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A comprehensive introduction to Coastal Storms and their associated impacts Coastal Storms offers students and professionals in the field a comprehensive overview and groundbreaking text that is specifically devoted to the analysis of Coastal Storms. Based on the most recent knowledge and contributions from leading researchers, the text examines Coastal Storms' processes and characteristics, the main hazards (such as overwash, inundation and flooding, erosion, structures overtopping), and how to monitor and model storms. The authors include information on the most advanced innovations in forecasting, prediction, and early warning, which serves as a foundation for accurate risk evaluation and developing adequate coastal indicators and management options. In addition, structural overtopping and damage are explained, taking into account the involved hydrodynamic and morphodynamic processes. The monitoring methods of Coastal Storms are analyzed based on recent results from research projects in Europe and the United States. Methods for vulnerability and risk evaluation are detailed, storm impact indicators are suggested for different hazards and coastal management procedures analyzed. This important resource includes: * Comprehensive coverage of storms and associated impacts, including meteorological coastal storm definitions and related potential consequences * A state-of-the-art reference for advanced students, professionals and researchers in the field * Chapters on monitoring methods of Coastal Storms, their prediction, early warning systems, and modeling of consequences * Explorations of methods for vulnerability and risk evaluation and suggestions for storm impact indicators for different hazards and coastal management procedures Coastal Storms is a compilation of scientific and policy-related knowledge related to climate-related extreme events. The authors are internationally recognized experts and their work reflects the most recent science and policy advances in the field.

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Table of Contents

Cover

Title Page

Copyright

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

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Guide

Cover

Table of Contents

Series Foreword

Introduction

Begin Reading

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 (upper panel) and a threshold of the total water-level (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 () at Egmond Beach (the Netherlands) during a storm with offshore significant wave heights of . 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. is the threshold velocity for sand transport and 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 () 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 m

3

/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)

Hydrometeorological Extreme Events

 

Published Titles in the Series

 

Hydrometeorological Hazards: Interfacing Science and Policy

Edited by Philippe Quevauviller

 

Forthcoming Titles in the Series

 

Flash Floods Early Warning Systems: Policy and Practice by Daniel Sempere-Torres

Coastal Storms

Processes and Impacts

 

Edited by

 

Paolo Ciavola

University of Ferrara

 

Giovanni Coco

University of Auckland

 

 

 

This edition first published 2017 © 2017 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

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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.

The Book Series on ‘Hydrometeorological Extreme Events’ has the ambition to gather available knowledge in this area, taking stock of research and policy developments at an international level. While individual publications exist on specific hazards, the proposed series is the first of its kind to propose an enlarged coverage of various extreme events that are generally studied by different (not necessarily interconnected) research teams.

The series encompasses several volumes dealing with various aspects of hydrometeorological extreme events, primarily discussing science – policy interfacing issues, and developing specific discussions about floods, coastal storms (including storm surges), droughts, resilience and adaptation. While the books are looking at the crisis management cycle as a whole, the focus of the discussions is generally oriented towards the knowledge base of the different events, prevention and preparedness, early warning, and improved prediction systems.

The involvement of internationally renowned scientists (from different horizons and disciplines) behind the knowledge base of hydrometeorological events makes this series unique in this respect. The overall series will provide a multidisciplinary description of various scientific and policy features concerning hydrometeorological extreme events, as written by authors from different countries, making it a truly international book series.

The book, Prevention of Hydrometeorological Extreme Events – Interfacing Sciences and Policies is the first book of this series; it has been written by policy-makers and scientific experts in the field. It offers the reader an overview of EU international policies, discussions on science – policy interfacing, and a snapshot of the knowledge base of various types of events which are developed in separate volumes of the series.

Philippe QuevauvillerSeries Editor

Introduction

Paolo Ciavola and Giovanni Coco

Coastal storms can be one of the most destructive natural hazards. In coastal cities, they can disrupt activities and affect large parts of the population; they can also cause major economic damage and often pose a threat to human lives. The problem of understanding the physical processes operating during a storm and predicting their impact is relevant for scientists and has clear societal implications. Here, we focus on some specific aspects of coastal storms, from inundation to the morphological changes along the coastline. An understanding of this is becoming increasingly relevant because of the ongoing climatic changes and the ever increasing population pressure along coastlines. We have tried to provide a textbook that we hope will be useful to advanced undergraduate and graduate students in variety of fields, ranging from ocean sciences to geomorphology, coastal engineering and geophysics. We decided to split the book in two parts. In the first part, we asked authors to provide a general overview of the present understanding of storms. In the second part we looked more closely at how storms impact different natural systems. In the first part, the definition of a ‘storm’ is addressed (Chapter 1) and detailed reviews of processes controlling hydrodynamics (Chapter 2), sediment transport (Chapter 3) and overwash processes (Chapter 4) under storm conditions are provided. The reader is then ready to tackle an understanding of how storms impact a variety of geomorphic landscapes, from barrier islands (Chapter 5) to cliffed coastlines (Chapter 6), tidal flats (Chapter 7) and coral reefs (Chapter 8). We also decided that a specific chapter should be dedicated to the role of storm clustering (Chapter 9) and to the most up-to-date advances on the numerical modelling of storm dynamics and effects (Chapter 10). The final chapters focus on the societal aspects of storms and show how to develop frameworks to assess hazards (Chapter 11) and risk management (Chapter 12).

We asked some of the most well-known scientists in the field to help us provide this overview on coastal storms by writing individual chapters. On several occasions the chapters report knowledge gained by the authors during years of research on their topic of expertise, developed with financial support from research agencies in Europe, USA, Australia and New Zealand. We are hugely indebted to the authors, it has been a privilege to share their passion for research and their effort to promote science. Finally, while reading the chapters, it will appear evident that there are still many poorly understood issues that require attention. Research on this topic is still constrained by a limited understanding of the analogies between theoretical process and natural system behaviour during extreme forcing. Field measurements still remain scarce, as acquisition of pre- and post-storm datasets requires quick and costly deployment of state-of-the-art equipment. We hope this book will stimulate scientists to advance knowledge on coastal storms and contribute to a better planning of measures to increase resilience of coastal communities.

Acknowledgments

This book is the result of many years of research and fruitful collaborations with scientists worldwide. None of this could have happened without funding from a number of agencies in a variety of countries. Here, we wish to specifically acknowledge the role of the EU in promoting research on coastal storm processes and impacts during the Seventh Research Framework (e.g. MICORE and RISC-KIT projects). The ongoing support from the EU-RISC-KIT Project-grant 603458 (Paolo Ciavola) and the MBIE-GNS Hazard Platform (Giovanni Coco) is gratefully acknowledged.

Paolo Ciavola would also like to express gratitude to his wife, Clara, and his son, Leonardo, for putting up with late nights trying to bridge the time zone with New Zealand. Together we have seen plenty of beaches around the world and not many mountains. Giovanni Coco would like to thank Mattia for reminding him that beaches are for fun, not for work. Thanks to Jennifer Montano for spotting the final typos.

Finally a little story about the editors of this book. We both grew up in Catania in Italy, on the foothills of the largest volcano in Europe. We both went to scientific high schools there, only a few kilometres from each other. But we never met at the time when we were living on the island. Once again, we studied different topics in different places in Italy and abroad, and destiny did not bring us together. Then life took us around the world and finally science enabled us to meet and discover another fellow who liked the idea of a book on storm processes. Some would call this serendipity, but without doubt it shows us the power of research in bringing people together, whatever their country, belief and personal opinions are.

Chapter 1Coastal Storm Definition

Mitchell Harley

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

1.1 Introduction

Storms represent nature in one of its most energetic and violent states. The word “storm” is synonymous with images of destruction – strong winds lashing at trees and buildings, intense precipitation flooding towns or dumping meters of snow, large seas eroding beaches and coastal properties, and rapid surges in ocean levels inundating entire islands and vast lowland areas. At the same time, storms are essential to human life and an integral part of the global weather and natural ecosystems. Storms help break droughts by delivering much needed water to drought-stricken areas, thereby recharging reservoirs, river systems and underground aquifers. Many ecosystems are also reliant on the episodic arrival of large storms for their rejuvenation after extended periods of calm, stable conditions (e.g. the flushing of hypersaline lagoons due to hurricanes, Tunnell, 2002).

Globally, storms rank as one of the deadliest of all natural hazards (International Federation of Red Cross and Red Crescent Societies, 2014). In the decade spanning the years 2004–2013, storms were responsible for over 180,000 deaths worldwide – second in terms of lives lost only to those of earthquakes and tsunamis (Table 1.1). Flooding, including marine flooding as a result of waves and storm surge, were meanwhile responsible for over 60,000 deaths worldwide and rank fourth on this list. In the United States, storms have contributed to the vast majority of monetary losses resulting from natural hazards over the last half century. Hurricanes and tropical storms alone have overwhelmingly been the most costly of all natural hazards, having resulted in a total of US$ 267 billion in monetary losses between the years 1960 and 2014 (Figure 1.1). Severe weather, flooding, tornadoes and miscellaneous coastal hazards (loosely defined as hazards including rip currents, coastal flooding, coastal erosion, strong winds, etc.) have also caused combined losses of US$ 364 billion (Hazards and Vulnerability Research Institute, 2015).

Table 1.1 Total number of people killed globally by natural disasters between 2004 and 2013 according to disaster type

Rank

Disaster Type

Total number of people killed

1

Earthquakes/tsunamis

650,321

2

Storms

183,457

3

Extreme temperatures

72,088

4

Floods

*

63,207

5

Mass movement: wet

8,739

6

Forest/scrub fires

705

7

Droughts/food insecurity

384

8

Volcanic eruptions

363

9

Mass movement: dry

273

Total

979,537

* includes wave and surge events

(Source: International Federation of Red Cross and Red Crescent Societies, 2014, p. 226)

Figure 1.1 Total hazard losses in the United States (1960–2014) by hazard type

(Source: Hazards and Vulnerability Research Institute, 2015).

There are few regions more vulnerable to storms than the narrow ribbon of the Earth's surface that constitutes the coastal zone. Situated at the interface between land and large water bodies such as oceans, seas and lakes, the coastal zone is a region in constant flux as consolidated and unconsolidated sediments are constantly shaped and re-shaped by Earth's forces. As these forces – winds, waves and currents – interact with coastal sediments, energy is dissipated to such a degree that under normal everyday conditions, their short-term effects on the adjacent coastal hinterland are minimal. During destructive storm conditions, however, the elevated energy and/or water levels may well be beyond the capacity of the coastal zone to dissipate, potentially exposing the backshore and coastal hinterland to unusually large forces and hazardous conditions.

Given the low-lying nature as well as the sheer density of people living close to the coast (with an estimated 23% of the world's population and population densities greater than three times the global average, Small & Nicholls, 2003), the exposure to elevated water levels, waves and currents that may occur during storm conditions can have devastating effects. Some historical examples of extreme storms striking the coast include the 1900 hurricane in Galveston, Texas that claimed the lives of an estimated 8000–12,000 people and is recognized even today as the deadliest natural disaster in the United States' history (Blake & Gibney, 2011). In 1953, a large storm surge in the North Sea inundated tens of thousands of hectares of coastal hinterland in the Netherlands, Belgium and the United Kingdom and claimed over 2500 lives. In Bangladesh, the Bhola cyclone of 1970 is considered one of the worst natural disasters of all time, generating a 10 m storm surge that killed up to 500,000 people and left a huge toll on the country's population and economy. Such devastation was repeated in the same region 21 years later, when another tropical cyclone caused a surge that extended 160 km inland and resulted in 138,000 deaths (Haque, 1997).

In more recent years, coastal storms have received considerable attention as access to news and information via the Internet has grown exponentially and the world has become more aware of the dangers associated with climate change. A particularly significant event that has remained in the conscience of many people was that of Hurricane Katrina that struck the Louisiana coastline in 2005. Hurricane Katrina demonstrated that even in an age of significant advancements in scientific understanding, technology and computer forecasts, nations can still be caught off-guard by the arrival of coastal storms. Hurricane Katrina also highlighted that when coastal storms do occur, it is often the most vulnerable people of a society that are affected the most (Laska & Morrow, 2006). Some other recent examples of coastal storms include Cyclone Sidr in Bangladesh (2007), the Xynthia cyclone in France (2010), Hurricane Sandy in the Caribbean, New Jersey and New York (2012), Typhoon Haiyan in the Philippines (2013), the 2013/2014 winter storms in the United Kingdom and Tropical Cyclone Pam in Vanuatu (2015). Figure 1.2 indicates a rare occurrence of three concurrent tropical cyclones close to the coastline that was observed in southern hemisphere waters in March 2015.

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

(Source: NASA Earth Observatory: http://earthobservatory.nasa.gov/).

Considering their destructiveness and relevance to today's world, surprisingly few books have dealt specifically with the subject of coastal storms and no overarching definition presently exists to assist in their identification. Indeed a degree of confusion surrounding the use of the term coastal storm is evident. An inspection of Table 1.1, for example, indicates that coastal storms fall into the category of both storms and floods, but are not recognized as a category on their own. This is in spite of the fact that the processes governing the formation and development of coastal storms are very different from those of, for instance, river floods. Figure 1.1, meanwhile, highlights the variety of ways in which coastal storms are classified in the commonly-used SHELDUS database for US disaster statistics, with hurricane/tropical storms and coastal hazards treated separately.

As this chapter discusses, the lack of clarity when it comes to defining coastal storms stems from the complexities surrounding the ways in which storm energy is generated, transported and interacts with the coastline. A robust definition of a coastal storm is, however, necessary if we want to answer important societal questions, such as:

How vulnerable are coastal communities and ecosystems to coastal storms?

Are coastal storms becoming more frequent or increasing in magnitude?

What influence is climate change having on coastal storms?

How near to the coast can we safely build infrastructure away from the influence of coastal storms?

How can we design coastal structures to withstand coastal storm forces?

This chapter begins by first summarizing the challenges of defining coastal storms. These challenges are then taken into consideration to form a general qualitative coastal storm definition that can be applied to all coastlines. Section 1.2 follows by describing the most common synoptic conditions associated with coastal storms. Section 1.3 then presents the various approaches taken to identify coastal storm events from observational records and summarizes ways of quantifying coastal storm severity.