Future Shoreline Research Articles

Risk averse choices of managed beach widths under environmental uncertainty

AbstractApplying a theoretical geo‐economic approach, we examined key factors affecting decisions about the choice of beach width when eroded coastal beaches are being nourished (i.e., when fill is placed to widen a beach). Within this geo‐economic framework, optimal beach width is positively related to its values for hazard protection and recreation and negatively related to nourishment costs and the discount rate. Using a dynamic modeling framework, we investigated the time paths of beach width and nourishment that maximized net present value under an accelerating sea level. We then analyzed how environmental uncertainty about expected future beach width, arising from natural shoreline dynamics, intermittent large storms, or sea‐level rise, leads to economic choices favoring narrower beaches. Risk aversion can affect a coastal property owner's choice of beach width in contradictory ways: the expected benefits of hazard protection must be balanced against the expected costs of repeated nourishment actions.Recommendations for Resource Managers Because of environmental uncertainty, coastal protection projects are risky investments. It is important to consider the effects of uncertainty on cost–benefit assessments of these projects. Without uncertainty, optimal beach width is positively related to its values for hazard protection and recreation and negatively related to nourishment costs and the discount rate. Uncertainty about future shoreline positions has a negative impact on the choice of an optimal beach width. Risk aversion can affect a coastal property owner's choice of beach width in contradictory ways: a wider beach to protect their home, and a narrower beach if the effectiveness of nourishment is uncertain.

Forecasting shoreline changes along the Egyptian Nile Delta coast using Landsat image series and Geographic Information System.

Before construction of the Aswan High Dam, the Nile Delta was expanding and advancing into the Mediterranean Sea. Subsequently, it became a highly destructive Delta due to the lack of sediment discharge, climate change, subsidence, and coastal processes (e.g. wind, waves, tides, and littoral currents). Many coastal structures have been erected to stop or mitigate coastal problems in the study area. We used 31 Landsat images to monitor the fluctuation of erosion and deposition along the study area. The shorelines in these huge datasets were extracted using standard techniques. Linear regression ratio (LRR) and end-point rate (EPR) were used with Digital Shoreline Analysis System (DSAS) software to determine the rates of beach changes; we then forecast future shoreline changes. The accuracy of the model's results was checked using the ground field measurements of several studies. This model also creates an estimate of the position uncertainty at each time step. The value of the uncertainty is low (approximately half a pixel) along the shorelines without coastal protection. This study aimed to forecast future beach evolution to the year 2041 to evaluate its sensibility and facilitate proposals for coastal protection for human safety and habitats if the coastal processes and climate change continue to worsen with time.

Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks

AbstractEvaluating shoreline retreat rate (SRR) on different spatial‐temporal scales is critical for effective coastal management. Large‐scale evaluations typically rely on data‐driven methods such as Discrete Bayesian networks (BNs). However, these BNs require discretization of continuous variables which can lead to information loss. Here, we propose a new method, the Hybrid BN to incorporate continuous variables without discretization. Both Discrete and Hybrid BNs were developed and compared to evaluate large‐scale (continental scale) SRR in Australia, using Digital Earth Australia data set. These BNs used forcing parameters (e.g., waves, tide, sediment sink/source, and sea level rise [SLR]) and geomorphic settings (e.g., geomorphology, backshore profile, and surfzone slope) to predict SRR. Validation of the BNs showed that Hybrid BNs, which provide a more realistic assessment of the range of SRR, outperform in predicting continuous variables, when compared with Discrete BNs. However, Discrete and Hybrid BNs provide consistent qualitative findings for the SRR of Australia. Among forcing parameters, the sediment sink/source was found to be the most informative variable to indicate the shoreline retreat, followed by tide, SLR rate, and wave processes. In the scenario of an increased SLR rate, tropical tidal flats were predicted as the most at risk coasts in Australia. We found that BNs can reflect the impact of different factors on coastal evolution, and predict future shoreline change by exploring historical data. The performance of these models can be further improved when more data sets become available.

VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network

ABSTRACT Coastal communities, land covers, and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid, and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. This paper develops and applies an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet’s 3 to 5 m) remote-sensing imagery, training a very deep convolutional neural network (holistically nested edge detection), to predict sequential vegetation line locations on annual to decadal timescales. Red, green, and near-infrared (RG-NIR) was found to be the optimum image spectral band combination during neural network training and validation. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (P A) at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, United Kingdom, has identified a landward retreat rate >3 m year−1 (2010–2020). Plausible vegetation lines were successfully retrieved from images in The Netherlands and Australia, which were not used to train the neural network, although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalizing to estimate retreat of sandy coastlines from Planet imagery in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, incorporating future shoreline change.

Traditional vs. Machine-Learning Methods for Forecasting Sandy Shoreline Evolution Using Historic Satellite-Derived Shorelines

Forecasting shoreline evolution for sandy coasts is important for sustainable coastal management, given the present-day increasing anthropogenic pressures and a changing future climate. Here, we evaluate eight different time-series forecasting methods for predicting future shorelines derived from historic satellite-derived shorelines. Analyzing more than 37,000 transects around the globe, we find that traditional forecast methods altogether with some of the evaluated probabilistic Machine Learning (ML) time-series forecast algorithms, outperform Ordinary Least Squares (OLS) predictions for the majority of the sites. When forecasting seven years ahead, we find that these algorithms generate better predictions than OLS for 54% of the transect sites, producing forecasts with, on average, 29% smaller Mean Squared Error (MSE). Importantly, this advantage is shown to exist over all considered forecast horizons, i.e., from 1 up to 11 years. Although the ML algorithms do not produce significantly better predictions than traditional time-series forecast methods, some proved to be significantly more efficient in terms of computation time. We further provide insight in how these ML algorithms can be improved so that they can be expected to outperform not only OLS regression, but also the traditional time-series forecast methods. These forecasting algorithms can be used by coastal engineers, managers, and scientists to generate future shoreline prediction at a global level and derive conclusions thereof.

The analysis of shoreline change dynamics and future predictions using automated spatial techniques: Case of El-Omayed on the Mediterranean coast of Egypt

The study area belongs to the coast of El-Omayed Biosphere Reserve on the western Mediterranean coast of Egypt. This work represents one of the limited studies analyzing historical change rates of its shoreline and predicting future changes to identify the parts requiring management interventions. The shorelines of five dates covering a period of 34 years between 1984 and 2018 were extracted from five Level-2 Landsat images. The Water Index (WI2015) provided the best extraction results of three common water indices when compared with a reference shoreline digitized from high resolution Google Earth imagery. End Point Rate (EPR) and Linear Regression Rate (LRR) were automatically calculated to determine shoreline change rates. The two methods provided very similar results (R2 exceeding 0.98). EPR was used for evaluating historical changes (1984–2018) and predicting future shoreline positions (2030 and 2050). The results showed that the shoreline exhibited alternation between erosion and accretion with varied rates. The average erosion rate is −1.67 m/year and highest accretion is 1.88 m/year. These changes result basically from longshore sediment transport generated by the waves and currents approaching mostly from NW quadrant. The western parts are also affected by the protective structures supporting the intensive touristic activities characterizing the study area. The future trends of predicted shorelines showed that urgent and long-term protective measures are required to avoid the partial and complete loss of the several parts of its beaches jeopardizing the sustainability of the tourism activities in the study area.

Shoreline change monitoring for coastal zone management using multi-temporal Landsat data in Mahi River estuary, Gujarat State

Coastline changes are caused by sea-level rise, erosion and anthropogenic activities, and have important consequences on coastal ecosystems and communities. In this study, shore line changes during the last 40 years near the Mahi estuarine belt in the Gulf of Khambhat region have been monitored using multi-temporal Landsat data from 1978 to 2018. Multi-date Landsat digital data with 10-year interval from 1978 to 2018 was downloaded from the website https://earthexplorer.usgs.gov/ . The Landsat data covering the study area with 15-km buffer was extracted and analyzed for monitoring the changes in the shoreline. The Normalized Difference Water Index (NDWI) images were also generated for delineation of the shoreline. The data was analyzed using the Digital Shoreline Analysis System in ArcGIS software, which provides a set of tools permitting transects-based calculation of shoreline displacement. A total number of 10 transects from the centre of the village up to the coast line were marked on the multi-date Landsat digital data. The changes in the shoreline from the centre of the village were calculated using the End Point Rate (EPR) and temporal linear regression model to predict future shoreline position. The decadal shoreline change indicated that shoreline has continuously increased from 113.9 to 831.4 m during the 40-year period from 1978 to 2018, respectively. This indicates that total migration of shoreline towards the land area of the study village during the last 40 years (1978 to 2018) was 1590.5 m with an alarming annual rate of change of 39.76 m year−1, which is very remarkably high and alarming which may lead to any future disaster for the study village. The coastline was not significantly changing until 1988; however, change in coastline is exponentially faster from 1989 to 2018. The linear regression method was adopted for predicting the shoreline change rates over the entire period of the study from 1978 to 2018 and from 2018 to 2028 and 2038. The results of shoreline change prediction for future 10 years (2028) and 20 years (2038) indicate the average predicted transect length of 1908 m and 1543 m, respectively. With the projected estimates of shoreline change during next 20 years at the rate of almost 120 m/year, it appears that the Chokari village may get submerged in the sea water. The analysis of change in the land surface area of the Chokari village indicated that it has decreased from 12.45 to 9.63 km2, which indicates that area reduced by 2.8 km2 may be due to the erosion along the river-bed which is the result of change in the shoreline during the last 40 years. Based on these results of shoreline change analysis, it is recommended that the Government of Gujarat needs to take immediate steps to protect the Chokari village from submergence by adopting Integrated Coastal Zone management strategies.

Assessment impact of the Damietta harbour (Egypt) and its deep navigation channel on adjacent shorelines

Deep navigation channels have a great impact on adjacent beaches and crucial economic effects because of periodic dredging operations. The navigation channel of the Damietta harbour is considered a clear example of the sedimentation problem and deeply affects the Northeastern shoreline of the Nile Delta in Egypt. The aim of the present study is to monitor shoreline using remote sensing techniques to evaluate the effect of Damietta harbour and its navigation channel on the shoreline for the last 45 years. Also, the selected period was divided into two periods to illustrate the effect of man-made interventions on the shoreline. Shorelines were extracted from satellite images and then the Digital Shoreline Analysis System (DSAS) was used to estimate accurate rates of shoreline changes and predict future shorelines evolution of 2030, 2040, 2050 and 2060. The Damietta harbour created an accretion area in the western side with an average rate of 2.13 m yr-1. On the contrary, the shoreline in the eastern side of the harbour retreated by 92 m on average over the last 45 years. So, it is considered one of the main hazard areas along the Northeastern shoreline of the Nile Delta that needs a sustainable solution. Moreover, a detached breakwaters system is predicted to provide shore stabilization at the eastern side as the implemented one at Ras El-Bar beach. Predicted shoreline evolution of 2060 shows a significant retreat of 280.0 m on average.

Risk of shoreline hardening and associated beach loss peaks before mid-century: O\u02bbahu, Hawai\u02bbi

Shoreline hardening, which causes beach loss globally, will accelerate with sea level rise (SLR), causing more beach loss if management practices are not changed. To improve beach conservation efforts, current and future shoreline hardening patterns on sandy beaches need deeper analysis. A shoreline change model driven by incremental SLR (0.25, 0.46, 0.74 m) is used to simulate future changes in the position of an administrative hazard zone, as a proxy for risk of hardening at all sandy beaches on the island of O‘ahu, Hawai ‘i. In Hawai ‘i, hardening can be triggered when evidence of erosion is within 6.1 m (“20 ft”) of certain structures, allowing an applicant to request emergency protection. Results show an increase in shoreline vulnerability to hardening with SLR governed by backshore land use patterns. The largest increase (+ 7.6%) occurred between modern-day and 0.25 m of SLR (very likely by year 2050) with half of all beachfront shoreline at risk by 0.74 m of SLR. Maximum risk of shoreline hardening and beach loss is projected to occur from modern-day and near-term hardening because of the heavily developed aspect of some shoreline segments. Adaptation to SLR should be considered an immediate need—not solely a future issue.

Spatio-temporal modelling of shoreline migration in Sagar Island, West Bengal, India

Sagar Island is a very popular pilgrimage destination located in the western part of Indian Sundarbans. This study employs Digital Shoreline Analysis System (DSAS) extension tool in ArcGIS platform to study and analyse the shoreline dynamics of Sagar Island by utilizing satellite images extending 40 years (1975–2015). 44 transects with 100 m spacing were laid and divided into six littoral cells (LCs). End Point Rate (EPR) and Linear Regression (LR) models were utilized to analyze the shoreline change patterns and also for predicting the future shoreline positions. It was observed that almost the entire southern portion of Sagar Island is susceptible to high rate of shoreline erosion. Most of the erosion occurred in Dhablat (LC 4 (a)) in the south eastern part at a rate of 11.695 ± 2.1 m/year. The mean shoreline change rate was also high in LC5 (±23.525 m/year). However, the overall shoreline change rate for the island was 4.94 m/year and uncertainty of total shoreline change rate was ±4.4 m/year. The study shows the usefulness of DSAS as a scientific tool for shoreline change studies and highlights state of erosion in the study area.

Large-scale erosion driven by intertidal eelgrass loss in an estuarine environment

Seagrasses influence local hydrodynamics by inducing drag on the flow and dampening near-bed velocities and wave energy. When seagrasses are lost, near-bed currents and wave energy can increase, which enhances bottom shear stresses, destabilizes sediment, and promotes suspension and erosion. Though seagrasses are being lost rapidly globally, the magnitude of change in sediment stabilization following ecosystem-wide eelgrass loss has rarely been measured. In this study, we explored the geomorphological changes associated with an unprecedented estuary-wide collapse of a seagrass (eelgrass, Zostera marina) in Morro Bay, CA, USA. Morro Bay has historically suffered from accelerated sedimentation and accretion. However, following massive eelgrass loss since 2010, over 90% of locations that previously had eelgrass experienced erosion. Elevation losses (erosion) reached 0.50 m in some places (mean loss of 0.10 m) with as much as a 50% decrease (median decrease of 13.6%) in elevation (i.e., increase in depth) compared to pre-decline levels. In comparison, the mouth of the estuary, where eelgrass was largely retained, had only 27.7% of the locations with prior eelgrass experiencing erosion and underwent a mean elevation increase (accretion) of 0.32 m. Thus, the loss of eelgrass appears to have altered dynamics at the seabed and transitioned large regions of the estuary from an environment that promotes deposition and accretion to one that promotes suspension and erosion. Large-scale erosion following seagrass loss may be predictive of future shoreline and coastal habitat changes and is likely to be exacerbated by increased storm surge and sea level rise expected with climate change.

Mapping the Shoreface of Coastal Sediment Compartments to Improve Shoreline Change Forecasts in New South Wales, Australia

The potential response of shoreface depositional environments to sea level rise over the present century and beyond remains poorly understood. The shoreface is shaped by wave action across a sedimentary seabed and may aggrade or deflate depending on the balance between time-averaged wave energy and the availability and character of sediment, within the context of the inherited geological control. For embayed and accommodation-dominated coastal settings, where shoreline change is particularly sensitive to cross-shore sediment transport, whether the shoreface is a source or sink for coastal sediment during rising sea level may be a crucial determinant of future shoreline change. While simple equilibrium-based models (e.g. the Bruun Rule) are widely used in coastal risk planning practice to predict shoreline change due to sea level rise, the relevance of fundamental model assumptions to the shoreface depositional setting is often overlooked due to limited knowledge about the geomorphology of the nearshore seabed. We present high-resolution mapping of the shoreface-inner shelf in southeastern Australia from airborne lidar and vessel-based multibeam echosounder surveys, which reveals a more complex seabed than was previously known. The mapping data are used to interpret the extent, depositional character and morphodynamic state of the shoreface, by comparing the observed geomorphology to theoretical predictions from wave-driven sediment transport theory. The benefits of high-resolution seabed mapping for improving shoreline change predictions in practice are explored by comparing idealised shoreline change modelling based on our understanding of shoreface geomorphology and morphodynamics before and after the mapping exercise.

Beach nourishment versus sea level rise on Florida’s coasts

Beach nourishment and sea level rise will dominate future shoreline changes on Florida’s 665 miles of sandy coast. Shoreline changes from 2020-2100 are projected along this entire coast using equilibrium profile theory that accurately predicted shoreline changes on Florida’s east coast from 1970-2017 (Houston 2019). Projections for 2020- 2100 are made assuming past rates of beach nourishment for the 30-yr period from 1988-2017 will continue and sea level will rise according to recent projections of the Intergovernmental Panel on Climate Change (IPCC) that include the latest knowledge on ice melting in Antarctica (IPCC 2019). Using the beach nourishment and sea level rise data, equilibrium profile theory is then used to predict shoreline change from 2020-2100 for each IPCC sea level rise projection. Beach nourishment is shown to produce shoreline advance seaward on average for all IPCC scenarios for both the entire Florida coast and east coast and for all scenarios except the upper confidence level of the worst scenario for the southwest and Panhandle coasts. Some of the 30 counties on these coasts will require a greater rate of nourishment than in the past to offset sea level rise for some or all of the scenarios, whereas some will offset sea level rise for all scenarios with lower nourishment rates than in the past. The annual beach nourishment volume for which a county has a shortfall or surplus in offsetting sea level rise for each IPCC scenario can be calculated with the information provided and examples are presented. The approach can be used on coasts outside Florida if beach nourishment and sea level rise are expected to dominate future shoreline change.

Assessment of the Kalman filter-based future shoreline prediction method

The prediction of the future position of the shoreline is of great importance in planning studies in coastal areas, in making effective decisions in coastal management, and in determining changes occurring on the coast. In this study, coastal change analyses were performed in two different study areas (the Gulf of Izmit and the Goksu Delta) by using satellite images of different dates, and the accuracy of the Kalman filter-based future shoreline prediction method was determined by statistical methods. In this context, by using the 19-period Landsat satellite image belonging to different dates between 1975 and 2019 for the Gulf of Izmit and the 10-period Landsat satellite image belonging to different dates between 1984 and 2018 for the Goksu Delta, shorelines were extracted automatically, and coastal changes were analyzed at a 95% CI by the statistical methods of end point rate, linear regression rate, and weighted linear regression rate (WLR). Afterward, the shorelines extracted automatically on the determined dates were compared with 10-year and 20-year predicted shorelines by the Kalman filter-based prediction method, and their accuracy was statistically analyzed. As a result, the 10-year predicted shorelines by the WLR method were found to provide the highest accuracy.

Holocene paleoshoreline changes of the Red River Delta, Vietnam

The Red River Delta (RRD), one of the two largest deltas in Vietnam, has been severely affected by natural hazards induced by global climate change. Understanding the Holocene paleoshoreline is crucial in developing adaptive planning in response to future shoreline changes based on past natural shoreline changes. This study presents new data pertaining to pollen, grain size, lithological characteristics, and 14C dating of six cores from the RRD. The purpose of this study was to determine the paleoenvironments and paleoshorelines of the RRD in the Holocene to provide information useful for modeling future changes, e.g., for calibration sea level in paleobathymetry and sea level change models; for making the necessary ecological and cultural adaption to survive. We identified 4 units and 10 sedimentary facies indicating periods of sedimentary evolution since late Pleistocene. The main indicators for paleoshoreline interpretation are true mangrove and backmangrove pollen. The inferred paleoshoreline changes and five paleogeographic maps for 10.5, 9.2, 7.8, 6.5, and 3.7 cal. kyr BP were constructed based on paleoshoreline indicators, and paleoenvironmental reconstructions from the dataset of our whole RRD study area.

Documenting a Century of Coastline Change along Central California and Associated Challenges: From the Qualitative to the Quantitative

Wave erosion has moved coastal cliffs and bluffs landward over the centuries. Now climate change-induced sea-level rise (SLR) and the changes in wave action are accelerating coastline retreat around the world. Documenting the erosion of cliffed coasts and projecting the rate of coastline retreat under future SLR scenarios are more challenging than historical and future shoreline change studies along low-lying sandy beaches. The objective of this research was to study coastal erosion of the West Cliff Drive area in Santa Cruz along the Central California Coast and identify the challenges in coastline change analysis. We investigated the geological history, geomorphic differences, and documented cliff retreat to assess coastal erosion qualitatively. We also conducted a quantitative assessment of cliff retreat through extracting and analyzing the coastline position at three different times (1953, 1975, and 2018). The results showed that the total retreat of the West Cliff Drive coastline over 65 years ranges from 0.3 to 32 m, and the maximum cliff retreat rate was 0.5 m/year. Geometric errors, the complex profiles of coastal cliffs, and irregularities in the processes of coastal erosion, including the undercutting of the base of the cliff and formation of caves, were some of the identified challenges in documenting historical coastline retreat. These can each increase the uncertainty of calculated retreat rates. Reducing the uncertainties in retreat rates is an essential initial step in projecting cliff and bluff retreat under future SLR more accurately and in developing a practical adaptive management plan to cope with the impacts of coastline change along this highly populated edge.

Effects of closure depth changes on coastal response to sea level rise: Insights from model experiments in southern Brazil

This study focuses on the impacts of variable shoreface closure depth limits on coastal responses to increases in sea levels along a sandy barrier in southern Brazil. Upper and lower shoreface limits for sediment exchanges are largely regulated by the wave climate and they tend to move offshore as the temporal scale increases. Therefore, because closure depth limits are a source of uncertainty in simulations of coastal response to sea level rise, to elucidate how important changes in these limits are under such conditions, four simulation experiments were performed with variable combinations of upper and lower shoreface closure depth values. Direct methods for closure depth delineation require long term data sets with field surveys, which are rarely available; therefore, indirect approaches are applied widely. To calculate closure depth values here, we apply Hallermeier's equations using two wave data sources: one measured (via wave buoys) and one modeled Wave Watch III and Simulating Waves Nearshore Model (WWIII/SWAN). Evaluation of coastal response under rising sea levels was possible via application of an aggregated coastal modeling approach using the random shoreface translation model (RanSTM). Shoreline retreat distances resulting from each combination of upper (hc) and lower (hi) shoreface closure depth values (cases) in model simulations were compared: Case 1 (hc = 7.4 m; hi = 42.1 m), Case 2 (hc = 7.4 m; hi = 35.7 m), Case 3 (hc = 6.2 m; hi = 35.7 m), and Case 4 (hc = 6.2 m; hi = 42.1 m). Statistical analysis via the Kruskal-Wallis test demonstrated that shoreline retreat was significantly affected (at P < 0.01) by the variations in lower shoreface limit. The recession distance was greater when the lower shoreface limit was deeper. Overall results indicate that the choice of lower shoreface limiting depth is indeed crucial in influencing coastal response to sea level rise, and hence in future shoreline position forecasts. Therefore, these results show the relevance of determining such limits with confidence when modeling coastal response to sea level rise, especially when this rise is being predicted over longer temporal scales.

Projections of future beach loss along the mediterranean coastline of Egypt due to sea-level rise

Beach loss and shoreline retreat caused by sea level rise (SLR) is considered one of the most worldwide significant issues. The Mediterranean coastline of Egypt (approximately 1066 km) is likely to face beach erosion, particularly in the low-lying and sandy coastal areas in the future as a direct response to SLR. Consequently, the projection of future shoreline recession and corresponding beach loss due to SLR using the Bruun rule were investigated to assess the proper impacts of SLR on the shoreline recession and beach loss. In addition, the uncertainties ratios associated with SLR scenarios and sediment sizes were assessed. Furthermore, this study investigated the influence of local land subsidence in combination with SLR scenarios on the shoreline recession and associated beach loss along the Nile Delta coastline. The ensemble-mean regional SLR data included representative concentration pathway (RCP) scenarios and 21 models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). The projected shoreline retreats and associated average beach loss in the future 2081–2100 were ranged from 12.6 m and 11.3 km2 to 41.9 m and 19.2 km2 for the ensemble-mean SLR RCP2.6 and RCP8.5, respectively. The uncertainty caused by the sediment size of 0.15 to 0.35 mm ranged from 17% to 30% for RCP2.6 and RCP8.5, respectively. The projected annual shoreline retreats ranged from 0.36 to 0.65 m/yr for the ensemble-mean SLR in combination with local land subsidence for RCP2.6 and 0.48 to 0.85 m/yr for RCP8.5, respectively. Highly vulnerable areas to shoreline recession for SLR and local land subsidence were detected from EL-Manzala lake to Port Said coastlines, Abo Qir bay, from Rosetta to Damietta promontories, and Alexandria coastline. Thus, shoreline retreat and associated beach loss due to SLR is an urgent issue that should be addressed through the integrated coastal zone management strategies of Egypt.

Governance and stakeholder perspectives of managed re-alignment: adapting to sea level rise in the Inner Forth estuary, Scotland

With climate change, coastal areas are faced with unprecedented sea level rise and flooding, raising questions as to how societies will choose to adapt. One option is to strengthen existing sea walls to maintain current land uses; however, scientists, policy-makers and conservationists increasingly see the benefits of managed realignment, which is a nature-based coastal adaptation that involves the conversion of reclaimed farmland back to wetlands, allowing periodic local flooding in designated areas to reduce the risk of flooding downstream. We interviewed 16 local organisations, landowners and farmers and held workshops with 109 citizens living the Inner Forth estuary in eastern Scotland, to examine how managed realignment is supported by stakeholder attitudes and their engagement. Most of the farmers we interviewed prefer strengthened sea walls, to maintain their livelihoods and agricultural heritage. Citizens and local organisations were mainly supportive of managed realignment, because it provided wildlife and flood regulation benefits. However, we identified several barriers that could present obstacles to implementing managed realignment, for example, uncertainty whether it would support their principles of economic and rational decision-making. Our findings suggest that the local capacity to cope with rising sea levels is limited by lack of engagement with all relevant stakeholder groups, the limited scope of existing stakeholder partnerships and poor short-term funding prospects of landscape partnerships that would facilitate collaboration and discussion. We suggest that including citizens, landowners, farmers and industries would strengthen existing stakeholder deliberation and collaboration, and support the Inner Forth’s transition towards a more sustainable future shoreline.

Willingness to restore jetty-created erosion at a famous tourism beach

This research revealed the intangible benefit of preserving the downdrift eroded shoreline at Cha-Am beach, Thailand. It integrated coastal engineering and environmental economics to urge for the beach restoration. Although providing some benefits, the jetty at Cha-am beach has also created severe downdrift coastal erosion. The research began with gathering the field data required to simulate the future shoreline position. After a satisfactory model calibration was attained, the future coastline change was predicted. It was found that the updrift part of the beach would be widened by approximately 8 m/yr, while the downdrift side of the jetty would experience severe coastal erosion by as much as 13 m/yr. A valuation of the downdrift eroded shore was consequently undertaken using a willingness-to-pay (WTP) study. Four hundred sets of questionnaires were surveyed using 10 different bid amounts. A logit model was implemented and the mean WTP was analyzed. It was found that the value of the downdrift eroding shoreline was approximately THB 20.1 billion or USD 609.9 million per year. Such a huge non-market value of the downdrift beach might urge decision makers to initiate certain continuous beach restoration measures.