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<p>This study evaluates the consistency between three precipitation-based drought indices—the Standardized Precipitation Index (SPI), Percent of Normal Index (PNI), and Deciles Drought Index (DDI)—in the Asi Basin, Türkiye. Using 32–52&#xa0;years of precipitation data from 11 stations, drought conditions were analyzed at monthly, seasonal, and annual scales. To ensure statistical robustness, Mann–Kendall trend tests were applied, and Bartlett-corrected effective sample sizes were used to account for temporal autocorrelation in correlation analyses. Additionally, a sensitivity analysis using a common 30th percentile threshold was conducted to evaluate the influence of classification schemes. The results reveal strong agreement between DDI and PNI, while consistency with SPI decreases during dry summer months (July–August) due to the mathematical instability of PNI under zero-dominated rainfall regimes. Trend analysis identified a statistically significant drying trend at the Hassa station (p &lt; 0.05) across all indices, despite no basin-wide trend being detected. The use of 95% confidence intervals highlighted increased uncertainty at longer temporal scales due to autocorrelation. For operational purposes, it is recommended to interpret PNI with caution during hyper-arid periods and to use SPI as a proxy for potential hydrological drought. Overall, the findings emphasize the necessity of multi-index frameworks that account for both climatic variability and methodological constraints.</p>

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Evaluation of the Consistency Between SPI, PNI, and DDI Drought Indices in the Asi Basin

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Mehmet Can Topboğazı,
  • Seda Sever,
  • Recep Yurtal

摘要

This study evaluates the consistency between three precipitation-based drought indices—the Standardized Precipitation Index (SPI), Percent of Normal Index (PNI), and Deciles Drought Index (DDI)—in the Asi Basin, Türkiye. Using 32–52 years of precipitation data from 11 stations, drought conditions were analyzed at monthly, seasonal, and annual scales. To ensure statistical robustness, Mann–Kendall trend tests were applied, and Bartlett-corrected effective sample sizes were used to account for temporal autocorrelation in correlation analyses. Additionally, a sensitivity analysis using a common 30th percentile threshold was conducted to evaluate the influence of classification schemes. The results reveal strong agreement between DDI and PNI, while consistency with SPI decreases during dry summer months (July–August) due to the mathematical instability of PNI under zero-dominated rainfall regimes. Trend analysis identified a statistically significant drying trend at the Hassa station (p < 0.05) across all indices, despite no basin-wide trend being detected. The use of 95% confidence intervals highlighted increased uncertainty at longer temporal scales due to autocorrelation. For operational purposes, it is recommended to interpret PNI with caution during hyper-arid periods and to use SPI as a proxy for potential hydrological drought. Overall, the findings emphasize the necessity of multi-index frameworks that account for both climatic variability and methodological constraints.