Tang et al. (2026) Characterizing extreme climate events at different time scales and their contributions to agricultural drought and flooding areas

Identification

• Journal: Natural Hazards
• Year: 2026
• Date: 2026-02-25
• Authors: Rong Tang, Ying Xia, Junda Du, Long Qian, Hui Wang, Yangzan Ou
• DOI: 10.1007/s11069-026-07985-2

Research Groups

• College of Water Resource and Civil Engineering, Hunan Agricultural University, Changsha, People’s Republic of China
• College of Water Resources and Hydropower, Changjiang Institute of Technology, Wuhan, People’s Republic of China

Short Summary

This study investigates multi-scale extreme climate events (1961–2020) in Hunan Province, China, and their contributions to agricultural drought and flooding using Modified Mann–Kendall, correlation, and Random Forest models, revealing asymmetric hydroclimatic shifts with distinct thermal-driven drought and precipitation-controlled flood mechanisms.

Objective

• To analyze the long-term spatio-temporal changes of climate indicators in Hunan Province, China, from 1961 to 2020 across seasonal, hydrological, and crop-growing seasonal scales.
• To analyze the correlations between areas impacted by drought and flood and the climate indicators.
• To reveal the dominant factors affecting the areas impacted by drought and flood using Random Forest models.

Study Configuration

• Spatial Scale: Hunan Province, China (108°47′–114°15′E, 24°38′–30°08′N), covering a total area of 211,800 square kilometers. Analysis distinguishes between lake and non-lake districts.
• Temporal Scale: 1961–2020 (60 years). Analysis conducted at seasonal (spring, summer, autumn, winter), hydrological (flood-season: April-September; non-flood season: October–March), and crop-growing seasonal (rice: 25 April–25 August; rapeseed: 25 September–10 May of the following year) scales.

Methodology and Data

• Models used:
  • Modified Mann–Kendall (MMK) trend analysis for significance of time series trends.
  • Pearson correlation coefficient for assessing relationships between climate indicators and affected areas.
  • Random Forest (RF) models for identifying critical driving factors and assessing variable importance.
• Data sources:
  • Daily precipitation (P, in millimeters), maximum temperature (Tmax, in degrees Celsius), minimum temperature (Tmin, in degrees Celsius), and mean temperature (Tm, in degrees Celsius) from 27 national meteorological stations in Hunan Province (1961–2020), obtained from the China Meteorological Data Network.
  • Area affected by floods and droughts (in hectares) in Hunan Province (1988–2020), obtained from the China Agricultural Statistical Yearbook.
  • Start and end dates of rice and rapeseed growth stages from agro-meteorological stations in Hunan Province, obtained from the China Meteorological Data Network.
  • Extreme climate indicators defined by the Expert Team on Climate Change Detections and Indices (ETCCDI).

Main Results

• Precipitation Trends: Precipitation decreased in spring and autumn (significant in 3.7% of stations for spring) but increased in summer and winter (96.6% of stations). Heavy rainfall days (R50) surged in 77.8% of stations during flood-seasons but decreased in 92.6% during non-flood seasons. R50 increased during rice (77.8%) and rapeseed (88.0%) growing seasons.
• Temperature Trends: Temperature extremes shifted towards warm events, with Warm days (TX90p) and summer days (SU) increasing, while cold nights (TN10p) and frost days (FD) decreased at over 92.6% of stations. Mean, maximum, and minimum temperatures generally showed an upward trend across all seasons and hydrological periods.
• Regional Heterogeneity: Lake districts exhibited "humid-warming" trends (increased precipitation, significant warming, frequent extreme heat) compared to "dry-cooling" characteristics in non-lake regions (reduced precipitation, declining summer Tmax). Lake areas showed simultaneous improvements in heavy precipitation (R50 increases) and drought mitigation (CDD decreases).
• Climate-Disaster Correlations: Correlations between climate indicators and agricultural water disaster-affected areas were most significant during the rice-growing season.
  • Drought-affected areas were negatively correlated with precipitation (r = -0.58) and R50 (r = -0.55), and positively correlated with TX90p (r = 0.39).
  • Flood-affected areas were positively correlated with precipitation (r = 0.62) and R50 (r = 0.62), and negatively correlated with Tm (r = -0.34) and TX90p (r = -0.37).
• Dominant Factors (RF Modeling):
  • Mean temperature (Tm) and consecutive dry days (CDD) were dominant cross-temporal climatic factors for drought.
  • Precipitation best explained variations in autumn/rapeseed-season flood.
  • Mechanistically, droughts were temperature-dependent (Tm, Tmax, Tmin), while floods were correlated with precipitation and extreme temperature events (TX90p, TN10p).
  • RF model performance for predicting affected areas showed R² values up to 0.78 for drought (rice season) and 0.90 for flood (rapeseed season).

Contributions

• Provided a comprehensive multi-scale (seasonal, hydrological, crop-growing seasonal) spatio-temporal analysis of extreme climate events and their complex relationships with agricultural drought and flood affected areas in Hunan Province, a critical agricultural region in China.
• Identified distinct regional vulnerabilities, showing that lake districts amplify climate risks with stronger warming and precipitation extremes compared to non-lake areas.
• Revealed fundamentally divergent mechanisms for agricultural disasters: droughts are primarily thermally driven, while floods are mainly precipitation-controlled but coupled with temperature extremes.
• Offered a scientific basis for developing regionally tailored adaptation strategies, such as water-saving measures for drought-prone uplands and flood-resilient infrastructure for lake basins, to enhance food security under intensifying climate volatility.

Funding

• Hunan Provincial Natural Science Foundation of China (Grant No. 2025JJ60379 and No. 2024JJ6251)
• Scientific Research project of Hunan Education Department, China (Grant No. 24B0211 and No. 24A0161)
• Hubei Provincial Natural Science Foundation of China (Grant No. 2025AFB861)

Citation

@article{Tang2026Characterizing,
  author = {Tang, Rong and Xia, Ying and Du, Junda and Qian, Long and Wang, Hui and Ou, Yangzan},
  title = {Characterizing extreme climate events at different time scales and their contributions to agricultural drought and flooding areas},
  journal = {Natural Hazards},
  year = {2026},
  doi = {10.1007/s11069-026-07985-2},
  url = {https://doi.org/10.1007/s11069-026-07985-2}
}