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The calibration of hydraulic models of water distribution networks (WDN) is of preeminent importance for their analysis and management. It is usually achieved by solving a constrained optimization problem based on some priors on decision variables and the demand-driven simulation of the entire network, given the observations of some hydraulic status variables (i.e. typically nodal heads and sometimes pipe flows). This paper presents a framework to perform the calibration of pipe hydraulic resistances considering two main issues: (i) the enhancements of WDN simulation models allowing us to simplify network topology with respect to serial nodes/trunks and/or to account for a more realistic representation of distributed demands and (ii) a different formulation of the calibration problem itself. Depending on the available measurements, the proposed calibration strategy reduces the hydraulic simulation model size and can permit the decomposition of the network. On the one hand, such a procedure allows for numerical and computational advantages, especially for large size networks. On the other hand, it allows a prompt analysis of observability of calibration decision variables based on actual observations and might help identifying those pipes (i.e. hydraulic resistances) which are more important for the whole network behaviour. [ABSTRACT FROM AUTHOR]

This paper describes the development of mathematical models, which represent static and dynamic properties of the AQUAI-MOD® hydraulic controller coupled with a standard pressure reducing valve (PRV) as well as a new experimental set-up for testing the controller and calibrating and validating the models. The purpose of the AQUAI-MOD® controller is to modulate the PRV outlet pressure according to the valve flow. The controller has been experimentally tested to assess its operation in different conditions and operating ranges and in all cases showed good performance. The mathematical models of the PRV and its controller have been developed and solved using the Mathematica software package to represent both steady state and dynamics conditions. The numerical results of simulation of the mathematical model have been compared with experimental data and showed a good agreement in magnitude and trends. The model can be used to simulate the behaviour of the PRV and the AQUAI-MOD® hydraulic controller in typical network applications. It can also be used at the design stage to size the controller components and to compute the required set points for the minimum and the maximum pressure before installing the controller in the field. [ABSTRACT FROM AUTHOR]

In many process-based models, parameters have to be estimated from data. It is important to obtain not only the optimum value of the parameters, but also to assess the uncertainty in the parameters and, hence, in the models' output. In this paper, the Bayesian Monte Carlo technique known as Generalised Likelihood Uncertainty Estimation (GLUE) is used to evaluate the parameter-induced predictive uncertainty of a three-parameter model that predicts alongshore currents over a nearshore barred profile. GLUE performs a fully random sampling of feasible-parameters space, assigning non-zero likelihoods to those model simulations that outperform a user-defined threshold. Based on data gathered at six cross-shore position across an inner bar at Egmond aan Zee, The Netherlands, non-zero likelihoods were found for a rather wide range of parameter values, largely induced by an interdependence between two parameters that affect the width of current jets across the bar. The width of the 95% uncertainty interval was found empirically to increase linearly with the predicted magnitude of the alongshore current, from about 0.02–0.06 m/s when the current magnitude is near zero to about 0.2 m/s when it is near its maximum of about 1.1 m/s. These widths are approximately equal to a rough estimate of the errors in the data. In many cases the 95% uncertainty interval brackets the observations, although there are also various instances where this is not the case and apparently model structural errors dominate over parameter-induced errors. Model non-linearity and parameter interdependence cause the marginal parameter posterior distributions to differ remarkably from those obtained from traditional first-order approximations. [ABSTRACT FROM AUTHOR]

Model calibration remains a critical step in numerical modelling. After many attempts to automate this task in water-related domains, questions about the actual need for calibrating physics-based models are still open. This paper proposes a framework for good model calibration practice for end-users of 1D hydraulic simulation codes. This framework includes a formalisation of objects used in 1D river hydraulics along with a generic conceptual description of the model calibration process. It was implemented within a knowledge-based system integrating a simulation code and expert knowledge about model calibration. A prototype calibration support system was then built up with a specific simulation code solving subcritical unsteady flow equations for fixed-bed rivers. The framework for model calibration is composed of three independent levels related, respectively, to the generic task, to the application domain and to the simulation code itself. The first two knowledge levels can thus easily be reused to build calibration support systems for other application domains, like 2D hydrodynamics or physics-based rainfall–runoff modelling. Keywords 1D river modelling; best practice; knowledge-based system; model calibration; conceptual description [ABSTRACT FROM AUTHOR]