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Dataset associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

Domain:

climateenvironment and energy

Record type:

dataset
Creator:
Che
Publisher:
fig
Host:avatar
Description of the dataset
This dataset is associated with the article “Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches.” It provides openly accessible monthly climatic data for three representative forest ecosystems in northeastern Algeria: Yabous, Messara, and El Hamma, covering the period 1993–2023 (31 years). The Excel file is organized into five sheets containing monthly averages of key climatic variables: precipitation (mm), mean temperature (°C), minimum temperature (°C), maximum temperature (°C), and wind speed (m/s).Methods
Climatic data were obtained from the Climate Engine platform (climateengine.org) and aggregated at the monthly scale to ensure consistency with drought index calculations. These variables represent primary drivers of drought dynamics in semi-arid forest ecosystems. The dataset is intended to support reproducibility of analyses in the associated article and may serve as a baseline for future studies on drought monitoring, climate variability, and ecosystem resilience in North Africa.File format
Excel file (.xlsx) with five sheets corresponding to each climatic variable.

Visit

doi.orgfigshare.com

Tags

MeteorologyAdverse weather events

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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Dataset and Codes associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

Dataset and Codes associated with the article <i>“Dry or humid? Modeling drought dynamics in North African semi-arid forests using machine learning, deep learning, and stochastic approaches”</i>

Description of the dataset
This dataset is associated with the article “Dr