August 19, 2021 Dataset Open Access

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

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    "description": "<p>This repository contains the codes and datasets used in the research article &quot;A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation&quot;.</p>\n\n<p>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.</p>\n\n<p>Formats: All scripts are in the programming language Python. The datasets are in HDF5 and NetCDF formats</p>", 
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    "title": "A Deep Learning-Based Hybrid Model of Global Terrestrial Evaporation", 
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    "keywords": [
      "Deep learning", 
      "Hybrid Modeling", 
      "Evaporation", 
      "Evaporative Stress", 
      "Transpiration", 
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      "Earth System Modeling"
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        "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
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        "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
        "name": "Rains, Dominik"
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        "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
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        "affiliation": "Hydro-Climate Extremes Lab (H-CEL), Ghent University", 
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