JAX‐CanVeg: A Differentiable Land Surface Model
Abstract Land surface models consider the exchange of water, energy, and carbon along the soil‐canopy‐atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opp...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
|
| Series: | Water Resources Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1029/2024WR038116 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849687892437237760 |
|---|---|
| author | Peishi Jiang Patrick Kidger Toshiyuki Bandai Dennis Baldocchi Heping Liu Yi Xiao Qianyu Zhang Carlos Tianxin Wang Carl Steefel Xingyuan Chen |
| author_facet | Peishi Jiang Patrick Kidger Toshiyuki Bandai Dennis Baldocchi Heping Liu Yi Xiao Qianyu Zhang Carlos Tianxin Wang Carl Steefel Xingyuan Chen |
| author_sort | Peishi Jiang |
| collection | DOAJ |
| description | Abstract Land surface models consider the exchange of water, energy, and carbon along the soil‐canopy‐atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process‐based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process‐based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX‐CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX‐CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball‐Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade‐off when learning observations of both LE and NEE, and (c) benefiting a multi‐layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade‐off over the process‐based model. Additionally, the multi‐layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land‐atmosphere interactions by leveraging the benefits of both data‐driven learning and process‐based modeling. |
| format | Article |
| id | doaj-art-b9b7b70821a741a091f9b107f8451e2b |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-b9b7b70821a741a091f9b107f8451e2b2025-08-20T03:22:12ZengWileyWater Resources Research0043-13971944-79732025-03-01613n/an/a10.1029/2024WR038116JAX‐CanVeg: A Differentiable Land Surface ModelPeishi Jiang0Patrick Kidger1Toshiyuki Bandai2Dennis Baldocchi3Heping Liu4Yi Xiao5Qianyu Zhang6Carlos Tianxin Wang7Carl Steefel8Xingyuan Chen9Atmospheric, Climate, and Earth Sciences Division Pacific Northwest National Laboratory Richland WA USACradle Bio Zurich SwitzerlandLawrence Berkeley National Laboratory Earth and Environmental Sciences Area Berkeley CA USADepartment of Environmental Science, Policy, and Management University of California Berkeley CA USADepartment of Civil and Environmental Engineering Washington State University Pullman WA USAAtmospheric, Climate, and Earth Sciences Division Pacific Northwest National Laboratory Richland WA USADepartment of Civil and Environmental Engineering Washington State University Pullman WA USADepartment of Environmental Science, Policy, and Management University of California Berkeley CA USALawrence Berkeley National Laboratory Earth and Environmental Sciences Area Berkeley CA USAAtmospheric, Climate, and Earth Sciences Division Pacific Northwest National Laboratory Richland WA USAAbstract Land surface models consider the exchange of water, energy, and carbon along the soil‐canopy‐atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process‐based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process‐based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX‐CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX‐CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball‐Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade‐off when learning observations of both LE and NEE, and (c) benefiting a multi‐layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade‐off over the process‐based model. Additionally, the multi‐layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land‐atmosphere interactions by leveraging the benefits of both data‐driven learning and process‐based modeling.https://doi.org/10.1029/2024WR038116differentiable land surface modelinghybrid modelingstomatal conductancewater stressoptimization trade‐off |
| spellingShingle | Peishi Jiang Patrick Kidger Toshiyuki Bandai Dennis Baldocchi Heping Liu Yi Xiao Qianyu Zhang Carlos Tianxin Wang Carl Steefel Xingyuan Chen JAX‐CanVeg: A Differentiable Land Surface Model Water Resources Research differentiable land surface modeling hybrid modeling stomatal conductance water stress optimization trade‐off |
| title | JAX‐CanVeg: A Differentiable Land Surface Model |
| title_full | JAX‐CanVeg: A Differentiable Land Surface Model |
| title_fullStr | JAX‐CanVeg: A Differentiable Land Surface Model |
| title_full_unstemmed | JAX‐CanVeg: A Differentiable Land Surface Model |
| title_short | JAX‐CanVeg: A Differentiable Land Surface Model |
| title_sort | jax canveg a differentiable land surface model |
| topic | differentiable land surface modeling hybrid modeling stomatal conductance water stress optimization trade‐off |
| url | https://doi.org/10.1029/2024WR038116 |
| work_keys_str_mv | AT peishijiang jaxcanvegadifferentiablelandsurfacemodel AT patrickkidger jaxcanvegadifferentiablelandsurfacemodel AT toshiyukibandai jaxcanvegadifferentiablelandsurfacemodel AT dennisbaldocchi jaxcanvegadifferentiablelandsurfacemodel AT hepingliu jaxcanvegadifferentiablelandsurfacemodel AT yixiao jaxcanvegadifferentiablelandsurfacemodel AT qianyuzhang jaxcanvegadifferentiablelandsurfacemodel AT carlostianxinwang jaxcanvegadifferentiablelandsurfacemodel AT carlsteefel jaxcanvegadifferentiablelandsurfacemodel AT xingyuanchen jaxcanvegadifferentiablelandsurfacemodel |