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<p>This study proposes a percentile-based Dynamic Threshold Run Theory (pDTRT) for drought event identification, using the Standardized Soil Moisture Index (SSI) and European Space Agency’s (ESA) Climate Change Initiative (CCI) Soil Moisture (SM) (ESACCI SM) data. Unlike traditional methods that use single or uniform thresholds across all grids to identify a drought event, pDTRT applies variable thresholds based on SSI percentiles, accounting for spatial variability in SM dynamics. The proposed framework is applied to the Godavari Basin, India, and results in systematic differences in drought characteristics, such as duration, frequency, and intensity. The method captured significant spatial variability, reflecting heterogeneous SM conditions across the basin. The use of ESACCI SM data further showcased the potential of satellite-based SM data for consistent drought assessment over large areas. Overall, the study underscores the method’s broad consistency and nuanced variations, highlighting spatial heterogeneity and methodological sensitivity, necessitating grid-specific considerations in drought assessment.</p>

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A Percentile-Based Dynamic Threshold Run Theory (pDTRT) for Characterizing Agricultural Drought Using ESACCI Soil Moisture

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • Hussain Palagiri,
  • Manali Pal

摘要

This study proposes a percentile-based Dynamic Threshold Run Theory (pDTRT) for drought event identification, using the Standardized Soil Moisture Index (SSI) and European Space Agency’s (ESA) Climate Change Initiative (CCI) Soil Moisture (SM) (ESACCI SM) data. Unlike traditional methods that use single or uniform thresholds across all grids to identify a drought event, pDTRT applies variable thresholds based on SSI percentiles, accounting for spatial variability in SM dynamics. The proposed framework is applied to the Godavari Basin, India, and results in systematic differences in drought characteristics, such as duration, frequency, and intensity. The method captured significant spatial variability, reflecting heterogeneous SM conditions across the basin. The use of ESACCI SM data further showcased the potential of satellite-based SM data for consistent drought assessment over large areas. Overall, the study underscores the method’s broad consistency and nuanced variations, highlighting spatial heterogeneity and methodological sensitivity, necessitating grid-specific considerations in drought assessment.