2017
DOI: 10.1016/j.coastaleng.2017.04.005
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Calibrating and assessing uncertainty in coastal numerical models

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Cited by 54 publications
(46 citation statements)
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References 57 publications
(133 reference statements)
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“…As has been previously noted, XBeach is particularly sensitive to choices of facAs and facSk e.g., [68][69][70]-often requiring separate parameter combinations for accretional and erosional conditions e.g., [53]. Preliminary model core testing (not shown) did not yield realistic behavior when using constant facAs and facSk for the full year period.…”
Section: Calibration Proceduresmentioning
confidence: 90%
See 1 more Smart Citation
“…As has been previously noted, XBeach is particularly sensitive to choices of facAs and facSk e.g., [68][69][70]-often requiring separate parameter combinations for accretional and erosional conditions e.g., [53]. Preliminary model core testing (not shown) did not yield realistic behavior when using constant facAs and facSk for the full year period.…”
Section: Calibration Proceduresmentioning
confidence: 90%
“…As the wave shape is not directly simulated, the effect of wave nonlinearity on sediment transport is parameterized via the short-wave-related velocity asymmetry (facAs) and skewness (facSk) coefficients. Many studies provide guidance on the effects of facAs and facSk on simulating erosional and accretional processes with XBeach e.g., [68][69][70]. Mean currents and long waves also contribute to sediment transport.…”
Section: Xbeachmentioning
confidence: 99%
“…For process-based simulation models that are used to predict short-term beach response to storms, model boundary conditions (e.g., waves and water levels) are relatively well known to high spatial and temporal resolutions, and so statistical techniques to manage the not insignificant uncertainty that is introduced by the selected parameterisation of complex models are of particular importance [77]. For behaviour-based models that are typically used to predict long-term shoreline change, similar techniques have been demonstrated to manage uncertainty in boundary conditions and model parameterisation [78][79][80][81].…”
Section: Uncertainty Managementmentioning
confidence: 99%
“…The drawbacks generated by the model in the calibration process are associated with the uncertainty of the parameters because the different sets of possible parameter values can have similar performance values (Loosvelt 205 et al, 2014;Mazzoleni et al, 2015;Ballinas-González, Alcocer-Yamanaka and Pedrozo-Acuña, 2016). For this reason, a Monte Carlo analysis (He et al, 2012;Simmons et al, 2017) has been used to estimate the best sets of parameters that generate a better performance. For this purpose, a data sampling with uniform distribution based on the interval reported in Table 1 was used.…”
Section: Parameter Estimationmentioning
confidence: 99%