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

Abstract

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.