This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. It examines four problem areas: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments to reduce critical uncertainties. The review is divided into two parts: a shorter, nontechnical version and a longer, more detailed version. The longer version discusses identifiability and experimental design, generating preliminary hypotheses, selection and evaluation of model structure, parameter estimation, checks and balances, and prediction error propagation. It also addresses the challenges of model identifiability, the importance of prior theory in model calibration, and the need for new questions in prediction. The paper highlights the difficulties in interpreting past system behavior due to a lack of model identifiability and the potential of larger, more comprehensive models to generate highly uncertain predictions. It concludes that much work has been done on uncertainty analysis in water quality modeling, but much remains to be done. The paper also discusses the importance of system identification in environmental modeling and the need for new approaches in the field of artificial intelligence. The review emphasizes the importance of understanding the relationships between causes and effects in environmental management and the need for robust methods in model calibration and verification. It also addresses the challenges of system identification and the need for experimental design to reduce uncertainties. The paper concludes that the analysis of uncertainty in water quality modeling is essential for effective environmental management and that further research is needed to improve the accuracy and reliability of models.This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. It examines four problem areas: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments to reduce critical uncertainties. The review is divided into two parts: a shorter, nontechnical version and a longer, more detailed version. The longer version discusses identifiability and experimental design, generating preliminary hypotheses, selection and evaluation of model structure, parameter estimation, checks and balances, and prediction error propagation. It also addresses the challenges of model identifiability, the importance of prior theory in model calibration, and the need for new questions in prediction. The paper highlights the difficulties in interpreting past system behavior due to a lack of model identifiability and the potential of larger, more comprehensive models to generate highly uncertain predictions. It concludes that much work has been done on uncertainty analysis in water quality modeling, but much remains to be done. The paper also discusses the importance of system identification in environmental modeling and the need for new approaches in the field of artificial intelligence. The review emphasizes the importance of understanding the relationships between causes and effects in environmental management and the need for robust methods in model calibration and verification. It also addresses the challenges of system identification and the need for experimental design to reduce uncertainties. The paper concludes that the analysis of uncertainty in water quality modeling is essential for effective environmental management and that further research is needed to improve the accuracy and reliability of models.