Toward the Probabilistic Simulation of Storm Surge and Inundation in a Limited-Resource Environment

Abstract

To create more useful storm surge and inundation forecast products, probabilistic elements are being incorporated. To achieve the highest levels of confidence in these products, it is essential that as many simulations as possible are performed during the limited amount of time available. This paper develops a framework by which probabilistic storm surge and inundation forecasts within the Curvilinear Hydrodynamics in 3D (CH3D) Storm Surge Modeling System and the Southeastern Universities Research Association Coastal Ocean Observing and Prediction Program's forecasting systems are initiated with specific focus on the application of these methods in a limited-resource environment. Ensemble sets are created by dividing probability density functions (PDFs) of the National Hurricane Center model forecast error into bins, which are then grouped into priority levels (PLs) such that each subsequent level relies on results computed earlier and has an increasing confidence associated with it. The PDFs are then used to develop an ensemble of analytic wind and pressure fields for use by storm surge and inundation models. Using this approach applied with official National Hurricane Center (OFCL) forecast errors, an analysis of Hurricane Charley is performed. After first validating the simulation of storm surge, a series of ensemble simulations are performed representing the forecast errors for the 72-, 48-, 24-, and 12-h forecasts. Analysis of the aggregated products shows that PL4 (27 members) is sufficient to resolve 90% of the inundation within the domain and appears to represent the best balance between accuracy and timeliness of computed products for this case study. A 5-day forecast using the PL4 set is shown to complete in 83 min, while the intermediate PL2 and PL3 products, representing slightly less confidence, complete in 14 and 28 min, respectively.

The reduction of storm surge by vegetation canopies: Three‐dimensional simulations

“…The benefit of this spatially integrated metric over the single-point metric (H v /H o at a given x, y location) is demonstrated by the significant spatial variability of H v /H o [Loder et al, 2009] which underscores the need for a spatially integrated vegetation dissipation potential (VDP) to quantify the impacts of vegetation on a regional scale. during hurricanes [Sheng et al, 2010a[Sheng et al, , 2010bDavis et al, 2010;Sheng and Liu, 2011]. The model includes a TKE model to represent the vegetation-induced skin friction drag and profile drag as well as the turbulence generation by the wakes behind the vegetation, by simplifying a vegetationresolving Reynolds Stress turbulence model [Lewellen and Sheng, 1980;Sheng, 1982].…”

Evaluation of coastal inundation hazard for present and future climates

“…To provide open boundary condition for SWAN, CH3D-SSMS uses the output of a large-scale wave model such as WaveWatch-III (Tolman 1999(Tolman , 2002. CH3D-SSMS has been used extensively to simulate storm surge and inundation due to various tropical storms including Hurricane Isabel (Sheng et al 2010a), Charley (Sheng et al 2006;Davis et al 2008Davis et al , 2010, Ivan (Sheng et al 2010b), and Wilma . Sheng and Paramygin (2010) combined the baroclinic circulation element of CH3D with CH3D-SSMS to forecast the storm surge, inundation, and 3D baroclinic circulation in northeast Florida during Tropical Storm Fay.…”

Assessing the temporal variability in extreme storm‐tide time series for coastal flood risk assessment

“…Understanding this uncertainty is essential as unrealistic expectations of accuracy can result in the misinterpretations of flood risk, with profound implications [Brown and Damery, 2002;Hall and Solomatine, 2008]. For this reason, many operational forecasting systems and risk assessments provide probabilistic predictions in an attempt to explicitly account for known uncertainties in the modeling approach used [e.g., Purvis et al, 2008;Bocquet et al, 2009;Pappenberger and Beven, 2006;Davis et al, 2010;Kim et al, 2010].…”