Numerical simulation of coastal and its mitigation by living shoreline methods: A case study in southern Rhode Island By

Scott Hayward1, M. Reza Hashemi2*, Marissa Torres2, Annette Grilli2, Stephan Grilli2, John King3, Chris Baxter2, and Malcolm Spaulding2 1) Ransom Consulting Inc., 400 Commercial Street, Portland, ME 04101; 2) Department of Ocean Engineering, University of Rhode Island, Narragansett, RI 02882, USA; 3) Graduate School of Oceanography, University of Rhode Island, Narragansett, RI 02882, USA; * Corresponding author: Email [email protected]

ABSTRACT Accelerated shoreline retreat due to sea level rise is a major chal- nested within this regional grid to simulate nearshore sediment lenge for coastal communities in many regions of the U.S. and transport processes and shoreline erosion. After validating the around the world. While many methods of erosion mitigation regional modeling system for a historical storm (Hurricane have been empirically tested, and applied in various regions, Sandy), Hurricane Irene (2011) was used to validate XBeach, on more research is necessary to understand the performance the basis of a unique dataset of pre- and post-storm beach pro- of these mitigation measures using process-based numerical files that was collected in our study area for this event. XBeach models. These models can potentially predict the response of showed a relatively good performance, being able to estimate a beach to these measures and help identify the best method. eroded volumes along three beach transects within 8% to 39%, Further, because nearshore sediment transport processes are with a mean error of 23%. The validated model was then used still poorly understood, there are many uncertainties in assess- to analyze the effectiveness of three living shoreline erosion ment of and mitigation measures. Hence, there mitigation methods that were recommended in a recent study is a need to better assess the capabilities and shortcomings of of coastal erosion in New England: , coastal numerical models as a way to improve them. In this work, a suite bank (engineered core), and submerged . Further, of numerical models was used to assess coastal erosion and the the effect of an artificial surfing reef on sediment transport performance of a number of recommended solutions, along a was also investigated. Conceptual designs were implemented section of in southern Rhode Island, US, which represents in the model and the eroded volume were computed, with and a typical coastal barrier system. The coupled modeling system without the presence of these solutions. Using two synthetic SWAN (Simulating Waves Nearshore), a third-generation storms, it was shown that erosion mitigation methods that fo- wave model, and ADCIRC (ADvanced CIRCulation Model), cus on reinforcing the beach face and dunes are more effective a two-dimensional depth integrated circulation model, was than those that reduce wave action. While this study showed applied over a regional grid encompassing northeastern U.S. how models such as XBeach can help examine the technical to compute offshore sea levels and wave conditions for speci- performance of erosion mitigation measures, more detailed fied storm scenarios, both historical and synthetic. The coastal assessments including cost-benefit analyses are necessary at wave-circulation and morphodynamic model XBeach was then the decision-making level. oastal communities on the East caused to coastal communities along the ADDITIONAL KEYWORDS: Coast of the United States (US) upper U.S. East Coast, the U.S. Army XBeach, ACDIRC, SWAN, living are likely to experience a greater Corps of Engineers (USACE) performed breakwater, beach nourishment, geo- Cimpact from tropical storms in the fu- the North Atlantic Coastal Comprehen- textile bank protection, recreational ture.1 In addition to sea level rise (SLR), sive Study (NACCS; Jensen et al. 2016), surfing reef. a number of studies have related climate which assessed resilience and adaptation change to an increase in the number of Manuscript submitted 13 July 2018, in the face of increased risk to ports, higher intensity tropical storm activity revised and accepted 19 October 2018. coastal communities, and businesses. As (Holland and Bruyere 2014; Yoshida et part of this study, both storm surge and al. 2017). Along the barrier beaches infrastructure is gradually reduced. With waves were modeled for 1,050 synthetic and barrier islands bordering the U.S. the perspective of increasing damage tropical and 100 historical extratropi- East Coast, severe storms often cause from the combination of more severe cal storms. Results of these simulations widespread dune and beach erosion, storms and SLR, coastal communities, were used to assess the vulnerability of resulting in beach recession and damage particularly along the U.S. East Coast, the U.S. East Coast to storms and SLR, to infrastructure. As beaches erode, the must adapt and increase their resilience and propose solutions to increase coastal natural protection they provide to coastal to these natural disasters. resilience. Although NACCS results were reasonably well validated in offshore 1) For more details visit the ongoing research at In the wake of superstorm Sandy regions, some studies have shown that NOAA’s GFDL lab: http://www.gfdl.noaa.gov/ (2012) and the widespread damage it global-warming-and-hurricanes. these results were not as accurate in near- Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 13 Figure 1. Computational domains of the study. Unstructured regional SWAN+ADCIRC mesh that covers the northeastern U.S. is shown in the top panel. The extent of the nearshore XBeach model in southern coast of Rhode Island is highlighted in the bottom panel. shore regions and around barrier beach these systems were significantly eroded, tures such as breakwaters, , and systems (Shaw et al. 2016). Nevertheless, flooding extent during an event similar similar structures and promote less in- both the synthetic tropical and historical to Hurricane Bob (1991) would increase vasive quasi-natural protection methods extratropical storms from NACCS can be by 200%, because natural dunes are the (Zeitlin-Hale et al. 2010). Besides beach useful to set up more accurate nearshore main protection against coastal flooding re-nourishment, such methods include models to estimate nearshore wave and in barrier beach systems. Schambach et dune restoration and using endemic storm surge conditions. al. (2018) recently used the coastal wave/ plants to reinforce the dunes. Narragan- circulation and geomorphodynamic sett town beach is one example of a RI With over 300 miles of mostly erodible model XBeach to simulate erosion of the beach community actively making ef- sandy coastline, the state of Rhode Island Charlestown, RI barrier beach system dur- forts to maintain the coastline by restor- (RI) in the Northeast of the U.S. is threat- ing Irene (2011) and for a synthetic 100- ing dunes and regularly re-nourishing ened by both tropical and extratropical year tropical storm. This study showed the most eroded sections of the beach storms. Barrier beach systems (i.e. coastal the effect of a dune system in reducing (Woods Hole Group 2011). The Woods ponds protected by barrier beaches, often coastal flooding and also the important Hole Group (2017) recently conducted confined between headlands) make up role of coastal vegetation in mitigating a coastal resilience study under the much of the southern RI coast and are dune erosion. Therefore, there is a growing auspice of the Northeastern Regional especially vulnerable to breaching and interest to protect and restore these valu- Association of Coastal Ocean Observ- overtopping due to a combination of able natural resources, which has led many ing Systems (NERACOOS). As part of storm waves and surge. Coastal commu- of the barrier systems along the southern that effort, they proposed simple tools nities such as Charlestown, Matunuck, coast of Rhode Island to be designated as and methodologies for most effectively Misquamicut, and Quonochontaug, RI, coastal barrier resource systems.2 increasing coastal resilience in New Eng- are located on and around barrier beach land. In particular, these tools can help systems, which recent studies have shown Regulations in several states, includ- determine the most effective approach are experiencing a trend of increased ing North Carolina and RI, prohibit for mitigating erosion in specific cases, coastal erosion (Boothroyd et al. 2016). using “hard” coastal protection struc- based on a number of factors such as Shaw et al. (2016) showed if dunes in 2) https://www.fws.gov/CBRA/). wave climate, available resources, tidal

Page 14 Shore & Beach  Vol. 86, No. 4  Fall 2018 range, and beach slope. Additionally, the tools can help decision-makers rule out the less effective approaches. Large-scale erosion mitigation proj- ects can be costly to develop and imple- ment. These projects are often recom- mended based on the success or failure of previous applications. Hence, for a case study, it is advantageous to use numeri- cal models to assess the performance of potential erosion mitigation methods, and reduce uncertainty before select- ing and further analyzing a design. As an illustration of this approach, in this work, we apply a suite of state-of-the-art numerical models to simulate the erosion of a barrier beach system in southern RI due to extreme storm surge and waves. We then perform additional simulations to assess and compare the performance of several conceptual living shoreline designs. Results and methodologies used in this work could be beneficial to similar case studies, particularly in areas that are protected by coastal barrier systems. Figure 2. Locations of observational water elevation (triangle), wave (diamond) METHODOLOGY and transect (dot) data used to validate the regional and nearshore models. Numerical models The zoomed region within the domain shows the XBeach model domain. The Two numerical models are used in location of the beach profile surveys at Charlestown Breakwater, Charlestown this work (Figure 1): (1) a coupled wave- Beach, and Green Hill Transects are also shown (CBW, CTB, GH). storm surge model SWAN-ADCIRC RI, coastline (Figure 1), which is over- (Booij et al. 1999; Dietrich et al. 2011; lapped by the nearshore grid. The main Luettich et al. 1992) was applied over a area of interest is a stretch of barrier large unstructured triangular regional beach located between Green Hill and the grid. This model was forced by hurricane Charlestown , which protects wind fields to simulate offshore waves and the eastern portion of Ninigret Pond, the storm surge caused by specific storms. (2) coastal lagoon onshore of the barrier (Fig- a hydrodynamic and morphodynamic ure 2). The beach morphology along this model XBeach (Roelvink et al. 2009) was stretch of the southern RI coast has been then applied over a high-resolution near- monitored biweekly in winter to monthly shore Cartesian grid to simulate nearshore Figure 3. Aerial photo of Ninigret and in summer since the early 1960s. Measure- hydrodynamics, sediment processes and Quonochontaug Ponds, view toward ments of the subaerial beach topography coastal erosion. the west, after Hurricane Carol were made along 8 beach transects using The unstructured regional grid (Fig- (1954). Credit Rhode Island Coastal a stadia-style technique (The location of ure 1) was developed by re-meshing the Resources Management Council. three of these transects are shown within NECOFS Gulf of Maine (GOM4) grid Topographic and bathymetric data the XBeach model domain in Figure 2). (Chen et al. 2006) along the Rhode Island were interpolated over the model grids, This dataset thus provides a time-series of coastline, to achieve a higher resolution from NOAA’s Montauk ⅓ arc-second beach profiles, which can be used to esti- of 30-100 m in coastal areas (Torres et (about 10 m) and Northeast Atlantic 3 mate volumetric changes (see e.g. Scham- al. 2018). The high resolution nearshore arc-second (about 90 m) DEMs (Digital bach et al. 2018). In this work, we selected Cartesian grid (Figures 1 and 2) is 7 km Elevation Models; NGDC 2007). For the this section of coastline due to the avail- alongshore by 3 km cross-shore, and nearshore grid, topographic data was ability of beach profile data, which can be rotated 15 deg. counter-clockwise to replaced by higher resolution LiDAR used for model calibration/validation. The align the offshore boundary with the datasets extracted from NOAA’s data maximum dune crest elevation in this area coast. The domain is discretized near the access viewer (collected in March 2011 is 3 m to 5 m NAVD88. The dunes have dunes with 5m by 10m resolution cells and extracted from USGS’ LiDAR of the been breached during significant storms, in the cross-shore and longshore direc- Northeast; OCM Partners 2018a). such as hurricane Carol (Figure 3), and the tions, respectively, while cell resolution Great Hurricane of 1938, when the storm CASE STUDY AREA then decreases to 25m by 10m near the surge in Newport, RI, was measured at 3.5 The primary focus of the study is a offshore boundary. m NAVD88. 3.5-km-long section of the Charlestown,

Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 15 Sandy, data is provided in three nested domains of increasing resolution from 27 km to 3 km. Note, the hindcast of Hur- ricane Sandy includes a synthetic (bogus) vortex to improve the predictive accuracy near the storm center (pers. comm C. Chen 12 Jan. 2017). The ECMWF (Euro- pean Centre for Medium Range Weather Forecast) ERA-interim dataset was used for Irene (the WRF dataset did not in- clude this event). ECMWF is based on a global circulation model and is publicly available for weather reanalysis (Dee et al. 2011) at 1/8-degree spatial resolution, with 3-hour time resolution were used for this meteorological forcing dataset. Observed hydrodynamic and beach erosion data Figure 4. Hurricane Figure 2 shows the location of offshore Tracks and radii of wave observation buoy, CDIP 157 buoy maximum winds for (cdip.ucsd.edu), and Newport and Provi- Hurricanes Irene dence, RI, NOAA water elevation gauges. (left), and Sandy These stations were used to validate the (right). regional SWAN-ADCIRC model. The Figure 5. Tracks of University of Rhode Island has collected the 1050 synthetic beach profile data at three stations within storms used in the area of study, representing one of the NACCS study the world’s longest continuous records (Cialone et al. 2015). of beach profiles, dating back as far as the early 1960. Data is sampled twice monthly in the spring, winter, and fall, and monthly during the summer. Eroded volume along the transects located at Charlestown Breachway, Charlestown Beach, and Green Hill Beach (CBW, CTB, MODELING DATA (Nadal-Caraballo et al. 2015; Cialone and GH in Figure 2) is compared to the Historical and synthetic storms et al. 2015) will be simulated, that were calculated erosion in the XBeach model. Hurricanes Irene (2011) and Sandy extracted from the set of 1,050 synthetic Additionally, grain size distributions, (2012) were used for validation and tropical storms (see Figure 5) to represent which were obtained from sediment calibration, respectively, of the SWAN- events with return periods of 10 and 20 samples taken at a number of locations ADCIRC and the XBeach models. Dam- years in the study area. by URI students along the beach, were ages caused by Hurricane Irene were the used as input to XBeach. Meteorological forcing data costliest of the 2011 tropical storm season For synthetic storms, wind and pres- MODEL CALIBRATION AND (Avila and Cangialosi 2011). It formed on sure fields were generated based on a VALIDATION 21 August 2011 and reached Category 3 parametric wind model: tracks and pa- Figure 6 shows a flowchart of the mod- before making landfall in North Carolina rameters were extracted from the NACCS eling process, and the data that are passed as a Category 1 storm. Hurricane Sandy, dataset, and used as input for ADCIRC’s among models. XBeach model settings also known as “Superstorm Sandy,” was internal parametric Asymmetric Holland are summarized in Table 1. the second costliest hurricane in terms model (Holland 1980) to generate the of damage to ever hit the U.S. until 2017 For validation of the regional model wind fields. when Hurricanes Harvey and Maria results, predicted water elevations were surpassed the damage cost of previous For historical storms, two meteoro- compared with those collected at New- hurricanes. Sandy formed on 22 October logical datasets were used to force the port and Providence, RI, tidal gauges; 2012 in the Caribbean and interacted regional storm surge and wave models, the wave parameters which were ex- with another storm system before mak- which were chosen based on availability tracted from the nearest node to CDIP ing landfall in New Jersey on 29 October and accuracy, depending on the historical 154 Station (Figure 2) were compared to (Blake et al. 2013). The tracks and radii of hurricane of interest: (1) the NECOFS- wave height observations. To force the maximum winds for these two storms are WRF (Northeast Coastal Ocean Fore- XBeach model during storm conditions, shown in Figure 4. After modeling these casting System-Weather Research and time series of the two-dimensional wave two historical storms, two NACCS storms Forecasting) wind dataset was used for spectrum, and water elevation were ex-

Page 16 Shore & Beach  Vol. 86, No. 4  Fall 2018 Figure 6. Flowchart illustrating the configuration and linkage of regional and nearshore models.

Figure 7. Illustration of four conceptual designs for that were simulated in the XBeach model: (a) beach nourishment; the region of beach that has been restored is shown with the dashed line; (b) coastal bank protection; the location of the engineered core is shown with cross marks; (c) living breakwater (green rectangles); and (d) the recreational surfing reef; the green triangle, which is shown with the crest elevation of -1m NADV88. tracted and implemented at the offshore Table 1. boundary of the XBeach model domain. Summary of XBeach model parameter values. Hurricanes Irene and Sandy were both used to validate the coupled SWAN- Variable Value Description (& units, if applicable) Wave Surf-Beat Wave forcing option: waves are phase ADCIRC model. Hydrodynamics averaged, wave groups are resolved. Sediment transport models generally Morfac 5 Morphological acceleration factor used have higher uncertainties in relation to to decrease computation time by hydrodynamic models; therefore, XBeach reducing frequency of bottom-updating. should be calibrated before validation. Dtbc 2 Boundary wave flux updating frequency The goal of the calibration process is to (seconds) tune the model parameters that are sub- Rt 3600 Frequency at which XBeach will read jected to uncertainties and site specific new wave spectrum (seconds) (e.g. friction coefficients, wave asym- Facua 0.15-0.35 Asymmetric onshore sediment transport metry) to increase the accuracy of the to parameterize wave asymmetry model predictions. Parameters that have Tsmin 0.1 Minimum time step in advection-diffusion been calibrated in the previous XBeach equation (seconds) applications at the site or at similar sites Wave forcing SWAN Wave boundary conditions include the facua (an onshore sediment 2-D spectrum transport parameter used to counteract Water level ADCIRC Water elevation forcing for offshore wave asymmetry), Manning friction coef- forcing boundary ficient, and the Shields parameter. McCall Friction Manning (0.02) Bottom friction formulation et al. (2010) calibrated an XBeach model in the Gulf of Mexico by tuning Shields parameter values. De Vet et al. (2015) and Nederhoff et al. (2015) showed that a facua parameter of 0.25 was best to

Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 17 A 1:12 beach slope was used to tie-in the replenished beach face with the exist- ing beach elevation. A transect within the XBeach domain, shown in Figure 8, illustrates the implementation of the hypothetical beach nourishment. The mean volume of sediment needed for this replenishment design was 25.5 m3/m. Figure 8. A transect showing the beach nourishment conceptual design. The Figure 7a illustrates the regions where dashed line shows the replenished beach face; slopes(m) of the beach face the elevation in the XBeach model was and foreshore face are shown. increased, representing the extent of the beach restoration. simulate the erosion during Hurricane with observed dune and beach profile Sandy in Fire Island, NY, and Bay Head, changes (https://coast.noaa.gov/). Method 2: Coastal bank NJ, respectively. Additionally, Schambach protection — engineered core SIMULATION OF LIVING et al. (2018) used a facua parameter of An Engineered Core-Coastal Bank SHORELINE PROPOSED 0.3 at the current study’s site to obtain uses tubes to support the toe SOLUTIONS the best agreement with observations of a coastal dune system. This will si- The XBeach model was used to ana- during Tropical Storm Irene. Elsayed and multaneously protect the toe of the dune lyze the performance and effectiveness Oumeraci (2017) showed that the facua from scour during storms, while provid- of coastal erosion mitigation methods. parameter can reliably be defined as a ing support for the rest of the dune. To A recent study in New England (US) by function of the beach slope. simulate an engineered core coastal bank, Woods Hole Group (2017) recommended a non-erodible layer was implemented at several living shorelines to address beach/ To evaluate the performance of the the toe of the dune in the XBeach model. dune erosion. These methods included XBeach model, changes in the beach The non-erodible layer is shown as the beach nourishment, coastal banks- profiles after two major historical hur- shaded region in Figure 7b. ricanes were examined. Observed beach engineered core, dune-engineered core, profile data did not extend far offshore, living breakwaters, and marsh creation. Method 3: Living breakwater meaning the observed transect data could Here, three of those methods, appropriate A living breakwater is designed to only be compared from the shoreline to for the area of interest were examined. break waves on nearshore rather than the dunes. Additionally, the transect data Additionally, a recreational surfing reef on the shoreline to mitigate erosion. A before each storm were not taken at the was also examined as a method that may living breakwater have the potential to same time as the LiDAR data was col- have a potential in areas that are used for improve habitat. Pre-cast reef balls are lected (note that the initial topographical/ surfing (Innes 2005). one example (e.g. see Woods Hole Group bathymetric elevation in the model runs 2017). A submerged living breakwater Four conceptual designs were imple- was based on LiDAR data and not the 1-D was simulated in the XBeach model by mented in the XBeach model to exam- transect data). creating a non-erodible reef approxi- ine the response of the beach to living mately 100 m offshore. The reef can be Therefore, the collected data were not shoreline methods (see Figure 7). We seen in Figure 7c, and consists of two sufficient to validate elevation changes must emphasize that a practical design 100-m segments. The elevation of the along the profiles in the model. Con- needs much more analysis considering crest is 0.0 m NAVD88. A non-erodible sequently, the eroded volume was used the benefit-cost, environmental impacts, layer was applied over the reef to prevent to compare the erosion in the XBeach and comparison of several design variants erosion of the reef. model against observed erosion as an (e.g. dimensions) in terms of the techni- approximation as done in Schambach et cal performance. A detailed engineering Method 4: Recreational surfing reef al. (2018). Hurricane Sandy was used to design is beyond the scope of this paper; Recreational geotextile breakwaters calibrate the XBeach model using facua therefore, each proposed living shore- have also been designed for combined rec- parameters ranging from 0.15 to 0.35. For line is a conceptual design. The living reational use and erosion mitigation. For validation, Hurricane Irene was simulated elements (i.e. dune vegetation, sea grass, instance, Mendonça et al. (2012) proposed using the facua parameter that provided geotextile fabric) within the conceptual an with an angle of 45 degrees the best results in the calibration stage. methods are not modeled in XBeach. which would create wave peel angles suit- Calculated eroded volumes were com- Simulations are meant to conceptually able for advanced surfers. The reef, shown pared to observations at the Charlestown show how coastal erosion is alleviated in Figure 7d, has a crest depth of -1 m Breakwater (CBW), Charlestown Beach during a storm by each method. They are NAVD88, and dimensions of 75 m and (CTB), and Green Hill (GH) Transects not intended to provide a final design. 100 m in the cross-shore and longshore (Figure 2). directions, respectively. The reef is located Method 1: Beach nourishment approximately 50 m offshore from the In addition to the qualitative valida- To simulate a replenished beach, the shoreline. The sides have a slope of -3/50 tion, differences in elevations between the elevation of the berm and foreshore were and decrease until the local bathymetry is April 2011 USGS LiDAR and the October increased for a section of the XBeach greater than the elevation of the reef. 2012 USACE Post Sandy LiDAR (OCM model. The beach face was extended 25 Partners 2018b) were used to compare the m seaward from an existing 2 m eleva- Two synthetic storms were selected spatial variation of erosion in the model tion (NAVD88) point with a 1/50 slope. with return periods of 20 years and 10

Page 18 Shore & Beach  Vol. 86, No. 4  Fall 2018 Figure 9. Validation of the SWAN-ADCIRC regional model. Model results were compared to observed offshore wave heights at CDIP 154 wave buoy, and water elevation data at the Newport water level gauge. A and C show the results for Hurricanes Irene, and B and D show the results for Hurricane Sandy. Peak modeled and observed water elevations and wave heights along with errors are shown in each. years. Such short return periods corre- acceptable because extreme water levels at series of wind speed and pressure were spond to a relatively high risk of occur- both locations are similar during extreme compared with those from a NACCS save rence, of 5% and 10% annually respective- events (Hashemi et al. 2016). Storm surge point near the XBeach model domain; the ly. These return periods are sometimes and waves were simulated using the differences were small and acceptable. used for the design of living shorelines. coupled SWAN-ADCIRC model for the XBeach was forced at the offshore For instance, Woods Hole Group (2011) selected synthetic storms. The synthetic boundary with wave spectral parameters performed a beach nourishment study at storms were simulated without tides, and and surge elevations, without the consid- nearby Narragansett Town Beach, using a tidal signal was added to the resulting eration of SLR. In addition to a control or 10- and 25-year return periods. Addi- storm surge. This tidal signal was shifted baseline simulation (no living shoreline), tionally, geosynthetics generally require to tune the peak water levels and the pre- simulations were performed for each of maintenance of around 25 years after dicted return period (1.62 m and 1.78 m the four erosion control methods and for installation (Greenwood et al. 2012). Due NAVD88 for the 10- and 20-year storms, the two synthetic storms (10- and 20- to the shorter lifetime of living shorelines, respectively). year return periods), resulting to a total it is unreasonable to design them for very Since the meteorological forcing (i.e. of 10 simulations. For each scenario, the strong storms such as 100-year storms. wind speed and pressure field over the effectiveness of the proposed mitigation To select the synthetic storms, peak entire domain) were not provided in the measure was assessed using two metrics: water elevations of the NACCS storms NACCS database, the wind field was re- the eroded foreshore volume and the at a save point near Charlestown, RI, created using the selected storm’s tracks eroded dune volume. These variables nearest to the XBeach model origin and parameters and ADCIRC internal were compared with a control simula- were compared with NOAA’s water level Asymmetric Holland model. To check tion in which no mitigation measure exceedance probability curves estimated that this method is consistent with the was implemented. The foreshore eroded at Newport (Figure 2). This assumption is original NACCS wind field, the time volume is calculated seaward of the 2 m

Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 19 (NAVD88) elevation contour, while the eroded dune volume is calculated over the entire dune. RESULTS AND DISCUSSION Model calibration and validation Regional and nearshore model (SWAN-ADCIRC) results were compared with observed significant wave height, and water elevation data for two selected historical storms. Figure 9 compares the observed water elevations and significant wave heights (circles) with those predicted by the model. Peak water elevations and wave heights were also compared. The result show a convincing model perfor- mance for wave height (less than 10% peak error, as shown in Figure 9). The estimated peak water elevation in Irene is underestimated, while the model shows better agreement for Hurricane Sandy. Erosion data during Hurricane Sandy were used to calibrate XBeach. Figure 10b shows the estimated bed level change during Hurricane Sandy. The volume of Figure 10. Simulated bed level change for Irene (A) and Sandy (B), with erosion was only compared along the facua=0.25. Red shaded areas illustrate erosion, while the blue shaded Charlestown Beach (CTB) and Green Hill regions show deposition. (GH) transects, as storm surge and waves had washed out the reference stakes for Figure 11. Simulated bed level change for NACCS # 551 (A; 10-year) and #377 the Charlestown Breachway (CBW) and (B; 20-year), with facua=0.25. Red shaded areas illustrate erosion, while the Green Hill during this storm. The Green blue shaded regions show deposition. Hill stake was replaced, but the Charles- town Breachway transect was decom- missioned. The model calibration results in a value of the facua parameter of 0.25 corresponding to a minimal value of the mean error, defined as the mean of the relative error in absolute value (Table 2). Hurricane Irene was used for vali- dation, simulated eroded volume are compared with volume changes based on surveyed profiles and are summa- rized in Table 3. Irene was a less severe and a faster moving storm, resulting in significantly less erosion in comparison to Sandy. Dune overtopping was not ob- served within the XBeach domain for the duration of the simulation, meaning the storm only entered the collision regime (De Vet et al. 2015). Table 3 summarizes the results of the XBeach model valida- tion, the observed and modeled eroded volumes (m3/m) are compared for each transect. The mean error along modeled transects was 23%. PERFORMANCE OF LIVING SHORELINE MITIGATION MEASURES Before examining the living shoreline solutions for erosion mitigation, a con-

Page 20 Shore & Beach  Vol. 86, No. 4  Fall 2018 Figure 12: Close-up views of the bed level change during the 10-year storm (NACCS #551) for the control run (a), and four erosion mitigation methods: beach nourishment (b), coastal bank (c), living breakwater (d), and recreational surfing reef (e). Shading illustrates magnitude of elevation change, cross-hatched areas show deposition. The longshore variation of foreshore (solid-dot), and dune volume change (dashed) are plotted over the erosion plots. The left and bottom axes correspond to the longshore and cross-shore distances, respectively, the top x-axis corresponds to m3/m of volume change. trol run for each storm was simulated nario (i.e. no living shoreline), and four [mean], 18.1 m3/m [max]), and higher in which no mitigation measure was erosion mitigation measures. Addition- dune erosion (16.7 m3/m [mean], 32.6 implemented in the area. The bed level ally, the longshore variability of both the m3/m [max]) in comparison. change during the control simulations eroded berm volume (m3/m), and eroded In terms of living shorelines, Method for the 10-year (NACCS Storm #551) and dune volume (m3/m) are shown with the 1 (beach nourishment; Figures 12b and 20-year (NACCS Storm #377) storms can bold solid and dashed lines, respectively. 13b) resulted in increased erosion of the be seen in Figure 11. These two synthetic The eroded volumes were computed by berm, as shown by the solid line between storms were of similar magnitudes to comparing the cross-shore profiles be- stations 3250 and 3750, likely due to the Hurricanes Irene and Sandy. Like Hur- fore and after each storm. The mean and increased shoreline volume. This in- ricane Irene, NACCS 551 caused minimal maximum erosion for these parameters creased berm erosion (11.1 m3/m [mean] dune erosion and scour. NACCS 377 en- are summarized in Table 4. for both the 10- and 20-year) is signifi- tered the over-wash regime, overtopping In the control run (Figures 12a and cantly higher than the rates observed in the dunes, and caused over-wash fans in 13a), berm and dune erosion rates were the control run. The increased berm size the southern portion of the domain as higher near longshore station 4000, and protected the dunes, resulting in lower shown in Figure 11. decreased heading in the direction of mean eroded volumes for both the 10- After performing the control simula- station 3000 for both storms. Referring and 20-year storms (3.2 m3/m [mean tions, the conceptual mitigation measures to Table 4, for the control scenario, the 10-year], 13.9 m3/m [mean 20-year]). were simulated for each storm. Figures mean eroded berm volume (m3/m) dur- Method 2 (coastal bank protection; 12 and 13 show the initial elevation con- ing the 10-year storm was 5.1 m3/m, with Figures 12c and 13c) also resulted in tours, and bed level change as a result of a maximum of 20.1 m3/m. The mean increased erosion of the berm, but to a the 10- and 20-year storm simulations, eroded dune volume was 3.3 m3/m, and lesser degree than Method 1. As shown respectively. The resulting bed level the maximum was 7.5 m3/m. The 20-year by the solid line in Figures 12c and 13c, changes are shown for the control sce- storm caused less berm erosion (3.9m3/m

Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 21 Figure 13. Close-up views of bed level change during the 20-year storm (NACCS #377) for the control run (a), and four erosion mitigation methods: beach nourishment (b), coastal bank (c), living breakwater (d), and recreational surfing reef (e). Red areas show erosion, while blue areas show deposition. The longshore variation of foreshore (solid), and dune volume change (dashed) are plotted over the erosion plots. The left and bottom axes correspond to the longshore and cross-shore distances, respectively; the top x-axis corresponds to m3/m of volume change. the trend more closely follows that of Methods 3 and 4 (living breakwater, sion in the lee of the structure. During the the control run (Figures 12a and 13a). surfing reef; Figures 12d-e and 13d-e) 10-year storm, both structures resulted in The slight increase of the berm erosion both cause significant spikes in both significant accretion of sediment to the (6.0 m3/m [mean 10-year] and 6.1 m3/m berm and dune erosion while some east of the structures (bottom in Figures [mean 20-year]) is significantly higher protection in the lee of breakwater can 13d-e). In Figure 13d, a large spike of than the rates observed in the control be seen. In Figure 12d, two regions of berm and dune erosion was observed to run. For the dunes, significantly lower increased erosion are observed on either the west (top) of the structure, a spike in mean eroded volumes for both the 10- side of the living breakwater, while only berm accretion occurred on the opposite and 20-year storms were observed [1.8 the area directly behind the structure is side of the structure, but to a lesser de- m3/m (mean 10-year), 10.2 m3/m (mean protected. In Figure 12e, as the surfing gree. In Figure 13e, a similar pattern was 20-year)]. reef accelerated the berm and dune ero- observed, but of a lesser magnitude. The mean eroded berm and dune volumes in Table 2. Table 4 were inconclusive for these two XBeach simulated eroded volume (m3/m) during Hurricane Sandy for cases; as areas of accumulated sediment transects CTB and GH for varying Facua values, used for model calibration. in the berm counteracted the eroded Percent errors (((modeled-obs)/obs)×100) are shown in parentheses. volume. Location of transect Observed Facua Parameter Value DISCUSSION 0.15 0.20 0.25 0.30 The regional model showed good CTB 32.72 78.42 67.39 36.93 45.24 (+139%) (+105%) (+13%) (+38%) agreement with both wave height and water level observations during Hur- GH 81.63 80.38 60.34 54.73 54.88 (-1.5%) (-26%) (-33%) (-32%) ricane Sandy. For Hurricane Irene, the prediction of significant wave height was Mean absolute error 70.58% 66.01% 22.90% 35.51% less than 10% at the CDIP buoy, but the

Page 22 Shore & Beach  Vol. 86, No. 4  Fall 2018 Table 3. Table 4. Observed versus modeled eroded Maximum and mean beach face (berm) and dune’s eroded volume(m3/m) for volumes (m3/m) along three each mitigation method shown in Figures 12 and 13. transects within the XBeach model Erosion Eroded volume (m3/m) Eroded volume (m3/m) for Hurricane Irene. Percent error assessment’s 10-yr storm (NACCS #551) 20-yr storm (NACCS #377) along each transect are shown in the Simulation location Max Mean Max Mean parentheses. Control Berm 20.1 5.1 18.1 3.9 Dune 7.5 3.3 32.6 16.7 Location Observed Modeled of transact (m3/m) (m3/m) Method 1 Berm 25.4 11. 2.0 11.1 CBW 18.53 14.39 (-22%) (Beach Dune 7.8 3.2 31.0 13.9 CTB 24.18 26.02 (+8%) nourishment) GH 21.89 13.28 (-39%) Method 2 Berm 20.3 6.0 19.2 6.1 Mean 21.53 17.90 (23%) (Coastal Dune 7.4 1.8 31.4 10.2 absolute error bank) Method 3 Berm 42.7 10.3 81.1 6.6 (Living Dune 21.4 4.8 67.2 19.8 storm surge was underestimated by ap- breakwater) proximately 20%. This can be related to Method 4 Berm 60.8 10.6 49.5 4.8 the accuracy of wind forcing as a better (Surfing Dune 14.4 4.7 44.7 18.1 reef) wind product (i.e. WRF-NECOFS) was available for Hurricane Sandy unlike Hurricane Irene. Torres et al. (2018) dis- cussed the sensitivity of regional storm XBeach model slightly under pre- themselves cannot be simulated with this surge models and showed that wind dicted the erosion across the domain, methodology. Therefore, the structural products such as WRF-NECOFS that which was most apparent near the bound- properties such as stability, and durabil- can simulate both the environmental aries. A possible source of error in the ity of the proposed mitigation measures wind (background winds distant from validation stage is the underestimation were not considered. In addition, the the storm) and a hurricane structure lead of storm surge elevation. Further, the response of the beach during the storm to much more accurate results compared spatial coverage of the three transects period was examined, and the impact of with wind data that are based only on within the model domain was relatively these living shoreline structures in long parametric hurricane models. poor. Figures 12 and 13 show the signifi- term and during calm periods were not Hurricane Sandy was chosen to cali- cance of longshore variability. Therefore, simulated. For a comprehensive assess- brate the nearshore sediment transport collection of DEM data before and after ment of living shorelines, these aspects as model because the storm had a greater a major storm can provide a much better well as cost, environmental benefits, and impact on the beach. However, the length basis for calibration and assessment of the local regulations should be considered of beach profiles were not sufficient XBeach model. (e.g. see Bilkovic et al. [2017]). (in the sea). Further, 2-D models like Although the peaks and time series of The beach nourishment simulations XBeach need a 2-D DEM before and the significant wave height at the CDIP experienced more berm erosion and less after the storm for model calibration buoy were similar during Hurricanes dune erosion during the selected syn- and validations which were not available Irene and Sandy, Sandy resulted in much thetic storms. The increased foreshore in this study. Nevertheless, the eroded more erosion in comparison to Hur- erosion is likely due to the increased volume provided an approximate basis ricane Irene as a result of higher storm amount of sediment volume in the berm for comparison. surge. This confirms the importance of compared to the control simulation. The In addition to the quantitative valida- combined surge and wave simulations for increased length of the nourished beach tions of eroded beach volumes at loca- erosion simulations. Munger and Kraus provided more protection to the dunes. tions of transects, available LiDAR data (2010) were able to classify the return The engineered core coastal bank also produced in April 2011 and November period of erosion during storms using experienced more berm erosion, and less 2012 can be used to assess (at least an integrated hydrograph method. They dune erosion in relation to the control qualitatively) the spatial performance of showed that erosion rates were propor- test. The non-erodible layer supported the XBeach. Figure 14a shows the difference tional to a combination of surge, wave dunes, but also prevented sediment in the in bed elevation between the two LiDAR height, and storm duration. dunes from moving offshore. datasets. Because the LiDAR was not A major assumption used in simula- The living breakwater and surfing reef produced immediately after Hurricane tion of living shorelines was the “non- simulations significantly impacted the Sandy, surge channels that occurred dur- erodible” layer in XBeach (used for erosion along the shoreline during the ing dune breaching were not as apparent Methods 2, 3 and 4). This method was storm-scale simulations. The wave direc- as the results of the simulation, as seen in applied over hard structures (e.g. break- tion during the selected 10-year storm Figure 14b. However, XBeach was able to waters, engineered core), and prevented (NACCS #551) was relatively perpendic- simulate where dune elevation had been any erosion for these structures. Al- ular to the shoreline, and little longshore decreased, and the location of surge chan- though XBeach can simulate scour, the current was present. Referring to Figure nels and over-wash fans. movement and collapse of the structures 12, a slight decrease of berm erosion di-

Shore & Beach  Vol. 86, No. 4  Fall 2018 Page 23 conclusions can be made from this study: The XBeach model could predict the eroded volume relatively well with er- rors on the order of 20%. Proper model calibration was the key step in which the facua parameter was tuned (using Hurricane Sandy). It was shown that the calibrated model performed well in the validation stage (Hurricane Irene), using meteorological forcing from Hur- ricane Irene. The XBeach model was able to assess the performance of three living shorelines methods (beach nourishment, coastal bank protection with engineered core, and living offshore breakwater) as well as a recreational surfing reef to reduce coastal erosion. However, limitations of this model such as inability to simulate the stability and durability of these struc- tures (e.g. engineered core coastal bank) should be noted. For this case study, which was focused on a barrier system in Rhode Island, Figure 14. Qualitative comparison of observed (a) (using the difference of methods that improved the beach face DEM in 2011 and 2012) and modeled (b) elevation change. and dunes volumes (e.g. beach nourish- rectly behind the artificial breakwater is shore, and other factors could significantly ment) were generally more effective than observed, but is dwarfed by the increase affect the beach response behind these those that reduced wave action (e.g. living of erosion to either side of the structure. structures, and a more in-depth study offshore breakwater) An increase of both dune and berm ero- would ideally include cost-benefit analyses Collection of data before, after, and sion was observed in lee of the surfing for projects of multiple scales. It is possible during storms can provide a much bet- reef. During the 20-year storm (NACCS to examine how the performance of a liv- ter understanding of the skill of XBeach, #377), the wave direction was more east- ing shoreline method could change with or similar models, for living shoreline erly, resulting in a more prominent East projected SLR scenarios. However, the simulations. These data include, wave to West longshore current (bottom to lifespan of the methods addressed in this and storm surge near the area of interest, top in Figure 13). This longshore current paper are short enough that performance DEM before, and after storm, as well as increased the rate of sediment deposition, changes due to SLR may be neglected. the conditions of these structures. There- as well as erosion for both the offshore CONCLUSIONS fore, traditional beach profile surveys, breakwater, and surfing reef. In both A suite of numerical models were used although useful, but cannot provide suf- instances, the reduction of wave energy to model erosion during both historical ficient data for implementation of more resulted in sediment deposition near the and synthetic tropical storms for a barrier advanced numerical models. bottom (East) breakwater due to wave system in RI. The regional hydrodynamic breaking. However, this in turn reduced REFERENCES model (SWAN-ADCIRC), covering most available sediments around the top (West) Avila, L.A., and J. Cangialosi, 2011. “Tropical of the northeastern United States, was Cyclone Report — Hurricane Irene” (PDF). breakwater, leading to increased erosion. used to calculate water elevations and National Hurricane Center. Retrieved 22 This can be observed in Figure 13d and wave conditions. The two-dimensional June 2017. 13e, an increase of foreshore volume was Blake, E.S., T.B. Kimberlain, R.J. Berg, J.P. 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