Articles | Volume 25, issue 4
https://doi.org/10.5194/nhess-25-1521-2025
https://doi.org/10.5194/nhess-25-1521-2025
Research article
 | 
25 Apr 2025
Research article |  | 25 Apr 2025

Evaluation of machine learning approaches for large-scale agricultural drought forecasts to improve monitoring and preparedness in Brazil

Joseph W. Gallear, Marcelo Valadares Galdos, Marcelo Zeri, and Andrew Hartley

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Short summary
In Brazil, drought is of national concern and can have major consequences for agriculture. Here, we determine how to develop forecasts for drought stress on vegetation health using machine learning. Results aim to inform future developments in operational drought monitoring at the National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) in Brazil. This information is essential for disaster preparedness and planning of future actions to support areas affected by drought.
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