No abstract

No abstract

Resampling techniques such as the Bootstrap and the Jack-knife are generic methods for the estimation of uncertainties in statistics. When applied in frequency analysis, resampling techniques can provide estimates of the uncertainties in both distribution parameters and quantile estimates in circumstances in which confidence limits cannot be obtained theoretically. Test experiments using two different parameter estimation methods on two types of distributions with different initial sample sizes and numbers of resamples has confirmed the utility of such methods. However, care is necessary in evaluating the skewness of the resampled quantiles, especially with small initial sample sizes.

SUMMARYParticle‐tracking models are often used for near field short‐term subgrid transport of substances. The consistency demand at the discrete level does not show up so dominantly for these applications. This demand refers to the use of a numerical advection scheme for particles that is fully compatible with the local mass conserving advection properties of the underlying flow field at the discrete level of that field. A noncompatible scheme will produce both local convergence and local divergence of particles in different parts of the model area and thus erroneous advection results and erroneous concentration patterns. This compatibility in particle tracking is especially important if smooth distributions over larger areas are modelled for longer times. These applications did not occur that often in the past because they require many particles and thus much computation time. These applications occur more frequently nowadays especially for environmental assessment such as for the modelling of transport of fish larvae growing during their journey in the model to juvenile stages. The advection scheme that is developed in this paper is shown to be exactly compatible with hydrodynamic flow fields computed by mass conserving curvilinear grid models. It is not only exact, it is fortunately also very simple to implement and fast, allowing for modelling a huge amount of particles with moderate computation time. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARYIn this paper we consider a parameter estimation procedure for shallow sea models. The method is formulated as a minimization problem. An adjoint model is used to calculate the gradient of the criterion which is to be minimized. In order to obtain a robust estimation method, the uncertainty of the open boundary conditions can be taken into account by allowing random noise inputs to act on the open boundaries. This method avoids the possibility that boundary errors are interpreted by the estimation procedure as parameter fluctuations. We apply the parameter estimation method to identify a shallow sea model of the entire European continental shelf. First, a space-varying bottom friction coefficient is estimated simultaneously with the depth. The second application is the estimation of the parameterization of the wind stress coefficient as a function of the wind velocity. Finally, an uncertain open boundary condition is included. It is shown that in this case the parameter estimation procedure does become more robust and produces more realistic estimates. Furthermore, an estimate of the open boundary conditions is also obtained. KEY WORDS Tidal models Maximum likelihood Modelling uncertain boundaries Parameter estimation

Wave overtopping is an important design criterion for coastal structures such as dikes, breakwaters and promenades. Hence, the prediction of the expected wave overtopping discharge is an important research topic. Existing prediction tools consist of empirical overtopping formulae, machine learning techniques like neural networks, and numerical models. In this paper, an innovative machine learning method—gradient boosting decision trees—is applied to the prediction of mean wave overtopping discharges. This new machine learning model is trained using the CLASH wave overtopping database. Optimizations to its performance are realized by using feature engineering and hyperparameter tuning. The model is shown to outperform an existing neural network model by reducing the error on the prediction of the CLASH database by a factor of 2.8. The model predictions follow physically realistic trends for variations of important features, and behave regularly in regions of the input parameter space with little or no data coverage.

No abstract

For the simulation of the transport of dissolved matter particle models can be used. In this paper a technique is developed for the identification of uncertain parameters in these models. This model calibration is formulated as an optimization problem and is solved with a gradient based algorithm. Here adjoint particle tracks are used for the calculation of the gradient of the cost function. The performance of the calibration method is illustrated by simulations and an application to a river Rhine water quality calamity in November 1986.