Table of Contents

Cover

Title Page

List of Contributors

Series Foreword

Introduction

Acknowledgments

Chapter 1: Coastal Storm Definition

1.1 Introduction
1.2 Synoptic systems and coastal storms
1.3 Statistical approaches to identifying coastal storms
1.4 Conclusion
References

Chapter 2: Hydrodynamics Under Storm Conditions

2.1 General introduction
2.2 Storm surges
2.3 Hydrodynamics of the surf zone during storms
2.4 Conclusions and future challenges
Acknowledgements
References

Chapter 3: Sediment Transport Under Storm Conditions on Sandy Beaches

3.1 Introduction
3.2 Morphological consequences of coastal storms
3.3 Sediment transport processes during storms
3.4 Observations of sediment transport on the upper shoreface during storm events
3.5 Observations of sediment transport on the lower shoreface during storm events
3.6 Conclusions
Acknowledgements
References

Chapter 4: Examples of Storm Impacts on Barrier Islands

4.1 Introduction
4.2 Barrier island response to storms
4.3 Quantifying the changes due to specific storms
4.4 Resilience
4.5 Summary
Acknowledgements
References

Chapter 5: Storm Impacts on the Morphology and Sedimentology of Open-coast Tidal Flats

5.1 Introduction
5.2 Sedimentologic characteristics
5.3 Erosion-deposition processes and morphodynamics of open-coast tidal flat
5.4 Conclusions
References

Chapter 6: Storm Impacts on Cliffed Coastlines

6.1 Introduction
6.2 Methodologies and their application
6.3 Storminess and the cliff record
6.4 Case study: Soft rock cliff geology and responses to storms
6.5 Modelling shoreline retreat for cliffed coasts and the incorporation of storminess
6.6 Future storm impacts on clifflines under accelerated sea-level rise and changing storminess
6.7 Conclusions
Acknowledgements
References

Chapter 7: Storms in Coral Reefs

7.1 Introduction
7.2 Geomorphic units of reefs
7.3 Storms on the forereef: Role of spurs and grooves
7.4 Storms on the reef flats: Development of rubble flats and rubble spits
7.5 Storms on the backreef: Sand aprons, reef islands and beaches
7.6 Conclusion
Acknowledgements
References

Chapter 8: Storm Clustering and Beach Response

8.1 Introduction
8.2 Storm clustering: Genesis and definitions
8.3 Approaches used to assess storm clustering impact on coasts
8.4 Beach response to storm cluster
8.5 Conclusions
References

Chapter 9: Overwash Processes: Lessons from Fieldwork and Laboratory Experiments

9.1 Introduction
9.2 Methods to study overwash processes
9.3 Hydrodynamic processes during overwash
9.4 Morpho-sedimentary dynamics by overwash processes
9.5 Conclusion
Acknowledgements
References

Chapter 10: Modeling the Morphological Impacts of Coastal Storms

10.1 Introduction
10.2 Outlook
Acknowledgements
References

Chapter 11: Preparing for the Impact of Coastal Storms: A Coastal Manager-oriented Approach

11.1 Introduction
11.2 Coastal vulnerability assessment framework
11.3 Coastal early warning systems
11.4 Conclusion
Acknowledgements
References

Chapter 12: Assessing Storm Erosion Hazards

12.1 Introduction
12.2 The diagnostic conundrum
12.3 Quantifying storm erosion volumes for coastal management/planning
12.4 Application of storm erosion volume estimates in coastal management/planning
12.5 Conclusions and recommendations
Acknowledgments
References

Conclusions and Future Perspectives

Index

End User License Agreement

List of Illustrations

Chapter 1: Coastal Storm Definition

Figure 1.1 Total hazard losses in the United States (1960–2014) by hazard type
Figure 1.2 A composite image taken from the NASA of three tropical cyclones occurring simultaneously in the southern hemisphere in March, 2015. Tropical Cyclone Pam to the right of the image struck the island of Vanuatu and is considered one of the worst natural disasters in the island's history
Figure 1.3 Large swell waves arriving on a coastline during a sunny day with clear skies (photo: A.D. Short).
Figure 1.4 Spatial distribution of tropical cyclone storm tracks (1946–2006) and their intensities according to the Saffir-Simpson Hurricane Scale
Figure 1.5 Pre- and post-storm shoreline measurements following the Pasha Bulker storm, an extra-tropical cyclone that struck the coastline of southeastern Australia in June 2007. A video monitoring station installed permanently at Narrabeen-Collaroy Beach (see Harley et al., 2011) measured 29 m of rapid retreat in beach width as a result of the storm.
Figure 1.6 The Peaks-Over-Threshold (POT) method for defining individual storm events from a significant wave-height time-series. P denotes the peak significant wave height of the storm, D the storm duration, I the meteorological independence criterion and H_thresh the threshold signignificant wave height. Individual storm events classified by this method are shaded gray.
Figure 1.7 Comparison between coastal storm definitions based on a non-tidal residual threshold $c01-math-0017$ (upper panel) and a threshold of the total water-level $c01-math-0018$ (lower threshold). Shaded regions highlight the different coastal storm periods identified according to these two definitions.

Chapter 2: Hydrodynamics Under Storm Conditions

Figure 2.1 Sea-surface drag coefficient as a function of wind speed, based on the dataset of Donelan et al. (2004), Hawkins & Rubsam (1968), Powell et al. (2003) and Takagaki et al. (2012).
Figure 2.2 Time series of wave energy spectra (top row), surface stress (middle row) and storm surge (bottom row) in La Rochelle (Bay of Biscay, France) during Xynthia (left) and Joachim (right). Adapted from Bertin et al. 2015.
Figure 2.3 (a) Simplified bathymetry of the Gulf of Mexico superimposed with the track of Ike and the location of the tide gauges used in this study; (b) sea-level pressure; and (c) 10 m wind speed 15 h before landfall, showing wind speed in the range 20–30 m/s parallel to the shore.
Figure 2.4 Modeled storm surge 15 h before landfall with (a) and without (b) Coriolis term. Observed (black) and modeled storm surge with (blue) and without (red) Coriolis term at Gavelston (c) and Freshwater (d).
Figure 2.5 Observed (black circles) and modeled storm surge with (blue) and without (red) wave forces at Arcachon (top) and Bayonne (bottom). Adapted from Arnaud & Bertin (2014).
Figure 2.6 Sea-surface elevation, wave-group envelope and associated bound wave (computed with the method of Guza et al., 1985) at station H5 located (Figure 2.7a) outside the surf zone, 9 September (1500 EST), see List (1992). The black line represents the measured free surface elevation, the blue line the wave envelope and the red line the bound wave.
Figure 2.7 (a) Beach profile and location of the pressure and velocity sensors during Duck85 field experiment. (b) Example of the directional wave energy transformation within the surf zone. Directional wave energy spectra at sensor R4 located thin the surf zone (left) and at sensor H3 located outside the surf zone (right). Velocity and pressure measurements, from which the directional wave spectra has been computed, completed during the Duck85 field experiment, 9 September (1500 EST).
Figure 2.8 Example of incident wave height, wave run-up and significant swash amplitude of a JONSWAP spectrum with constant peak period, as a function of the ratio between the swash period and the peak period. Results are computed using the wave-resolving model XBeach-G (McCall et al., 2014) on a 1/20 slope.

Chapter 3: Sediment Transport Under Storm Conditions on Sandy Beaches

Figure 3.1 Example cross-shore profile for a non-tidal coast showing the morphological and hydrodynamic zones (italics) considered in this chapter. MWL is mean water level. The beach comprises the beach face and the backshore.
Figure 3.2 Example vertical profiles of cross-shore suspended sediment transport components during storms. Positive transport is onshore. Circles indicate oscillatory transport at incoming wave frequencies, dots are oscillatory transports at infragravity (IG) wave frequencies and crosses are Eulerian mean transports (due to undertow). The sum of these three components represents the net transport, which is indicated by squares. The data was collected using optical and fibre-optical backscatter sensors in the inner surf zone ( $c03-math-0013$ ) at Egmond Beach (the Netherlands) during a storm with offshore significant wave heights of $c03-math-0014$ . The IG transport component was oriented oppositely in the two cases, which strongly affected net transport magnitude.
Figure 3.3 Models for net IG-driven sediment transport: (a) shows schematic time series of an offshore-skewed IG velocity signal and the sediment transport rate associated with it, (b), (c) and (d) illustrate IG transport in the spatial domain (shoreline to the right) associated with (b) smaller water depths and/or larger incoming wave heights at IG wave troughs cause increased sediment suspension and offshore net transport, (c) larger incoming wave heights at IG wave crests cause increased suspension and onshore net transport, and (d) when IG waves are cross-shore standing, local sediment suspension maxima, for example at the wave breakpoint, may result in net IG transport away from the suspension maxima. The arrows indicate IG transport rates and directions. In panels (b), (c) and (d) the solid lines represent the incoming wave forms, the dotted lines are the infragravity wave shapes (envelopes) and the horizontal line is mean water level. See text for further explanation.
Figure 3.4 Orthorectified image of Skallingen. Graadyb inlet is seen at the bottom of the image whilst the cuspate foreland of Blaavands Huk is at the upper left corner.
Figure 3.5 Cross-shore profiles at P6420 from the period 1981–2012. The dunes eroded consistently in the period 1981–2008 but have recovered somewhat since then.
Figure 3.6 Mean water levels recorded at Esbjerg Harbour 1 January 1999–31 December 2008. The circle indicates the storm event in October 2000 during which sediment transport measurements were made.
Figure 3.7 Cross-shore profile change during the storm surge of 30–31 October 2000, at Skallingen. The grey dashed line is the profile prior to the storm, the thick solid line is the survey immediately after the storm on 1 November and the thin solid line is the profile on 6 November. Circles indicate positions where waves and currents were recorded and crosses signify positions of sediment transport measurements. The horizontal line indicates the maximum water level reached during data collection.
Figure 3.8 Measurements of (a) mean water level (circles) and significant wave height (crosses) at the offshore subtidal station, (b) mean undertow speeds at the four instrument stations; the subtidal station is shown by circles and the most landward station is indicated by black dots, (c) cross-shore suspended sediment fluxes at the landward sediment transport station due to: incoming wind waves and swell (circles), IG waves (black dots), undertow (crosses) and the resulting net flux (squares), (d) similar data for the seaward sediment transport station. Negative current speeds and sediment fluxes are offshore directed. Instrument runs were spaced one hour apart.
Figure 3.9 Cross-shore sediment transport measurements at the upper/lower shoreface transition: (a) shows local significant wave height during the measurement campaign; (b) illustrates cross-shore suspended sediment transport rates due to oscillatory wave motions (dotted grey line), Eulerian currents (thin solid) and Lagrangian currents (thick solid line), and (c) is cumulated wave (dotted grey), current (Eulerian plus Lagrangian; thin solid line) and resulting net (thick solid line) transport.

Chapter 4: Examples of Storm Impacts on Barrier Islands

Figure 4.1 Assateague Island (Virginia/Maryland, USA) showing 1 km of shoreline retreat (left) and 5 km of spit elongation (right). The historic shorelines (Himmelstoss et al., 2010) from the 1880s (red), 1940s (yellow), and 2000s (blue) are overlain on the imagery showing the long-term evolution.
Figure 4.2 Barrier island migration illustrated at the Northern Chandeleur Islands (Louisiana, USA). The background images shows the island in 2015 and are overlain with shorelines (Miller et al., 2004) from the 1930s (left) and in 2001 (right).
Figure 4.3 Evolution of the Northern Chandeleur Islands. Multiple hurricanes and other storms caused significant land loss over a decade-long time span. A sand berm was constructed in 2010 and appears as a thin ribbon of sand at the northern tip of the island. The water level at the time of Landsat imagery acquisition and date of each image are labelled at the bottom and the most recent morphologic event is labelled at the top.
Figure 4.4 Depiction of the Sallenger (2000) storm-response model showing storm-induced water levels corresponding to swash, collision, overwash and inundation regimes. Dashed lines show the storm-induced mean water level and solid lines indicate the wave runup level under each regime.
Figure 4.5 Elevation-change differences along the Chandeleur Islands after Hurricane Lili.
Figure 4.6 Comparison of (top) pre-storm island elevation to the storm surge, wave setup, and R2% (which includes wave runup) estimated for Hurricane Lili. Resulting island elevation changes (bottom left) and shoreline changes (bottom right) measured with lidar. The mean values, μ, are labelled with a black vertical line on each histogram. Zero change is denoted by the red vertical line.
Figure 4.7 Comparison of (top) pre- and post-storm island elevation to the storm surge, wave setup, and R2% estimated for Hurricane Katrina (asterisks mark alongshore locations of profiles shown in Figure 4.8). Histograms show variability of island elevation change (bottom left) and shoreline change (bottom right).
Figure 4.8 Profiles across the width of the Chandeleur Islands at three locations, showing classic overwash evolution associated with Hurricane Lili and island levelling due to the inundation associated with Hurricane Katrina. Profile locations are marked with an asterisk on Figure 4.7, with profile A at the northern-most location and profile C at the southern-most location.
Figure 4.9 Comparison of pre-storm island elevation to the storm surge estimated for Hurricane Gustav, 2008 (top). The storm surge level was uncertain, so a range is indicated with two dashed lines. Resulting island elevation changes (bottom left) and shoreline changes (bottom right). Black lines are mean values; red lines are zero-change.
Figure 4.10 Comparison of shoreline change histograms from three storms.
Figure 4.11 Elevation time series collected prior to man-made berm construction (March 2010), during construction (February 2011), and after winter storms and the tropical storm season (February 2012).

Chapter 5: Storm Impacts on the Morphology and Sedimentology of Open-coast Tidal Flats

Figure 5.1 Distribution of open-coast tidal flats along the Chinese coast (modified from Li et al., 2004).
Figure 5.2 Schematic model showing the deposition and erosion of sandy and muddy laminae couplets. $c05-math-0001$ is the threshold velocity for sand transport and $c05-math-0002$ is the velocity below which mud deposition occurs (modified from Fan et al., 2004b).
Figure 5.3 Alternation of sand-dominated layer (SDL) and mud-dominated layer (MDL). Left panel: SDL and MDL from modern Changjiang River delta tidal flat. Right panel: SDL and MDL from Upper Ordovician rock record in east-central China (right panel modified from Fan et al., 2004a).
Figure 5.4 Average tidal flat elevation change at Donghai Farm on the southern flank of the Changjiang (aka Yangtze) River delta, spring-neap tidal cycles and wave heights observed at the Ship Observation Station during a four-month study in 1992. Wave heights lower than 1 m were not plotted (modified from Li et al., 2000).
Figure 5.5 Lamina-thickness variations in a section of the Upper Ordovician Tonglu rhythmite. (A) A three-point averaged sand-lamina thickness; (B) Frequency distribution of the thickness variation (modified from Fan et al., 2004a).
Figure 5.6 Schematic illustration of time-velocity asymmetry. Because transport rate is generally proportional to velocity cubed, much more sediment is transported in the direction of the greater velocity, which results in a net transport toward that direction.
Figure 5.7 Schematics of scour lag (A) and settling lag (B) for fine-grain sediments. A: Scour lag: a particle on the bed is suspended into the water column when the threshold velocity is exceeded at point 1. However, it does not achieve the depth-averaged velocity until point 2, a relatively seaward position. It then travels with the water trajectory to point 3, where we assume it is instantaneously deposited. On the following ebb tide, the particle is suspended, but again lags the flow until point 4 is reached. It is eventually re-deposited at point 5. A net landward movement has occurred during the tidal cycle because of the scour lag. B: Settling lag: at position 1, the particle is entrained from the bed and travels with water until point 2, where it starts to settle. Because of the settling lag, it reaches the bed at point 3. On the following ebb tide, it is not entrained until later in the tide cycle when the threshold velocity (greater than the velocity for settling) is reached. The deposition at low water is at position 6. Consequently, the particle has a net shoreward movement due the settling lag (modified from Dyer, 1994).
Figure 5.8 A conceptual model of mud shore equilibrium shape (the Mehby Rule). The two end members, accretion-dominated (modeled with Equation 5.1) and erosion-dominated (Equation 5.2) profiles, are illustrated (modified from Kirby, 2000).
Figure 5.9 (A) Time series of measured wind speed and direction, and (B) Time series of predicted astronomic tides and daily forecasted wave heights ( $c05-math-0012$ ) at the Changjiang River mouth area from 23 July to 13 August 13, 1999. Two distal storms, with offshore waves exceeding 3 m, occurred during the study period (modified from Fan et al., 2006).
Figure 5.10 Tidal flat zonations based on vegetation cover, tidal level and morphodynamics, including: the Spartina alterniflora dominated marsh (M1), the Scirpus marsh (M2), the pioneer marsh (M3), the upper and lower sections of the bare middle mudflat (B1, B2), and the upper and lower sections of the lower mudflat (B3, B4). Elevation is referred to Wusong Datum (WD) (modified from Fan et al., 2006).
Figure 5.11 Short-term (2–3 days) changes in bed level across the tidal flat during a stormy period from 23 July to 13 August 1999, with mean tidal ranges and wave heights during each monitoring period listed. The six morphological zones responded differently to the typhoon-induced swells. Mean changes in bed level over each zone were calculated and indicated in the Figure (modified from Fan et al., 2006).
Figure 5.12 A conceptual model depicting intertidal morphodymics and sediment transport in response to storm wave processes at different tidal regimes: (a) spring tide, (b) intermediate tide, and (c) neap tide (modified from Fan et al., 2006).

Chapter 6: Storm Impacts on Cliffed Coastlines

Figure 6.1 Locations of cliffs from around the UK that are discussed in this chapter.
Figure 6.2 Five-stage model of cliff retreat for chalk cliffs, Hunstanton, UK (A) Early stage with few blocks at cliff base; (B) Undercutting of cliff and blocky collapse; (C) Overhanging large blocks fail through cliff base unloading; (D) Further undercutting and development of significant cliff overhang; (E) Cliff collapse and development of talus ramp at the cliff base (redrawn from Drake & Phipps, 2006).
Plate 6.1 Cliff spectrum: (A) 500–800 m high volcanic cliffs at Los Gigantes, Tenerife, Canary Islands (photo: T. Spencer); (B) White Chalk, Red Chalk and Carstone cliffs of Hunstanton, UK (photo: S. Brooks); (C) Liassic Clay cliffs of Charmouth, Dorset, UK (photo: S. Brooks); (D) Tertiary (Eocene) siltstones, sandstones and volcanic ash with overlying basalts on the cliffs of Oregon, USA; (E) Bluff erosion in soft sediments of Pacifica, California, USA; (F) Soft rock cliffs in glacial and pre-glacial sands and silts at Covehithe, Suffolk, UK (photo: S. Brooks).
Figure 6.3 Shoreline retreat in soft rock cliffs over different timescales: (A) Long-term and medium-term rates of change; (B) Event-driven short-term retreat.
Figure 6.4 Historic storminess identified from cliff-top storm deposits: (A) Icelandic low proxy record; (B) Greenland sea salt observed record; (C) Shetland cliff-top storm record (redrawn from Hansom & Hall, 2009).
Figure 6.5 (A) Location and; (B) geological composition of soft rock cliff, Covehithe, Suffolk, UK.
Figure 6.6 Modelled outputs showing changing negative pore water pressure (suction) responses in three cliff types of contrasting geological composition. Numbers refer to days since start of simulated rainfall.
Figure 6.7 Waves recorded at Southwold approaches (http://www.cefas.defra.gov.uk/our-science/observing-and-modelling/monitoring-programmes/wavenet.aspx) plotted alongside tidal variations at Lowestoft (http://www.ntslf.org/data/uk-network-real-time) to show coincident high still water levels and large waves.
Figure 6.8 Suggested retreat mechanisms for soft rock cliffs of varying geological composition, based on modelled and observed responses at Covehithe at different points alongshore.

Chapter 7: Storms in Coral Reefs

Figure 7.1 Main shallow morphological elements in coral reefs: (a) Schematic coral reef cross-section; (b) Satellite image of the SE corner of One Tree Reef in the southern Great Barrier Reef showing the coral zones.
Figure 7.2 Conceptual model showing ecomorphodynamics of coral reefs. Shaded components represent storm effects.
Figure 7.3 World map showing coral reefs of the world (database downloaded from http://www.wri.org/publication/reefs-risk-revisited) superimposed on historical tropical storm tracks between 1842 and 2014 (database downloaded from NOAA; Knapp et al. (2010, updated in 2014). Please note that bright red large points correspond to coral reef location.
Figure 7.4 One Tree Reef, Southern Great Barrier Reef, Australia (see Figure 7.1): (a) A diver on the forereef, a spur can be seen to the left of the photo and a groove to the right, the spur is covered with laminar corals (plates); (b) Laminar corals on the forereef; (c) A rubble dominated reef flat showing old (dark colours) and new (white) coral rubble; (d) Close-up photo of rubble dominated reef flat, presence of laminar corals (plates); (e) Another view of the rubble dominated reef flat, SE corner of One Tree Reef.
Figure 7.5 Storm depositional products on coral reefs. Reef flat storm blocks in Bonnaire (a) and Funafuti atoll, Tuvalu (b), intertidal rubble sheet increasing reef elevation at Funafuti atoll (c), shingle ridge at Bewick Island, Great Barrier Reef (d), hurricane Bebe storm rampart (e) and subsequent deposition of gravel against island shoreline, Funafuti atoll, Tuvalu (f).
Figure 7.6 Long-term evolution of sedimentary deposits at One Tree Reef, Great Barrier Reef, Australia: (a) An example of rubble spit formation and evolution showing its recurrent position on the northern margin. (b) Sand apron evolution on the southern margin (modified from Vila-Concejo et al. (2013)). In both cases, the background image is WorldView 2 from December 2009. GIS analyses by Amelia Shannon.
Figure 7.7 Erosion impacts on islands in the Belize barrier reef following Hurricane Hattie 1961. Grey shading is the island area prior to the hurricane. Black area is the post hurricane island area. Based on Stoddart (1969).
Figure 7.8 The modification of the Hurricane Bebe storm rampart on Funafuti Atoll, Tuvalu: (a) Sequential surveys showing the migration of rampart rubble toward the shoreline. (b) The rampart deposited on the reef flat. (c) Expansion of Funamanu Island, Funafuti 1971–2013 as a result of delivery of rampart rubble to the shoreline.
Figure 7.9 Storm and high wave washover deposits in the Maldives: (a) Limits of a washover sand sheet. (b) Depth of the sand sheet as a consequence of the 2004 Indian Ocean tsunami. (c) Multiple washover layers resulting from the 2004 tsunami and 2007 long-period swell event.
Figure 7.10 Conceptual model of reef island shoreline dynamics in response to storm events: (a) Storm frequency and intensity. (b) Morphological response of sand and gravel islands as reflected in sediment volume. After Bayliss-Smith (1988).

Chapter 8: Storm Clustering and Beach Response

Figure 8.1 Logistic exceedance return period contour for Long Reef Point storm data. Crosses indicate measured storms; solid red circle – average of a group of two storms; hollow red circle – maximum of a group of two storms; solid blue circle – average of a group of three storms; hollow blue circle – maximum of a group of three storms. Black curves correspond to a range of storm return periods from one to fifty years (Figure by Karunarathna et al., 2014).
Figure 8.2 Beach volume change against storm return period (black line with dots – single storms; blue solid line – average of a group of two storms; blue dotted line – maximum of a group of two storms; red solid line – average of a group of three storms; red broken line – maximum of a group of three storms) (Figure by Karunarathna et al., 2014).
Figure 8.3 Time series of significant wave height (black) and peak wave period (blue) during the winter of 2013/2014 measured in about 50 m depth offshore of Truc Vert beach (SW France). The horizontal colour lines indicate the occurrence of storm and storm clusters using six different definitions.
Figure 8.4 Timex collected under consecutive storm conditions showing (from (a) to (f)) the straigthening and the reconstruction of the outer bar crescentic patterns (from Almar et al., 2010).
Figure 8.5 Conceptual model describing beach morphological evolution phases of steep-sloping beaches during consecutive storms: (a) beach-face shows rapid erosion/recovery; (b) the morphological change at the (eroded) sub-aerial beach decelerates and adaptation of the surf-zone bathymetry becomes more important; (c) the beach reaches equilibrium, becomes more resilient to wave forcing and is vulnerable mostly to increased mean water levels (Figure by Vousdoukas et al., 2012).
Figure 8.6 (a) Aerial photograph of the Gironde coast (SW France) on 7 March 2014, showing megacusp embayments cutting the dune (dune foot indicated by the red dashed line) with, at this section of coast, a mean alongshore and cross-shore length scale of 500 m and 20 m, respectively (photo Julien Lestage). (b, c) LANDSAT satellite images of the Gironde coast (b) prior to the winter of 2013/2014 on 10 July 2013 for waves with Hs≈0.6 m and Tp≈8.8 s and (c) after the winter of 2013/2014 on 23 March 2014, for waves with Hs≈3.6 m and Tp≈12.9 s. In (b, c) the shoreline (dune foot proxy) measured with the ATV on 3–4 April 2014 is superimposed with colour bar indicating the deviation from mean shoreline in meters. The orange arrows in (c) show the location of rip current occurrence during storm waves clearly facing megacusp embayments. The grey line in (b, c) indicates the location of hard coastal structures in the coastal town of Lacanau.
Figure 8.7 Alongshore variability beach response. Top panel: Offshore Hs. Bottom panel: Each black line represents the mean alongshore position for different contour lines (each contour line is labelled on the left). Vertical bars indicate the alongshore variability denoted by one standard deviation of the mean contour line position. The blue line indicates the high tide mean waterline position as measured from a pressure sensor on the inner sandbar. Offshore direction is upwards (modified from Coco et al., 2014).

Chapter 9: Overwash Processes: Lessons from Fieldwork and Laboratory Experiments

Figure 9.1 Pictures of overwash. (a) Overview of overwash flow over the crest (left) towards the backbarrier (right) contouring dune remnants on Barreta Island, Ria Formosa, Portugal. (b) Overwash at the barrier crest confined at throat by dune scarps on Barreta Island, Ria Formosa, Portugal. (c) Overwash over Roi-Namur Island, Republic of the Marshall Islands, during 2 March 2014 (Swarzenski, 2014). Photographs (a) and (b) taken by Alexandra Cunha; and (c) taken by Peter Swarzenski.
Figure 9.2 Illustration of both applications of the word ‘overtopping’. (a) Photograph of overtopping on Barreta Island, Ria Formosa, Portugal. (b) Photograph of overtopping of a groin in Albufeira, Portugal. (c) Scheme with definition of overtopping and overwash processes (modified from Orford & Carter, 1982).
Figure 9.3 Picture of washovers in Salthouse, East England, UK, after North Sea storm surge in December 2014. Photo courtesy of Mike Page, www.mike-page.co.uk.
Figure 9.4 Pictures of BARDEX and BARDEX II experiments. (a) View towards the ‘lagoon’, with scaffolding on top of gravel barrier of BARDEX experiment. (b) Time-series of overwash depth recorded during Test Series D34 (BARDEX II), with the peak depth of each overwash event marked with a circle. (c) View towards the paddle, with overwash on top of the gravel barrier of BARDEX experiment. (d) View towards the paddle, with overwash on top of the sandy barrier of BARDEX II experiment.
Figure 9.5 Relationship between overwash depth and overwash velocity for available fieldwork data sets. Symbols represent average values and bars represent standard deviation of velocity. Data sets used in this plot are identified by the authors of studies as: Fisher et al., 1974; Leatherman, 1976; Fisher & Stauble, 1977; Leatherman & Zaremba, 1987; Holland et al., 1991; Bray & Carter, 1992; Matias et al., 2010.
Figure 9.6 Example of pre- and post-overwash cross-shore barrier morphology. (a) Profiles located on Santa Rosa Island, Florida, USA, before and after Hurricane Opal in 1995; adapted from Stone et al. (2004). (b) Profiles located on Sillon de Talbert, Brittany, France, before and after March 2008 storm; adapted from Stéphan et al. (2010). (c) St George Island, Florida, USA, before and after Hurricane Dennis in 2005; adapted from Priestas & Fagherazzi (2010). (d) Profiles located on Barreta Island, Ria Formosa, Portugal, before and after equinoctial spring tides of September 2012; adapted from Matias et al. (2009). (e) Profiles located on Assateague Island, Maryland, USA, before and after northeastern storm of August 1976; adapted from Fisher & Stauble (1977). (f) Profiles located on Lido di Dante, Emilia-Romagna Region, Italy, before and after December 2008 storm, adapted from Armaroli et al. (2012). The sea is always to the left of the profiles. Note that the horizontal and vertical scales are different for the various examples.
Figure 9.7 (a) Elevation change map of Dauphin Island, Alabama, USA, after Hurricane Katrina in 2005, elevation gains in green and losses in red (Sallenger et al., 2007). (b) Elevation change map of St George Island, Florida, USA, after Hurricane Dennis in 2005, bar in meters of elevation (from Priestas & Fagherazzi, 2010).
Figure 9.8 Barrier cross-shore profiles from test series with overwash. (a) Profiles from Test Series E of BARDEX II; and (b) Profiles from Test Series E10 of BARDEX (from Matias et al., 2014). Comparison of gravel and sand profiles and feedback processes of BARDEX 2008 and BARDEX II.

Chapter 10: Modeling the Morphological Impacts of Coastal Storms

Figure 10.1 Areas (marked in red) along the Dutch coast where the assumption of alongshore uniformity is violated (courtesy of Dr M. Boers, Deltares).
Figure 10.2 A principle sketch of short wave motions (black), the short wave envelope (dark blue), the incoming bound long wave (light blue) and the reflected free long wave (red) (courtesy of Dr Ad Reniers).
Figure 10.3 Four stages of the morphodynamical impact of Hurricane Ivan on Santa Rosa Island, Florida. The Gulf of Mexico is in the front and the Santa Rosa Sound is at the back. Top left: collision regime, top right: overwash regime, bottom left: inundation regime, bottom right: topography after recession of the flood. Reprinted from McCall et al., 2010, Coastal Engineering, with permission from Elsevier.
Figure 10.4 Example of measured (black) and modeled (red) post-storm beach profiles for five separate events and locations. The pre-storm profiles are shown in grey, whereas the maximum still water levels are represented by the blue lines. Modified from McCall et al. (2015) under Creative Commons Attribution License (CC BY).
Figure 10.5 Example result of measured (squares and circles) and modeled wave height (top panel) and mean water level (middle panel). The effect on the mean water level is indicated by the difference between the red and blue line. Figure, courtesy of Arnold van Rooijen, Deltares.
Figure 10.6 The impact of hard elements – both in cross-shore (left) and in the longshore direction(right). The blue line indicates the response of a coast without the structure, while the red line indicates the response in the presence of a structure. Figure, courtesy of Kees Nederhoff.
Figure 10.7 Pre (left) and post-Sandy (right) in a three dimensional plot with both bed and water levels as simulated by XBeach (Courtesy Kees Nederhoff).

Chapter 11: Preparing for the Impact of Coastal Storms: A Coastal Manager-oriented Approach

Figure 11.1 Coastal vulnerability assessment framework to storm impacts.
Figure 11.2 Functional relationship to compute vulnerability values.
Figure 11.3 The Catalonian coast.
Figure 11.4 Vulnerability of the Catalonian coast to storm-induced erosion (top) and inundation (bottom) associated with different return periods.
Figure 11.5 The Emilia-Romagna coast.
Figure 11.6 Scheme of the modules included into the Emilia-Romagna EWS described in section 11.3.3.
Figure 11.7 Screenshot of the Emilia-Romagna Coastal EWS web page.

Chapter 12: Assessing Storm Erosion Hazards

Figure 12.1 May 1997 storm at Narrabeen Beach, in which the offshore significant wave height peaked at 8.1 m or 10-year RP wave height (left), resulting in 4-year RP erosion volume of 73 m³/m (right).
Figure 12.2 Extrapolated wave height-frequency-duration curves for Narrabeen Beach, Sydney, Australia (from Callaghan et al., 2009).
Figure 12.3 Temporal evolution of various RP design storms at Narrabeen Beach, Sydney, Australia, obtained via the SDS approach (from Callaghan et al., 2009).
Figure 12.4 Operational structure of the JPM approach (from Callaghan et al., 2013). H_s is significant wave height, D is storm duration, T_p is peak wave period, R is storm surge, θ_m is mean storm wave direction and δt is time between individual storm events.
Figure 12.5 Return period distribution for Narrabeen Beach, Sydney, Australia using the JPM Approach. In this application KD93 was used as the structural function. The 95% confidence limits (shaded grey) were obtained by bootstrapping the 1000-year Montecarlo simulation 2000 times. The triangles indicate the RP distribution of measured storm erosion volumes (modified from Callaghan et al., 2013).
Figure 12.6 The 100-year forecasted percentage increases of the significant wave height (dotted line), water level (dashed line) and erosion volume (solid line) associated with a 31-year RP storm event at one cross-shore profile in the Durban Bight, South Africa as calculated by the CS approach (from Corbella & Stretch, 2012).
Figure 12.7 Dune stability schema proposed by Nielsen et al. (1992) for converting predicted storm erosion volumes to coastal setback lines (or storm buffer zones). AHD is Australian Height datum, which is approximately at mean sea level (i.e. MSL is AHD = 0).
Figure 12.8 Erosion contours of different exceedance probabilities obtained by applying the JPM approach along a number of cross-shore profiles at Narrabeen Beach, Sydney, Australia. The green shaded areas indicate 2010 property values.
Figure 12.9 Risk map and economically optimal coastal setback line (blue line) for Narrabeen Beach, Sydney Australia, obtained by using the probabilistic output of the JPM approach in conjunction with property values and economic modelling methods described by Jongejan et al. (2011).
Figure 12.10 JPM application to Narrabeen Beach, Sydney, Australia with three different structural functions with varying levels of complexity (from Callaghan et al., 2013). The solid lines with symbols indicate measured storm erosion volumes calculated in two different ways (for details, please see Callaghan, 2013).

List of Tables

Chapter 1: Coastal Storm Definition

Table 1.1 Total number of people killed globally by natural disasters between 2004 and 2013 according to disaster type
Table 1.2 Overview of different storm classifications based on significant wave heights and the peaks-over-threshold method
Table 1.3 Site-specific classifications of coastal storms based on water-level information
Table 1.4 Coastal storm severity classifications for the coastlines of the East Coast, USA and Catalonia, Spain based on the total storm energy methodology of Dolan and Davis (1992)

Chapter 7: Storms in Coral Reefs

Table 7.1 Summary of destructive and constructive effects on the zones of coral reefs as described in this chapter

Chapter 8: Storm Clustering and Beach Response

Table 8.1 Storm and storm cluster characteristics computed from the storm sequence shown in Figure 8.3 using six storm cluster definitions

Chapter 11: Preparing for the Impact of Coastal Storms: A Coastal Manager-oriented Approach

Table 11.1 Recommended minimum lifetime for coastal protection works (Puertos del Estado, 2001)
Table 11.2 Recommended maximum values of failure probability for coastal protection works as a function of their importance (Puertos del Estado, 2001)

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List of Contributors

Troels Aagaard

Department of Geoscience and Natural Resources, University of Copenhagen, Copenhagen, Denmark

Clara Armaroli

Department of Physics and Earth Sciences, University of Ferrara, Ferrara, Italy

Xavier Bertin

UMR 7266 LIENSs, CNRS-Université de La Rochelle, La Rochelle, France

Eva Bosom

Laboratori d'Enginyeria Marítima, Universitat Politécnica de Catalunya Barcelona Tech, Barcelona, Spain

Sue Brooks

Department of Geography, Environment and Development Studies Birkbeck, University of London, UK

Karin R. Bryan

School of Science, University of Waikato, Hamilton, New Zealand

David Callaghan

School of Civil Engineering, University of Queensland, Brisbane, Australia

Bruno Castelle

Univ. Bordeaux, UMR EPOC, Pessac, France

CNRS, UMR EPOC, Pessac, France

Jun Cheng

School of Geosciences, University of South Florida, Tampa, USA

Paolo Ciavola

Dipartimento di Fisica e Scienze della Terra, Università di Ferrara, Ferrara, Italy

Giovanni Coco

School of Environment, University of Auckland, Auckland, New Zealand

Ap van Dongeren

Deltares, Delft, The Netherlands

Kara Doran

US Geological Survey, Saint Petersburg, Florida, USA

Mitchell Harley

Water Research Laboratory, School of Civil and Environmental Engineering, UNSW Sydney, Manly Vale, NSW, Australia

Jose Jimenez

Laboratori d'Enginyeria Marítima, Universitat Politécnica de Catalunya Barcelona Tech, Barcelona, Spain

Paul Kench

School of Environment, University of Auckland, New Zealand

Aart Kroon

Department of Geoscience and Natural Resources, University of Copenhagen, Copenhagen, Denmark

Gerhard Masselink

School of Marine Science and Engineering, University of Plymouth, Plymouth, UK

Ana Matias

Centro de Investigação Marinha e Ambiental (CIMA), Universidade do Algarve, Portugal

Robert McCall

Deltares, Delft, The Netherlands

Melisa Menéndez

Grupo de Clima Marino y Cambio Climático IH Cantabria, Universidad de Cantabria Avda, Santander, Spain

Kees Nederhoff

Deltares, Delft, The Netherlands

Maitane Olabarrieta

Civil and Coastal Engineering Department, ESSIE, University of Florida, Gainesville, Florida, USA

Nathaniel Plant

US Geological Survey, Saint Petersburg, Florida, USA

Roshanka Ranasinghe

UNESCO-IHE Institute, Delft, The Netherlands

Dano Roelvink

Deltares, Delft, The Netherlands

UNESCO-IHE Institute, Delft, The Netherlands

Arnold van Rooijen

Deltares, Delft, The Netherlands

University of Western Australia, Crawley, WA, Australia

Nadia Sénéchal

Univ. Bordeaux, UMR EPOC, Pessac, France

CNRS, UMR EPOC, Pessac, France

Tom Spencer

Cambridge Coastal Research Unit, University of Cambridge, UK

Hilary Stockdon

US Geological Survey, Saint Petersburg, Florida, USA

Ana Vila-Concejo

Geocoastal Research Group, School of Geosciences, The University of Sydney, NSW, Australia

Ping Wang

School of Geosciences, University of South Florida, Tampa, USA

Series Foreword

The increasing frequency and severity of hydrometeorological extreme events are reported in many studies and surveys, including the 5th IPCC Assessment Report. This report and other sources highlight the increasing probability that these events are partly driven by climate change, while other causes are linked to the increased exposure and vulnerability of societies in exposed areas (which are not only due to climate change but also to mismanagement of risks and ‘lost memories’ about them). Efforts are ongoing to enhance today's forecasting, prediction and early warning capabilities in order to improve the assessment of vulnerability and risks and develop adequate prevention, mitigation and preparedness measures.

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