New coefficient for water quality modelling in meandering rivers: Fatigue Factor

The accuracy of water quality predictions is essential, especially in countries affected by climate change and ecological water diversity. Water quality modelling in rivers is a valuable tool for enabling decision-making in surface water management because water quality prediction using sampling methods is expensive and time-consuming. The collection of technical knowledge of river characteristics and information about the sources of pollution plays a vital role in this context. This research focused on the effects of river geometry and meandering on the one-dimensional pollutant transport process. Flow velocity magnitude and direction in meandering rivers are frequently variable, leading to uncertain dispersion coefficients and massive changes in pollution concentration even over short distances of these rivers. So, the geometry of meandering rivers has a significant effect on their ecological indicators. A new coefficient called Fatigue Factor was introduced and defined in this study to consider this effect. Colidale Beck (CB) and Tyne rivers were selected for water quality modelling and implementation of the Fatigue Factor. The simulation-optimization method was employed to calculate zinc concentrations along the CB river using measured data for performance assessment of the model. The genetic algorithm performed well in predicting measured zinc concentration with high accuracy. Results of the model demonstrated that the mean effect of the Fatigue Factor in reducing the peak concentration of zinc increases by 3.8% compared to ignoring the Fatigue Factor along the CB length. With the Fatigue Factor consideration, the Mean Percentage Error between model outputs and measured data is 4%, while without it is 18%. Also, the Fatigue Factor had a greater impact on river pollution transport than the dispersion coefficient. With a 50% increase in the Fatigue Factor, the zinc concentration decreased by 6.1% more than the same in-crease in the dispersion coefficient. Moreover, results indicated that a 100% increment in the Fatigue Factor increases the assimilation capacity up to 3.5 times in CB.