Process-based morphodynamic modelling is one of the relatively new tools at the disposal of coastal scientists, engineers and managers. On paper, it offers the possibility to analyse morphological processes and to investigate the effects of various measures one might consider to alleviate some problems. For these to be applied in practice, a model should be relatively straightforward to set up. It should be accurate enough to represent the details of interest, it should run long enough and robustly to see the real effects happen, and the physical processes represented in such a way that the sediment generally goes in the right direction at the right rate. Next, practitioners must be able to judge if the patterns and outcomes of the model are realistic and finally, translate these colour pictures and vector plots to integrated parameters that are relevant to the client or end user. In a nutshell, this book provides an in-depth review of ways to model coastal processes, including many hands-on exercises.

This thesis contributions belong to the general framework of data-based and physically-based data-driven modelling. An efficient approach for Machine Learning (ML), as well as a speed-up technique for Data Assimilation (DA), have been developed. For this purpose, Dimensionality Reduction (DR) and stochastic spectral modelling were used. In particular, a coupling between Proper Orthogonal Decomposition (POD) and Polynomial Chaos Expansion is at the center of this thesis contributions. POD and PCE have widely proved their worth in their respective frameworks, and the idea was to combine them for optimal field measurement based forecasting, and ensemble-based acceleration technique for variational DA. For this purpose, (i) a physically interpretable POD-PCE ML for non-linear multidimensional fields was developed in the Neural Networks (NN) paradigm and (ii) a hybrid ensemble-variational DA approach for parametric calibration was proposed with adapted calculations of POD-PCE metamodelling error covariance matrix. The proposed techniques were assessed in the context of an industrial application, for the study of sedimentation in a coastal power plant's water intake. Water intakes ensure plant cooling via a pumping system. They can be subject to sediment accumulation, which represents a clogging risk and requires costly dredging operations. For monitoring and safety reasons, the power plant stakeholders asked for a predictive tool that could be run in operational conditions. Data collected during many years of monitoring in the study area were provided. The objective was then to achieve comprehensive analysis of the flow and sediment dynamics, as well as to develop an optimal model in terms of forecasting accuracy, physical meaning, and required computational time. Uncertainty reduction and computational efficiency were therefore starting points for all proposed contributions. In addition to the previously proposed methods, Uncertainty Quantification (UQ) studies were undertaken. Specifically, (i) uncertainties related to tidal hydrodynamic modelling, resulting from common modelling choices (domain size, empirical closures) were investigated. POD patterns resulting from measurements and numerical scenarios were compared; (ii) UQ study of the sediment transport modelling in the intake, in a highdimensional framework, was achieved. Investigations were based on appropriate DR. In fact, POD patterns of Boundary Conditions (BC) and Initial Conditions (IC), resulting from hydrodynamic simulations outputs and from bathymetry measurements respectively, were used. A perspective of this work would be to implement a hybrid POD-PCE model, using both measured and numerically emulated data, to better understand and predict complex physical processes. This approach would offer a complete, fast and efficient tool for operational predictions.

This new edition of Guide to Process Based Modeling of Lakes and Coastal Seas brings the modeling up to date, taking into account multiple stressors acting on aquatic systems. The combination of acidification and increasing amounts of anoxic waters associated with eutrophication puts severe stress on the marine environment. The detection and attribution of anthropogenic changes in coastal seas are therefore crucial and transparent modeling tools are increasingly important. Modeling the marine CO2–O2 system makes systematic studies on climate change and eutrophication possible and is fundamental for understanding the Earth system. This second edition also includes new sections on detection and attribution and on modeling future changes, as well as improved exercises, updated software, and datasets. This unique book will stimulate students and researchers to develop their modeling skills and make model codes and data transparent to other research groups. It uses the general equation solver PROBE to introduce process-oriented numerical modeling and to build understanding of the subject step by step. The equation solver has been used in many applications, particularly in Sweden and Finland with their numerous lakes, archipelago seas, fjords, and coastal zones. It has also been used for process studies in the Polar Seas and the Mediterranean Sea and the approach is suitable for applications in many other environmental applications. Guide to Process Based Modeling of Lakes and Coastal Seas: • is a unique teaching tool for systematic learning of aquatic modeling; • approaches lake and ocean modeling from a new angle; • introduces aquatic numerical modeling using a process-based approach; • enables the thorough understanding of the physics and biogeochemistry of lakes and coastal seas; • provides software, datasets, and algorithms needed to reproduce all calculations and results in the book; • provides a number of creative and stimulating exercises with solutions; • addresses the interaction between climate change and eutrophication and is a good basis for learning Earth System Sciences.

Since the computing revolution, modelling has become the most important way in which we further our knowledge about how the sea moves and how the processes in the sea operate. The coast and the continental shelf are two of the most important areas of the sea to understand. Coastal and Shelf Sea Modelling is therefore very timely and important. In this text, modelling the processes that occur in the sea is motivated continually through real life examples. Sometimes these are incorporated naturally within the text, but there are also a number of case studies taken from the recent research literature. These will be particularly valuable to students as they are presented in a style more readily accessible than that found in a typical research journal. The motivation for modelling is care for the environment. The well publicised problem of global warming, the phenomenon of El Niño, more localised pollution scares caused by tanker accidents and even smaller scale coastal erosion caused by storms all provide motivation for modelling and all get coverage in this text. Particularly novel features of the book include a systematic treatment of the modelling process in a marine context, the inclusion of diffusion in some detail, ecosystems modelling and a brief foray into wave prediction. The final chapter provides the reader with the opportunity to do some modelling; there are many worked examples followed by exercises that readers can try themselves. All answers are provided. Throughout, the style is informal and the technicalities in term of mathematics are kept to a minimum. Coastal and Shelf Sea Modelling is particularly suitable for graduate marine and oceanographic modelling courses, but will also prove useful to coastal engineers and students at any level interested in the quantitative modelling of marine processes. It is stressed that only a minimal level of mathematics (first year calculus or less) is required; the style and content is introductory.

This collection contains 109 papers presented at the Fourth Conference on Coastal Dynamics, held in Lund, Sweden, June 11-15, 2001.

Modeling is now a major tool for important environmental strategies. This book allows the non-specialist reader to understand and criticize current models of the shallow sea and coastal environments. Sufficient background on mathematics and statistics is covered, but readers disinclined to spend time on this may use the book as a reference guide in modeling. Topics include the numerical schemes used, modeling the sea bed, modeling shallow sea dynamics and, unusually for this type of book, modeling ecosystems and animals.

Laboratory physical models are a valuable tool for coastal engineers. Physical models help us to understand the complex hydrodynamic processes occurring in the nearshore zone and they provide reliable and economic engineering design solutions.This book is about the art and science of physical modeling as applied in coastal engineering. The aim of the book is to consolidate and synthesize into a single text much of the knowledge about physical modeling that has been developed worldwide.This book was written to serve as a graduate-level text for a course in physical modeling or as a reference text for engineers and researchers engaged in physical modeling and laboratory experimentation. The first three chapters serve as an introduction to similitude and physical models, covering topics such as advantages and disadvantages of physical models, systems of units, dimensional analysis, types of similitude and various hydraulic similitude criteria applicable to coastal engineering models.Practical application of similitude principles to coastal engineering studies is covered in Chapter 4 (Hydrodynamic Models), Chapter 5 (Coastal Structure Models) and Chapter 6 (Sediment Transport Models). These chapters develop the appropriate similitude criteria, discuss inherent laboratory and scale effects and overview the technical literature pertaining to these types of models. The final two chapters focus on the related subjects of laboratory wave generation (Chapter 7) and measurement and analysis techniques (Chapter 8).

The authors have developed models for predicting beach changes applicable to various problems on real coasts. One of them is the contour-line-change model to predict long-term beach changes caused by the imbalance in longshore sand transport, which is a kind of N-line model. Because the calculation of the nearshore current is not needed in this model, and the computational load is small, it has an advantage in the prediction of long-term topographic changes on an extensive coast. However, the handling of boundary conditions becomes difficult when offshore coastal structures are constructed in a complicated manner, and in this regard the so-called 3D model has an advantage. Taking this point into account, the authors developed a morphodynamic model (BG model) by applying the concept of the equilibrium slope and the energetics approach, in which depth changes on 2D horizontal grids are calculated.

Mechanistic models are often employed to simulate processes in coastal environments. However, these predictive tools are highly specialized, involve certain assumptions and limitations, and can be manipulated only by experienced engineers who have a thorough understanding of the underlying principles. This results in significant constraints on their manipulation as well as large gaps in understanding and expectations between the developers and users of a model. Recent advancements in soft computing technologies make it possible to integrate machine learning capabilities into numerical modelling systems in order to bridge the gaps and lessen the demands on human experts. This book reviews the state-of-the-art in conventional coastal modelling as well as in the increasingly popular integration of various artificial intelligence technologies into coastal modelling. Conventional hydrodynamic and water quality modelling techniques comprise finite difference and finite element methods. The novel algorithms and methods include knowledge-based systems, genetic algorithms, artificial neural networks, and fuzzy inference systems. Different soft computing methods contribute towards accurate and reliable prediction of coastal processes. Combining these techniques and harnessing their benefits has the potential to make extremely powerful modelling tools.