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<p>Understanding the variability of long-term aridity is essential for the sustainable management of water resources in arid regions. This study evaluates the spatiotemporal trends and abrupt regime shifts in the annual and seasonal Aridity Index (AI) across Saudi Arabia from 1985 to 2022. The AI was computed as the ratio of precipitation to potential evapotranspiration (PET), with PET estimated using the Hargreaves-Samani method. Monotonic trends were analyzed using the Mann–Kendall (MK) test along with Sen’s slope, while structural breaks were identified through both the Pettitt test and cumulative sum (CUSUM) analysis. To control for multiple testing across the station network, a Benjamini–Hochberg false discovery rate (BH-FDR) correction was applied at Q = 0.05. The results reveal widespread negative trends in AI, with 83% of stations exhibiting a decline at the annual scale, 96% in winter, and 91% in spring. Significant decreasing trends at the station level are primarily concentrated in winter, with fewer trends observed at the annual scale, and none detected in spring, summer, or autumn. Attribution analysis indicates that the decline in cool-season AI is primarily linked to precipitation deficits, while rising PET serves as a secondary amplifying factor. Both change-point detection methods reveal a consistent shift in the late 1990s across the annual, winter, and spring series. The observed aridity transition broadly coincides with a phase shift in the Atlantic Multidecadal Oscillation (AMO). However, because the 1985–2022 record does not span a complete AMO cycle, this relationship should be interpreted as a statistical association rather than evidence of a causal mechanism. These findings highlight the significance of integrating seasonal diagnostics, attribution analysis, change-point detection, and multiple-testing control in assessments of hydroclimatic conditions in arid regions.</p>

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Long-term spatiotemporal evolution of annual and seasonal aridity across Saudi Arabia

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • Ali O. Alnahit,
  • Saleh H. Alhathloul

摘要

Understanding the variability of long-term aridity is essential for the sustainable management of water resources in arid regions. This study evaluates the spatiotemporal trends and abrupt regime shifts in the annual and seasonal Aridity Index (AI) across Saudi Arabia from 1985 to 2022. The AI was computed as the ratio of precipitation to potential evapotranspiration (PET), with PET estimated using the Hargreaves-Samani method. Monotonic trends were analyzed using the Mann–Kendall (MK) test along with Sen’s slope, while structural breaks were identified through both the Pettitt test and cumulative sum (CUSUM) analysis. To control for multiple testing across the station network, a Benjamini–Hochberg false discovery rate (BH-FDR) correction was applied at Q = 0.05. The results reveal widespread negative trends in AI, with 83% of stations exhibiting a decline at the annual scale, 96% in winter, and 91% in spring. Significant decreasing trends at the station level are primarily concentrated in winter, with fewer trends observed at the annual scale, and none detected in spring, summer, or autumn. Attribution analysis indicates that the decline in cool-season AI is primarily linked to precipitation deficits, while rising PET serves as a secondary amplifying factor. Both change-point detection methods reveal a consistent shift in the late 1990s across the annual, winter, and spring series. The observed aridity transition broadly coincides with a phase shift in the Atlantic Multidecadal Oscillation (AMO). However, because the 1985–2022 record does not span a complete AMO cycle, this relationship should be interpreted as a statistical association rather than evidence of a causal mechanism. These findings highlight the significance of integrating seasonal diagnostics, attribution analysis, change-point detection, and multiple-testing control in assessments of hydroclimatic conditions in arid regions.