Journal of Hydroinformatics 5 (2003) 259-274

Uncertainty and risk in water quality modelling and management

Neil R. McIntyre, Thorsten Wagener, Howard S. Wheater and Zeng Si Yu

Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU, UK Tel: +44 207 594 6019 Fax: +44 207 823 9401 E-mail: n.mcintyre@ic.ac.uk

SAHRA Hydrology and Water Resources, Harshbarger Building, University of Arizona, Tucson, AZ 85721, USA Tel: +1 520 6268799; Fax: +1 520 6211422; E-mail: thorsten@sahra.arizona.edu

Department of Civil and Environmental Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BU, UK Tel: +44 207 594 6019 Fax: +44 207 823 9401 E-mail: h.wheater@ic.ac.uk

Department of Environmental Science and Engineering, Tsinghua University, Beijing 100084, People's Republic of China Tel.: +86 106 2785610; Fax: +86 106 2785687; E-mail: szeng@tsinghua.edu.cn

The case is presented for increasing attention to the evaluation of uncertainty in water quality modelling practice, and for this evaluation to be extended to risk management applications. A framework for risk-based modelling of water quality is outlined and presented as a potentially valuable component of a broader risk assessment methodology. Technical considerations for the successful implementation of the modelling framework are discussed. The primary arguments presented are as follows. (1) For a large number of practical applications, deterministic use of complex water quality models is not supported by the available data and/or human resources, and is not warranted by the limited information contained in the results. Modelling tools should be flexible enough to be employed at levels of complexities which suit the modelling task, data and available resources. (2) Monte Carlo simulation has largely untapped potential for the evaluation of model performance, estimation of model uncertainty and identification of factors (including pollution sources, environmental influences and ill-defined objectives) contributing to the risk of failing water quality objectives. (3) For practical application of Monte Carlo methods, attention needs to be given to numerical efficiency, and for successful communication of results, effective interfaces are required. A risk-based modelling tool developed by the authors is introduced.

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