The paper presents a framework for addressing uncertainty in environmental modeling, emphasizing its importance in integrated water resources management (IWRM) and the EU Water Framework Directive (WFD). It reviews 14 methods commonly used for uncertainty assessment and characterization, including data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modeling, Monte Carlo analysis, multiple model simulation, NUSAP, quality assurance, scenario analysis, sensitivity analysis, stakeholder involvement, and uncertainty matrix. The applicability of these methods is mapped according to their purpose, stage in the modeling process, and type of uncertainty addressed. The paper argues that uncertainty assessment should be an ongoing process throughout the modeling study, starting from the initial problem definition and identification of modeling objectives. It also highlights the role of stakeholders in the modeling process, emphasizing the need for participatory approaches to ensure that all relevant uncertainties are considered. The paper concludes by providing guidance on selecting appropriate methodologies for uncertainty assessment at different stages of the modeling process.The paper presents a framework for addressing uncertainty in environmental modeling, emphasizing its importance in integrated water resources management (IWRM) and the EU Water Framework Directive (WFD). It reviews 14 methods commonly used for uncertainty assessment and characterization, including data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modeling, Monte Carlo analysis, multiple model simulation, NUSAP, quality assurance, scenario analysis, sensitivity analysis, stakeholder involvement, and uncertainty matrix. The applicability of these methods is mapped according to their purpose, stage in the modeling process, and type of uncertainty addressed. The paper argues that uncertainty assessment should be an ongoing process throughout the modeling study, starting from the initial problem definition and identification of modeling objectives. It also highlights the role of stakeholders in the modeling process, emphasizing the need for participatory approaches to ensure that all relevant uncertainties are considered. The paper concludes by providing guidance on selecting appropriate methodologies for uncertainty assessment at different stages of the modeling process.