Evaluation of Water Quality Models Hydrological Variabilities Considering Event-Based Scenarios: A case study

Abstract

Abstract Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognized the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, hydrological events' magnitude in terms of intensity has not been extensively scrutinized in previous studies. Hence, this paper aims to propose an adjusted Monte Carlo event-based scenarios framework that considers four scenarios (1- 35 years of flow rate records, 2- events with ARI ≤ 10 years, 3- events with ARI ≤ 5 years, 4- events with ARI ≤ 1) to evaluate hydrological variabilities and quantify embedded uncertainties. The study employs a Qual2k model that simulates five water quality variables in the Zaroub river, Iran, as a case study. The model's uncertainty boundaries are quantified using five statistical metrics: Plevel, ARIL, SU, CU, and EU. The results of the study considering extreme hydrological events when examining the flow rate time histories leads to a significant increase in uncertainty in the water quality model. On the other hand, reducing the ARI values of hydrological events not only minimizes the uncertainty boundaries but also improves the accuracy of the model simulations. These findings highlight the crucial role of selecting hydrological scenarios based on the water quality variable under investigation. Furthermore, the proposed framework can be applied to any water quality model and water body. The study's outcomes suggest that the presented methodology reduces uncertainty and provides more reliable simulations for decision-making in water resources conundrums.