Preprints
https://doi.org/10.5194/nhess-2024-60
https://doi.org/10.5194/nhess-2024-60
30 Apr 2024
 | 30 Apr 2024
Status: a revised version of this preprint is currently under review for the journal NHESS.

Application of machine learning to forecast agricultural drought impacts for large scale sub-seasonal drought monitoring in Brazil

Joseph William Gallear, Marcelo Valadares Galdos, Marcelo Zeri, and Andrew Hartley

Abstract. Drought events have increased in frequency and severity in recent years, and result in significant economic losses. Although the Brazilian semi-arid northeast has been historically associated with the impacts of drought, drought is of national concern, from 2011–2019, drought events were recorded in all Brazilian territories. Droughts can have major consequences for agricultural production, which is of particular concern given the importance of soybeans for socio-economic development. Due to its regional heterogeneity, it is important to develop accurate drought forecast and assessment tools for Brazil. We explore machine learning as a method to forecast the vegetation health index (VHI), for large scale monthly drought monitoring across agricultural land in Brazil. Furthermore, we also determine spatio-temporal drivers of VHI across the wide variation in climates, as well as evaluate machine learning performance for ENSO variation, forecasting of the onset of drought impact, and how the trade off between spatial variation and sample size affects model performance. We show that machine learning methods such as gradient boosting methods are able to more easily forecast vegetation health in the north and north east Brazil than south Brazil, and perform better during La Niña events than El Niño events. Drought impacts which reduce VHI below the commonly used 40 % threshold can be forecast across Brazil with similar model performance. SPEI is shown to be a useful indicator of drought impact, with 3 month accumulation periods preferred over 1 and 2 months. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). Future work should build upon methods discussed here to improve drought forecasts for agricultural drought response and adaptation.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Joseph William Gallear, Marcelo Valadares Galdos, Marcelo Zeri, and Andrew Hartley

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2024-60', Anonymous Referee #1, 30 May 2024
    • AC2: 'Reply on RC1', Joseph Gallear, 26 Jul 2024
  • RC2: 'Comment on nhess-2024-60', Anonymous Referee #2, 26 Jun 2024
    • AC1: 'Reply on RC2', Joseph Gallear, 26 Jul 2024
Joseph William Gallear, Marcelo Valadares Galdos, Marcelo Zeri, and Andrew Hartley
Joseph William 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 impacts on vegetation health using machine learning. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). This information is essential for disaster preparedness and planning of future actions to support areas affected by drought.
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