J4.5 Coupling a Computationally Efficient Wave Model to the SLOSH Storm Surge Model

Tuesday, 24 January 2017: 11:30 AM
Conference Center: Chelan 4 (Washington State Convention Center )
Dongming Yang, IMSG@NOAA/NWS/NCEP, College Park, MD; and A. Van der Westhuysen, J. Rhome, B. C. Zachry, and K. Zhang

In order to establish a surge and inundation forecasting capability in reef-fringed islands environments, the National Weather Service is currently participating in the USAID/WMO Coastal Inundation and Flooding Demonstration Project (CIFDP) for the Island of Hispaniola (Dominican Republic and Haiti). From evidence in the literature, it is known that waves have a significant impact on surge levels in island environments. However, third-generation wave models such as SWAN are prohibitively expensive for operational use when coupled with the computationally-efficient surge model SLOSH, currently used at the National Hurricane Center. This constraint is even more acute in developing countries with flood hazard, such as the Dominican Republic and Haiti. This limits the use of coupled wave-surge modeling both in the development of planning tools such as the Maximum Envelope of Water (MEOW) and Maximum of Maximum (MOM) surge hazard databases, and it is prohibitive in probabilistic storm surge forecasting, which requires an ensemble of hundreds of model runs.

To address the above issue, we are studying the application of a more computationally-efficient wave model to the coupled surge problem, in which the frequency-directional space and wave growth processes are parameterized, compared to the first-principles physics approaches in third-generation wave models. This paper presents details of the wave model being considered, the energy balance equation and source term extensions implemented for shallow water application, and the model validation using benchmark tests and field cases over the Caribbean Islands of Hispaniola and Puerto Rico. This parametric model coupled to SLOSH yields coastal wave fields that are comparable to those produced by SWAN at reduced computational cost.

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