Zhao et al. (2025) Evaluating drought conditions and predicting return periods with the standard soil moisture index: a three-threshold run theory and GH copula approach

Identification

• Journal: Stochastic Environmental Research and Risk Assessment
• Year: 2025
• Date: 2025-11-16
• Authors: Jun Zhao, Xiaodong Wang, Jinchao Xu, Nuo Chen, Min Liu, Y. B. Zhao, Sadashiv Chaturvedi
• DOI: 10.1007/s00477-025-03121-x

Research Groups

• School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, Jiangsu Province, China
• Nanjing Hydraulic Research Institute, Nanjing, Jiangsu Province, China
• College of Hydraulic and Architectural Engineering, Tarim University, Alar, Xinjiang Uygur Autonomous Region, China
• Dipartimento di Ingegneria Civile, Edile e Ambientale (DICEA), University of Naples Federico II, Naples, Italy
• Institute of Environment and Sustainable Development (IESD), Banaras Hindu University, Varanasi, India
• IUSS, Pavia, University Institute of Higher Studies, Pavia, Italy

Short Summary

This study develops an integrated methodology using the Standard Soil Moisture Index (SSMI), a three-threshold run theory, and the GH copula function to identify agricultural drought processes and predict their return periods. The approach, validated in the Huai River Basin, demonstrates high consistency with observational data and effectively quantifies drought duration and severity for improved risk assessment.

Objective

• To develop a methodological framework that integrates the Standard Soil Moisture Index (SSMI), the three-threshold run theory, and the GH copula function to overcome existing limitations in drought identification and characterization.
• To construct the SSMI using remote sensing data, ensuring accurate threshold determination and establishing standardized criteria for drought process identification.
• To quantify drought event frequency and return period to offer a comprehensive and systematic approach for resolving challenges in drought identification and sub-drought event combination analysis.

Study Configuration

• Spatial Scale: Huai River Basin, China (approximately 270,000 square kilometers). Satellite data resolution: 0.25° × 0.25° (approximately 756.25 square kilometers per grid point).
• Temporal Scale: 1979–2016 (38 years) for soil moisture data. Drought analysis conducted at monthly, 3-monthly, 6-monthly, and 12-monthly time scales.

Methodology and Data

• Models used:
  • Standard Soil Moisture Index (SSMI)
  • Empirical Probability Distribution Function (ePDF) / Kernel Density Estimation (KDE) for SSMI calculation.
  • Three-threshold run theory for drought process identification (using thresholds R0, R1, R2).
  • Pearson type III (P-III) distribution for fitting marginal distributions of drought duration and severity.
  • GH Copula function for establishing the joint distribution of drought duration and severity, and for calculating return periods.
• Data sources:
  • Essential Climate Variable (ECV)-derived Soil Moisture (SM) data from multiple European Space Agency (ESA) satellites (Edition 3.3).
  • Measured soil moisture content at agricultural weather stations (e.g., Chahua, Yingshang) for validation (2002–2010).

Main Results

• ECV-based soil moisture data showed high consistency with agricultural station observations, with an average absolute relative error less than 8% from 2002 to 2010.
• The SSMI effectively characterized drought conditions, exhibiting an approximate normal distribution in inter-annual changes and reflecting a spatial trend of decreasing wetness from southeast to northwest in the Huai River Basin.
• The three-threshold run theory successfully identified major severe drought events in the Huai River Basin (e.g., 1978–1979, 1987–1989, 2002–2003, and 2014), with identified drought periods aligning well with historical records.
• The GH Copula function proved suitable for modeling the joint distribution of drought duration and severity, particularly for upper-tail and positive correlations, accurately calculating drought return periods.
• Analysis of nine stations indicated that Lu'an, Fuyang, Bengbu, and Kaifeng experienced more severe drought conditions (longer duration, higher intensity, shorter return periods) compared to Xinyang and Zhumadian.
• A discernible trend of increasing drought severity was observed from coastal areas towards the inland, and the southern Huai River Basin experienced frequent droughts despite abundant precipitation.

Contributions

• Proposes a novel, integrated methodological framework combining SSMI, three-threshold run theory, and the GH copula function for comprehensive agricultural drought identification and return period prediction.
• Validates the effectiveness of remote sensing-derived SSMI for drought assessment, demonstrating its superior capability in capturing regional drought dynamics compared to traditional methods.
• Develops a robust and generalizable method for determining drought index thresholds based on actual drought frequency and WMO classification, addressing a critical challenge in drought research.
• Quantifies the joint probability and return periods of drought duration and severity using a GH Copula, providing a more comprehensive and statistically robust approach to drought risk assessment than single-variable analyses.
• Offers theoretical support and a reproducible, quantifiable methodology for natural hazard prevention, early warning systems, agricultural management, and resilient water resource planning in drought-prone regions.

Funding

• National Key Research and Development Program of China (Nos. 2021YFC3201100 and 2022YFC3202300)
• China Postdoctoral Science Foundation (Nos. 2020T130309 and 2019M651892)
• Jiangsu Water Resources Science and Technology Project, China (Nos. 2020022 and 2021024)
• Jiangsu Province Key Research and Development Project, China (No.BE2020633)
• Jiangbei New Area Key Research and Development Project, Jiangsu Province, China (No. ZDYF20200129)
• Yili Science and Technology Project, China (No.YZ2022A005)

Citation

@article{Zhao2025Evaluating,
  author = {Zhao, Jun and Liu, Yu and Wang, Xiaodong and Xu, Jinchao and Chen, Nuo and Liu, Min and Zhao, Y. B. and Chaturvedi, Sadashiv},
  title = {Evaluating drought conditions and predicting return periods with the standard soil moisture index: a three-threshold run theory and GH copula approach},
  journal = {Stochastic Environmental Research and Risk Assessment},
  year = {2025},
  doi = {10.1007/s00477-025-03121-x},
  url = {https://doi.org/10.1007/s00477-025-03121-x}
}