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

Abstract

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 m3/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 m3/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 extremely high and low discharge changes, ultimately reducing the risk of flood and drought damage. HIGHLIGHTS For better flood management, high discharge changes are estimated via quantile regression model.; Quantile regression models are suitable for analyzing changes in different ranges of data series.; Bayesian quantile regression is more accurate than frequency-oriented quantile regression models.; High rates of trend variation are visible in extreme values of discharge.;

Keywords

• bayesian quantile regression
• flow
• frequentist quantile regression
• gorganroud
• trend