Articles | Volume 15, issue 5
https://doi.org/10.5194/gmd-15-1971-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-1971-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
09 Mar 2022
Model evaluation paper |
| 09 Mar 2022
| 09 Mar 2022
Global evaluation of the Ecosystem Demography model (ED v3.0)
Lei Ma
CORRESPONDING AUTHOR
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
George Hurtt
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
Lesley Ott
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD 20771, USA
Ritvik Sahajpal
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
Justin Fisk
Regrow Agriculture Inc., Durham, NH 03824, USA
Rachel Lamb
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
Department of Geography, National University of Singapore, 117570,
Singapore
Steve Flanagan
Wildland Fire Science, Tall Timbers Research Station and Land
Conservancy, Tallahassee, FL 32312, USA
Louise Chini
Department of Geographical Sciences, University of Maryland,
College Park, MD 20770, USA
Abhishek Chatterjee
Global Modeling and Assimilation Office, NASA Goddard Space Flight
Center, Greenbelt, MD 20771, USA
Universities Space Research Association, Columbia, MD 21046, USA
now at: NASA Jet Propulsion Laboratory, Caltech, Pasadena, CA 91326, USA
Joseph Sullivan
Department of Plant Science & Landscape Architecture, University
of Maryland, College Park, MD 20770, USA
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9 citations as recorded by crossref.
- A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0) L. Li et al. 10.5194/gmd-16-4017-2023
- Parameterization of Tree and Shrub Stem Wood Density Adaptions to Multiple Climate and Soil Factor Gradients X. Song et al. 10.1007/s00376-024-4034-9
- Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network J. Sipps & L. Magruder 10.3390/rs15235594
- Monitoring Earth’s climate variables with satellite laser altimetry L. Magruder et al. 10.1038/s43017-023-00508-8
- Beyond MRV: combining remote sensing and ecosystem modeling for geospatial monitoring and attribution of forest carbon fluxes over Maryland, USA G. Hurtt et al. 10.1088/1748-9326/ad9035
- Parameterization of height–diameter and crown radius–diameter relationships across the globe X. Song et al. 10.1093/jpe/rtae005
- Grassland vegetation dynamic modeling and production potential estimation D. Wang et al. 10.1360/TB-2024-0266
- Scientific land greening under climate change: Theory, modeling, and challenges J. Chen et al. 10.1016/j.accre.2024.08.003
- Global Carbon Budget 2023 P. Friedlingstein et al. 10.5194/essd-15-5301-2023
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
We present a global version of the Ecosystem Demography (ED) model which can track vegetation 3-D structure and scale up ecological processes from individual vegetation to ecosystem scale. Model evaluation against multiple benchmarking datasets demonstrated the model’s capability to simulate global vegetation dynamics across a range of temporal and spatial scales. With this version, ED has the potential to be linked with remote sensing observations to address key scientific questions.
We present a global version of the Ecosystem Demography (ED) model which can track vegetation...
