Report and papers with guidelines on calibration of urban flood models
Computer modelling offers a sound scientific framework for well-structured analysis and management of urban drainage systems and flooding. Computer models are tools that are expected to simulate the behaviour of the modelled real system with a reasonable level of accuracy. Assurance of accurate representation of reality by a model is obtained through the model calibration. Model calibration is an essential step in modelling. This report present concepts and procedures for calibration and verification of urban flood models. The various stages in the calibration process are presented sequentially. For each stage, a discussion of general concepts is followed by descriptions of process elements. Finally, examples and experiences regarding application of the procedures in the CORFU Barcelona Case Study are presented. Calibration involves not only the adjustment of model parameters but also other activities such as model structural and functional validation, data checking and preparation, sensitivity analysis and model verification, that support and fortify the calibration process as a whole. The objective in calibration is the minimization of differences between model simulated results and observed measurements. This is normally achieved through a manual iterative parameter adjustment process but automatic calibration routines are also available, and combination parameter adjustment methods also exist. The focus of a model calibration exercise is not the same for all types of models. But regardless of the model type, good modelling practice should involve thorough model verification before application. A well-calibrated model can give the assurance that, at least for a range of tested conditions, the model behaves like the real system, and that the model is an accurate and reliable tool that may be used for further analysis. However, calibration could also reveal that the model cannot be calibrated and that the correctness of the model and its suitability as a tool for analysis and management of real-world systems could not be proven. The conceptualisation and simplification of real-world systems and associated processes in modelling inevitably lead to errors and uncertainty. Various modelling components introduce errors such as the input parameters, the model concept, scheme and corresponding model output, and the observed response measurements. Ultimately, the quality of the model as quantified by how much it deviates from reality is an aggregate of the errors that have been brought into it during the modelling process. Thus, it is important to identify the different error sources in a model and also account for and quantify them as part of the modelling.
The work described in this publication was supported by the European Community’s Seventh Framework Programme through the grant to the budget of CORFU Collaborative Research on Flood Resilience in Urban Areas, Contract 244047.
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