Ding et al. (2026) Three-dimensional identification and attribution of flash and slow agricultural droughts in the North China Plain

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-01-26
• Authors: Yiding Ding, Haishen Lü, Yonghua Zhu, ALİ LEVENT YAĞCI, Qiqi Gou, Jianbin Su, Mengwei Chen, Yuan Yao, Wen Liu, Jiaying Liu, Yinghao Fu
• DOI: 10.1016/j.jhydrol.2026.135030

Research Groups

• The National Key Laboratory of Water Disaster Prevention, College of Hydrology and Water Resources, Hohai University, Nanjing, China
• College of Geography and Remote Sensing, Hohai University, Nanjing, China
• Geomatics Engineering Department, Gebze Technical University, Kocaeli, Turkey
• School of Atmospheric Sciences, Key Laboratory of Mesoscale Severe Weather/Ministry of Education, Nanjing University, Nanjing, China
• Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing, China
• Nanjing Institute of Geography and Limnology, Chinese Academy of Science, Nanjing, China

Short Summary

This study proposes a novel three-dimensional framework integrating DBSCAN spatiotemporal clustering with Local Transfer Entropy (LTE) to identify and attribute flash and slow agricultural droughts in the North China Plain. It reveals that precipitation deficits are the primary driver of drought occurrences, with flash droughts rapidly intensified by water vapor flux divergence and temperature anomalies, while slow droughts are governed by prolonged precipitation deficits.

Objective

• To propose a novel three-dimensional framework (DBSCAN-LTE) to disentangle the stage-dependent contributions of key meteorological factors and clarify the physical mechanisms governing the initiation and acceleration of flash and slow agricultural droughts in the North China Plain (NCP).

Study Configuration

• Spatial Scale: North China Plain (NCP)
• Temporal Scale: 1951–2023 (73 years)

Methodology and Data

• Models used: DBSCAN spatiotemporal clustering, Local Transfer Entropy (LTE), integrated into a novel three-dimensional framework (DBSCAN-LTE).
• Data sources: Historical meteorological observations/reanalysis (inferred from the analysis of meteorological factors over the study period).

Main Results

• A total of 155 agricultural drought events were reconstructed in the NCP between 1951 and 2023.
• These events were characterized by an average onset speed of 7.9 percentile pentad⁻¹ and a mean maximum area of 75,000 km².
• The drought-affected area in NCP exhibited an annual expansion trend of 458.31 km² over the past seven decades.
• LTE identified precipitation deficits as the primary driver of drought occurrences, contributing 46% of the total information flow.
• Flash droughts are rapidly intensified by sharp anomalies in water vapor flux divergence (r = 0.57) and temperature (r = 0.40) during the onset phase (p < 0.05).
• Slow droughts are predominantly governed by prolonged precipitation deficits.

Contributions

• Introduces a novel three-dimensional framework (DBSCAN-LTE) for identifying and attributing agricultural droughts, addressing limitations of traditional drought indices in representing spatiotemporal propagation and divergent causal drivers.
• Provides a detailed understanding of the stage-dependent contributions of meteorological factors and the distinct physical mechanisms underlying the initiation and acceleration of flash versus slow droughts.
• Reconstructs a comprehensive dataset of agricultural drought events in the North China Plain over a 73-year period.

Funding

• Not specified in the provided text.

Citation

@article{Ding2026Threedimensional,
  author = {Ding, Yiding and Lü, Haishen and Zhu, Yonghua and YAĞCI, ALİ LEVENT and Gou, Qiqi and Su, Jianbin and Chen, Mengwei and Yao, Yuan and Liu, Wen and Liu, Jiaying and Fu, Yinghao},
  title = {Three-dimensional identification and attribution of flash and slow agricultural droughts in the North China Plain},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135030},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135030}
}