switch between different climate scenarios (default: "CRU_4")
scenario

scenario specifications (eg. ssp126_co2_limN)
sar

Average range for smoothing annual variations

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("CollectSoilCarbonPastr", past_mngmt = "me2")

## End(Not run)

calcDegradationYieldReduction

Description

Function creates dummy file for including yield reduction coefficients to represent land degradation

Usage

calcDegradationYieldReduction(cells = "lpjcell")

Arguments

cells

number of halfdegree grid cells to be returned. Options: "magpiecell" (59199), "lpjcell" (67420)

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
calcOutput("DegradationYieldReduction", aggregate = FALSE)

## End(Not run)

calcEFRRockstroem

Description

This function calculates environmental flow requirements (EFR) for MAgPIE retrieved from LPJmL monthly discharge and water availability following the definition of the planetary boundary in Rockström et al. 2023

Usage

calcEFRRockstroem(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  stage = "harmonized2020",
  seasonality = "grper"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios
stage

Degree of processing: raw, smoothed, harmonized, harmonized2020
seasonality

grper (default): EFR in growing period per year; total: EFR throughout the year; monthly: monthly EFRs

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier, Jens Heinke

Examples

## Not run: 
calcOutput("EFRRockstroem", aggregate = FALSE)

## End(Not run)

calcEFRSmakthin

Description

This function calculates environmental flow requirements (EFR) for MAgPIE retrieved from LPJmL monthly discharge and water availability using the method of Smakthin et al. (2004)

Usage

calcEFRSmakthin(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  stage = "harmonized2020",
  LFR_val = 0.1,
  HFR_LFR_less10 = 0.2,
  HFR_LFR_10_20 = 0.15,
  HFR_LFR_20_30 = 0.07,
  HFR_LFR_more30 = 0,
  seasonality = "grper",
  cells = "lpjcell"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios
stage

Degree of processing: raw, smoothed, harmonized, harmonized2020
LFR_val

Strictness of environmental flow requirements
HFR_LFR_less10

High flow requirements (share of total water for cells) with LFR<10percent of total water
HFR_LFR_10_20

High flow requirements (share of total water for cells) with 10percent < LFR < 20percent of total water
HFR_LFR_20_30

High flow requirements (share of total water for cells) with 20percent < LFR < 30percent of total water
HFR_LFR_more30

High flow requirements (share of total water for cells) with LFR>30percent of total water
seasonality

grper (default): EFR in growing period per year; total: EFR throughout the year; monthly: monthly EFRs
cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier, Abhijeet Mishra

Examples

## Not run: 
calcOutput("EFRSmakthin", aggregate = FALSE)

## End(Not run)

calcEnvmtlFlow

Description

This function calculates environmental flow requirements (EFR) for MAgPIE retrieved from LPJmL monthly discharge and water availability

Usage

calcEnvmtlFlow(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  stage = "harmonized2020",
  seasonality = "grper"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios
stage

Degree of processing: raw, smoothed, harmonized, harmonized2020
seasonality

grper (default): EFR in growing period per year; total: EFR throughout the year; monthly: monthly EFRs

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier

Examples

## Not run: 
calcOutput("EnvmtlFlow", aggregate = FALSE)

## End(Not run)

calcFoodDemandGridded

Description

Calculates grid-level food demand, note also includes food and feed

Usage

calcFoodDemandGridded(
  attribute = "dm",
  prod = "k",
  feed = TRUE,
  cells = "lpjcell"
)

Arguments

attribute

dm or calories ("ge") or other massbalance attribute
prod

for memory reasons
feed

whether to include feed demand in the gridded demand
cells

magpiecell or lpjcell (default)

Value

Gridded magpie object of food demand disaggregated by rural urban pop

Author(s)

David M Chen

Examples

## Not run: 
calcOutput("FoodDemandGridded")

## End(Not run)

calcGCMClimate

Description

Disaggregate CO2 global atmospheric concentration to cellular level NOTE: This function will be depreciate soon, please use mrland::calcLPJmLClimate

Usage

calcGCMClimate(
  subtype = "ISIMIP3bv2:IPSL-CM6A-LR:ssp126:1850-2100:tas:annual_mean",
  smooth = 0,
  cells = "lpjcell"
)

Arguments

subtype

type of climate data to collect, consisting of data source, GDM, RCP, time period, variable and time resolution separated by ":"
smooth

set averaging value for smoothing trajectories
cells

number of halfdegree grid cells to be returned. Options: "magpiecell" (59199), "lpjcell" (67420)

Value

magpie object in cellular resolution

Author(s)

Marcos Alves, Kristine Karstens, Felicitas Beier

Examples

## Not run: 
calcOutput("GCMClimate", subtype = "ISIMIP3b:IPSL-CM6A-LR:ssp126:1850-2100:tas:annual_mean")

## End(Not run)

calcGrasslandBiomass

Description

Calculates pasture biomass demand for the historical period split between rangelands andmanaged pastures.

Usage

calcGrasslandBiomass(cells = "lpjcell")

Arguments

cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

Regional biomass demand

Author(s)

Marcos Alves

See Also

calcOutput, calcFAOmassbalance, readSource

Examples

## Not run: 
calcOutput("GrasslandBiomass")

## End(Not run)

calcGrasslandsYields

Description

Calculates rangelands maximum output and managed pastures yields

Usage

calcGrasslandsYields(
  lpjml = "lpjml5p2_pasture",
  climatetype = "MRI-ESM2-0:ssp370",
  cells = "lpjcell",
  subtype = "/co2/Nreturn0p5",
  lsu_levels = c(seq(0, 2, 0.2), 2.5),
  past_mngmt = "mdef"
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Global Circulation Model to be used
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells
subtype

Switch between different climate scenarios
lsu_levels

Livestock unit levels in the source folder
past_mngmt

pasture areas management option

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("GrasslandsYields", lsu_levels, past_mngmt = "me2", subtype)

## End(Not run)

calcGrassPastureShare

Description

Calculate glassland shareas os pasture managed lands.

Usage

calcGrassPastureShare()

Value

List of magpie object with results on cluster level

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("GrassPastureShare")

## End(Not run)

calcGrassSoilEmu

Description

Read files related to the training and optimization of the LPJml emulators.

Usage

calcGrassSoilEmu(
  subtype = "ISIMIP3b:IPSL_CM6A_LR:ssp126:1965_2100",
  model = "5f5fa2",
  mfile = "weights"
)

Arguments

subtype

Subtype of file to be opened. Subtypes available: 'weights', 'inputs', 'stddevs' and 'means'.
model

trained model ID
mfile

model file name

Value

Magpie objects with a diverse inforamtion

Author(s)

Marcos Alves

Examples

## Not run: 
readSource("GrassSoilEmu",
  subtype = "ISIMIP3b:IPSL_CM6A_LR:ssp126:1965_2100",
  model = "5f5fa2", mfile = "weights"
)

## End(Not run)

calcGridPop

Description

Past and future (SSP1-5) population based on HYDE3.2 and Jones & O'Neill (2016) Data is scaled to match WDI data from calcPopulation NOTE that some scaling factors for the future (for small countries Gambia and Djibouti) are off, data read in is 50

Usage

calcGridPop(
  source = "ISIMIP",
  subtype = "all",
  cellular = TRUE,
  cells = "lpjcell",
  FiveYear = TRUE,
  scale = TRUE,
  harmonize_until = 2015,
  urban = FALSE
)

Arguments

source

default source (ISIMIP) or Gao data (readGridPopGao) which is split into urban and rural.
subtype

time horizon to be returned. Options: past (1965-2005), future (2005-2010) or all (divergence starts at year in harmonize_until)
cellular

if true: half degree grid cell data returned
cells

number of halfdegree grid cells to be returned. Options: "magpiecell" (59199), "lpjcell" (67420)
FiveYear

TRUE for 5 year time steps, otherwise yearly from source
scale

if true: scales sum of gridded values to match country level totals
harmonize_until

harmonization year until which SSPs diverge (default: 2015)
urban

TRUE to return only urban gridded population based on iso share

Value

Population in millions.

Author(s)

David Chen, Felicitas Beier

Examples

## Not run: 
calcOutput("GridPop", aggregate = FALSE)

## End(Not run)

calcInitialLsu

Description

Loads the LSU that provides the maximum grass harvest as a initial values for MAgPIE

Usage

calcInitialLsu(model = "f41f19be67")

Arguments

model

Grass harvest machine learning model ID

Value

MAgPIE objects with optimal lsu on a cellular level.

Author(s)

Marcos Alves

Examples

## Not run: 
calOutput("InitialLsu", model = "f41f19be67")

## End(Not run)

calcIrrigation

Description

This function extracts irrigation water (airrig: water applied additionally to rainfall) from LPJmL for MAgPIE

Usage

calcIrrigation(
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical",
  cells = "lpjcell",
  rainfedweight = 0.01
)

Arguments

lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios
cells

Number of cells to be returned: "magpiecell" for 59199 cells or "lpjcell" for 67420 cells
rainfedweight

For clustering airrig is weighted with cropland_irrigated + rainfedweight * cropland_rainfed (default: 0.01)

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier, Abhijeet Mishra

Examples

## Not run: 
calcOutput("Irrigation", aggregate = FALSE)

## End(Not run)

calcLabourProdImpact

Description

Labour productivity impacts

Usage

calcLabourProdImpact(
  timestep = "5year",
  subtype = "Orlov",
  cellular = TRUE,
  cells = "lpjcell"
)

Arguments

timestep

5year or yearly
subtype

data source comes from
cellular

cellular is true
cells

"magpiecell" or "lpjcell"

Value

List of magpie objects with results on 0.5deg grid level, weights based on production value, unit (ratio) and description.

Author(s)

David Chen

calcLabourProdImpactEmu

Description

Spatial and temporal aggr. of labour productivity impacts from climate change emulated by LAMACLIMA

based on method of Orlov et al. 2019. Economics of Disasters and Climate Change, 3(3), 191-211.

Usage

calcLabourProdImpactEmu(
  timestep = "5year",
  cellular = TRUE,
  subtype = "impact",
  cells = "lpjcell"
)

Arguments

timestep

5-year or yearly
cellular

cellular is true
subtype

impact for rcp based laborprod decrease, relief for LCLM based relief of impact
cells

"magpiecell" or "lpjcell"

Value

List of magpie object of gridded (0.5) labour productivity as percentage of full labour prod 1

Author(s)

Michael Windisch, Florian Humpenöder

calcLivestockDistribution

Description

Disaggregate Livestock estimates based on the GLW3 dataset.

Usage

calcLivestockDistribution(cells = "lpjcell")

Arguments

cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

MAgPIE objects with livestock numbers on a cellular level.

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("LivestockDistribution")

## End(Not run)

calcLsuDensityHist

Description

Calculate livestock historical livestock densities

Usage

calcLsuDensityHist(disagg_type = "grassland", cells = "lpjcell")

Arguments

disagg_type

select the disaggregaton weights for biomass production (can be either grassland or livestock)
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

List of magpie object with results on cluster level

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("LsuDensityHist")

## End(Not run)

calcLuh2SideLayers

Description

Function extracts biodiversity data for LUH2 land cover types

Usage

calcLuh2SideLayers(cells = "lpjcell")

Arguments

cells

number of cells to be returned: magpiecell (59199), lpjcell (67420)

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
calcOutput("Luh2SideLayers", aggregate = FALSE)

## End(Not run)

calcMAPSPAM

Description

MAPSPAM data

Usage

calcMAPSPAM(subtype = "physical")

Arguments

subtype

it can be either "physical" or "harvested" area

Value

magpie object in cellular resolution

Author(s)

Edna J. Molina Bacca

Examples

## Not run: 
calcOutput("MAPSPAM", subtype = "physical", aggregate = FALSE)

## End(Not run)

calcMaxPastureSuit

Description

Calculate maximum glassland suitable for pasture management based on population and aridity criteria.

Usage

calcMaxPastureSuit(
  climatetype = "MRI-ESM2-0:ssp126",
  lpjml = "LPJmL4_for_MAgPIE_44ac93de",
  cells = "lpjcell"
)

Arguments

climatetype

Switch between different climate scenarios
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
cells

number of halfdegree grid cells to be returned. Options: "magpiecell" (59199), "lpjcell" (67420)

Value

List of magpie object with results on cluster level

Author(s)

Marcos Alves, Kristine Karstens, Alexandre Köberle

Examples

## Not run: 
calcOutput("MaxPastureSuit")

## End(Not run)

calcNonLocalProduction

Description

Calculates grid-level amount of food that would need to be transported, assuming that food produced in the grid cell is first consumed by local population i.e. amount of food greater than local rural demand, split into that which feeds the local urban population, and that which exceeds total local demand and is available to export

Usage

calcNonLocalProduction(cells = "lpjcell")

Arguments

cells

magpiecell or lpjcell (default)

Author(s)

David M Chen

Examples

## Not run: 
calcOutput("NonLocalTransport")

## End(Not run)

calcNpiNdcAdAolcPol

Description

Function creates dummy NPI/NDC policies

Usage

calcNpiNdcAdAolcPol(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze, Michael Windisch

Examples

## Not run: 
calcOutput("NpiNdcAdAolcPol", aggregate = FALSE)

## End(Not run)

calcNpiNdcAffPol

Description

Function creates dummy NPI/NDC policies

Usage

calcNpiNdcAffPol(cells = "lpjcell")

Arguments

cells

lpjcell for 67420 cells or magpiecell for 59199 cells

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze, Michael Windisch

Examples

## Not run: 
calcOutput("NpiNdcAffPol", aggregate = FALSE)

## End(Not run)

calcPackagingMarketingCosts

Description

calculates per-ton marketing and packaging costs for food that leaves a cell Currnetly assume expert guess 50 USD / ton of packaging/marketing costs (100 USD/t in model, of which half is already in GTAP)

Usage

calcPackagingMarketingCosts()

Value

List of magpie objects with results on country level, weight on country level, unit and description.

Author(s)

David M Chen

calcPastr_new

Description

Calculates managed pasture yields

Usage

calcPastr_new(
  past_mngmt = "me2",
  lpjml = "lpjml5p2_pasture",
  climatetype = "MRI-ESM2-0:ssp370",
  scenario = "/co2/Nreturn0p5/limN",
  cells = "lpjcell"
)

Arguments

past_mngmt

pasture areas management option
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios (default: "CRU_4")
scenario

specify ssp scenario
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("Pastr_new", past_mngmt = "me2", lpjml = "LPJml_pastr", climatetype)

## End(Not run)

calcPastrMngtLevels

Description

Calculates managed pasture potential yields for different combinations of SSP+RCP and grassland management options

Usage

calcPastrMngtLevels(
  climatetype = "MRI-ESM2-0:ssp370",
  options = c("brazil_1", "brazil_2", "brazil_4"),
  cost_level = c(1, 2, 3),
  cells = "lpjcell"
)

Arguments

climatetype

SSP+RCP combination
options

Management options simulated by LPJml
cost_level

level cost for different past management options
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

magpie object in 0.5 degree resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("PastrMngtLevels", ssps, options)

## End(Not run)

calcPastrTauHist

Description

Calculates managed pastures Tau based on FAO yield trends for 1995.

Usage

calcPastrTauHist(past_mngmt = "mdef", cells = "lpjcell")

Arguments

past_mngmt

Pasture management reference yield
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

List of magpie objects with results on country level, weight on country level, unit and description.

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("PastrTauHist", past_mngmt)

## End(Not run)

calcPeatland

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 (GPD) at the national level and downscales the peatland area to grid cell level using gridded potential peatland area. The GPD has been provided by Alexandra Barthelmes. The potential peatland area has been provided by Leifeld_2018 (DOI 10.1038/s41467-018-03406-6).

Usage

calcPeatland(subtype = "degraded", cells = "lpjcell")

Arguments

subtype

degraded (default) or intact
cells

"magpiecell" or "lpjcell"

Value

magpie object in cellular resolution

Author(s)

Florian Humpenoeder

Examples

## Not run: 
calcOutput("Peatland", aggregate = FALSE)

## End(Not run)

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.

Usage

calcPeatland2(cells = "magpiecell", countryLevel = FALSE)

Arguments

cells

number of cells to be returned: magpiecell (59199), lpjcell (67420)
countryLevel

Whether output shall be at country level. Requires aggregate=FALSE in calcOutput.

Value

magpie object in cellular resolution

Author(s)

Florian Humpenoeder

Examples

## Not run: 
calcOutput("Peatland2", aggregate = FALSE)

## End(Not run)

calcPotentialForestArea

Description

Calculates the area than can be potentially covered by forests, based on environmental conditions.

Usage

calcPotentialForestArea(
  refData = "lpj",
  countryLevel = FALSE,
  cells = "lpjcell",
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de"),
  climatetype = "MRI-ESM2-0:ssp370"
)

Arguments

refData

Determines the reference data that the estimated potential forest area is derived from (currently only "lpj")
countryLevel

Whether output shall be at country level. Requires aggregate=FALSE in calcOutput.
cells

magpiecell (59199 cells) or lpjcell (67420 cells)
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs. Only relevant, if refData = "lpj".
climatetype

Switch between different GCM climate scenarios. Only relevant, if refData = "lpj".

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
calcOutput("PotentialForestArea", aggregate = FALSE)

## End(Not run)

calcRangelandsMaxNew

Description

Calculates rangelands maximum output

Usage

calcRangelandsMaxNew(
  lsuLevels = c(seq(0, 2.2, 0.2), 2.5),
  lpjml = "lpjml5p2_pasture",
  climatetype = "MRI-ESM2-0:ssp370",
  scenario = "/co2/Nreturn0p5/limN",
  report = "harvest",
  cells = "lpjcell"
)

Arguments

lsuLevels

Livestock unit levels in the source folder
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios (default: "CRU_4")
scenario

specify ssp scenario
report

Either 'harvest' or 'lsu/ha' controlling what values are output by the function.
cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("ContGrazMax_new", lsuLevels = 0, lpjml, climatetype, report)

## End(Not run)

calcRangeSoilCarbonHist

Description

calculates soil carbon for rangelands

Usage

calcRangeSoilCarbonHist(
  subtype = "ISIMIP3b:IPSL-CM6A-LR:ssp126:1965-2100",
  lpjml,
  model = "9eaf9b"
)

Arguments

subtype

subtypes
lpjml

lpjml version
model

trained model ID

Value

List of magpie objects with results on country level, weight on country level, unit and description.

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("GrassSoilCarbonHist ", subtype, model)

## End(Not run)

calcRRLayer

Description

Function extracts range-rarity as used for biodiversity loss

Usage

calcRRLayer(cells = "lpjcell")

Arguments

cells

number of cells to be returned: magpiecell (59199), lpjcell (67420)

Value

magpie object in cellular resolution

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
calcOutput("RRLayer", aggregate = FALSE)

## End(Not run)

calcScaledPastSoilCarbon

Description

calculates the mean and sd of the scaled pasture soil carbon dataset

Usage

calcScaledPastSoilCarbon(
  lsu_levels = c(seq(0, 2, 0.2), 2.5),
  lpjml = "LPJML5.2_pasture",
  climatetype = "IPSL_CM6A_LR",
  scenario = "ssp126_co2_limN",
  sar = 20,
  aggr = FALSE
)

Arguments

lsu_levels

Livestock unit levels in the source folder
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios (default: "CRU_4")
scenario

scenario specifications (eg. ssp126_co2_limN)
sar

Average range for smoothing annual variations
aggr

aggregation level

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("ScaledPastSoilCarbon", lsu_levels = c(seq(0, 2, 0.2), 2.5), scenario)

## End(Not run)

calcScaleEnvironmentData_new

Description

Scale climate, CO2 and soil environmental conditions on cellular level

Usage

calcScaleEnvironmentData_new(
  subtype = "ISIMIP3b:IPSL-CM6A-LR:ssp126:1965-2100",
  aggr = FALSE,
  sar = 20,
  sel_feat = c("tas", "pr", "lwnet", "rsds", "CO2", "Ks", "Sf", "w_pwp", "w_fc", "w_sat",
    "hsg")
)

Arguments

subtype

Switch between different climate scenarios
aggr

aggregation level
sar

Average range for smoothing annual variations
sel_feat

features names to be included in the output file

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("ScaleEnvironmentData_new", climatetype = "HadGEM2_ES:rcp8p5:co2", sar = 20, sel_feat)

## End(Not run)

calcSCScalingFactors

Description

calculates the mean and sd of the scaled pasture soil carbon dataset

Usage

calcSCScalingFactors(
  lsu_levels = c(seq(0, 2, 0.2), 2.5),
  lpjml = "LPJML5.2_pasture",
  climatetype = "IPSL_CM6A_LR",
  scenario = "ssp126_co2_limN",
  sar = 20
)

Arguments

lsu_levels

Livestock unit levels in the source folder
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different climate scenarios (default: "CRU_4")
scenario

scenario specifications (eg. ssp126_co2_limN)
sar

Average range for smoothing annual variations

Value

magpie object in cellular resolution

Author(s)

Marcos Alves

Examples

## Not run: 
calcOutput("SCScalingFactors", lsu_levels = c(seq(0, 2, 0.2), 2.5), scenario)

## End(Not run)

calcSoilCharacteristics

Description

Calculate Soil Characteristics based on a HWDS soil classification map

Usage

calcSoilCharacteristics()

Value

Magpie objects with results on cellular level.

Author(s)

Marcos Alves

See Also

readSoilClassification,

Examples

## Not run: 
readSource("SoilClassification", subtype = "HWSD.soil", convert = "onlycorrect")

## End(Not run)

calcSOMinitialsiationPools

Description

calculates Soil Organic Matter Pool, accounting for the management history as initialisation to magpie

Usage

calcSOMinitialsiationPools(cells = "lpjcell")

Arguments

cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells

Value

List of magpie object with results on country or cellular level, weight on cellular level, unit and description.

Author(s)

Benjamin Leon Bodirsky, Kristine Karstens

Examples

## Not run: 
calcOutput("SOMinitialsiationPools")

## End(Not run)

calcTopsoilCarbon

Description

This function extracts topsoil carbon densities from LPJ to MAgPIE

Usage

calcTopsoilCarbon(
  cells = "lpjcell",
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb"),
  climatetype = "GSWP3-W5E5:historical"
)

Arguments

cells

"magpiecell" for 59199 cells or "lpjcell" for 67420 cells
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
climatetype

Switch between different GCM climate scenarios

Value

magpie object in cellular resolution

Author(s)

Kristine Karstens

Examples

## Not run: 
calcOutput("TopsoilCarbon", aggregate = FALSE)

## End(Not run)

calcTransportCosts

Description

calculates country-level transport costs from GTAP total transport costs, cellular production, and cellular travel time

Usage

calcTransportCosts(transport = "all", gtapVersion = "9")

Arguments

transport

"all" or "nonlocal". "all" means all production incurs transport costs, while "nonlocal" sees only production greater than local rural consumption with transport costs
gtapVersion

"9" or "81"

Value

List of magpie objects with results on country level, weight on country level, unit and description.

Author(s)

David M Chen

See Also

[calcTransportTime()], [calcGTAPTotalTransportCosts()]

Examples

## Not run: 
calcOutput("TransportCosts_new")

## End(Not run)

calcTransportDistance

Description

Function extracts travel time to major cities in minutes This function now deprecated - use calcTransportTime instead

Usage

calcTransportDistance()

Value

magpie object in cellular resolution

Author(s)

David Chen

Examples

## Not run: 
calcOutput("TransportDistance", aggregate = FALSE)

## End(Not run)

calcTransportTime

Description

Function extracts travel time to major cities in minutes

Usage

calcTransportTime(subtype = "cities50", cells = "lpjcell")

Arguments

subtype

currently only cities of 5, 20, or 50 thousand people ("cities5", "cities20", "cities50") or ports of various sizes ("portsLarge|Medium|Small|VerySmall|Any")
cells

number of cells to be returned: magpiecell (59199), lpjcell (67420)

Value

magpie object in cellular resolution

Author(s)

David Chen

Examples

## Not run: 
calcOutput("TransportTime", aggregate = FALSE)

## End(Not run)

convertGPD

Description

convert GPD

Usage

convertGPD(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on iso level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("GPD", convert = TRUE)

## End(Not run)

convertGPD2022

Description

convert GPD2022

Usage

convertGPD2022(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on iso level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("GPD2022", convert = TRUE)

## End(Not run)

correctAvlLandSi

Description

Read Available Land Si

Usage

correctAvlLandSi(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

David Chen

See Also

readAvlLandSi

Examples

## Not run: 
readSource("AvlLandSi", convert = "onlycorrect")

## End(Not run)

readBendingTheCurve

Description

Read bending the curve data

Usage

correctBendingTheCurve(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Patrick v. Jeetze, Michael Windisch

Examples

## Not run: 
readSource("BendingTheCurve", subtype = "rr_layer", convert = "onlycorrect")

## End(Not run)

correctGCMClimate

Description

Correct GCMs climate variables NOTE: This function will be depreciate soon, please use mrland::correctLPJmLClimate

Usage

correctGCMClimate(x)

Arguments

x

magpie object provided by the read function

Value

Magpie objects with results on cellular level, weight, unit and description.

Author(s)

Marcos Alves, Felicitas Beier

See Also

readGCMClimate

Examples

## Not run: 
readSource("GCMClimate", subtype, convert="onlycorrect")

## End(Not run)

correctGFAD

Description

Correct Global Forest Age Dataset

Usage

correctGFAD(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Abhijeet Mishra, Felicitas Beier

See Also

readGFAD

Examples

## Not run: 
  readSource("GFAD", convert="onlycorrect")

## End(Not run)

correctGPM2

Description

correct peatland area

Usage

correctGPM2(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
  readSource("GPM2", convert="onlycorrect")

## End(Not run)

correctGrassYldEmu

Description

Correct files related to the training and optimization of the LPJml emulators

Usage

correctGrassYldEmu(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects.

Author(s)

Marcos Alves

See Also

readGrassYldEmu

Examples

## Not run: 
  readSource("GrassYldEmu", subtype = "GrassYldEmu:20f33a2280.weights", convert="onlycorrect")

## End(Not run)

correctLabourProdImpactEmu

Description

correct labour productivity impacts from climate change emulated by the LAMACLIMA project

based on method of Orlov et al. 2019. Economics of Disasters and Climate Change, 3(3), 191-211.

Usage

correctLabourProdImpactEmu(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Michael Windisch

See Also

readLabourProdImpactEmu

Examples

## Not run: 
  readSource("LabourProdImpactEmu", convert="onlycorrect")

## End(Not run)

correctLeifeld2018

Description

correct potential peatland area from Leifeld2018

Usage

correctLeifeld2018(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
  readSource("Leifeld2018", convert="onlycorrect")

## End(Not run)

correctMehta2024

Description

correct Global Area Equipped for Irrigation Dataset 1900-2015 from Mehta et al., 2024

Usage

correctMehta2024(x)

Arguments

x

magpie object provided by the read function

Value

magpie object in cellular resolution

Author(s)

Felicitas Beier

Examples

## Not run: 
  readSource("Mehta2024", convert="onlycorrect")

## End(Not run)

correctRamankutty

Description

Read Available Land Si

Usage

correctRamankutty(x)

Arguments

x

magpie object provided by the read function

Value

magpie object

Author(s)

Felicitas Beier

See Also

readRamankutty

Examples

## Not run: 
  readSource("Ramankutty", convert="onlycorrect")

## End(Not run)

correctSoilClassification

Description

Correct soil classification

Usage

correctSoilClassification(x)

Arguments

x

Magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Marcos Alves, Kristine Karstens

See Also

readSoilClassification,

Examples

## Not run: 
readSource("SoilClassification", subtype = "HWSD.soil", convert = "onlycorrect")

## End(Not run)

correctTransportDistance

Description

Read transport distance file

Usage

correctTransportDistance(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

David Chen

See Also

readTransportDistance

Examples

## Not run: 
readSource("TransportDistance", convert = "onlycorrect")

## End(Not run)

correctWindisch2021

Description

correct data to calculate BphEffect, BphTCRE or BphMask depending on the chosen subtype. BphEffect: Biogeophysical temperature change of afforestation (degree C). (File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018). BphMask: Mask of Datapoints of biogeophysical temperature change of afforestation (degree C) to be used as weight. (File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018). BphTCRE: Transient Climate Response to accumulated doubling of CO2. (File is based on CMIP5 +1perc CO2 per year experiment. To be used in the translation to carbon equivalents of BphEffect)

Usage

correctWindisch2021(x)

Arguments

x

magpie object provided by the read function

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Felicitas Beier, Michael Windisch

See Also

readWindisch2021

Examples

## Not run: 
  readSource("Windisch2021", convert="onlycorrect")

## End(Not run)

downloadCO2Atmosphere_new

Description

Download CO2 atm. inputs used for Lpjml runs

Usage

downloadCO2Atmosphere_new(subtype = "ISIMIP3b:ssp126")

Arguments

subtype

Switch between different inputs (eg. "ISIMIP3b:IPSL-CM6A-LR:historical:1850-2014:tas") It consists of GCM version, climate model, scenario and variable.

Value

metadata entry

Author(s)

Marcos Alves

Examples

## Not run: readSource("CO2Atmosphere_new",convert="onlycorrect")

downloadGCMClimate

Description

Download GCM climate input used for Lpjml runs NOTE: This function will be depreciate soon, please use mrland::downloadLPJmLClimate

Usage

downloadGCMClimate(subtype = "ISIMIP3b:IPSL-CM6A-LR:ssp126:2015-2100:tas")

Arguments

subtype

Switch between different inputs (e.g. "ISIMIP3b:IPSL-CM6A-LR:historical:1850-2014:tas") Argument consists of GCM version, climate model, scenario and variable, separated by ":"

Value

metadata entry

Author(s)

Marcos Alves

Examples

## Not run: 
readSource("GCMClimate", convert = "onlycorrect")

## End(Not run)

downloadMAPSPAM

Description

Downloads the MAP-SPAM (SPAM) data set for harvested and physical croparea

Usage

downloadMAPSPAM()

Value

raw files for MAPSPAM

Author(s)

Edna J. Molina Bacca

See Also

[downloadSource()]

Examples

## Not run: 
a <- download("downloadMAPSPAM")

## End(Not run)

downloadMehta2024

Description

download Global Area Equipped for Irrigation Dataset 1900-2015 from Mehta et al. (2024). Gridded dataset is created based on (sub-)national statistics from FAOSTAT, AQUASTAT, EUROSTAT and country's census data downscaled using two alternative gridded irrigation maps (GMIA from Siebert et al. 2013 and Meier et al. 2018)

Usage

downloadMehta2024(subtype = "GMIA")

Arguments

subtype

data subtype to be downloaded. Subtypes available: 'GMIA': gridded base map for downscaling from Stefan et al. (2013). Global Map of Irrigation Areas version 5. 'Meier2018': gridded base map for downscaling from Meier, et al. (2018). Global Irrigated Areas.

Author(s)

Felicitas Beier

See Also

[downloadSource()] [readMehta2024()]

Examples

## Not run: 
a <- downloadSource()

## End(Not run)

downloadRamankutty

Description

download Ramankutty available land si (Source: Ramankutty N, Foley JA, Norman J and McSweeney K (2002) The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11, 377-392.)

Usage

downloadRamankutty()

Author(s)

Felicitas Beier

See Also

downloadSource readRamankutty

Examples

## Not run:  a <- downloadSource()

downloadTravelTimeNelson2019

Description

download Nelson 2019 paper

Usage

downloadTravelTimeNelson2019()

Author(s)

David M Chen

fullCELLULARMAGPIE

Description

Function that produces the complete cellular data set required for running the MAgPIE model.

Usage

fullCELLULARMAGPIE(
  rev = numeric_version("0.1"),
  dev = "",
  ctype = "c200",
  climatetype = "MRI-ESM2-0:ssp370",
  lpjml = c(natveg = "LPJmL4_for_MAgPIE_44ac93de", crop =
    "ggcmi_phase3_nchecks_9ca735cb", grass = "lpjml5p2_pasture"),
  isimip = NULL,
  clusterweight = NULL,
  emu_id = NULL
)

Arguments

rev

data revision which should be used as input (numeric_version).
dev

development suffix to distinguish development versions for the same data revision. This can be useful to distinguish parallel lines of development.
ctype

aggregation clustering type, which is a combination of a single letter, indicating the cluster methodology, and a number, indicating the number of resulting clusters. Available methodologies are - hierarchical clustering (h), - normalized k-means clustering (n) and - combined hierarchical/normalized k-means clustering (c). In the latter hierarchical clustering is used to determine the cluster distribution among regions whereas normalized k-means is used for the clustering within a region.
climatetype

Global Circulation Model to be used
lpjml

Defines LPJmL version for crop/grass and natveg specific inputs
isimip

Defines isimip crop model input which replace maiz, tece, rice_pro and soybean
clusterweight

Should specific regions be resolved with more or less detail? Values > 1 mean higher share, < 1 lower share e.g. cfg$clusterweight <- c(LAM=2) means that a higher level of detail for region LAM if set to NULL all weights will be assumed to be 1. Examples: c(LAM=1.5,SSA=1.5,OAS=1.5) or c(LAM=2,SSA=2,OAS=2) setConfig (e.g. for setting the mainfolder if not already set properly).
emu_id

Pasture Soil carbon emulator ID

Author(s)

Kristine Karstens, Jan Philipp Dietrich

See Also

readSource,getCalculations,calcOutput,setConfig

Examples

## Not run: 
retrieveData("CELLULARMAGPIE", rev = numeric_version("12"),
             mainfolder = "pathtowhereallfilesarestored")

## End(Not run)

readAvl_Land_Si

Description

Read si0 and nsi0 areas based on Ramankutty dataset"

Usage

readAvlLandSi()

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

David Chen

Examples

## Not run: 
  readSource("AvlLandSi", convert="onlycorrect")

## End(Not run)

readBendingTheCurve

Description

Read bending the curve data

Usage

readBendingTheCurve(subtype)

Arguments

subtype

Data used in the Bending the Curve initiative. Type "rr_layer" for the range-size rarity layer and "luh2_side_layers" for the LUH2 Side Layers.

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Patrick v. Jeetze

Examples

## Not run: 
  readSource("BendingTheCurve", subtype="rr_layer", convert="onlycorrect")

## End(Not run)

readCO2Atmosphere

Description

Read CO2 global atmospheric concentration

Usage

readCO2Atmosphere_new(subtype = "ISIMIP3b:ssp126")

Arguments

subtype

Switch between different inputs

Value

Magpie objects with results on global level

Author(s)

Marcos Alves, Kristine Karstens

Examples

## Not run: 
readSource("CO2Atmosphere_new", subtype = "ISIMIP3b:ssp126", convert = FALSE)

## End(Not run)

readFishCatches

Description

Read soil classification data used as input for lpjml

Usage

readFishCatches()

Value

Magpie object with results on cellular level for soil types

Author(s)

Marcos Alves, Kristine Karstens

Examples

## Not run: 
readSource("SoilClassification")

## End(Not run)

readGCMClimate

Description

Read Climate data used as LPJmL inputs into MAgPIE objects NOTE: This function will be depreciate soon, please use mrland::readLPJmLClimate

Usage

readGCMClimate(
  subtype = "ISIMIP3bv2:IPSL-CM6A-LR:historical:1850-2014:tas",
  subset = "annual_mean"
)

Arguments

subtype

Switch between different inputs, e.g. "ISIMIP3b:IPSL-CM6A-LR:historical:1850-2014:tas" Available variables are: * tas - * wet - * per -
subset

Switch between different subsets of the same subtype Available options are: "annual_mean", "annual_sum", "monthly_mean", "monthly_sum", "wet"

Value

MAgPIE objects with results on cellular level.

Author(s)

Marcos Alves, Kristine Karstens, Felicitas Beier

See Also

readGCMClimate

Examples

## Not run: 
readSource("GCMClimate", subtype, convert = "onlycorrect")

## End(Not run)

readGFAD

Description

Read GLobal Forest Age Dataset derived from MODIS and COPENICUS satellite data

Usage

readGFAD()

Value

magpie object in cellular resolution

Author(s)

Abhijeet Mishra, Felicitas Beier

Examples

## Not run: 
readSource("GFAD", convert = "onlycorrect")

## End(Not run)

readGPD

Description

read GPD 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)",

Usage

readGPD()

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("GPD", convert = "onlycorrect")

## End(Not run)

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)",

Usage

readGPD2022()

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("x", convert = "onlycorrect")

## End(Not run)

readGPM2

Description

read peatland area from GPM2

Usage

readGPM2(subtype = "1km")

Arguments

subtype

resolution ("1km" or "500m")

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("GPM2", convert = "onlycorrect")

## End(Not run)

readGrassSoilEmu

Description

Read files related to the training and optimization of the LPJml emulators.

Usage

readGrassSoilEmu(
  subtype = "ISIMIP3b:IPSL_CM6A_LR:ssp126:1965_2100:5f5fa2:stddevs_lab"
)

Arguments

subtype

Subtype of file to be opened. Subtypes available: 'weights', 'inputs', 'stddevs' and 'means'.

Value

Magpie objects with a diverse inforamtion

Author(s)

Marcos Alves

Examples

## Not run: 
readSource("GrassSoilEmu",
  subtype =
    "ISIMIP3b:IPSL_CM6A_LR:ssp126:1965_2100:5f5fa2:weights", convert = F
)

## End(Not run)

readGrassYldEmu

Description

Read files related to the training and optimization of the LPJml emulators.

Usage

readGrassYldEmu(subtype = "109325f71e.inputs")

Arguments

subtype

Subtype of file to be opened. Subtypes available: 'max_harvest', 'weights', 'inputs', 'stddevs' and 'means'.

Value

Magpie objects with a diverse inforamtion

Author(s)

Marcos Alves

Examples

## Not run: 
readSource("GrassYldEmu", subtype = "109325f71e.inputs", convert="onlycorrect")

## End(Not run)

readGridPopGao

Description

Read gridded population, by urban and rural, from Gao O'Neill and JOnes dataset, see https://www.cgd.ucar.edu/iam/modeling/spatial-population-scenarios.html https://doi.org/10.7927/m30p-j498

Usage

readGridPopGao(subtype = "future")

Arguments

subtype

only "future" post-2000 available for this source

Author(s)

David Chen, Felicitas Beier

readGridPopIsimip

Description

Reads in past and future (SSP1-5) gridded population data, from ISIMIP database, Past data is based on HYDE3.2, while future SSPs are based on projections from Jones & O'Neill 2016

Usage

readGridPopIsimip(subtype)

Arguments

subtype

past (1965-2005) or future (2010-2100)

Value

A MAgPIE object, cellular 0.5deg resolution, of population (millions)

Author(s)

David Chen, Marcos Alves, Felicitas Beier

readLabourProdImpactEmu

Description

read in labour productivity impacts from climate change emulated by the LAMACLIMA project

based on method of Orlov et al. 2019. Economics of Disasters and Climate Change, 3(3), 191-211.

Usage

readLabourProdImpactEmu()

Value

magpie object of gridded productivity loss in percent (0-100)

Author(s)

Michael Windisch, Florian Humpenöder, Felicitas Beier

See Also

readSource

readLabourProdImpactOrlov

Description

read in labour productivity impacts from climate change from Orlov (see Orlov et al. 2019. Economic Losses of Heat-Induced Reductions in Outdoor Worker Productivity: a Case Study of Europe. Economics of Disasters and Climate Change, 3(3), 191-211.)

Usage

readLabourProdImpactOrlov(
  subtype = "IPSL-CM5A-LR_rcp85_wbgtod_hothaps_400W.nc"
)

Arguments

subtype

subtype of choice between indoor outdoor work, GCM, work intesnsity (300W medium, 400W high, rcp)

Value

magpie object of gridded productivity as share of 1 (full productivity)

Author(s)

David Chen

See Also

readSource

readLeifeld2018

Description

read potential peatland area from Leifeld2018

Usage

readLeifeld2018()

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Florian Humpenoeder

Examples

## Not run: 
readSource("Leifeld2018", convert = "onlycorrect")

## End(Not run)

readMAPSPAM

Description

Reads the MAP-SPAM crop data per year (mapping each year different)

Usage

readMAPSPAM(subtype = "harvested")

Arguments

subtype

It can be either "harvested" or "physical" area

Value

magpie object with croparea data in ha

Author(s)

Edna J. Molina Bacca, Felicitas Beier

See Also

[readSource()]

Examples

## Not run: 
a <- readSource("MAPSPAM")

## End(Not run)

readMehta2024

Description

reads in Global Area Equipped for Irrigation for years 1900-2015 from Mehta et al. (2022)

Usage

readMehta2024(subtype = "GMIA")

Arguments

subtype

data subtype to be downloaded. Subtypes available: 'GMIA': gridded base map for downscaling from Stefan et al. (2013). Global Map of Irrigation Areas version 5. 'Meier2018': gridded base map for downscaling from Meier, et al. (2018). Global Irrigated Areas.

Author(s)

Felicitas Beier

See Also

[correctMehta2024()]

Examples

## Not run: 
a <- readSource("Mehta2024")

## End(Not run)

readRamankutty

Description

Read in data of Ramankutty dataset (Source: Ramankutty N, Foley JA, Norman J and McSweeney K (2002) The global distribution of cultivable lands: current patterns and sensitivity to possible climate change. Global Ecology and Biogeography, 11, 377-392.). Link to data: https://www.nelson.wisc.edu/sage/data-and-models/global-land-use/grid.php

Usage

readRamankutty()

Value

magpie object

Author(s)

Felicitas Beier

Examples

## Not run: 
readSource("Ramankutty", convert = "onlycorrect")

## End(Not run)

readSoilClassification

Description

Read soil classification data used as input for lpjml

Usage

readSoilClassification(subtype = "HWSD.soil")

Arguments

subtype

Switch between different inputs

Value

Magpie object with results on cellular level for soil types

Author(s)

Marcos Alves, Kristine Karstens

Examples

## Not run: 
readSource("SoilClassification", subtype="HWSD.soil", convert="onlycorrect")

## End(Not run)

readTransportDistance

Description

Read transport distance

Usage

readTransportDistance()

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

David Chen

Examples

## Not run: 
  readSource("TransportDistance", convert="onlycorrect")

## End(Not run)

readTravelTimeNelson2019

Description

Read minimum travel time to cities and ports and ports of various size, see metadata file in source folder

Usage

readTravelTimeNelson2019(subtype = "cities50")

Arguments

subtype

currently only cities of 5, 20, or 50 thousand people ("cities5", "cities20", "cities50") or ports of various sizes ("portsLarge|Medium|Small|VerySmall|Any")

Value

gridded magpie object for 2015, minimum travel time to cities in minutes

Author(s)

David M Chen

readWindisch2021

Description

Reads in data to calculate BphEffect, BphTCRE or BphMask depending on the chosen subtype. BphEffect: Biogeophysical temperature change of afforestation (degree C). (File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018). BphMask: Mask of Datapoints of biogeophysical temperature change of afforestation (degree C) to be used as weight. (File is based on observation datasets of Bright et al. 2017 and Duveiller et al. 2018). BphTCRE: Transient Climate Response to accumulated doubling of CO2. (File is based on CMIP5 +1perc CO2 per year experiment. To be used in the translation to carbon equivalents of BphEffect)

Usage

readWindisch2021(subtype)

Arguments

subtype

refordefor_BPHonly_05_new, annmean_pertCha_05_EW1, annstd_diff_pertCha_05_EW1

Value

List of magpie objects with results on cellular level, weight, unit and description.

Author(s)

Felicitas Beier, Michael Windisch, Patrick v. Jeetze

Examples

## Not run: 
  readSource("Windisch2021", convert="onlycorrect")

## End(Not run)

Apply region names

Description

This tool function replaces country names with region names in the spatial dimension of the object. To avoid mixing up of cache files with different regional aggregation the regioncode needs to supplied and checked as well. Only if the supplied regions code agrees with the region mapping currently chosen the function will return the data.

Usage

toolApplyRegionNames(cdata, regionscode)

Arguments

cdata

a cluster data file as produced by cluster_base
regionscode

regionscode of the regional mapping to be used. Must agree with the regionscode of the mapping mentioned in the madrat config! Can be retrieved via regionscode().

Value

the cluster data file with region names in spatial dimension rather than country names

Author(s)

Jan Philipp Dietrich, Felicitas Beier

See Also

calcClusterKMeans, calcClusterBase

toolClusterPerRegion

Description

This function calculates an appropriate number of clusters per region as it is needed for ClusterKMeans

Usage

toolClusterPerRegion(cells, ncluster, weight = NULL)

Arguments

cells

spatial names as returned by getCells
ncluster

The desired total number of clusters.
weight

named vector with weighting factors for each region for the cluster distribution, e.g. weight=c(AFR=3,EUR=0.5). weight > 1 will grant more cluster to a region and weight < 1 less cluster than by default.

Value

A matrix with regions in rows and number of cells and clusters in columns

Author(s)

Jan Philipp Dietrich

See Also

calcClusterKMeans, calcClusterBase

toolClusterPerRegionManual

Description

This function translates weights into number of clusters per region as it is needed for ClusterKMeans. Weights have to sum up to total number of clusters.

Usage

toolClusterPerRegionManual(cells, ncluster, ncluster2reg)

Arguments

cells

spatial names as returned by getCells
ncluster

The desired total number of clusters.
ncluster2reg

named vector with numbers per region

Value

A matrix with regions in rows and number of cells and clusters in columns

Author(s)

Kristine Karstens

See Also

calcClusterKMeans, calcClusterBase

toolMoveValues

Description

Distances are calculated from the lat and lon coordinates. Therefore, all magpie objects must have location information (see addLocation). Values are only moved within a country. If no suitable cell is available in the same country, the undesirable values are discarded. This function takes only magpie objects with only one time and data dimensions to allow for more flexibility. Whenever more than one dimension is available in the magpie objects, I suggest using a loop (see for and apply).

Usage

toolMoveValues(x, y, z, w = NULL)

Arguments

x

Unidimensional magpie object (one time and one data dimension) with location information caring for the values that must be checked and moved if necessary.
y

Unidimensional magpie object (one time and one data dimension) that has a binary or logical mapping (see as.logical) of the unsuitable areas for the values in x
z

Unidimensional magpie object (one time and one data dimension) that has a binary or logical (see as.logical) mapping of the areas that can receive the values from x.
w

Unidimensional magpie object (one time and one data dimension) that has a binary or logical (see as.logical) mapping of the areas that have to be zeroed. If left empty, the inverse of 'z' is assumed.

Details

Move values in an undesirable cell to the nearest desirable neighbor (Euclidian distance).

Value

Unidimensional magpie object with summed values of the moved values to the nearest suitale neighbor. All the unmoved and discarded values are set to 0.

Author(s)

Marcos Alves

Neural Network Reconstruction

Description

Reconstructs and evaluate a neural network from the weights and biases provided as arguments

Usage

toolNeuralNet(inputsMl, weights, activation)

Arguments

inputsMl

Neural Network input features properly scaled with the scale and center attributes of the scaled training set in a matrix format.
weights

The learned weights and biases in a list format as outputed by the function keras::get_weights().
activation

Name of the activation function used for training. Currently implemented functions: 'relu', 'softplus', 'sigmoid'. Optionally, a custom activation function can be passed using a "." to indicate where the layer inputs should be piped.

Value

The evaluated result of the neural network for the input_ml parameter.

Author(s)

Marcos Alves

Refold weights from NN training Refold weights into their original configuration.

Description

Refold weights from NN training Refold weights into their original configuration.

Usage

toolRefoldWeights(x)

Arguments

x

magpie object containing weights.

Author(s)

Marcos Alves