Published August 19, 2021
| Version v1
Dataset
Open
A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation
- 1. Hydro-Climate Extremes Lab (H-CEL), Ghent University
Description
This repository contains the codes and datasets used in the research article "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation".
The repository contains the following files: 1) Codes - contains scripts used for training the deep learning models used in the study, and for creating the figures in the article. 2) Input - contains all the processed input used for training the deep learning models and the datasets used for creating the figures in the article. 3) 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 formats
Files
code_koppa_et_al_hybrid_model.zip
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