Huang et al. (2026) Assessment of dynamic drought risk and transition characteristics by combining an indicator-based approach and Markov chain model

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-02-01
• Authors: Bin Huang, Tao Peng, Tianyi Fan, Vijay P. Singh, Xiaohua Dong, Qingxia Lin, Wenjuan Chang, Ji Liu, Yan Huang, Jiali Guo, Yinghai Li, Dan Yu
• DOI: 10.1007/s11069-025-07820-0

Research Groups

• Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering, and Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang, China
• Hunan Provincial Water Resources and Hydropower Survey, Design, Planning and Research Co., Ltd, Changsha, China
• Department of Biological and Agricultural Engineering, and Zachry Department of Civil and Environmental Engineering, Texas A&M University, College Station, TX, USA
• National Water and Energy Center, UAE University, Al Ain, UAE

Short Summary

This study assessed dynamic drought risk and its spatial transition characteristics in Hunan Province, China, from 1960 to 2021, by integrating an indicator-based approach (vulnerability, exposure, resilience) with traditional and spatial Markov chain models, revealing significant spatial autocorrelation and neighborhood influence on drought risk transitions.

Objective

• To identify the spatiotemporal evolution of meteorological drought in Hunan Province.
• To assess regional comprehensive drought risk using three risk indicators: vulnerability, exposure, and resilience.
• To quantitatively analyze the spatiotemporal transition patterns of drought risk, considering the influence of neighboring regions.

Study Configuration

• Spatial Scale: Hunan Province, China (area of 2.118 × 10^5 km^2), at the county level.
• Temporal Scale: 1960–2021 (62 years).

Methodology and Data

• Models used: Standardized Precipitation Index (SPI), Run theory, Breaks for Additive Seasonal and Trend (BFAST) algorithm, Mann–Kendall (MK) test, Indicator-based drought risk assessment (Vulnerability, Exposure, Resilience), Traditional Markov chain model, Spatial Markov chain model, Global Moran’s I index, Random Forest (RF) model, Cross Wavelet Transform (XWT).
• Data sources:
  • Monthly precipitation data from 83 meteorological gauges in Hunan Province (1960–2021), obtained from the Hunan Meteorological Bureau.
  • Eight climate indices (El Niño–Southern Oscillation (ENSO) - Nino3.4 index, Atlantic Multidecadal Intergenerational Oscillation (AMO), Indian Ocean Dipole (IOD) - Dipole Mode Index (DMI), Arctic Oscillation (AO), North Pacific Oscillation (NPO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Southern Oscillation Index (SOI)) from the Climate Prediction Centre of the National Oceanic and Atmospheric Administration (NOAA).

Main Results

• The annual SPI series (SPI-12) in Hunan Province (1960–2021) exhibited a fluctuating trend: an upward trend from 1960 to 1993, followed by a downward trend between 1994 and 2014, and a subsequent upward tendency from 2015 to 2021. Four significant change points were detected in 1984, 1993, 2002, and 2014.
• Spatially, an increasing trend of SPI-12 was observed in most areas of Hunan Province, except for Shaoyang, Hengyang, and the northern parts of Xiangxi and Yongzhou, which were identified as drought-prone.
• Approximately 24.6% of counties had medium–high drought risk, and 23.8% had high drought risk. High-risk areas were predominantly clustered in the northwestern and southern regions of Hunan, while lower-risk areas were concentrated in the northeast and southwest.
• Global Moran’s I analysis confirmed a significant positive spatial autocorrelation of drought risk at the county level, with the intensity of spatial aggregation gradually strengthening over time.
• Traditional Markov chain analysis indicated that drought risk was unstable and tended to shift toward lower risk levels, with low-risk regions exhibiting greater stability in maintaining their initial state.
• Spatial Markov chain analysis revealed that drought risk transitions were influenced by both a county's intrinsic risk level and the risk levels of its neighboring counties, demonstrating a distinct spatial spillover effect. 89% of counties experienced a simultaneous downward transition of regional and neighborhood risk categories, mainly in the western and southern parts of Hunan Province.
• Teleconnection factors, particularly Nino3.4 and AO, were identified as the most important drivers of meteorological drought in Hunan Province, showing significant correlations with drought risk indicators (vulnerability, exposure, resilience) across various periods.

Contributions

• Developed an innovative drought risk assessment framework that integrated an indicator-based approach (vulnerability, exposure, and resilience) and a Markov chain model.
• Quantitatively analyzed the spatiotemporal transition patterns of drought risk, specifically incorporating the influence of neighboring regions using a spatial Markov chain model, addressing a limitation in previous drought evolution studies.
• Provided a deeper understanding of regional drought risk patterns and transition dynamics, enriching the methodological framework for drought risk mitigation.
• Offered valuable information and policy suggestions for regional drought early warning and water resources management in Hunan Province and similar drought-prone regions.

Funding

• National Natural Science Foundation of China (No. U2340211, 52479018)
• Natural Science Foundation of Hubei Province of China (2024AFD212)
• Hubei Provincial Key Laboratory of Construction and Management in Hydropower Engineering, Three Gorges University, China (2023KSD30)

Citation

@article{Huang2026Assessment,
  author = {Huang, Bin and Peng, Tao and Fan, Tianyi and Singh, Vijay P. and Dong, Xiaohua and Lin, Qingxia and Chang, Wenjuan and Liu, Ji and Huang, Yan and Guo, Jiali and Li, Yinghai and Yu, Dan},
  title = {Assessment of dynamic drought risk and transition characteristics by combining an indicator-based approach and Markov chain model},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-025-07820-0},
  url = {https://doi.org/10.1007/s11069-025-07820-0}
}