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ClimaLand.jl

Create and run land models in integrated or standalone modes.
https://github.com/clima/climaland.jl

Category: Natural Resources
Sub Category: Soil and Land

Keywords

climate gpu julia land land-surface-model

Keywords from Contributors

data-assimilation climate-change earth-observation ocean ordinary-differential-equations ode sciml pde partial-differential-equations neural-differential-equations

Last synced: about 1 hour ago
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Repository metadata

Clima's Land Model

README.md

Documentation dev
GHA CI gha ci
Code Coverage codecov
Downloads downloads

Introduction

This is the repository of the CliMA land model code. Here are some notable features:

  • ClimaLand has a modular design, models can be run as standalone (e.g., soil moisture/energy only) or integrated (e.g., soil moisture/energy AND canopy AND snow, etc.)
  • ClimaLand can simulate single columns, regional boxes, and global runs
  • ClimaLand is CPU and GPU compatible
  • ClimaLand welcomes contributions! Please feel free to reach out to us with questions about how to get started, create a branch, and extend our code.

Installation

To use ClimaLand.jl, first you need to install Julia.
Then, you can install ClimaLand.jl by doing:

julia> using Pkg
julia> Pkg.add("ClimaLand")

You are now ready to use ClimaLand.jl.
To run a simple first simulation, please see our documentation page Running your first simulation.

Models

In our code base, a "model" define a set of prognostic variables which must be timestepped. The equations which govern the time evolution likely contain parameters and are informed by parameterization and physical domain choices. Any ClimaLand model contains all of the information needed to evaluate these equations. Below are the current models we support:

Component Models:

  • RichardsModel: Soil model option; runnable only in standalone mode

  • EnergyHydrology: Soil model option; runnable in standalone mode, or as part of an integrated model

  • CanopyModel: runnable in standalone mode, or as part of an integrated model

  • SnowModel: runnable in standalone mode, or as part of an integrated model

  • SoilCO2Model: runnable in standalone mode, or as part of an integrated model. Please note that this model is in beta version.

Combined Models:

  • SoilCanopyModel: an integrated model made of individual component models EnergyHydrology + CanopyModel
  • LandModel: an integrated model made of individual component models EnergyHydrology + CanopyModel + SnowModel + SoilCO2Model

Notes

Recommended Julia Version: Stable release v1.11.x. CI tests Julia v1.10 and 1.11.

ClimaLand.jl is a different model from the original CliMA Land,
which aims to utilize remote sensing data through more complex canopy RT
and plant physiology modules. For more details, please refer to
https://github.com/CliMA/Land.

  • Wang, Yujie, et al. "Testing stomatal models at the stand level in deciduous angiosperm and evergreen gymnosperm forests using CliMA Land (v0. 1)." Geoscientific Model Development 14.11 (2021): 6741-6763.
  • R. K. Braghiere, Y. Wang, R. Doughty, D. Souza, T. Magney, J. Widlowski, M. Longo, A. Bloom, J. Worden, P. Gentine, and C. Frankenberg. 2021. Accounting for canopy structure improves hyperspectral radiative transfer and sun-induced chlorophyll fluorescence representations in a new generation Earth System model. Remote Sensing of Environment. 261: 112497.
  • Wang, Yujie, and Christian Frankenberg. "On the impact of canopy model complexity on simulated carbon, water, and solar-induced chlorophyll fluorescence fluxes." Biogeosciences 19.1 (2022): 29-45.
  • Wang, Yujie, et al. "GriddingMachine, a database and software for Earth system modeling at global and regional scales." Scientific data 9.1 (2022): 258.
  • Holtzman, Nataniel, et al. "Constraining plant hydraulics with microwave radiometry in a land surface model: Impacts of temporal resolution." Water Resources Research 59.11 (2023): e2023WR035481.
  • Braghiere, R. K., Wang, Y., Gagné-Landmann, A., Brodrick, P. G., Bloom, A. A., Norton, A. J., Ma, S., Levine, P., Longo, M., Deck, K., Gentine, P., Worden, J. R., Frankenberg, C., & Schneider, T. (2023). The Importance of Hyperspectral Soil Albedo Information for Improving Earth System Model Projections. AGU Advances, 4(4), e2023AV000910. link
  • Wang, Y., Braghiere, R. K., Yin, Y., Yao, Y., Hao, D., & Frankenberg, C. (2024). Beyond the visible: Accounting for ultraviolet and far-red radiation in vegetation productivity and surface energy budgets. Global Change Biology, 30(5), e17346. link

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Last synced: 11 days ago

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Avg Commits per committer: 31.452
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Commits in past year: 397
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Avg Commits per committer in past year: 19.85
Development Distribution Score (DDS) in past year: 0.67

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Last synced: 2 days ago

Total issues: 333
Total pull requests: 1,197
Average time to close issues: 4 months
Average time to close pull requests: 14 days
Total issue authors: 17
Total pull request authors: 27
Average comments per issue: 0.86
Average comments per pull request: 0.98
Merged pull request: 721
Bot issues: 0
Bot pull requests: 43

Past year issues: 123
Past year pull requests: 540
Past year average time to close issues: 23 days
Past year average time to close pull requests: 9 days
Past year issue authors: 12
Past year pull request authors: 18
Past year average comments per issue: 0.39
Past year average comments per pull request: 0.79
Past year merged pull request: 294
Past year bot issues: 0
Past year bot pull requests: 13

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/clima/climaland.jl

Top Issue Authors

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  • Calibration (4)
  • Run test calibration (4)
  • bugfix (3)
  • github_actions (3)
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  • GPU (1)

Package metadata

juliahub.com: ClimaLand

Clima's Land Model

  • Homepage: https://clima.github.io/ClimaLand.jl/
  • Documentation: https://docs.juliahub.com/General/ClimaLand/stable/
  • Licenses: apache-2.0
  • Latest release: 0.16.2 (published 8 months ago)
  • Last Synced: 2025-12-24T13:52:00.747Z (1 day ago)
  • Versions: 35
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 53 Total
  • Rankings:
    • Dependent repos count: 9.832%
    • Average: 24.71%
    • Dependent packages count: 39.587%

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Score: 12.741091244893637