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<p>The seasonal index is a key tool for analyzing river flow variations, identifying discharge patterns, forecasting seasonal hydrology, and improving water resource management. In this study, the seasonality of river discharge was assessed using the Markham Seasonal Index (MSI) at 32 river gauge stations across Ardabil Province over a 40-year statistical period (1981–2021). Daily discharge data from these stations were employed, and the Markham method was applied to calculate the mean event timing and MSI, utilizing the components S (the magnitude of seasonal concentration), and C (the timing of peak flow within the year), and the annual mean rainfall vector magnitude. The Markham Seasonal Index served as the key metric for quantifying discharge seasonality. Subsequently, the stations were clustered based on MSI values using statistical clustering techniques implemented in R software. Spatial autocorrelation analysis and hotspot detection were performed using Moran’s I and Getis-Ord Gi* statistics, respectively. Cluster analysis revealed four distinct groups of river gauge stations, reflecting varying degrees of seasonal variability in river flows within the region. The clusters were relatively balanced, with the largest number of stations in the first cluster. The Neur station exhibited the highest seasonality index at 85% during March to May, whereas Viladaragh station recorded the lowest seasonality of 18% year-round. Moran’s I results indicated that the spatial and temporal distribution of flow variations across Ardabil Province is largely random and influenced by climate, topography, and land use. Hotspot analysis via the Getis-Ord Gi* statistic identified specific areas with significant spatial clustering of high or low MSI values. These hotspots determined areas with significant potential for targeted water management and flow prediction. The results offer a strong scientific basis for improving water management and hydrological planning in Ardabil, emphasizing focused attention on key areas for better flood and drought risk mitigation.</p>

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Spatio-Temporal Pattern and Hotspots of River Flow Discharge Variability and Seasonality in Northwestern Iran

  • 期刊论文

  • 发表日期：

    2025年11月19日

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  • Raoof Mostafazadeh,
  • Nazila Alaei

摘要

The seasonal index is a key tool for analyzing river flow variations, identifying discharge patterns, forecasting seasonal hydrology, and improving water resource management. In this study, the seasonality of river discharge was assessed using the Markham Seasonal Index (MSI) at 32 river gauge stations across Ardabil Province over a 40-year statistical period (1981–2021). Daily discharge data from these stations were employed, and the Markham method was applied to calculate the mean event timing and MSI, utilizing the components S (the magnitude of seasonal concentration), and C (the timing of peak flow within the year), and the annual mean rainfall vector magnitude. The Markham Seasonal Index served as the key metric for quantifying discharge seasonality. Subsequently, the stations were clustered based on MSI values using statistical clustering techniques implemented in R software. Spatial autocorrelation analysis and hotspot detection were performed using Moran’s I and Getis-Ord Gi* statistics, respectively. Cluster analysis revealed four distinct groups of river gauge stations, reflecting varying degrees of seasonal variability in river flows within the region. The clusters were relatively balanced, with the largest number of stations in the first cluster. The Neur station exhibited the highest seasonality index at 85% during March to May, whereas Viladaragh station recorded the lowest seasonality of 18% year-round. Moran’s I results indicated that the spatial and temporal distribution of flow variations across Ardabil Province is largely random and influenced by climate, topography, and land use. Hotspot analysis via the Getis-Ord Gi* statistic identified specific areas with significant spatial clustering of high or low MSI values. These hotspots determined areas with significant potential for targeted water management and flow prediction. The results offer a strong scientific basis for improving water management and hydrological planning in Ardabil, emphasizing focused attention on key areas for better flood and drought risk mitigation.