Abstract: 

Predictive simulation of flow and transport in coastal ocean environments is a problem of interest worldwide. The coastal ocean is home to vital ecosystems, and nearly half of the world's population lives within 150 km of the coast. Coastal regions are extremely vulnerable to natural and man-made catastrophes. The coastal U.S. and parts of Asia and the Middle East in particular have been inundated repeatedly by surge due to tropical cyclones and tsunamis, leading to some of the most costly disasters in terms of human loss and economic damage in history. An increase in hurricane activity over the past decade has led to increasingly devastating coastal flooding. Hurricanes Katrina (2005), Rita (2005), Gustav (2008), Ike (2008) and Cyclone Nargis (2008), for example, killed thousands to tens of thousands and caused hundreds of billions of dollars in damage. The Deepwater Horizon oil spill in the Gulf of Mexico impacted the Gulf coast in ways that we are still struggling to determine. The recent Japanese tsunami also points to the urgency of better understanding the vulnerability of coastal communities to flooding, and improving coastal protection and sustainability. Uncertaintly in global climate change, population growth, rising sea levels and our increasing dependence on offshore energy production, will only increase the chances for similar disasters to occur in the future.

In this joint project between KAUST and The University of Texas at Austin (UT Austin), we propose to significantly advance the predictive capabilities of coastal ocean models through the development of more accurate models coupled with state-of-the-art data assimilation methods. We will focus on short-duration extreme events such as hurricane storm surge and longer-term events such as oil spills, and apply data assimilation techniques to improve estimation of unknown parameters in the near-shore and inland coastal regions. The project is headed by Prof. C. Dawson at UT Austin, who has expertise in modeling and high performance computing for coastal ocean applications, and Prof. I. Hoteit at KAUST, who has expertise in data assimilation methodologies with applications to oceanography and meteorology. This project is a spin-off of a Round 2 KAUST AEA project, "Storm Surge Forecasting Using Nonlinear Filtering." In this project we have successfully implemented a prototype data assimilated storm surge forecast system, based on the use of ensemble Kalman filters and the Advanced Circulation (ADCIRC) storm surge model.

The planned approach will extend the current research along three related but separate objectives. First, we will produce a more complete and robust hurricane forecasting system, whereby we assimilate not only water levels, but wind and significant wave heights. Second, we will study the use of nonlinear filters for estimation of uncertain coastal parameters. Third, we will extend the methodologies beyond storm surge to other coastal flow and transport processes, including transport of chemical constituents such as oil, salinity and temperature.

We anticipate that this research will lead to new insights into data assimilation methodologies and their use in coastal ocean modeling. In particular, we will focus on reduced order ensemble methods which limit the evolution of the ensemble to a few members. Furthermore, we will extend these data assimilation techniques to complex coupled systems of equations describing flow and transport processes.

The broader impacts of the proposed research are significant and far-reaching. Most hurricane-related fatalities are due to flooding and could be prevented with improved planning, warning systems and emergency response. Accurate forecasts of coastal inundation, provided in real-time to agencies in charge of emergency operations, will result in more timely and orderly evacuations, and help significantly with deployment of first responders and emergency personnel. Improved forecasts require not only predicting water levels, but also forecasting winds and wind generated waves, as these processes are intricately related. In addition, there are many unknown parameters in any model of the coast, perhaps the most significant being bathymetry and bottom friction. Coastal inundation is basically a contest between water being forced inland and frictional resistance to this force. Therefore, better parameterizations of bottom friction and bathymetry are essential to capturing the characteristics and timing of inundation and the recession after the event. Finally, the methods and technologies developed for modeling waves, circulation and winds can be coupled with transport models and applied to many applications of coastal processes, and can provide better predictive capabilities for other extreme events, such as oil spills.