Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning’s friction coefficient

Mayo, Talea; Butler, Troy; Dawson, Clint; Hoteit, Ibrahim

doi:10.1016/j.ocemod.2014.01.001

Cited by 64 publications

(69 citation statements)

References 27 publications

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“…However, even if the initial system state can be described awlessly, model parameters simplify the physical processes and, by doing so, will result in the growth of prediction errors. At present, assimilation methods being developed to improve morphological forecast reliability are producing encouraging results (e.g., [133][134][135][136][137]). For example, ad hoc data assimilation schemes and techniques using more re ned heuristic tuning of model state variables are being used to improve the performance of suspended sediment transport models (e.g., [138,139]).…”

Section: Improving Predictions and Reducing Uncertaintymentioning

confidence: 99%

Guidance on Setup, Calibration, and Validation of Hydrodynamic, Wave, and Sediment Models for Shelf Seas and Estuaries

Williams

Esteves

2017

Advances in Civil Engineering

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e paper is motivated by a present lack of clear model performance guidelines for shelf sea and estuarine modellers seeking to demonstrate to clients and end users that a model is t for purpose. It addresses the common problems associated with data availability, errors, and uncertainty and examines the model build process, including calibration and validation. It also looks at common assumptions, data input requirements, and statistical analyses that can be applied to assess the performance of models of estuaries and shelf seas. Speci cally, it takes account of inherent modelling uncertainties and de nes metrics of performance based on practical experience. It is intended as a reference point both for numerical modellers and for specialists tasked with interpreting the accuracy and validity of results from hydrodynamic, wave, and sediment models.

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Section: Improving Predictions and Reducing Uncertaintymentioning

confidence: 99%

Guidance on Setup, Calibration, and Validation of Hydrodynamic, Wave, and Sediment Models for Shelf Seas and Estuaries

Williams

Esteves

2017

Advances in Civil Engineering

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“…Previous studies have demonstrated that hydrodynamic model parameter uncertainty has a significant effect on coastal simulations, e.g., on sediment transport (Briere et al, 2010), on water quality (Li et al, 2013), on nearshore currents and wave growth (Adrani and Kaihatu, 2012), on tidal propagation (Mayo et al, 2014), on tsunami generation and propagation (Knighton and Bastidas, 2015;Sraj et al, 2014), and on storm surge (Ferreira et al, 2014;Holt et al, 2015). Despite these findings, several recent studies on the validation of the Delft3D model have not considered potential effects of uncertainty in model parameter values, e.g., Elias et al (2000) and Golshani (2011).…”

Section: Surge and Wave Model Parameter Sensitivitymentioning

confidence: 99%

Parameter sensitivity and uncertainty analysis for a storm surge and wave model

Bastidas¹,

Knighton

Kline³

2016

Nat. Hazards Earth Syst. Sci.

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Abstract. Development and simulation of synthetic hurricane tracks is a common methodology used to estimate hurricane hazards in the absence of empirical coastal surge and wave observations. Such methods typically rely on numerical models to translate stochastically generated hurricane wind and pressure forcing into coastal surge and wave estimates. The model output uncertainty associated with selection of appropriate model parameters must therefore be addressed. The computational overburden of probabilistic surge hazard estimates is exacerbated by the high dimensionality of numerical surge and wave models. We present a model parameter sensitivity analysis of the Delft3D model for the simulation of hazards posed by Hurricane Bob (1991) utilizing three theoretical wind distributions (NWS23, modified Rankine, and Holland). The sensitive model parameters (of 11 total considered) include wind drag, the depth-induced breaking γ B , and the bottom roughness. Several parameters show no sensitivity (threshold depth, eddy viscosity, wave triad parameters, and depth-induced breaking α B ) and can therefore be excluded to reduce the computational overburden of probabilistic surge hazard estimates. The sensitive model parameters also demonstrate a large number of interactions between parameters and a nonlinear model response. While model outputs showed sensitivity to several parameters, the ability of these parameters to act as tuning parameters for calibration is somewhat limited as proper model calibration is strongly reliant on accurate wind and pressure forcing data. A comparison of the model performance with forcings from the different wind models is also presented.

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“…Smith et al, 2013;Mayo et al, 2014;Figure 1b). These techniques keep model parameters fixed and produce an updated model state that matches as closely as possible the true state by combining observational data with model predictions.…”

Section: Improving Predictions and Reducing Uncertaintymentioning

confidence: 99%

“…At present assimilation methods being developed to improve morphological forecast reliability are producing encouraging results (e.g. Scott and Mason, 2007;Smith et al, 2013;Chu et al, 2013;Margvelashvili et al, 2013;Mayo et al, 2014). For example ad hoc data assimilation schemes and techniques using more refine heuristic tuning of model state variables are being used to improve the performance of suspended sediment transport models (e.g.…”

Section: Improving Predictions and Reducing Uncertaintymentioning

confidence: 99%

Modelling Long-Term Morphodynamics in Practice: Uncertainties and Compromises

Williams¹,

Conduché²,

Esteves

2015

The Proceedings of the Coastal Sediments 2015

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Two problems limit the ability of morphological models to reproduce the real-world behaviour of geomorphological systems: (a) the dominance of short-term observations which fail to capture the full character of morphological evolution and cannot quantify fully the primary phenomena and mechanisms of change; and (b) incomplete understanding of processes at all relevant scales. Present efforts to reduce uncertainty in morphological models assume that: (a) observations are the key to locate, quantify and reduce uncertainty; and (b) means used to quantify, minimise and control uncertainty will improve model performance. This paper first outlines the principles underpinning morphological model development concerning coastal sandy environments. Then it discusses the use of morphological models and considers approaches that improve predictions through reduced uncertainty. The discussion is supported by two examples that illustrate the compromises that must be reached between what a morphological model is required to predict and what the model can deliver in practice.

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Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning’s friction coefficient

Cited by 64 publications

References 27 publications

Guidance on Setup, Calibration, and Validation of Hydrodynamic, Wave, and Sediment Models for Shelf Seas and Estuaries

Guidance on Setup, Calibration, and Validation of Hydrodynamic, Wave, and Sediment Models for Shelf Seas and Estuaries

Parameter sensitivity and uncertainty analysis for a storm surge and wave model

Modelling Long-Term Morphodynamics in Practice: Uncertainties and Compromises

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