Coastal Systems and Low-Lying Areas

Field, Christopher B.; Barros, Vicente; Dokken, David Jon; Mach, Katharine J.; Mastrandrea, Michael D.

doi:10.1017/cbo9781107415379.010

Cited by 27 publications

(13 citation statements)

References 227 publications

(285 reference statements)

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“…Storm surge intensity and frequency are both expected to change in future 6,17,20 , while riverflows are expected to change by up to 40% in some regions 21 . These climate change driven variations in natural forcing are likely to result in significant morphological impacts along, especially, the sandy coastlines of the world 3,5,22,23 , which constitute 31% of the global coastline 24 and are subject to a very high level of human utilisation 2,25 . The potential first order climate change driven morphological impacts on sandy coasts, are summarized in Table 1, together with their main drivers and manifestation time scales.…”

Section: Climate Change Driven Coastal Changementioning

confidence: 99%

On the need for a new generation of coastal change models for the 21st century

Ranasinghe

2020

Sci Rep

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The combination of climate change impacts, declining fluvial sediment supply, and heavy human utilization of the coastal zone, arguably the most populated and developed land zone in the world, will very likely lead to massive socioeconomic and environmental losses in the coming decades. Effective coastal planning/management strategies that can help circumvent such losses require reliable local scale (<~10 km) projections of coastal change resulting from the integrated effect of climate change driven variations in mean sea level, storm surge, waves, and riverflows. Presently available numerical models are unable to adequately fulfill this need. A new generation of multi-scale, probabilistic coastal change models is urgently needed to comprehensively assess and optimise coastal risk at local scale, enabling risk informed, climate proof adaptation measures that strike a good balance between risk and reward. With approximately 10% of the global population living in the coastal zone 1 , the potentially massive impact of climate change on the world's coastal zones is now well recognized 2-6. Moreover, continued human attraction to the coast has resulted in rapid expansions in settlements, urbanization, infrastructure, economic activities and tourism, as exemplified by 15 of the world's 20 megacities being located in the coastal zone 7. The combination of climate change impacts, declining fluvial sediment supply 8,9 , and the ever increasing human utilization of the coastal zone is very likely to result in unprecedented socioeconomic and environmental losses in the coming decades 10-15. For example, the economic losses due to flooding alone in coastal cities is expected to be around US $ 1 Trillion by 2050 16. Similarly, the cost of forced migration due to just sea level rise (SLR) driven coastal erosion over the 21 st century is also expected to be around US $ 1 Trillion 2. Effective coastal planning/management strategies that can help circumvent such losses through adaptation require reliable projections of coastal change. This perspective addresses the question of how we may obtain such projections using numerical modelling techniques.

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Section: Climate Change Driven Coastal Changementioning

confidence: 99%

On the need for a new generation of coastal change models for the 21st century

Ranasinghe

2020

Sci Rep

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“…Understanding the climate of extreme water levels is important for coastal protection, particularly as climate change affects wind and weather conditions and sea level rise, subsequently modifying extreme water levels (McInnes et al, 2016;Vitousek et al, 2017;Vousdoukas et al, 2018;Wong et al, 2014). The contributing processes to water level extremes include ocean basin scale steric and barotropic sea levels, astronomical tide, atmospheric forced coastal storm surge, wind wave-driven wave setup, and wave runup, each of which can occur in isolation or coincidentally.…”

Section: Introductionmentioning

confidence: 99%

Extreme Water Levels for Australian Beaches Using Empirical Equations for Shoreline Wave Setup

et al. 2019

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Empirical equations for wave breaking and wave setup are compared with archived shoreline wave setup measurements to investigate the contribution of wind waves to extreme Mean Total Water Levels (MTWL, the mean height of the shoreline), for natural beaches exposed to open ocean wind waves. A broad range of formulations is compared through linear regression and quantile regression analysis of the highest measured values. Shoreline wave setup equations are selected based on the availability of local beach slope data and the ability of the quantile regression to show a good representation of the highest measured levels. Wave parameters from an existing spectral wave hindcast are used as input to the selected equations and are combined with a storm tide time series to quantify the relative contribution of shoreline wave setup to the extreme MTWL climate along Australian beaches. A multipass analysis is provided to understand the ability to capture the shoreline wave setup estimates with and without considering beach slope. The national scale analysis which does not include beach slope indicates there are multiple contributing factors to MTWL. Examples are provided at two locations of differing local beach slope to show the importance of including local beach slope in determining the contribution of waves to MTWL. A tool is in development for further investigation of wave setup for Australian beaches.

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“…On the other hand, coastal scientists ask for ever more frequent observations of sand and shoreline displacements to better understand the underlying physics and to improve the accuracy of prediction tools. Demand will likely increase with rising sea level and changing storm patterns [2,3], which has fostered the development and application of new surveying methods [4][5][6], and the so-called Blue Economy [7] is a chance to contribute to this effort.…”

Section: Introductionmentioning

confidence: 99%

Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

Nahon

Molina²,

Blázquez³

et al. 2019

Remote Sensing

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Recurrent monitoring of sandy beaches and of the dunes behind them is needed to improve the scientific knowledge on their dynamics as well as to develop sustainable management practices of those valuable landforms. Unmanned Aircraft Systems (UAS) are sought as a means to fulfill this need, especially leveraged by photogrammetric and LiDAR-based mapping methods and technology. The present study compares different strategies to carry UAS photogrammetric corridor mapping over linear extensions of sandy shores. In particular, we present results on the coupling of a UAS with a mobile laser scanning system, operating simultaneously in Cap Ferret, SW France. This aerial-terrestrial tandem enables terrain reconstruction with kinematic ground control points, thus largely avoiding the deployment of surveyed ground control points on the non-stable sandy ground. Results show how these three techniques-mobile laser scanning, photogrammetry based on ground control points, and photogrammetry based on kinematic ground control points-deliver accurate (i.e., root mean square errors < 15 cm) 3D reconstruction of beach-to-dune transition areas, the latter being performed at lower survey and logistic costs, and with enhanced spatial coverage capabilities. This study opens the gate for exploring longer (hundreds of kilometers) shoreline dynamics with ground-control-point-free air and ground mapping techniques.coverage [11]. Indeed, spatial coverage of airborne LiDAR is unmatched [4]. Nonetheless, it involves technological, operational and logistic costs that prevent fast acquisition of pre-and post-storm data for instance. On the other hand, UAS photogrammetric surveys [10,12] are easier to perform, and incorporating post-processed or real-time differential GNSS processing techniques (PPK/RTK) and low-cost, low-to-medium tactical grade Inertial Measurement Units (IMU) implies a reduction in the use of surveyed Ground Control Points (GCPs) [13,14]. However, concerning linear coastal landforms, GCPs remain largely necessary to mitigate accuracy degradation along the corridor, i.e., "bowl effects" [15,16]. This constraint hampers the use of UAS over hundreds of kilometers of shore, as for instance, to survey long sandy beaches after storms [17].Over the last decade, Mobile Laser Scanning (MLS) has emerged as a credible alternative for surveying coastal beaches and foredunes [18][19][20][21]. MLS mainly consist of one or several terrestrial laser scanners mounted on a moving vehicle together with a medium-to-high grade GNSS or IMU/GNSS system. Along the vehicle path, scanners collect 3D point clouds with an accuracy comparable to airborne LiDAR standards at reduced technological and logistic costs. Typical achieved point cloud densities are over 100 points per m 2 , therefore offering a valuable opportunity to study foredune growth with high resolution [20]. Yet, from a coverage standpoint, steep dune slopes create shadow areas and leave large portions of the dune top unscanned. This limitation gives rise to one of the core value proposit...

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Coastal Systems and Low-Lying Areas

Cited by 27 publications

References 227 publications

On the need for a new generation of coastal change models for the 21st century

On the need for a new generation of coastal change models for the 21st century

Extreme Water Levels for Australian Beaches Using Empirical Equations for Shoreline Wave Setup

Corridor Mapping of Sandy Coastal Foredunes with UAS Photogrammetry and Mobile Laser Scanning

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