1 Introduction

A byproduct of a warming climate is sea level rise (SLR), which is mainly caused by thermal expansion of the sea and ice loss [Parris et al., 2012]. Long‐term tide gage records show that global mean sea levels have risen 1.7 ± 0.3 mm/yr over the last century [Church and White, 2006; Holgate, 2007]. Since 1993, satellite altimetry data have recorded an increase to 3.3 ± 0.4 mm/yr [Ablain et al., 2009; Nicholls and Cazenave, 2010]. These are global means, however, and the tide and satellite altimetry data indicate large spatial and regional variations across the Earth, with some areas experiencing up to three times the global average. Regional SLR trends are dominated by local circulation and a redistribution of heat, salinity, and water mass [Cazenave and Cozannet, 2014].

Estimates of the magnitude and rates of future sea levels are based on potential carbon emission scenarios, and are constructed from different assessments of ocean warming and ice sheet loss. Regardless of the methods and emission scenarios used to estimate future sea levels, the consensus is that sea levels are rising and its rate is expected to accelerate [Jevrejeva et al., 2010; Parris et al., 2012; Intergovernmental Panel of Climate Change, 2013]. Parris et al. [2012] derived four SLR scenarios spanning 0.2–2.0 m for the year 2100 based on different contributions of carbon emissions and ice sheet loss. The continued rise and acceleration of global sea level put low‐lying coastal regions at risk with increases in frequency and magnitude of flooding [Cazenave and Cozannet, 2014]. Increased flooding because of SLR, in addition to regions that experience storm surges from tropical cyclones further increases the vulnerability of inundation in coastal regions.

Storm surge models, which vary in complexity and computational efficiency, are commonly used to estimate maximum water levels caused by tropical cyclones. Physics‐based storm surge models require a variety of inputs such as topography, bathymetry, and bottom friction as well as astronomical tide and meteorological forcings. The benefits of hydrodynamic storm surge models are that they allow for analysis of regions that have little observational hydrodynamic data, and permit varying scenarios and changes to the input data. As described in this work, it is useful to project coastal flooding under a variety of potential climate change scenarios. The hydrodynamic models also allow for a more detailed analysis of the nonlinear interactions of storm surge and SLR [Bilskie et al., 2014], and permit more accurate assessments than static, or “bathtub,” modeling techniques that are not always straightforward to apply [Hagen and Bacopoulos, 2012; Zhang et al., 2013].

McInnes et al. [2009] outlined three general methodologies to examine potential impacts of flood risk under a changing climate that employ hydrodynamic models: direct nesting approach, perturbed historical baseline approach, and the statistical dynamic approach. The direct nesting approach considers model forcing from global or regional climate models. This method is computationally expensive and heavily reliant on the accuracy of the climate model used [Knutson and Tuleya, 2004; Woth et al., 2006]. The perturbed historical approach is similar to the direct nesting approach, however, the model is forced with historical extreme sea level events to obtain a baseline and then changes to mean sea level and hurricane intensity, for example, are included in the model simulations [Bernier et al., 2007; Atkinson et al., 2013; Ding et al., 2013; Zhang et al., 2013; Yang et al., 2014; Orton et al., 2015]. This method is more computationally efficient than the direct nesting approach and permits variations of multiple climate change scenarios. However, it depends upon a rich meteorological history to develop a population of extreme sea levels and in many cases flood risk is only assessed through a selected few events. In the statistical dynamic approach, a statistical model is employed to generate a population of potential storms used to force a hydrodynamic model [McInnes et al., 2003; Resio et al., 2009; Mousavi et al., 2011; Hagen and Bacopoulos, 2012; Lin et al., 2012]. This method performs well in regions with limited meteorological records [McInnes et al., 2009]. Regardless of the applied approach, the model solution is only as good as the input data. To this end, it is of critical importance to evaluate the model's numerical description of nature and how the landscape may change in the future under normal or climate change conditions. This includes changes to the coastal landscape such as shoreline morphology, marsh migration, and land use.

Ali [1999] performed one of the first studies that examined the combined effect of storm surge with an emphasis on SLR, coastal morphology, and a change in hurricane intensity. They forced a GCOM2D hydrodynamic model with 489 individual events based on an increase in sea surface temperature of 2°C and 4°C and a SLR of 0.2 and 1.0 m to evaluate the climate change impact on storm tide return periods along the southeastern Australia coastline. Wang et al. [2012] employed the MIKE21 hydrodynamic model to evaluate coastal flood risk under SLR and land subsidence in Shanghai, China for the years 2030, 2050, and 2100 to provide data and suggestions for coastal protection design. Mousavi et al. [2011] estimated the increase in storm surge flooding for Corpus Christi, Texas for the years 2030 and 2080. The model was forced by 23 hurricane scenarios based on three historical storms (Bret, Beulah, and shifted Carla) that were selected from numerical simulations derived from surge response functions. Studies like Atkinson et al. [2013] and Smith et al. [2010] began to include waves into the computation of storm surge and SLR in addition to introducing changes to bottom friction based on new regions exposed to daily tidal flooding. Smith et al. [2010] demonstrated that in regions of maximum storm surge, the additional surge was equal to the SLR, but in wetland fronted areas, the storm surge was highly nonlinear and surge heights increased by 1–3 m in addition to the change in sea level. Atkinson et al. [2013] concluded that there is not a “one‐size‐fits‐all” approach to storm surge under SLR because it is highly dependent on storm characteristics and the local geography. These studies advanced the modeling of hurricane storm surge and SLR using a dynamic modeling framework.

Bilskie et al. [2014] showed that hurricane storm surge flooding under SLR is a complex nonlinear process and changes to the coastal landscape for past and future scenarios can amplify storm surge by 80% or reduce storm surge by 100%, relative to the amount of SLR. They found that altering the coastal floodplain topographic elevations, including barrier island morphology, and incorporating changes in land use land cover (LULC) based on past conditions and future scenarios altered the path, pattern, and magnitude of flooding. From this work, the normalized nonlinearity (NNL) index was purposed to quantify and determine the influencing factors of the nonlinear interaction of storm surge under climate change conditions. This work served as a proof‐of‐concept to the dynamic modeling system and focused only on the Mississippi and Alabama coast, and included limited changes to the coastal landscape and sea level.

Passeri et al. [2015a] began to examine the potential changes to the shoreline position and profile under SLR along the Florida Panhandle coast. They extrapolated historic rates of shoreline change in conjunction with an equilibrium beach profile approach to estimate future shoreline positions under an intermediate SLR scenario for the year 2050. Using hydrodynamic simulations, they indicated that the simulated storm surge was highly sensitive to shoreline change, and back bays protected from barrier islands were highly vulnerable to increased storm surge because of additional barrier island overtopping. Passeri et al. [2016] continued this work with a focus on validating the modeling framework for astronomic tides and shoreline morphology. They used a high‐resolution astronomic tide, wind‐wave, and hurricane storm surge presented by Bilskie et al. [2015b] that contains the entire coast of Mississippi, Alabama, and the Florida Panhandle. The model's representation of the coastal landscape was modified based on an estimate of future shoreline positions and primary dune heights for four SLR derived from a Bayesian network purposed in Plant et al. [2016].

Based on the conclusions and recommendations of these previous studies, the natural progression is to develop a modeling system to estimate and investigate storm surge flooding under climate change conditions by taking into account three major landscape changes: (1) shoreline and barrier island morphology, (2) marsh evolution, and (3) LULC change. As stated in Passeri et al. [2015c], a large‐scale, physics‐based storm surge modeling framework that accounts for these changes and their interrelation with varying climate and SLR scenarios does not exist. To properly examine the additional flood risk and the vulnerability of coastal communities to extreme flooding in the future, these conditions must be incorporated into the computational modeling approach. The objective of this paper is to assess the state of the practice and provide a suggested physics‐based storm surge modeling framework to simulate hurricane storm surge in a changing climate while considering changes to the coastal landscape. This work is a culmination of the research presented in Bilskie et al. [2014], Passeri et al. [2016], and Plant et al. [2016]. The developed methodology is applied across the coastal floodplain regions of Mississippi, Alabama, and the Florida Panhandle, henceforth, referred to as the northern Gulf of Mexico (NGOM).