Abstract
Uncertainty-aware design and management of urban water systems lies on the generation of synthetic series that should precisely reproduce the distributional and dependence properties of residential water demand process (i.e., significant deviation from Gaussianity, intermittent behaviour, high spatial and temporal variability and a variety of dependence structures) at various temporal and spatial scales of operational interest. This is of high importance since these properties govern the dynamics of the overall system, while prominent simulation methods, such as pulse-based schemes, address partially this issue by preserving part of the marginal behaviour of the process (e.g., low-order statistics) or neglecting the significant aspect of temporal dependence. In this work, we present a single stochastic modelling strategy, applicable at any fine time scale to explicitly preserve both the distributional and dependence properties of the process. The strategy builds upon the Nataf’s joint distribution model and particularly on the quantile mapping of an auxiliary Gaussian process, generated by a suitable linear stochastic model, to establish processes with the target marginal distribution and correlation structure. The three real-world case studies examined, reveal the efficiency (suitability) of the simulation strategy in terms of reproducing the variety of marginal and dependence properties encountered in water demand records from 1-min up to 1-h.
Highlights
In the planning and management of urban water systems, the temporal and spatial variability of residential water demand is one of the most influential sources of uncertainty [1,2]
Conventional modelling approaches often neglect this aspect, by involving on the one hand, identical multiplier patterns to provide a coarse representation of the diurnal variation of water demand and on the other hand, simplified allocation techniques to express the variation of demand across different points of the network
We focus on simulation that allows the generation of an arbitrarily large number of synthetic, yet statistical consistent, realizations of water demand events, which can serve as non-deterministic inputs to a water distribution systems (WDS), allowing for the probabilistic assessment of its performance under different input scenarios
Summary
In the planning and management of urban water systems, the temporal and spatial variability of residential water demand is one of the most influential sources of uncertainty [1,2]. This approach fails to capture adequately the high spatio-temporal variability and uncertainty of water demand (e.g., [3,4,5,6,7]), which becomes more and more intense as the scales of analysis become lower [8]. Such a modelling concept does not allow the design, evaluation and management of water distribution systems (WDS) within an uncertainty-aware framework since it implies the use of deterministic water demand patterns as inputs in, typically, deterministic simulation models. We focus on simulation that allows the generation of an arbitrarily large number of synthetic, yet statistical consistent, realizations of water demand events, which can serve as non-deterministic inputs to a WDS, allowing for the probabilistic assessment of its performance under different input scenarios
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