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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. [ABSTRACT FROM AUTHOR]

This paper combines a risk-based two-stage stochastic robust programming (RTSRP) model into an agricultural-ecological system dynamics (AESD) to conduct agricultural-ecological water resources management. It integrates the chance- constrained programming, conditional risk-at-value, two-stage stochastic robust programming with the AESD model. The RTSRP model improves upon the TSRP model by introducing the risk control measurements. The agricultural-ecological dynamic system (AEDS) model can build water connections amid wheat, field corn, seed corn, economic crops, woodland, meadow, and city greening and farmland shelterbelts. Besides, the ecological service value is transformed into the irrigation benefit of ecological sector to guide water allocation amid sub-ecological sectors at a more applicable detailed scale. It can tradeoff relationships between economic benefit and ecological recovery by formulating two enlarging ecological areas scenarios. The combinations of the RTSRP model and the AEDS model can get optimal water allocation schemes, and simulate future sustainable degree (SD) of optimal water allocation schemes and explore relationships amid water allocation, economic benefit, and SD and risk and robustness of model. It is used to the middle reaches of the Heihe River Basin in China to verify its application. The results show that water allocation schemes have different responses on various risk scenarios. Higher water allocation corresponds to bigger water shortage risk and higher robustness of the model. The economic benefit positive with water allocation is contradictory with SD. Enlarging ecological areas contributes to improvements of economic benefit and ecological recovery, but not the SD under the condition of enough water resources. [ABSTRACT FROM AUTHOR]