Methodological study demonstrates enhanced drought sensitivity and earlier detection using a modified daily index in China, indicating superior tracking of agricultural drought dynamics.
The daily standardized precipitation evapotranspiration index (DSPEI) aggregates water surplus and deficit series with equal-weight accumulation, which is inconsistent with the physical principle that recent impacts are prominent and distant impacts attenuate during drought accumulation process. In this study, nonlinear attenuation coefficients are constructed using an exponential function. The attenuation intensity is modulated by an attenuation parameter to quantify temporal attenuation of water surplus and deficit; therefore, a modified DSPEI (MDSPEI) incorporating such attenuation characteristics is proposed. China serves as the study area. The correlation coefficients between the water surplus-deficit series and soil moisture series are sequentially calculated under varying attenuation parameters, and the parameter corresponding to the maximum correlation coefficient is defined as the optimal value. The dataset is split into training and test sets for parameter calibration and applicability validation, respectively. Three approaches, namely, comparison with historical drought events, correlation analysis with the standardized soil moisture index (SSMI), and correlation analysis against drought disaster areas, are adopted to systematically verify the advantages of the MDSPEI. The results demonstrate that the MDSPEI presents higher sensitivity and earlier drought identification and can effectively capture intense drought evolution. The MDSPEI outperforms the DSPEI in correlation with the SSMI, suggesting better consistency with the physical nature of drought. Stronger negative correlations between the MDSPEI and drought-affected areas are observed at annual and annually averaged scales, indicating that the MDSPEI identifies and characterizes agricultural drought more realistically. This study contributes to theories of daily SPEI and offers new ideas for drought monitoring.
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Pei et al. (2026) studied this question.
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