Over the past decade, a series of natural disasters caused by waves have attacked coastal communities resulting in substantial loss of life and property. From the 2004 Boxing Day tsunami to the damage wrought by Hurricane Sandy, these events are expected to become increasingly common due to rising seas and population growth in the coastal zones threatened by these events.
Modeling and simulation efforts in the past decades have led to improved forecasting and prediction of these coastal hazards, but there are still a number of situations where current methods have failed to produce accurate risk assessment and predictive capabilities. To address these issues, this four-day workshop will discuss three primary themes and hopefully draw many newcomers into this exciting field:
(1) Assessment of the underlying model equations— Since the full governing equations of fluid dynamics are often too costly to compute on the scales needed, models are often formulated that capture salient features of the fluid dynamics but are computationally less expensive to compute. The question of what these salient features are and what physics need to be included in the model equations to capture these features is an active topic of research and one that this workshop will attempt to address.
(2) Numerical methods and software development—Numerical methods are at the core of risk assessment and the considerations that go into what method to use is based on a number of factors including the type of model equations used, the inherent multi-scale physics present, and the computational cost among others. A related issue is the task of developing the software that will implement the numerical methods. Issues here include software design, community development, code availability, and testing.
(3) Verification, uncertainty quantification, and probalistic approaches to hazard assessment—Coastal hazards are often difficult to fully characterize due to a number of factors including uncertainty in the bathymetry, friction, observations, and other forcing parameters such as an earthquake fault model or hurricane forcing. To mitigate this uncertainty, we need to characterize the uncertainty in the input data and develop methods that can adapt to these uncertainties, providing metrics for which uncertainties have the greatest impact on risk assessment and therefore guide future allocation of observational programs.