Nagai et al. (2026) A Spatiotemporal Analysis of Heterogeneity and Non-Stationarity of Extreme Precipitation in the Ayeyarwady River Basin, Myanmar, and Their Linkages to Global Climate Variability

Identification

• Journal: Water
• Year: 2026
• Date: 2026-01-15
• Authors: Masahiko Nagai, Arnob Bormudoi
• DOI: 10.3390/w18020227

Research Groups

• Center for Research and Application of Satellite Remote Sensing (YUCARS), Yamaguchi University, Ube, Japan
• New Space Intelligence, Ube, Japan
• Faculty of Engineering, Assam downtown University, Guwahati, India

Short Summary

This study characterized distinct rainfall regimes, quantified non-stationary extreme precipitation, and identified teleconnections with global climate variability in the Ayeyarwady River Basin. It revealed four distinct monsoonal regimes with heterogeneous extreme event dynamics and regime-specific, non-stationary linkages to ENSO and IOD.

Objective

• To delineate and characterize distinct spatiotemporal rainfall regimes within the Ayeyarwady River Basin using unsupervised K-means clustering.
• To quantify the magnitude, frequency, and temporal evolution of extreme rainfall events for each identified regime through non-stationary Generalized Pareto Distribution (GPD) modeling.
• To investigate regime-specific teleconnections between regional precipitation extremes and large-scale climate oscillations (ENSO and IOD) using multivariate wavelet coherence analysis.

Study Configuration

• Spatial Scale: Ayeyarwady River Basin, Myanmar (approximately 412,500 km²), using gridded data with approximately 4 km (1/24th degree) resolution.
• Temporal Scale: 66 years (1958–2023), utilizing monthly precipitation data.

Methodology and Data

• Models used: K-means clustering, non-stationary Generalized Pareto Distribution (GPD) modeling (Peaks-Over-Threshold method with time-varying scale parameter), Wavelet Coherence Analysis (Morlet wavelet).
• Data sources: TerraClimate (high-resolution global dataset of monthly climate and water balance), HydroSHEDS database (basin boundary shapefile), Oceanic Niño Index (ONI), Indian Ocean Dipole (DMI).

Main Results

• Four distinct spatiotemporal rainfall clusters were delineated, exhibiting fundamentally different monsoonal characteristics with mean seasonal peaks ranging from 188 mm to 686 mm.
• Extreme precipitation behavior demonstrated substantial heterogeneity, with 100-year return periods varying from 501 mm in subdued northern zones (Cluster 2) to 983 mm in hyper-intense coastal regions (Cluster 4).
• GPD shape parameters (ξ) varied by cluster: negative for high-intensity clusters (e.g., -0.421 for Cluster 3, -0.279 for Cluster 4), implying a theoretical upper bound, and positive for drier northern regimes (e.g., 0.100 for Cluster 1, indicating heavy-tailed distributions with no theoretical upper limit).
• Wavelet coherence analysis revealed regime-specific teleconnections: Cluster 2 exhibited the strongest ENSO influence (0.536 coherence strength, 64-month median duration, peak activity in the 1960s), while Cluster 4 demonstrated unique IOD dominance (0.479 strength, 74-month duration) extending beyond annual timescales, with peak activity in the 1990s.
• Teleconnection effectiveness varied substantially across regimes (0.428–0.536 strength) with significant decadal non-stationarity, highlighting temporally evolving climate-rainfall relationships.

Contributions

• Developed a novel hybrid analytical framework integrating spatiotemporal clustering, non-stationary extreme value theory, and wavelet coherence analysis for the Ayeyarwady River Basin.
• Delineated and characterized distinct spatiotemporal rainfall regimes, overcoming limitations of basin-averaged approaches that obscure sub-regional dynamics.
• Quantified non-stationary extreme rainfall events for each regime, explicitly accounting for climate change-induced shifts in extreme event characteristics.
• Investigated regime-specific, time-frequency dependent teleconnections between regional precipitation extremes and large-scale climate oscillations (ENSO and IOD), revealing non-uniform and temporally evolving influences.
• Provided critical implications for differentiated flood risk assessment and climate-informed water resources management across Myanmar’s most vital river basin.

Funding

This research received no external funding.

Citation

@article{Nagai2026Spatiotemporal,
  author = {Nagai, Masahiko and Bormudoi, Arnob},
  title = {A Spatiotemporal Analysis of Heterogeneity and Non-Stationarity of Extreme Precipitation in the Ayeyarwady River Basin, Myanmar, and Their Linkages to Global Climate Variability},
  journal = {Water},
  year = {2026},
  doi = {10.3390/w18020227},
  url = {https://doi.org/10.3390/w18020227}
}