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Dataset Open Access

A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

Koppa, Akash; Rains, Dominik; Hulsman, Petra; Miralles, Diego G.

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 (30.3 GB)
Name Size
code_koppa_et_al_hybrid_model.zip
md5:ef1307b7432000c8c04d8c88705e4112
30.5 kB Download
input_koppa_et_al_hybrid_model.zip
md5:7886621409904deae90fdf4a3dfb22e6
1.5 GB Download
output_koppa_et_al_hybrid_model.zip
md5:0b1432afdb97fdc16951c19fad92d8f0
28.8 GB Download
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