Dataset Open Access

A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation

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

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<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Koppa, Akash</dc:creator>
  <dc:creator>Rains, Dominik</dc:creator>
  <dc:creator>Hulsman, Petra</dc:creator>
  <dc:creator>Poyatos, Rafael</dc:creator>
  <dc:creator>Miralles, Diego G.</dc:creator>
  <dc: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:</dc:description>
  <dc:subject>Deep learning</dc:subject>
  <dc:subject>Hybrid Modeling</dc:subject>
  <dc:subject>Evaporative Stress</dc:subject>
  <dc:subject>Machine Learning</dc:subject>
  <dc:subject>Earth System Modeling</dc:subject>
  <dc:title>A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation</dc:title>
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