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Publication - Professor Thorsten Wagener

    How important are model structural and contextual uncertainties when estimating the optimized performance of water resource systems?

    Citation

    Dobson, B, Wagener, T & Pianosi, F, 2019, ‘How important are model structural and contextual uncertainties when estimating the optimized performance of water resource systems?’. Water Resources Research, vol 55., pp. 2170-2193

    Abstract

    Uncertainty in simulating water resource systems (WRSs) makes it difficult to assess how effective different water management decisions will be. Uncertainty in simulation models can undermine the credibility of simulation and optimization studies and the uptake of their results. We identify different sources of uncertainty in WRS models and find that structural uncertainty (i.e., around definition of interrelationships within the system) and contextual uncertainty (i.e., around definition of the system boundaries) are rarely considered when simulating and optimizing WRSs. We propose a methodology to quantify the effects of structural and contextual uncertainties on the estimated performance of optimized water management decisions and demonstrate that they have a significant impact on a real-world case study of a pumped-storage system in the UK. To the best of the authors' knowledge, this is the first study to consider the impact of these types of uncertainty on optimized operating policies and their simulated performances. Our main finding is that of all the considered uncertainties, the assumptions made about context—specifically around the level of cooperation between neighboring water companies—had the greatest impact on performance estimates. This is important because few WRSs exist in isolation, yet discussion of the effects that a given definition of the system boundaries have on the simulation/optimization results is uncommon. We also highlight the significance of adequately considering aleatory uncertainty when evaluating performance estimates—something that few studies do—and present a simple technique to justify the sample size used for the evaluation of optimization results.

    Full details in the University publications repository