Transition of the Coastal and Estuarine Storm Tide Model to an Operational Storm Surge Forecast Model: A Case Study of the Florida Coast (2024)

Keywords: Storm surges; Operational forecasting; Numerical analysis/modeling

1. Introduction

One of the major hazards caused by hurricanes in the United States is storm surge flooding, which can damage buildings and infrastructure, block escape routes, and drown people in low-lying coastal areas along the Atlantic and Gulf coasts. In 2005, storm surge from Hurricane Katrina impacted the Louisiana, Mississippi, Alabama, and Florida coasts, resulting in the death of about 1200 people and $108 billion in property damage (Blake et al. 2011). To avoid loss of life because of storm surge flooding, evacuation plans have been developed and enacted in U.S. coastal areas (Forbes and Rhome 2011). Evacuation zones are determined by a combination of coastal topographic elevations and predicted storm surge heights over the land, which are computed by the National Weather Service's (NWS) numerical storm surge model: Sea, Lake, and Overland Surges from Hurricanes (SLOSH). The National Hurricane Center (NHC) performs real-time storm surge forecasts using SLOSH during the hurricane season to provide critical information for evacuation decision making in response to storms that threaten the U.S. coastline (Glahn et al. 2009).

There are two ways to perform real-time surge forecasts: 1) pregenerated composite and 2) real-time simulation methods. In the pregenerated composite method, storm surge inundations for a coastal area (basin) are precomputed based on a set of climatologically generated synthetic hurricanes that can affect the basin. Storm surge forecasts are generated by selecting the precomputed storm surge from a hurricane closest to the forecasted hurricane through a lookup table listing the parameters of synthetic hurricanes or by combining precomputed surge heights from synthetic hurricanes using a statistical method (Irish et al. 2011). The advantage of this method is that surge simulations can be performed in advance and are less limited by computation time. Therefore, more computationally intensive models, such as the Advanced Circulation (ADCIRC; Luettich et al. 1992) model and Finite Volume Coastal Ocean Model (FVCOM; Chen et al. 2003), which cover large coastal areas with high-resolution grids, can be employed in this manner. The disadvantage of the pregenerated composite method is that actual storms may not match precomputed ones because the synthetic hurricanes cannot perfectly represent real storms. Often, a reasonable estimate of peak storm surge heights along the coast can be derived by this method (Irish et al. 2011). However, a reliable estimate of the surge flooding extent that is critical for evacuation is difficult to achieve because of variability in the wind and track parameters.

The second method generates the storm surge forecast by running hydrodynamic models in a real-time fashion based on the actual hurricane forecasts rather than synthetic ones. The advantage of the real-time simulation method is that it takes into account the variability of wind parameters and the track as the hurricane approaches land. The disadvantage is that the resolution and coverage of the model domain are limited by the available computational resources because multiple model executions in a short period are required to accommodate the uncertainty in the hurricane forecast. The strict constraint on computation time allows only a handful of hydrodynamic models with high computational efficiency to perform real-time surge forecasts.

SLOSH is one example of the hydrodynamic models that can be used to perform real-time forecasts because of its low computational complexity due to the utilization of a linearized momentum equation. The SLOSH model has served the nation well for several decades, but recent studies suggest that improvement in surge forecasts could be made through updating the model physics and algorithms (Bunya et al. 2010; Forbes et al. 2007; Rego and Li 2008). For example, the SLOSH model suffers from several drawbacks, such as excluding advective acceleration and horizontal diffusion terms in the momentum equation (Jelesnianski et al. 1992; Murty 1984), neglecting the effect of land cover on inundation, and insufficient spatial resolutions to fully resolve the details of overland flooding. The further refinement of the model grid is limited by the allowable reduction in the time step that is required to maintain the numerical stability of the explicit scheme used by SLOSH in terms of the Courant–Friedrichs–Lewy condition. The utilization of a filtering method can improve the stability of the explicit scheme (Taylor and Forbes 2011), but cannot completely remove the limitation set by the explicit scheme.

Many numerical models for coastal and oceanic hydrodynamics have been developed for simulating tide, wind-driven circulation, and storm surge over the past two decades. Examples of these models include the Princeton Ocean Model (POM; Blumberg and Mellor 1987); the Curvilinear Hydrodynamics in Three Dimensions (CH3D) model (Sheng 1987); the ADCIRC model (Luettich et al. 1992), the Environmental Fluid Dynamics Code (EFDC) model (Hamrick 1992); the Unstructured Tidal, Residual, and Intertidal Mudflat (UnTRIM) model (Casulli and Walters 2000); FVCOM (Chen et al. 2003); the Eulerian–Lagrangian Circulation (ELCIRC) model (Zhang et al. 2004); the Coastal and Estuarine Storm Tide (CEST) model (Zhang et al. 2008); and the Semi-Implicit Eulerian–Lagrangian Finite Element (SELFE) model (Zhang and Baptista 2008). These models solve the full momentum equations together with the continuity equation by maintaining nonlinear advective acceleration and diffusion terms. These models and their extensions also include the wetting–drying component and have recently been applied to the simulation of overland flooding (Bunya et al. 2010; Forbes et al. 2009; Forbes et al. 2010; Huang et al. 2010; Shen et al. 2006; Sheng et al. 2010; Xie et al. 2004; Xu et al. 2010). However, these models cannot be used directly for operational surge forecasts because most of them are developed in research mode and have not been tested to meet the requirements of an operational center such as the NHC. The lack of a clear research-to-operation (R2O) pathway for hydrodynamic models hinders the usage of cutting-edge hydrodynamic models for operational surge forecasting.

Although the Joint Hurricane Testbed program at the NHC provides a funding opportunity for a R2O transition of hurricane-related products, few studies place emphasis on the R2O transition process of a hydrodynamic model for surge forecasting because 1) the requirements for real-time surge forecasting have not been clearly defined and 2) the procedure to perform the R2O transition has not been explored. The main objective of this research is to explore a possible pathway for transitioning a storm surge model in a research mode into an operational one using CEST as an example. The secondary objective is to identify the requirements of operational storm surge forecasts at the NHC to facilitate the interaction between research and operational communities. This paper is arranged as follows. Section 2 describes the requirements of a hydrodynamic model for performing real-time surge forecasts. Section 3 briefly presents the SLOSH and CEST models. Section 4 describes the procedure to convert a SLOSH basin to the CEST grid. Section 5 compares the SLOSH and CEST simulations on the Miami basin and other basins in Florida, and presents and discusses the CEST simulations on the refined grids. Section 6 consists of discussion. Section 7 draws conclusions.

2. Requirements of operational storm surge forecasting

There is considerable uncertainty in forecasting hurricanes (e.g., their tracks, intensities, and sizes) that drive storm surge models. Exact flood magnitude and extent cannot be determined in advance because of this uncertainty, even if hydrodynamic models are very accurate in predicting storm surges. In addition, evacuation orders have to be issued well before hurricanes make landfall because time is needed to evacuate residents. The NWS overcomes these difficulties by using conservative approaches by first generating the maximum envelope of water (MEOW) in a coastal area. The MEOW is composed of the maximum surge at each grid cell computed by SLOSH based on a large ensemble of storms of a given category, track direction, and forward speed with varying sizes and landfall locations (Shaffer et al. 1989). Then, the maximum of MEOWs (MOMs) for a given category of hurricanes, indicating coastal areas that could potentially be flooded by this category of storms from all possible directions, is generated. Finally, the evacuation zones are created through hurricane evacuation studies based on the storm surge threat depicted by the MEOWs and MOMs. In reality, the extent of flooding from a single event is usually less than those from the MEOWs and MOMs corresponding to the category of the event. This is because the MEOWs–MOMs are intended to help develop evacuation plans rather than forecast a single event.

Because of the inherent uncertainty associated with pinpointing the exact nature and landfall location of a given hurricane, it is necessary to develop conservative evacuation zones. However, if more forecast accuracy could be attained, more efficient hurricane evacuations could be executed with less societal disruption and costs. One improvement in hurricane evacuation decision making is the use of Probabilistic Hurricane Storm Surge (P-Surge) forecasts (Taylor and Glahn 2008), which are produced based on a possible range of hurricane intensities, sizes, and tracks determined by the NHC's forecast and its uncertainty, rather than based on MOMs for synthetic hurricanes from all directions. The P-Surge model generates more realistic forecasts of the possible ranges of inundation magnitudes and extents for a hurricane than the forecasts based on MOMs. Real-time storm surge simulations are required to produce P-Surge products because the NHC updates the hurricane forecast/advisory every 6 h. Obviously, the forecasts of storm surges are only useful if they are available to users before the arrival of the next advisory. The ideal situation for emergency managers is that the surge forecasts are available immediately after the NHC issues a hurricane advisory so they can use the advisory and surge forecasts for decision making. Although it is impractical to derive surge forecasts immediately after issuing an advisory, the needs of emergency managers for fast responses require that the storm surge guidance based on an updated advisory be available in minutes rather than hours.

The NWS offers three-tiered products for decision making (Fig. 1) related to storm surge flooding. For planning and mitigation work prior to 120 h of landfall, the usage of MOMs is recommended. Both MEOWs and MOMs, which provide potential storm surge for a given hurricane category at a regional level, are recommended between 48 and 120 h before landfall. The usage of P-Surge and MEOWs is recommended for possible and potential storm surge scenarios during a period from 0 to 48 h before landfall, which is often critical for decision making. To produce reliable real-time probabilistic forecasts of surge flooding, thousands of simulations using storms of varying tracks, intensities, sizes, and forward speeds for affected basins need to be completed (Glahn et al. 2009). The simulations and the creation of P-Surge are completed in approximately 20–30 min using National Oceanic and Atmospheric Administration's (NOAA) operational forecasting supercomputer. Additionally, because the NHC generates and iteratively refines the hurricane forecast between advisories, unplanned quick diagnostic predictions need to be completed within 5–10 min in response to adjustments to the hurricane forecast. Currently, the NHC performs “as needed” diagnostic forecasts by running SLOSH using multicore PC workstations. Often, several surge forecasts need to be generated to accommodate the uncertainty in the forecast of hurricane track, intensity, size, and forward speed. This approach is called a mini-MEOW (or miniensemble) forecasting method. The ability to generate real-time P-Surge and mini-MEOW forecasts places a strict requirement on the computation time of hydrodynamic models.

In addition to time constraints on the storm surge forecast, the modeling approach must be consistently employed over the U.S. Atlantic and Gulf coasts. The simulations have to be robust without becoming unstable during the forecast, which means that the model must undergo rigorous testing using a set of climatologically generated hurricanes that could affect the U.S. Atlantic and Gulf coasts. Finally, the results of surge simulations should be available in geographic information system (GIS) format to facilitate the usage of surge flooding data across many platforms by evacuation planners, decision makers, and the scientific community.

3. SLOSH and CEST models

a. Hydrodynamic model

Two-dimensional (2D) models are often used to model storm surges to reduce the time complexity of numerical computation. With the Boussinesq and hydrostatic pressure approximations, the 2D depth-integrated momentum equations for shallow water in a Cartesian coordinate system can be expressed by

The continuity equation is

where x and y are the horizontal coordinates, t represents time, H is the water depth from the still water level to the bottom, ζ is the water surface elevation reference to the still water level, U and V are depth-integrated velocities along the x and y directions, f is the Coriolis parameter, g is the gravitational acceleration,

is the atmospheric pressure drop,

is the water density,

is the horizontal eddy diffusivity,

and

are bottom stresses, and

and

are surface wind stresses.

4. Conversion of SLOSH basins to CEST grids

Since maintenance of a surge modeling system, especially basin development and maintenance, is expensive and the products such as MEOWs and MOMs for SLOSH basins have been extensively used in emergency management communities at the federal, state, and county levels, it would be difficult for the NHC and its partner agencies to immediately switch from SLOSH products to those from another hydrodynamic model. One way for a new operational model to alleviate this transition problem is to develop the capability of performing surge simulations over existing SLOSH basins and to demonstrate the ability to reproduce associated MEOW and MOM products. This would allow an operational center such as the NHC to potentially adopt an additional modeling system without incurring the huge costs associated with transitioning, building, and maintaining a new set of model grids. Therefore, the first step in exploring the R2O transition process for converting a research hydrodynamic modeling system such as CEST into an operational model is to test the feasibility of running the model over existing SLOSH basins.

The computation scheme of the SLOSH model is based on the Arakawa B grid (Jelesnianski et al. 1992; Purser and Leslie 1988; Taylor and Forbes 2011) with velocity components at the four corners of a grid cell and the elevation at the center (Fig. 3a). The following set of shapefiles (Fig. 4) has been created in ArcGIS by the MDL to maintain and update SLOSH basins (Conver et al. 2010):

1. a grid-cell polygon shapefile;

2. a cell center point shapefile with the average cell elevation for the cell center;

3. a 1D flow point shapefile with property indicating two types of 1D flows, where 1D flow with a width value of −1 represents the water flow that crosses the entire cell when the cell is wet and 1D flow with width value of 0.1–0.9 indicates that the flow moves through a channel limited by banks with width of 0.1–0.9 times of the cell edge length; the position of the 1D flow point coincides with the centers of cell edges;

4. a cut point shapefile, which is used to connect 1D flows with channels of varying widths; cut points are on the centers of cell edges;

5. a barrier point shapefile representing levees and barrier islands with points coinciding with the vertices of grid cells;

6. a momentum point shapefile, which represents the maximum elevation height of the four surrounding grid cells, and is used by the SLOSH model to determine the water flowing between cells; the momentum points coincide with the vertices of grid cells; and

7. a tree value point shapefile for computing surface wind stress at the momentum points. Tree values are divided into three main categories:

   1. land and channels:

      • value 1 for lake and tree—forest land with elevation between 0 and 11.0 m (36 ft) in reference to NAVD88;

      • value 3 for lake only—nonforested land with elevation between 0 and 11.0 m, as well as channels and bays below 0 m; and

      • value 4 for high terrain—land with elevation ≥11.0 m;

   2. oceans:

      • value 2 for deep water—the water with elevation <−9.1 m (−30 ft); and

      • value 5 for shallow water—the water with elevation ≥−9.1 m;

   3. boundary values (along the edges of the model grid):

      • value 4 for solid wall (no flow)—all land;

        See Also

        Marine Forecasting at the 1996 Centennial Olympic Games

      • value 6 for static height—elevation ≤−9.1 m;

      • value 8 for intermediate—elevation between −9.1 and −22.9 m (−75 ft); and

      • value 9 for shallow—elevation from 0 to −9.1 m.

The computation scheme of the CEST model is based on the modified Arakawa C grid (Blumberg and Mellor 1987; Purser and Leslie 1988) with velocity components at the four edges of a grid cell and the elevation at the center (Fig. 3b). The elevations at the four edges are also included in the numerical scheme to simulate wetting and drying processes using the accumulative volume method (Zhang et al. 2008). The attributes of a CEST grid cell include horizontal coordinates of four vertices and the center, the five elevation values at the center and four edges, four velocities of water flows perpendicular to the edges, a tree category, and five flags for four edges and the cell indicating whether they are dry or wet. To run CEST on a SLOSH grid, a SLOSH grid was converted into the CEST grid using the following procedure:

1. grid coordinate—extract the grid coordinates from the SLOSH shapefile and create the grid for CEST;

2. cell center depth—set the center depth of a CEST cell to be the depth of SLOSH cell center;

3. edge depth—set edge depths of a CEST cell by averaging center depths of two adjacent CEST cells;

4. barrier depth—update the depth of an edge by averaging the depths of two adjacent SLOSH barrier points that are connected by the edge;

5. flow depth:

   1. update the center depths of the cells at the left H_L and right H_R sides of the edge coinciding with a flow point using a width-weighting method,

      

      

      

      where w is the ratio of the flow width to the edge width, H_flow is the depth of the SLOSH flow point, H_LO is the original center depth of the cell at the left side of the edge, and H_RO is the original center depth of the cell at the right side of the edge;

   2. update the depth H_E of the edge that coincides with the flow point using center depths of two adjacent cells,

      

      

      

6. cut depth—update edge and center depths using the same method for flow depth;

7. tree flag—set up the tree flag of a CEST cell based on the value of SLOSH tree point at the top-right vertex of the CEST cell, where the tree flag is only used for computation of the wind field in CEST; similar to SLOSH, the wind speed in a cell covered by vegetation is adjusted using a coefficient C_T based on the ratio of the surge water depth D to the vegetation height H_T (Jelesnianski et al. 1992),

   

   

   

   the effect of trees on the wind speed decreases based on this equation as the water submerges the vegetation gradually;

8. Manning coefficients—calculate Manning coefficients for ocean and land grid cells; the CEST model uses the Chezy formula (LeMehaute 1976; Zhang et al. 2012) with Manning's roughness coefficient to calculate bottom stresses; the Manning coefficients for ocean grid cells are computed by an empirical formula based on the water depth H,

   

   

   

   or set up to be constants, for example,

   

   

   

   where C ranges from 0.01 to 0.03. Manning coefficients for grid cells over the land were estimated according to the 2006 National Land Cover Database (NLCD) created by the U.S. Geological Survey (USGS) (Fry et al. 2011). Table 1, which is modified from Mattocks and Forbes (2008), lists the Manning coefficient for each category of land cover. Since the spatial resolution of the NLCD is 30 m, which is much smaller than the cell size of a SLOSH grid, an average Manning coefficient n_a for a grid cell was calculated using

   

   

   

   where n_i is the Manning coefficient value of an NLCD pixel within a model grid cell, α is the area of an NLCD pixel, N is the total number of NLCD pixels within a model cell, and n_w is the Manning coefficient for the oceanic area β that is not covered by NLCD pixels.

5. Comparison of SLOSH and CEST simulations

6. Discussion

The differences between SLOSH and CEST algorithms in handling of wetting and drying processes, overland bottom friction, and nonlinear terms have varying effects on the computation of storm surges in the open ocean, shallow water, and land areas. The differences in the peak surge heights computed by SLOSH and CEST are usually small for the open ocean and large in the shallow bays and lagoons. This spatial difference in peak surge heights is probably caused by including, versus ignoring, the nonlinear terms in the momentum equations or by the differences in wetting and drying algorithms, and needs to be further investigated for each SLOSH basin to determine their contributions. The overland inundation extents and magnitudes from CEST are less than those from SLOSH in some cases. This difference in inundation is mainly caused by the disparity in the handling of the bottom friction. The overland flow of CEST is influenced by the Manning coefficients, which are determined based on land cover types. In the CEST model, the surge inundation is reduced in the heavily vegetated and highly developed areas because of increased bottom friction, rendering a more realistic simulation (Zhang et al. 2012). In contrast, the bottom friction of the SLOSH model does not include the effects of variations in land cover. Additionally, the CEST model uses a minimum accumulation water volume in a grid cell to determine whether the cell is wetting during the flood surge, which generates a slower inundation process than the SLOSH model.

The Manning coefficient is an important parameter used by the CEST model that is not available in a SLOSH basin. Over the land, the computation of Manning coefficients based on the national land cover dataset is somewhat subjective. More field measurements of overland flooding processes on different types of land cover are needed to verify the current Manning coefficients. It is also problematic to determine the Manning coefficients for the shallow coastal water. Theoretically, the Manning coefficients for the underwater portion should be based on categories of bottom floors. Unfortunately, there is no national dataset that classifies underwater bottom floors. The CEST model uses either a constant Manning coefficients ranging from 0.01 to 0.03 or Manning coefficients for the ocean bottom that vary with water depths [Eq. (7)]. A constant Manning coefficient of 0.015 for the ocean bottom and the varying Manning coefficients based on Eq. (7) produce similar results for the case of Hurricane Andrew. Ideally, several historical hurricanes with abundant field observations for each basin are needed to calibrate Manning coefficients and verify the model; unfortunately, very few basins have such field measurements. Numerous field observations have been collected by various agencies including the USGS, NOAA, the U.S. Army Corps of Engineers, and the Federal Emergency Management Agency for recent hurricanes after realizing the importance of field observations for the better understanding of surge inundation (Soderqvist and Byrne 2007). This effort needs to be continued in the future when hurricanes approach the shore.

The improvement of simulations using finer-resolution grids while accounting for computation time has to be balanced for real-time forecasting. The simulation speed of the CEST model can be improved by parallelizing and distributing the CEST code to a cluster. However, in order to accommodate the uncertainty in track forecast, concurrent simulations of 2000–4000 tracks across affected basins using multiple CPUs with each CPU handling one or more cases is more practical for generating real-time P-Surge forecasts during the 48 h prior to landfall. The stability of the surge model is also important for real-time forecasting. One way to reduce the possibility of a model becoming unstable during an operational forecast run is to test the model against possible hurricanes affecting a basin. The CEST model has been tested for the nine Florida SLOSH basins using more than 100 000 tracks of synthetic hurricanes created by the NHC. Passing these tests ensures that the model will provide stable guidance in most cases, although there is no guarantee that the model will not have problems during a real-time forecast. Therefore, it is safer to use at least two surge models (e.g., SLOSH and CEST) than to use one model to perform real-time forecasts. The other advantage to running multiple surge models is the ability to perform cross validation of simulations and to produce an ensemble forecast.

7. Summary and conclusions

The operational real-time forecasting of storm surge flooding requires that a numerical hydrodynamic model be capable of completing multiple surge simulations in minutes rather than in hours. Also, the numerical model must be highly stable and able to produce simulations in GIS format to facilitate the wide dissemination of computed storm surges. Therefore, in the process of transitioning a research model into an operational model, computational efficiency and numerical stability of the model, as well as the compatibility of the model products with operational guidance, must be considered in addition to accuracy. Such considerations have historically been lacking in traditional studies of hydrodynamic models. In this paper, a four-step procedure was developed for converting a hydrodynamic modeling system for operational usage using CEST as an example. The four-step procedure includes 1) testing the feasibility of running CEST on existing SLOSH basins, 2) comparing the CEST and SLOSH simulations for individual historical storm events, 3) comparing the CEST and SLOSH simulations for multiple synthetic storms and recreating MEOWs and MOMs, and 4) examining the potential of CEST in improving surge forecasts by conducting simulations on refined SLOSH grids. This procedure has proven to be effective in exploring the process of transitioning CEST into an operational model and, therefore, provides a valuable reference to converting other research surge models into operational ones.

Surge simulations for more than 100 000 synthetic hurricanes affecting the coast indicate that CEST is numerically stable on the nine Florida SLOSH basins. Comparison of MEOW and MOM data indicates that CEST and SLOSH produce generally similar results in most cases. However, several distinct results were generated in a few cases because of the differences between SLOSH and CEST in handling the wetting–drying processes, bottom friction, and nonlinear items. CEST is capable of completing 72-h surge simulations on SLOSH basins using a single 2.5-GHz Intel Xeon processor within 2 min in most cases, which is comparable to the computational speed of SLOSH. Therefore, the CEST model can be used to run real-time surge forecasts. The simulations conducted on the refined grids for the Miami basin demonstrate that CEST has the potential to produce more detailed real-time forecasts than those based on the current SLOSH grids.

The requirement for a new surge forecast system that is capable of conducting surge simulations on existing SLOSH basins and producing associated MEOW and MOM products is particularly useful for the surge forecast operation with the limited resources at the NHC. This capability would allow the NHC to potentially adopt an additional modeling system without incurring the cost associated with transitioning, building, and maintaining new model grids. This capability also allows the users of SLOSH surges to utilize the results from the new surge forecast system without significant additional training. Therefore, the four-step procedure is recommended for the conversion of the other research surge models into operational ones whenever it is possible. In addition, a fifth step, which involves the development of a set of tools to convert the outputs from multiple models into a standard GIS format and existing operational dissemination formats such as those used by the SLOSH display program, is worth exploring in the future to further facilitate the R2O transition of surge models.