Model:GLUDM

GLUDM

Also known as
Model type	Single
Model part of larger framework
Note on status model
Date note status model

Incorporated models or components:

Spatial dimensions	2D
Spatial extent	Global
Model domain	Terrestrial
One-line model description	Global future agricultural land use dynamics model
Extended model description	The model calculates a unique regression equation for each grid-cell between a the relative area of a specific land use (e.g. cropland) and global population. The equation is used to extrapolate that land use are into the future in each grid cell with predicted global population predictions. If the relative area of a land use reach a value of 95%, additional expansion is migrated to neighboring cells thus allowing spatial expansion. Geographic limitations are imposed on land use migration (e.g. no cropland beyond 60 degree latitude). For more information: Haney, N., Cohen, S. (2015), Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model. Applied Geography, 62: 366-376.

Keywords:	land use, agriculture, Future,

Supported platforms	Unix, Linux, Mac OS, Windows
Other platform
Programming language	Java
Other program language
Code optimized	Single Processor
Multiple processors implemented
Nr of distributed processors
Nr of shared processors
Start year development	2013
Does model development still take place?	No
If above answer is no, provide end year model development	2014
Code development status	As is, no updates are provided
When did you indicate the 'code development status'?	2020
Model availability	As code
Source code availability (Or provide future intension)	Through CSDMS repository
Source web address
Source csdms web address	https://github.com/csdms-contrib/gludm
Program license type	GPL v2
Program license type other
Memory requirements
Typical run time	Minutes

Describe input parameters	Rasters containing the relative area of a specific land use (e.g. cropland) in the past (e.g. 1960, 1980, 1990, 2005). A table of historic and predicted global population.
Input format	ASCII
Other input format	text file
Describe output parameters	Rasters containing the relative area of a specific land use in the future.
Output format	ASCII
Other output format
Pre-processing software needed?	Yes
Describe pre-processing software	GIS
Post-processing software needed?	Yes
Describe post-processing software	GIS
Visualization software needed?	Yes
If above answer is yes	ESRI
Other visualization software	Any GIS

Describe processes represented by the model	Regression based interpolation. Different regression equation type can be used. Land use area in each grid cell is the dependent variable and global population is the independent variable.
Describe key physical parameters and equations	Global population values is assumed to be the most important controlling factor on the area of a specific agricultural land use area.
Describe length scale and resolution constraints	At the moment the resolution of the input controls the resolution of the output. This is a global model but can be applied to smaller domains.
Describe time scale and resolution constraints	Yearly, 1960-2100.
Describe any numerical limitations and issues

Describe available calibration data sets	We used the HYDE 3.1 dataset and the USDA's CropScape dataset for validation. See: Haney, N., Cohen, S. (2015), Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model. Applied Geography, 62: 366-376.
Upload calibration data sets if available:
Describe available test data sets	Cropland area for ...
Upload test data sets if available:
Describe ideal data for testing

Do you have current or future plans for collaborating with other researchers?

Is there a manual available?	Yes
Upload manual if available:
Model website if any	http://sdml.ua.edu/models/
Model forum / discussion board

Comments	See model description, validation and analysis in: Haney, N., Cohen, S. (2015), Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model. Applied Geography, 62: 366-376. http://sdml.ua.edu/publications/

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Featured reference(s)	Open Access Status	Year	Type of Reference	Cited
Haney, Nicholas; Cohen, Sagy; 2015. Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model. Applied Geography, 62366–376. 10.1016/j.apgeog.2015.05.010	closed	2015	a module application description	14