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Received Aug 2, 2017; Accepted Oct 4, 2017
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Although a need to standardise model build, calibration, and validation processes around one agreed approach is widely acknowledged, only limited guidance is available (e.g., [1, 2]) and often ambiguous and sometimes conflicting advice if offered in the grey literature (e.g., [3, 4]). A wide variety of different modelling practices are employed by consultants and academics, and frequently insufficient attention is given to the potential errors associated with the measured (and modelled) data used for model calibration and validation. This can result in poor model performance and unreliable model predictions. Without an agreed methodology and a performance standard for model calibration and validation, there is a risk that the quality of different approaches will vary, efforts will be wasted following inefficient or inappropriate calibration methods, and inconsistencies in methodologies will make model intercomparisons problematic.
This paper provides an evidence-based review and presents examples of calibration data sources and of model calibration and validation practices for estuarine and shelf sea models. It is intended to provide guidance to the assessment and use of model calibration data and to offer procedural clarity and simplification to the model calibration and validation process. In doing so, it acknowledges that some degree of compromise between the complexity of the natural system and the model representation must be reached. For this reason, the paper does not address complex modelling issues around wave-driven currents, littoral drift, and shoreline evolution where specialist models (e.g., the nonhydrostatic version of XBeach and CFD) must be employed.
Since the accuracy of the model calibration depends critically on the calibration data used, attention is given to some of the most common issues associated with data quality. The paper also provides (a) the end users of model data more specialist guidance on modelling approaches, (b) the calibration procedures most frequently applied, and (c)...