Grace-based assessment of hydrometeorological droughts and their Possible teleconnection Mechanisms using wavelet based quantitative approach

• Hydro-meteorological drought conditions in Tunisia are evaluated using multiple drought indices. • Tunisia faces severe drought hazards with high stability and low recovery probabilities. • GDDI identifies more frequent and prolonged droughts than traditional drought indices. • Robust connections between ENSO and droughts are found in Tunisia. • Groundwater drought occurrence in Tunisia is predominantly influenced by anthropogenic activities. Climate change and recurrent extreme climatic events have intensified the vulnerability of water-stressed regions like Tunisia to droughts, severely impact agriculture, the economy, and society. This study analyzes hydro-meteorological drought patterns using the Gravity Recovery and Climate Experiment (GRACE) satellite-derived Groundwater Drought Index (GGDI), alongside traditional indices, including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI). A stochastic analysis of monthly SPEI-GGDI values was conducted using a first-order Markov chain model, to investigate regional drought hazards formation, persistence, and evolution. Pearson’s correlation coefficient and wavelet coherence were applied to evaluate interactions among indices and their teleconnections with large-scale climate patterns. Results reveal persistent droughts, with extreme events exhibiting high stability and low recovery probabilities. The most severe groundwater drought occurred in 2014–2015, averaging a GGDI value of −1.36, while 2002–2003 was the driest based on SPEI, SPI, and SRI, averaging −1.9. Correlation analysis highlights complex interactions between meteorological and hydrological droughts, with GDDI-identified droughts exhibit greater severity in frequency, intensity, and duration, indicating significant anthropogenic influence. El Niño-Southern Oscillation (ENSO) significantly influenced drought evolution, with intense negative phases exacerbating severity. This study highlights the potential of GRACE satellite data for integrated drought monitoring and provides novel insights for developing sustainable drought management strategies in Tunisia.

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Publication Details

Journal
International Journal of Applied Earth Observation and Geoinformation
Published
2025-02-16
DOI
https://doi.org/10.1016/j.jag.2025.104410
Citations
2
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
2.21

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article

Grace-based assessment of hydrometeorological droughts and their Possible teleconnection Mechanisms using wavelet based quantitative approach

Houssem Terwayet Bayouli, Wanchang Zhang, Olfa Terwayet Bayouli, Zhijie Zhang et al.
2 citations
International Journal of Applied Earth Observation and Geoinformation
Hydrology and Drought Analysis
2.21
article

Grace-based assessment of hydrometeorological droughts and their Possible teleconnection Mechanisms using wavelet based quantitative approach

Houssem Terwayet Bayouli, Wanchang Zhang, Olfa Terwayet Bayouli, Zhijie Zhang, Qianying Ma
article en
2 citations

Abstract

• Hydro-meteorological drought conditions in Tunisia are evaluated using multiple drought indices. • Tunisia faces severe drought hazards with high stability and low recovery probabilities. • GDDI identifies more frequent and prolonged droughts than traditional drought indices. • Robust connections between ENSO and droughts are found in Tunisia. • Groundwater drought occurrence in Tunisia is predominantly influenced by anthropogenic activities. Climate change and recurrent extreme climatic events have intensified the vulnerability of water-stressed regions like Tunisia to droughts, severely impact agriculture, the economy, and society. This study analyzes hydro-meteorological drought patterns using the Gravity Recovery and Climate Experiment (GRACE) satellite-derived Groundwater Drought Index (GGDI), alongside traditional indices, including Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI). A stochastic analysis of monthly SPEI-GGDI values was conducted using a first-order Markov chain model, to investigate regional drought hazards formation, persistence, and evolution. Pearson’s correlation coefficient and wavelet coherence were applied to evaluate interactions among indices and their teleconnections with large-scale climate patterns. Results reveal persistent droughts, with extreme events exhibiting high stability and low recovery probabilities. The most severe groundwater drought occurred in 2014–2015, averaging a GGDI value of −1.36, while 2002–2003 was the driest based on SPEI, SPI, and SRI, averaging −1.9. Correlation analysis highlights complex interactions between meteorological and hydrological droughts, with GDDI-identified droughts exhibit greater severity in frequency, intensity, and duration, indicating significant anthropogenic influence. El Niño-Southern Oscillation (ENSO) significantly influenced drought evolution, with intense negative phases exacerbating severity. This study highlights the potential of GRACE satellite data for integrated drought monitoring and provides novel insights for developing sustainable drought management strategies in Tunisia.

International Journal of Applied Earth Observation and GeoinformationVol. 137
Utah State University (US), Shanxi University (CN), Chinese Academy of Sciences (CN), Beijing Institute of Big Data Research (CN), Institut National Agronomique de Tunisie (TN), Aerospace Information Research Institute (CN), University of Chinese Academy of Sciences (CN), Taiyuan University of Technology (CN)
Third World Academy of Sciences, National Major Science and Technology Projects of China
Climate action
Openalex Percentile: Top 15%
Hydrology and Drought Analysis
2.21
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