Helali et al. (2026) Analysis of precipitation anomalies in basins of Iran based on transition phases and different intensities of ENSO

Identification

• Journal: Journal of Hydrology Regional Studies
• Year: 2026
• Date: 2026-02-06
• Authors: Jalil Helali, Mehdi Mohammadi Ghaleni, Ebrahim Asadi Oskouei, Mostafa Safarikomeil, Ali Afruzi, Mohammad Ahmadi, Alireza Karbalaee, Ali Akbar Sabziparvar
• DOI: 10.1016/j.ejrh.2026.103222

Research Groups

• Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
• Department of Water Science and Engineering, Faculty of Agriculture and Environment, Arak University, Arak, Iran
• Research Institute for Water Science and Engineering, Arak University, Arak, Iran
• Atmospheric Science and Meteorology Research Center (ASMERC), Tehran, Iran
• Hamedan Regional Water Authority, Hamedan, Iran
• Kermanshah Regional Meteorological Office, Kermanshah, Iran
• Department of Climatology, Faculty of Geographical Sciences, Kharazmi University, Tehran, Iran
• Department of Water Science Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Short Summary

This study analyzed annual precipitation anomalies in Iran's basins from 1960 to 2019, revealing that ENSO intensities have a more pronounced impact than ENSO phases, with El Niño generally causing positive precipitation anomalies and La Niña causing negative ones, significantly influencing regional hydrology.

Objective

• To investigate how specific ENSO phases affect precipitation variability at different scales (basin vs. sub-basin) in Iran.
• To determine the impact of neutral transition phases (El Niño-Neutral and La Niña-Neutral) on precipitation anomalies compared to El Niño and La Niña transitions.
• To assess how the probability of precipitation occurrence during different ENSO phases influences the management of hydroclimatic variables.

Study Configuration

• Spatial Scale: Iran's six main basins (Caspian Sea, Persian Gulf–Oman Sea, Urmia Lake, Central Plateau, Eastern Border, Qara Qum) and 135 sub-basins.
• Temporal Scale: Annual precipitation data from 1960 to 2019 (60-year period).

Methodology and Data

• Models used:
  • Statistical characteristics: Mean, Standard Deviation, Coefficient of Variation (CV).
  • Precipitation Anomaly calculation.
  • Probability Density Function: Weibull Probability Density Function (WDF).
  • Trend analysis: Mann-Kendall method, Sen’s slope estimator.
  • Autocorrelation removal: Pre-whitening test.
• Data sources:
  • 1,200 rain gauge stations from the Ministry of Energy.
  • Rain gauge and synoptic stations from the Islamic Republic of Iran Meteorological Organization (IRIMO).
  • ENSO classification based on the Oceanic Niño Index (ONI) from the Climate Prediction Center of NOAA.
  • Average precipitation for basins and sub-basins calculated using the Thiessen method.

Main Results

• Annual precipitation differences under varying ENSO intensities are more pronounced than those across ENSO phases.
• El Niño events are associated with predominantly positive precipitation anomalies (average +11.3%), increasing in magnitude and frequency across most sub-basins (98% positive anomalies). Strong El Niño showed the highest positive anomaly (+17.2%).
• La Niña phases produce predominantly negative precipitation anomalies (average -7.0%), with reductions observed across varying intensities (98% negative anomalies in sub-basins). Strong La Niña showed the highest negative anomaly (-17.6%).
• The neutral phase generally exhibits negative anomalies (average -5.1%), with 87% of sub-basins showing negative anomalies.
• El Niño has a more stable and pronounced impact on Iran’s annual precipitation compared to La Niña.
• During ENSO transition phases, the El Niño–La Niña transition shows more positive precipitation differences than the La Niña–El Niño phase across a greater number of sub-basins (93 sub-basins).
• The Persian Gulf–Oman Sea basin records the highest coefficient of variation (CV) at 74.1% during the Neutral–La Niña transition, indicating substantial climatic diversity and variability.
• The Qara Qum basin exhibits the lowest CV at 13.2% during the La Niña–El Niño transition, reflecting greater climatic uniformity.
• Trend analysis revealed varying precipitation trends across basins and ENSO phases (e.g., Caspian Sea showed a positive trend during El Niño and La Niña, while the Persian Gulf–Oman Sea showed a decreasing trend during El Niño and La Niña but an increasing trend during the neutral phase).
• Weibull probability distribution analysis indicates that during El Niño phases, precipitation is generally higher than the long-term average across most probabilities, while in La Niña and neutral phases, it tends to be lower, with regional exceptions.

Contributions

• Provided a comprehensive statistical analysis of annual precipitation anomalies in Iran's basins, considering ENSO phases, intensities, and transition phases.
• Demonstrated that ENSO intensities exert a more distinct influence on precipitation variability than ENSO phases, offering new hydrological insights for the region.
• Introduced an empirical method for forecasting annual precipitation based on ENSO phase differentiation, which can enhance the management of surface and groundwater resources.
• Highlighted the critical importance of integrating ENSO variability into long-term water resource planning and climate change adaptation strategies for Iran.

Funding

The authors state that they did not receive any funds, grants, or other support during the preparation of this manuscript.

Citation

@article{Helali2026Analysis,
  author = {Helali, Jalil and Ghaleni, Mehdi Mohammadi and Oskouei, Ebrahim Asadi and Safarikomeil, Mostafa and Afruzi, Ali and Ahmadi, Mohammad and Karbalaee, Alireza and Sabziparvar, Ali Akbar},
  title = {Analysis of precipitation anomalies in basins of Iran based on transition phases and different intensities of ENSO},
  journal = {Journal of Hydrology Regional Studies},
  year = {2026},
  doi = {10.1016/j.ejrh.2026.103222},
  url = {https://doi.org/10.1016/j.ejrh.2026.103222}
}