This paper presents a terminology and typology of uncertainty, along with a framework for the modelling process, its interaction with the broader water management process, and the role of uncertainty at different stages in the modelling process. It reviews 14 methods commonly used in uncertainty assessment and characterisation, including data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modelling (parameter estimation), inverse modelling (predictive uncertainty), 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 purpose, stage of the modelling process, and source and type of uncertainty addressed. It is concluded that uncertainty assessment should be integrated throughout the modelling study, starting from the beginning, where the identification and characterisation of all uncertainty sources should be performed jointly by the modeller, the water manager, and the stakeholders. The paper focuses on uncertainty in the modelling process, touching upon aspects related to broader policy and public participation processes but not fully covering these aspects. The modelling process is decomposed into five major steps, each involving different actors and tasks. The paper also discusses the nature of uncertainty, its sources, and the uncertainty matrix as a tool for identifying and prioritising uncertainties. It provides guidance on selecting appropriate methodologies for uncertainty assessment based on the modelling process and level of ambition. The paper highlights the importance of uncertainty assessment in water management decisions and the need for a structured approach to address uncertainties throughout the modelling process.This paper presents a terminology and typology of uncertainty, along with a framework for the modelling process, its interaction with the broader water management process, and the role of uncertainty at different stages in the modelling process. It reviews 14 methods commonly used in uncertainty assessment and characterisation, including data uncertainty engine (DUE), error propagation equations, expert elicitation, extended peer review, inverse modelling (parameter estimation), inverse modelling (predictive uncertainty), 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 purpose, stage of the modelling process, and source and type of uncertainty addressed. It is concluded that uncertainty assessment should be integrated throughout the modelling study, starting from the beginning, where the identification and characterisation of all uncertainty sources should be performed jointly by the modeller, the water manager, and the stakeholders. The paper focuses on uncertainty in the modelling process, touching upon aspects related to broader policy and public participation processes but not fully covering these aspects. The modelling process is decomposed into five major steps, each involving different actors and tasks. The paper also discusses the nature of uncertainty, its sources, and the uncertainty matrix as a tool for identifying and prioritising uncertainties. It provides guidance on selecting appropriate methodologies for uncertainty assessment based on the modelling process and level of ambition. The paper highlights the importance of uncertainty assessment in water management decisions and the need for a structured approach to address uncertainties throughout the modelling process.