Preprints
https://doi.org/10.5194/essd-2024-344
https://doi.org/10.5194/essd-2024-344
22 Aug 2024
 | 22 Aug 2024
Status: a revised version of this preprint is currently under review for the journal ESSD.

The Application of Machine Learning Algorithms to the Global Forecast of Temperature-Humidity Index with High Temporal Resolution

Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira

Abstract. Climate change poses a significant threat to agriculture, with potential impacts on food security, economic stability, and human livelihoods. Dairy cattle, a crucial component of the livestock sector, are particularly vulnerable to heat stress, which can adversely affect milk production, immune function, feed intake, and in extreme cases, lead to mortality. The Temperature Humidity Index (THI) is a widely used metric to quantify the combined effects of temperature and humidity on cattle. However, most studies estimate THI using daily-level data, which fails to capture the full extent of daily thermal load and cumulative heat stress, especially during nights when cooling is inadequate. To address this limitation, we developed a machine learning approach to temporally downscale daily climate data to hourly THI values. Utilizing historical ERA5 reanalysis data, we trained an XGBoost model and generated hourly THI datasets for 12 NEX-GDDP-CMIP6 climate models under two emission scenarios (SSP2-4.5 and SSP5-8.5) extending to the end of the century. This high-resolution THI data provides a more accurate assessment of heat stress in dairy cattle, enabling better predictions and management strategies to mitigate the impacts of climate change on this vital agricultural sector.

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.
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2024-344', Anonymous Referee #1, 12 Sep 2024
  • RC2: 'Comment on essd-2024-344', Anonymous Referee #2, 04 Oct 2024
  • AC1: 'Comment on essd-2024-344', Pantelis Georgiades, 14 Nov 2024
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira

Data sets

Temperature Humidity Index GDDP-NEX-CMIP6 ML projections Pantelis Georgiades https://doi.org/10.26050/WDCC/THI

Model code and software

The Application of Machine Learning Algorithms to the Global Forecast of Temperature-Humidity Index with High Temporal Resolution Pantelis Georgiades https://github.com/pantelisgeor/Temperature-Humidity-Index-ML

Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira

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Short summary
Climate change is posing increasing challenges in the dairy cattle farming sector, as heat stress adversely affects the animals' health and milk production. To accurately asses these impacts, we developed a machine learning model to downscale daily climate data to hourly Temperature Humidity Index (THI) values. We utilised historical weather data to train our model and applied it to future climate projections, under two climate scenarios.
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