H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
<p>The proposed hybrid hydrological model with vegetation (H2MV) uses dynamic meteorology and static features as input to a long short-term memory (LSTM) to model uncertain parameters of process formulations that govern water fluxes and states. In the hydrological model, vegetation states are...
Saved in:
| Main Authors: | Z. Baghirov, M. Jung, M. Reichstein, M. Körner, B. Kraft |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-05-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/2921/2025/gmd-18-2921-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
On the added value of sequential deep learning for the upscaling of evapotranspiration
by: B. Kraft, et al.
Published: (2025-08-01) -
Preface
by: Baghirov Hussein
Published: (2025-01-01) -
Study of the Greater Caucasus glaciers and forest distribution routes
by: Baghirov Hussein
Published: (2025-01-01) -
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
by: G. Tang, et al.
Published: (2025-04-01) -
Where Are Global Vegetation Greening and Browning Trends Significant?
by: José Cortés, et al.
Published: (2021-03-01)