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<p>Soil moisture (SM) serves as a crucial linkage between the terrestrial water and energy cycles. Remote sensing technology enables real-time monitoring of SM across extensive areas, critical for constructing a drought index aligning with drought conditions. This study investigates agricultural drought identification and return period calculation using the Standard Soil Moisture Index (SSMI), employing the three-threshold run theory and GH copula function. Initially, Essential Climate Variable (ECV)-derived SM data from multiple European Space Agency (ESA) satellites (1979–2016) in the Huai River Basin are validated against agricultural weather station observations. Remote sensing SM is transformed into SSMI via empirical probability distribution function (ePDF) fitting. Subsequently, the three-threshold run theory identifies drought processes based on SSMI. Threshold values for drought levels are determined via a combination of drought classification methods and actual drought frequency. Drought duration is defined as the time interval when the drought index falls below the threshold, and severity as the cumulative difference from the threshold within that interval. The fitting curve method calculates marginal distributions of drought duration and severity, establishing a joint distribution function using the GH copula. This allows estimation of return periods for drought events in the Huai River Basin. Results indicates, high consistency between ECV-based SM and observational data, with SSMI effectively characterizing drought drivers and processes, aiding widespread drought research.</p>

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Evaluating drought conditions and predicting return periods with the standard soil moisture index: a three-threshold run theory and GH copula approach

  • 期刊论文

  • 发表日期：

    2025年11月16日

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  • Jun Zhao,
  • Yu Liu,
  • Xiaodong Wang,
  • Jinchao Xu,
  • Nuo Chen,
  • Min Liu,
  • Yuhan Zhao,
  • Sadashiv Chaturvedi

摘要

Soil moisture (SM) serves as a crucial linkage between the terrestrial water and energy cycles. Remote sensing technology enables real-time monitoring of SM across extensive areas, critical for constructing a drought index aligning with drought conditions. This study investigates agricultural drought identification and return period calculation using the Standard Soil Moisture Index (SSMI), employing the three-threshold run theory and GH copula function. Initially, Essential Climate Variable (ECV)-derived SM data from multiple European Space Agency (ESA) satellites (1979–2016) in the Huai River Basin are validated against agricultural weather station observations. Remote sensing SM is transformed into SSMI via empirical probability distribution function (ePDF) fitting. Subsequently, the three-threshold run theory identifies drought processes based on SSMI. Threshold values for drought levels are determined via a combination of drought classification methods and actual drought frequency. Drought duration is defined as the time interval when the drought index falls below the threshold, and severity as the cumulative difference from the threshold within that interval. The fitting curve method calculates marginal distributions of drought duration and severity, establishing a joint distribution function using the GH copula. This allows estimation of return periods for drought events in the Huai River Basin. Results indicates, high consistency between ECV-based SM and observational data, with SSMI effectively characterizing drought drivers and processes, aiding widespread drought research.