oliver-mcmanamay2025scidata

United States multi-sector land use and land cover base maps to support human and Earth system models

Jay Oliver^1* and Ryan McManamay¹

¹ Department of Environmental Science, Baylor University, Waco, TX, USA

* corresponding author: Ryan_McManamay@baylor.edu

Abstract

Earth System Models (ESMs) require current and future projections of land use and landcover change (LULC) to simulate land-atmospheric interactions and global biogeochemical cycles. Among the most utilized land systems in ESMs are the Community Land Model (CLM) and the Land-Use Harmonization 2 (LUH2) products. Regional studies also use these products by extending coarse projections to finer resolutions via downscaling or by using other Multisector Dynamic (MSD) models. One such MSD is the Global Change Analysis Model (GCAM), which has its own independent land model, but often relies on CLM or LUH2 as spatial inputs for its base years. However, this requires harmonization of thematically incongruent land systems at multiple spatial resolutions, leading to uncertainty and error propagation. To resolve these issues, we develop a thematically consistent LULC system for the conterminous United States adaptable to multiple MSD frameworks to support research at a regional level. Using empirically derived spatial products, we developed a series of base maps for multiple contemporary years of observation at a 30-m resolution that support flexibility and interchangeability amongst LUH2, CLM, and GCAM classification systems.

Journal reference

Oliver, J., R.A. McManamay. 2025. United States Multi-Sector Dynamics land use and land cover base maps to support Human and Earth System Models. Scientific Data 10.1038/s41597-025-04713-6

Code reference

Oliver, J., R.A. McManamay. 2025. United States Multi-Sector Dynamics land use and land cover base maps to support Human-Earth System Modeling. IMMM-SFA GitHub Repository

Data reference

Oliver, J., & McManamay, R. (2024). United States Multi-Sector Dynamics land use and land cover base maps to support Human-Earth System Modeling (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2246609

Input data

Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R., & Riitters, K. Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 184–199. (2020).

Multi-Resolution Land Characteristics (MRLC) Consortium (2025). National Land Cover Database. https://www.mrlc.gov/

United States Department of Agriculture Crop Data Layer (2025). https://www.nass.usda.gov/ResearchandScience/Cropland/SARS1a.php

U.S. Geological Survey (USGS). 2022. Gap Analysis Project (GAP), Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release. https://doi.org/10.5066/P96WBCHS.

United States Department of Agriculture Forest Service (2025. LANDFIRE dataset. https://landfire.gov/version_download.php

United States Department of Agriculture Forest Service (2025). USDA Forest Service Grazing Dataset. https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=grazing.

Falcone, J. (2016). Mapping enhanced grazing potential based on the NAWQA Wall-to-wall Anthropogenic Land-use Trends (NWALT) product. US Geological Survey. DOI 10.5066/F7CR5RFF. https://www.usgs.gov/data/mapping-enhanced-grazing-potential-based-nawqa-wall-wall-anthropogenic-land-use-trends-nwalt

Xie, Y., Gibbs, H. K., & Lark, T. J. (2021). Landsat-based Irrigation Dataset (Lanid): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. Earth System Science Data, 13(12), 5689–5710. https://doi.org/10.5194/essd-13-5689-2021 https://data.usgs.gov/datacatalog/data/USGS:63b5dbd9d34e92aad3caa517#:~:text=The%20Landsat%2Dbased%20Irrigation%20Dataset%20(LANID)%20uses%20a%20random,train%20and%20validate%20the%20model.

Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. (2018).

Olson, David M., and Eric Dinerstein. “The Global 200: Priority Ecoregions for Global Conservation.” Annals of the Missouri Botanical Garden, vol. 89, no. 2, p. 199., (2002). https://globil-panda.opendata.arcgis.com/maps/panda::wwf-global-200-ecoregions/about

National Aeronautics and Space Administration’s Shuttle Radar Topography Mission (SRTM). Digital Elevation Model data (from the USGS SRTM data: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1).

Output data

Oliver, J., & McManamay, R. (2024). United States Multi-Sector Dynamics land use and land cover base maps to support Human-Earth System Modeling (Version v1) [Data set]. MSD-LIVE Data Repository. https://doi.org/10.57931/2246609

Contributing modeling software

| Model | Version | Repository Link | |-------|---------|-----------------| | Python| version 3 | https://www.python.org/|
| R | version 4.4.3 | https://cran.r-project.org/|

Reproduce my experiment

1. Install the software components required to conduct the experiment from contributing modeling software
2. Download and install the supporting input data required to conduct the experiment
3. Refer to Oliver and McManamay (2025) Scientific Data. Review all tables of land classes, as well as Supplementary material of all MSD classes. Also review Figure 1 (sankey diagram)
4. Run the following scripts in the workflow directory to re-create the first part of this experiment

| Script Name | Description | How to Run | | --- | --- | --- | | CDL_Crop_Mask_OS.py | Script to override NLCD cultivated crops with USDA CDL values | python3 CDL_Crop_Mask_OS.py| |RainfedIrrigatedClassificationOS.py| Script to assign crops as rainfed or irrgated |python3 RainfedIrrigatedClassificationOS.py |

1. Classifying managed and unmanaged forest and pasture lands was conducted manually in ArcPro. However, this processed followed a similar rationale to 'RainfedIrrigatedClassification_OS.py'. Please refer to Oliver and McManamay (2025) Scientific Data for specific steps.
2. Classying land classes as boreal, temperate, and tropical classes was conducted manually in ArcPro using input data(Köppen-Geiger climate classification, Ecoregions, and Digital Elevation Model). Please refer to Oliver and McManamay (2025) Scientific Data for specific steps.
3. Download and unzip the output data from my experiment
4. Run the following scripts in the workflow directory to compare my outputs to those from the publication

| Script Name | Description | How to Run | | --- | --- | --- | | CLM_MSE_Calculations_OpenSource.ipynb | Script to calculate error values (MSE) comparing new MSD basemap with CLM values| python3 CLM_MSE_Calculations_OpenSource.ipynb | |LUH2MSECalculation.ipynb| Script to calculate error values (MSE) comparing new MSD basemap with LUH2 values |python3 LUH2MSECalculation.ipynb | | Tabulate_Data_Final.py | Script to tabulate MSD land class areas within CLM grids and LUH2 grids | `python3 TabulateDataFinal.py -output /path/to/outuptdir/"PFTAppendedWt_Prop.csv" |

Reproduce my figures

Use the scripts found in the figures directory to reproduce the figures used in this publication.

| Figure Number(s) | Script Name | Description | How to Run | | --- | --- | --- | --- | |1| Updated_Sankey_Diagram-checkpoint.ipynb | Sankey diagram depicting MSD development workflow | python3 Updated_Sankey_Diagram-checkpoint.ipynb -input /path/to/inputs/Workflow_Plot_Outline.csv | |6|CircosePlotAnalysisCLM.R| Circose diagrams comparing pairwise associations (as proportions of area) between CLM PFTs and MSD landcover classes |R CircosePlotAnalysisCLM.R -input /path/to/inputs/PFTAppendedWtProp.csv | |7| `LUHCircoseAnalysis (1).R| Circose diagrams comparing pairwise associations (as proportions of area) between LUH2 states and MSD landcover classes |R LUHCircoseAnalysis (1).R -input /path/to/inputs/PFTAppendedWtProp.csv |