Mostafazadeh et al. (2025) Spatio-Temporal Pattern and Hotspots of River Flow Discharge Variability and Seasonality in Northwestern Iran

Identification

• Journal: Iranian Journal of Science and Technology Transactions of Civil Engineering
• Year: 2025
• Date: 2025-11-19
• Authors: Raoof Mostafazadeh, Nazila Alaei
• DOI: 10.1007/s40996-025-02090-z

Research Groups

• Department of Natural Resources and Water Management Research Center, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.
• Watershed Management Science and Engineering, Faculty of Natural Resources, Urmia University, Urmia, Iran.

Short Summary

This study evaluates the spatio-temporal seasonality of river discharge across 32 stations in Ardabil Province, Iran, using the Markham Seasonal Index (MSI) over a 40-year period. The research identifies distinct hydrological clusters and spatial hotspots of flow variability, providing a scientific basis for localized flood and drought risk mitigation.

Objective

• To quantify the degree and timing of river flow seasonality and identify spatial-temporal hotspots of discharge variability in Northwestern Iran.

Study Configuration

• Spatial Scale: Regional (Ardabil Province, Iran), covering 32 river gauge stations across diverse elevations ranging from 30 m to 4,811 m above sea level.
• Temporal Scale: 40-year statistical period (1981–2021) using daily discharge data.

Methodology and Data

• Models and Methods used: Markham Seasonality Index (MSI) to calculate seasonal concentration and peak timing; Mann-Kendall (M-K) test for temporal trend analysis; Quantile-based statistical clustering (implemented in R); Global and Local Moran’s I for spatial autocorrelation; Getis-Ord Gi* for hotspot detection.
• Data sources: Daily river discharge data from the Regional Water Company of Ardabil; topographic and climatic data (precipitation and temperature) for regional characterization.

Main Results

• Seasonality Intensity: MSI values ranged from a minimum of 18% at Viladaragh station (indicating stable, year-round flow) to a maximum of 85% at Neur station (indicating high concentration during March to May).
• Peak Flow Patterns: 71.87% of stations (23 stations) exhibited an autumn-winter peak pattern, while high-altitude stations like Neur showed spring-winter dominance driven by snowmelt.
• Clustering: Stations were divided into four distinct clusters reflecting varying degrees of seasonal variability, with Cluster 1 being the largest (28.13% of stations).
• Spatial Autocorrelation: Global Moran’s I (0.10, p-value 0.19) suggested that the overall spatial distribution of flow seasonality is largely random, influenced by localized topography and microclimates.
• Hotspot Identification: Getis-Ord Gi* analysis identified significant hotspots of high seasonality at Samian (99% confidence), Nanakaran (95% confidence), and Neur (90% confidence) stations.
• Temporal Trends: Mann-Kendall results showed heterogeneous trends; stations like Polesoltani and Kozetopraghi showed significant increases in seasonality, while Mashiran and Lai showed significant decreases.

Contributions

• Establishes the first integrated framework combining the Markham Seasonality Index with spatial clustering and hotspot analysis for river discharge in Northwestern Iran.
• Identifies specific localized areas (hotspots) that require prioritized water management and flood forecasting due to high seasonal variability.
• Demonstrates that river flow seasonality in semi-arid mountainous regions is driven by a complex interplay of winter precipitation, spring snowmelt, and localized land-use factors rather than uniform regional drivers.

Funding

• University of Mohaghegh Ardabili (Project/Contract Reference Code: 18612).

Citation

@article{Mostafazadeh2025SpatioTemporal,
  author = {Mostafazadeh, Raoof and Alaei, Nazila},
  title = {Spatio-Temporal Pattern and Hotspots of River Flow Discharge Variability and Seasonality in Northwestern Iran},
  journal = {Iranian Journal of Science and Technology Transactions of Civil Engineering},
  year = {2025},
  doi = {10.1007/s40996-025-02090-z},
  url = {https://doi.org/10.1007/s40996-025-02090-z}
}

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