Roohinia et al. (2025) Spatiotemporal connections in high precipitation events in Iran: Application of complex networks

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2025
• Date: 2025-11-20
• Authors: Mahnoor Roohinia, Banafsheh Zahraie
• DOI: 10.1016/j.ejrh.2025.102938

Research Groups

• School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran
• Water Institute, University of Tehran, Tehran, Iran

Short Summary

This study applies Complex Networks Theory, Event Synchronization (ES), and Event Coincidence Analysis (EC) to investigate synchronous and lagged teleconnections of high precipitation events in Iran and surrounding regions from 1980 to 2019, revealing shifts in critical moisture pathways and offering opportunities for seasonal forecasting.

Objective

• To investigate the complex correlation patterns and teleconnections of high precipitation events in Iran with surrounding regions.
• To determine how these interlinkages differ for concurrently occurring (synchronous) and time-lagged events.
• To assess the impact of global climate change on these interlinkages over time.

Study Configuration

• Spatial Scale: A large area covering East and West Asia, North Africa, and Europe, with a focus on Iran (25.06° N to 38.47° N latitude, 44.03° E to 63.33° E longitude). Data resolution is 2.5° x 2.5°.
• Temporal Scale: Monthly precipitation data from 1980 to 2019, analyzed using twenty-one 20-year annual sliding windows (e.g., 1980–1999, 1981–2000, ..., 2000–2019). Time lags of 2–3 months were considered for lagged analysis.

Methodology and Data

• Models used:
  • Complex Networks Theory
  • Event Synchronization (ES) for synchronous events
  • Event Coincidence Analysis (EC) for lagged events (2–3 month delay)
  • Network measures: Clustering coefficient and Betweenness centrality
  • High precipitation events defined as exceeding the 70th percentile in each grid cell.
  • Network links established when synchronization strength or coincidence rate is ≥ 70%.
• Data sources:
  • Global Precipitation Climatology Project (GPCP) dataset, providing monthly precipitation values.
  • GPCP data merges ground and satellite observations.

Main Results

• Synchronous (zero-lag) connections (ES):
  • Large-scale local precipitation clusters develop synchronously over both eastern and western Iran.
  • Central provinces (e.g., Isfahan, Fars) and northern Iran show a decrease in clustering coefficient over time, indicating weaker network interactions.
  • Khorasan Razavi, Khorasan Shomali, and western Iran exhibit relative stability in clustering coefficient.
  • Sistan & Baluchestan Province (southeast Iran) and the Pakistan border consistently show high betweenness centrality, acting as key conduits for precipitation.
  • Khuzestan province (southwest Iran) shows a sharp decline in betweenness centrality.
  • Significant shifts in synchronous connections were observed around the year 2000, with some pre-2000 patterns disappearing and new ones emerging.
• Lagged (2–3 month delay) connections (EC):
  • Precipitation clustering becomes dominant in southeast Iran.
  • Critical moisture transition nodes shift from synchronous regions in southeast Iran and its border with Pakistan towards delayed routes passing through northwest Iran, southern India, and the Yemen–Saudi Arabian border.
  • Southeastern Iran (Sistan & Baluchestan Province) shows a high clustering coefficient in recent years.
  • Northwestern Iran shows increased betweenness centrality in recent years.
  • Southern India consistently exhibits high betweenness centrality, suggesting a role in transmitting delayed moisture to Iran.
  • Fluctuations in Saudi Arabia’s betweenness centrality indicate a potential contribution to moisture transfer affecting southern and southwestern Iran.
  • Stable teleconnections were identified with India, the Indian Ocean, the North Atlantic Ocean, and the Mediterranean Sea, indicating their influence on Iran's high precipitation periods with a 2–3 month lead time.
  • Similar to synchronous connections, lagged connections also showed changes around 2000, with some pre-2000 links disappearing.

Contributions

• Provides new hydrological insights by demonstrating distinct spatial patterns for synchronous versus 2–3 month lagged high precipitation events in Iran.
• Offers a robust dual approach (ES and lagged EC) for identifying both immediate and delayed precipitation teleconnections, enhancing understanding beyond traditional methods.
• Identifies specific zones with high clustering and centrality across multiple temporal scales, crucial for developing effective early flood warning systems and mid-term water resources planning and management.
• Differentiates itself from previous studies by combining ES and EC over a large study area encompassing Iran, its neighbors, Europe, and East Asia.
• Highlights the potential for seasonal forecasting of high precipitation events in Iran, particularly with a 2–3 month lead time from regions like the Indian Ocean and southern India.

Funding

Not explicitly stated in the provided text.

Citation

@article{Roohinia2025Spatiotemporal,
  author = {Roohinia, Mahnoor and Zahraie, Banafsheh},
  title = {Spatiotemporal connections in high precipitation events in Iran: Application of complex networks},
  journal = {Journal of Hydrology Regional Studies},
  year = {2025},
  doi = {10.1016/j.ejrh.2025.102938},
  url = {https://doi.org/10.1016/j.ejrh.2025.102938}
}