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This paper applies an ontological politics approach for studying how complexity, uncertainty, and ignorance are being dealt with in the Netherlands by looking at how knowledges are produced and incorporated in decision-making on uncertain climate change. On the basis of work done in the Netherlands, this paper shows two things in particular. First, how decision making responses historically have been subject to change under the influence of floods and how the emergence of climate change has significantly changed these floods. Second, based on the analysis of processes dealing with a blue-green algae problem in a lake, climate change not only changed decision making responses but also changed the very reality that is being enacted. Consequently, this brings an ethical dimension to the fore, related to the intrinsic tension between the growing awareness that 'all is interconnected' on the one hand and the realization we cannot take all into account. [ABSTRACT FROM AUTHOR]

Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows. [ABSTRACT FROM AUTHOR]