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gcamland v1.0 – An R Package for Modelling Land Use and Land Cover Change Cover

gcamland v1.0 – An R Package for Modelling Land Use and Land Cover Change

Open Access
|Oct 2019

Figures & Tables

Figure 1

Default nesting structure used in gcamland.

Figure 2

Sample land allocation by use and type in the USA for a Reference scenario.

Table 1

Table of the main methods in the gcamland v1.0 package, including their inputs and outputs. Note that only methods that can be called directly are listed in the table.

MethodDescriptionInputsOutput
NameDescription
run_modelCalculates land use and land cover for a given region and set of time periodsaScenarioInfoScenario-related information, including names, logits, expectations.Table of model results
aPeriodsInteger vector of periods to run. Default is all periods defined for the scenario type.
aVerboseIf TRUE, output additional debugging information.
run_ensembleGenerates a suite of different scenarios and calculates land use and land cover for each (by calling run_model)NNumber of parameter sets to selectList of ScenarioInfo objects for the ensemble members
aOutputDirDirectory where outputs are saved
skipNumber of iterations to skip (i.e., if building on another run.)
atypeScenario type: either “Reference” or “Hindcast”
logparallelName of directory to use for parallel workers’ log files. If NULL, then don’t write log files.
export_resultsSaves results from a specified scenario as a csv fileaScenarioInfoScenario-related information, including names, logits, expectations.csv file with model results
plotLandAllocationPlots allocation over time by land type, with subregional detailaScenarioInfoScenario-related information, including names, logits, expectations.ggplot plot
plotRegionalLand AllocationPlots allocation over time by land type, aggregated to region (as in Figure 2)aScenarioInfoScenario-related information, including names, logits, expectations.ggplot plot
ScenarioInfoCreates a scenario information object that can be used in the functions aboveaExpectationTypeString indicating whether to use “Perfect”, “Lagged”, or “Linear” expectationsScenarioInfo object
aLaggedShareOldWeight to put on older information if “Lagged” expectations are used
aLinearYearsNumber of years to use in extrapolation if “Linear” expectations are used
aLogitUseDefaultBoolean indicating whether to use default logits
aLogitAgroForestAgroForest logit exponent (assuming mLogitUseDefault == FALSE)
aLogitAgroForest_NonPastureAgroForest_NonPasture logit exponent (assuming mLogitUseDefault == FALSE)
aLogitCroplandCropland logit exponent (assuming mLogitUseDefault == FALSE)
aScenarioTypeType of scenario to run: either “Reference” or “Hindcast”
aScenarioNameComplete scenario name, with expectations and logit information
aFileNameFile name
aOutputDirOutput directory
aSerialNumSerial number for a run that is part of a series.
aRegionRegion to use in the calculation. Right now we only run a single region at a time.
DOI: https://doi.org/10.5334/jors.233 | Journal eISSN: 2049-9647
Language: English
Submitted on: Jun 4, 2018
Accepted on: Oct 7, 2019
Published on: Oct 22, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Katherine Calvin, Robert Link, Marshall Wise, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.