Spatiotemporal changes in river flow seasonality and predictability using Colwell indices in Northwestern Iran

Abstract

Abstract The study aimed to examine seasonal-spatial changes in river flow regimes in northwestern Iran using Colwell indices, which assess the regularity and seasonal pattern of flow through three components: Predictability, Constancy, and Contingency. This study is the first to apply Colwell indices across rivers in northwestern Iran, revealing varied trends influenced by climate and human activities. In this study, river flow data from various rivers in northwestern Iran with climate diversity were used to analyze the time and spatial changes of river flow regimes. In this regard, using the Colwell indicators, the variations of the different components of the river flow regime were evaluated from three main aspects including Predictability (P), Constancy (C) and Contingency (M) on different daily, monthly and seasonal time scales. The results showed that the ArbabKandi river had the highest Predictability and Constancy in its currents with values of P_MaxM = 0.739 and C_MaxM = 0.651 and Nir with values of P_MinS = 0.64 and C_MinS = 0.42, while the dostbeglo and Hir Rivers had the lowest values. Analysis of maximum, minimum and average of Colwell indicators of the flow regime showed significant differences between rivers due to the influence of various climatic factors or human exploitation. Trend analysis in Ardabil Province based on the Modified Mann-Kendall test reveals significant river flow decreases at stations such as Samian (S = − 649), Borran (S = − 639), Dostbeglo (S = − 643), and Mashiran (S = − 665), while Abgarm ( p  = 0.55), AhleIman ( p  = 0.50), Hir ( p  = 0.22), and ShamsAbad ( p  = 0.15) show no significant trends. Moderate but significant declines are observed at Arbabkandi, Barough, Gilandeh, Hajahmadkandi, Nir, Nuran and Viladaragh ( p  < 0.05). The study emphasizes the importance of understanding the variability of river flow regimes and river flow discharge values and can help improve water resource management strategies and better predict future changes.