Comparison of Bayesian and frequentist quantile regressions in studying the trend of discharge changes in several hydrometric stations of the Gorganroud basin in Iran

This research utilized Bayesian and quantile regression techniques to analyze trends in discharge levels across various seasons for three stations in the Gorganroud basin of northern Iran. The study spanned a period of 50 years (1966-2016). Results indicate a decrease in high discharge rates during springtime for the Arazkouseh and Galikesh stations, with a steep slope of -0.31 m(3)/s per year for Arazkouseh and -0.19 and -0.17 for Galikesh. Furthermore, Tamar station experienced an increase in very high discharge during summer, with a slope of 0.12 m(3)/s per year. However, low discharge rates remained relatively unchanged. Arazkouseh station showed a higher rate of decreasing discharge levels and this trend was most prominent during spring. Additionally, the Bayesian quantile regression model proved to be more accurate and reliable than the frequency-oriented quantile regression model. These findings suggest that quantile regression models are a valuable tool for predicting and managing extreme high and low discharge changes, ultimately reducing the risk of flood and drought damage.

Automated summary

This research used Bayesian and quantile regression techniques to analyze the trends in discharge levels across different seasons in the Gorganroud basin of northern Iran. The study covered a 50-year period (1966-2016). The results showed a decrease in high discharge rates during spring for the Arazkouseh and Galikesh stations, with a steep slope of -0.31 m(3)/s per year for Arazkouseh and -0.19 and -0.17 for Galikesh. On the other hand, the Tamar station experienced an increase in very high discharge rates during summer, with a slope of 0.12 m(3)/s per year. The findings highlight the importance of quantile regression models in predicting and managing extreme discharge changes, reducing the risk of flood and drought damage.