Dataset Open Access
A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
Koppa, Akash;
Rains, Dominik;
Hulsman, Petra;
Poyatos, Rafael;
Miralles, Diego G.
This repository contains the datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation".
The repository contains the following files: 1) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 2) Output - contains the final deep learning models and the outputs (evaporation and transpiration stress factor) outputs from the hybrid model developed in the study.
Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF file formats.
The codes related to the research article and deep learning model are available in the following repository: https://github.com/akashkoppa/StressNet
| Name | Size | |
|---|---|---|
|
data_figures_hybrid_paper.zip
md5:2e3ce593336da4d6a02c4157aea8809b |
1.5 GB | Download |
|
output_hybrid_model_output.zip
md5:5ae99fea254d871265699289443dc7cf |
27.6 GB | Download |
| All versions | This version | |
|---|---|---|
| Views | 924 | 412 |
| Downloads | 6,466 | 497 |
| Data volume | 181.2 TB | 11.7 TB |
| Unique views | 787 | 369 |
| Unique downloads | 255 | 134 |
- Publication date:
- January 21, 2022
- DOI:
-
- Keyword(s):
- Deep learning Hybrid Modeling Evaporation Evaporative Stress Transpiration GLEAM Machine Learning Earth System Modeling
- Grants:
-
European Commission:
-
DOWN2EARTH - DOWN2EARTH: Translation of climate information into multilevel decision support for social adaptation, policy development, and resilience to water scarcity in the Horn of Africa Drylands (869550)
- DRY-2-DRY - Do droughts self-propagate and self-intensify? (715254)
-
DOWN2EARTH - DOWN2EARTH: Translation of climate information into multilevel decision support for social adaptation, policy development, and resilience to water scarcity in the Horn of Africa Drylands (869550)
- License (for files):
- Creative Commons Attribution 4.0 International