A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
- 1. Hydro-Climate Extremes Lab (H-CEL), Ghent University
- 2. CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Catalonia, Spain
Description
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
Files
data_figures_hybrid_paper.zip
Files
(29.1 GB)
| Name | Size | Download all |
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md5:2e3ce593336da4d6a02c4157aea8809b
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1.5 GB | Preview Download |
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md5:5ae99fea254d871265699289443dc7cf
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27.6 GB | Preview Download |
Additional details
Funding
- 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
- European Commission
- DRY-2-DRY – Do droughts self-propagate and self-intensify? 715254
- European Commission