Wang et al. (2026) Subseasonal Ensemble Prediction of the 2024 Abrupt Drought-to-Flood Transition in Henan Province, China

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Identification

• Journal: MDPI (MDPI AG)
• Year: 2026
• Date: 2026-03-07
• Authors: Yifei Wang, Xing Yuan, Shiyu Zhou
• DOI: 10.3390/w18050635

Research Groups

Not explicitly mentioned in the provided text.

Short Summary

This study developed a three-dimensional method using soil moisture percentiles to identify and evaluate the spatiotemporal evolution of an abrupt drought-to-flood transition (ADFT) event in Henan Province, China, in 2024, using ECMWF S2S reforecasts. It found that while the ECMWF model captured the transition at a 1-week lead, its skill significantly decreased at a 2-week lead due to model errors and atmospheric circulation biases.

Objective

• To develop a three-dimensional identification method for spatiotemporally detecting abrupt drought-to-flood transition (ADFT) events using soil moisture percentiles.
• To evaluate the deterministic and probabilistic forecast skill of ECMWF S2S reforecasts for the 2024 ADFT event in Henan Province, China, at subseasonal scales (1-week and 2-week leads).

Study Configuration

• Spatial Scale: Henan Province, China.
• Temporal Scale: 2024 ADFT event; subseasonal prediction leads of 1 week and 2 weeks.

Methodology and Data

• Models used: ECMWF S2S reforecasts.
• Data sources: Soil moisture percentiles (derived from ECMWF S2S reforecasts).

Main Results

• The ECMWF ensemble mean successfully captured the abrupt drought-to-flood transition at a 1-week lead.
• At a 2-week lead, the spatial extent of the transition was substantially underpredicted.
• Probabilistic forecast skill (Brier skill scores) at a 1-week lead were 0.38 for drought, 0.57 for transition, and 0.38 for flood stages.
• These Brier skill scores dropped sharply at a 2-week lead, particularly for the transition and flood stages.
• The decreased forecast skill at longer leads is attributed to internal dynamical errors within the model and biases in the positions of subtropical high- and low-pressure systems.

Contributions

• Proposed a novel three-dimensional identification method for spatiotemporally detecting abrupt drought-to-flood transition (ADFT) events.
• Provided a comprehensive assessment of a numerical model's capability to predict a compound extreme event (ADFT) from both deterministic and probabilistic perspectives at subseasonal scales.
• Highlighted the critical role of accurate atmospheric circulation representation for achieving skillful subseasonal predictions of compound extreme events.

Funding

Not explicitly mentioned in the provided text.

Citation

@article{Wang2026Subseasonal,
  author = {Wang, Yifei and Yuan, Xing and Zhou, Shiyu},
  title = {Subseasonal Ensemble Prediction of the 2024 Abrupt Drought-to-Flood Transition in Henan Province, China},
  journal = {MDPI (MDPI AG)},
  year = {2026},
  doi = {10.3390/w18050635},
  url = {https://doi.org/10.3390/w18050635}
}