Evaluation of hydrological variabilities of water quality models considering event-based scenarios: A case study

Given the significant momentum of developing water quality models to simulate water quality variables and support decision-making, the literature recognizes the importance of addressing uncertainties embedded in the water quality models, such as inherent, parametric, and hydrological uncertainties. However, previous studies have not extensively examined the magnitude of hydrological events in terms of their intensity. Therefore, 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 study's findings reveal that when considering the flow rate time histories during extreme hydrological events, there is a significant increase in uncertainty in the water quality model, highlighting the challenge of incorporating various scenarios within the framework such as high computational demand, integrating different scenarios, sparse data, and its temporal resolution. 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 results underscore the critical importance 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.