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<p>Global climate change has intensified drought impacts in arid regions, posing significant threats to ecological and socioeconomic stability. This study presents a comprehensive assessment of drought dynamics in Kachchh District, Gujarat, India, using 53 years (1970–2022) of rainfall data from nine meteorological stations. Multi-temporal Standardized Precipitation Index (SPI) analysis revealed pronounced spatial heterogeneity in annual rainfall (363.6–522.7&#xa0;mm), with coastal stations receiving higher precipitation than interior regions due to topographic and climatic influences. Modified Mann-Kendall trend analysis indicated encouraging recovery trends in moisture availability over the past two decades, despite persistent rainfall volatility. Drought propagation analysis identified three distinct pathways, with 100% of meteorological droughts cascading into agricultural and hydrological droughts. The Meteorological→Agricultural pathway exhibited a mean lag of 3.02 months, whereas the Agricultural→Hydrological and direct Meteorological→Hydrological pathways showed longer lags of 4.95 and 6.81 months, respectively. A novel contribution of this study is the quantification of agricultural buffering in true arid environments, with a 17% reduction in drought severity during propagation from meteorological to agricultural stages. The Meteorological→Agricultural pathway emerged as the optimal early warning indicator, offering a 1–2-month lead time with high correlation (<i>r</i> = 0.75–0.80). Extreme value analysis using the Generalized Extreme Value distribution estimated return periods of 10–25 years for severe droughts (SPI-12 &lt; − 2.0), with marked spatial variability. These results support the implementation of real-time, multi-scale drought early warning systems and region-specific water management strategies, providing critical guidance for climate adaptation planning in arid landscapes.</p>

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Drought propagation dynamics in arid regions: multi-temporal analysis of cascade effects, early warning potential, and risk assessment in Kachchh, western India

  • 期刊论文

  • 发表日期：

    2026年3月21日

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  • Saurabh Vaza,
  • Shilpesh Rana,
  • Naveena K.,
  • Surendran U.

摘要

Global climate change has intensified drought impacts in arid regions, posing significant threats to ecological and socioeconomic stability. This study presents a comprehensive assessment of drought dynamics in Kachchh District, Gujarat, India, using 53 years (1970–2022) of rainfall data from nine meteorological stations. Multi-temporal Standardized Precipitation Index (SPI) analysis revealed pronounced spatial heterogeneity in annual rainfall (363.6–522.7 mm), with coastal stations receiving higher precipitation than interior regions due to topographic and climatic influences. Modified Mann-Kendall trend analysis indicated encouraging recovery trends in moisture availability over the past two decades, despite persistent rainfall volatility. Drought propagation analysis identified three distinct pathways, with 100% of meteorological droughts cascading into agricultural and hydrological droughts. The Meteorological→Agricultural pathway exhibited a mean lag of 3.02 months, whereas the Agricultural→Hydrological and direct Meteorological→Hydrological pathways showed longer lags of 4.95 and 6.81 months, respectively. A novel contribution of this study is the quantification of agricultural buffering in true arid environments, with a 17% reduction in drought severity during propagation from meteorological to agricultural stages. The Meteorological→Agricultural pathway emerged as the optimal early warning indicator, offering a 1–2-month lead time with high correlation (r = 0.75–0.80). Extreme value analysis using the Generalized Extreme Value distribution estimated return periods of 10–25 years for severe droughts (SPI-12 < − 2.0), with marked spatial variability. These results support the implementation of real-time, multi-scale drought early warning systems and region-specific water management strategies, providing critical guidance for climate adaptation planning in arid landscapes.