Although uncertainty about structures of environmental models (conceptual uncertainty) is often acknowledged to be the main source of uncertainty in model predictions, it is rarely considered in environmental modelling. Rather, formal uncertainty analyses have traditionally focused on model parameters and input data as the principal source of uncertainty in model predictions. The traditional approach to model uncertainty analysis, which considers only a single conceptual model, may fail to adequately sample the relevant space of plausible conceptual models. As such, it is prone to modelling bias and underestimation of predictive uncertainty. In this paper we review a range of strategies for assessing structural uncertainties in models. The existing strategies fall into two categories depending on whether field data are available for the predicted variable of interest. To date, most research has focussed on situations where inferences on the accuracy of a model structure can be made directly on the basis of field data. This corresponds to a situation of 'interpolation'. However, in many cases environmental models are used for 'extrapolation'; that is, beyond the situation and the field data available for calibration. In the present paper, a framework is presented for assessing the predictive uncertainties of environmental models used for extrapolation. It involves the use of multiple conceptual models, assessment of their pedigree and reflection on the extent to which the sampled models adequately represent the space of plausible models.
Integrated Water Resources Management (IWRM) can be viewed as a complex process in which the effect of adopted water management measures must be monitored and adjusted in an iterative way as new information and technology gradually become available under changing and uncertain external impacts, such as climate change. This paper identifies and characterises uncertainty as it occurs in the different stages of the IWRM process with respect to sources, nature and type of uncertainty. The present study develops a common terminology that honour the most important aspects from natural and social sciences and its application to the entire IWRM process. The proposed framework is useful by acknowledging a broad range of uncertainties regarding data, models, multiple frames and context. Relating this framework to the different steps of the IWRM cycle is helpful to determine the strategies to better handle and manage uncertainties. Finally, this general framework is illustrated for a case study in the transboundary Rhine river basin.
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