Uncertainties in projections of sandy beach erosion due to sea level rise: an analysis at the European scale

Athanasiou, Panagiotis; Dongeren, Ap van; Giardino, Alessio; Vousdoukas, Michalis; Ranasinghe, Roshanka; Kwadijk, Jaap

doi:10.1038/s41598-020-68576-0

Cited by 61 publications

(34 citation statements)

References 57 publications

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“…In principle, the extra-probabilistic framework can be used with any shoreline change model. In this study, we adopt the perspective of coastal adaptation practitioners that generally rely on empirical models that extrapolate observed shoreline changes to anticipate better their future evolution (Peter et al, 2003;Le Cozannet et al, 150 2019a;Vousdoukas et al, 2020;Cowell et al, 2003). In the absence of estuaries or other major sediment sources or sinks, our empirical model expresses shoreline change ΔS following Eq.…”

Section: Setting-up Shoreline Change Projections Within the Extra-promentioning

confidence: 99%

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

Thiéblemont¹,

Cozannet²,

Rohmer³

et al. 2021

Preprint

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Abstract. Global mean sea-level rise and its acceleration are projected to aggravate coastal erosion over the 21st century, which constitutes a major challenge for coastal adaptation. Projections of shoreline retreat are highly uncertain, however, namely due to deeply uncertain mean sea-level projections and the absence of consensus on a coastal impact model. An improved understanding and a better quantification of these sources of deep uncertainty are hence required to improve coastal risk management and inform adaptation decisions. In this work we present and apply a new extra-probabilistic framework to develop shoreline change projections of sandy coasts that allows considering intrinsic (or aleatory) and knowledge-based (or epistemic) uncertainties exhaustively and transparently. This framework builds upon an empirical shoreline change model to which we ascribe possibility functions to represent deeply uncertain variables. The model is applied to two local sites in Aquitaine (France) and Castellón (Spain). First, we validate the framework against historical shoreline observations and then develop shoreline change projections that account for possible (although unlikely) low-end and high-end mean sea-level scenarios. Our high-end projections show for instance that shoreline retreats of up to 200 m in Aquitaine and 130 m in Castellón are plausible by 2100, while low-end projections revealed that 58 m and 37 m modest shoreline retreats, respectively, are also plausible. Such extended intervals of possible future shoreline changes reflect an ambiguity in the probabilistic description of shoreline change projections, which could be substantially reduced by better constraining SLR projections and improving coastal impact models. We found for instance that if mean sea-level by 2100 does not exceed 1 m, the ambiguity can be reduced by more than 50 %. This could be achieved through an ambitious climate mitigation policy and improved knowledge on ice-sheets.

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Section: Setting-up Shoreline Change Projections Within the Extra-promentioning

confidence: 99%

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

Thiéblemont¹,

Cozannet²,

Rohmer³

et al. 2021

Preprint

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“…It can thus be seen that in the last two centuries, there has been an intense process of littoralization of human societies, whose development is exponential. The coastal area in question is based, directly and indirectly, on the exploitation of marine resources, especially those from coastal areas [10].…”

Section: International Journal Of Environmental Sciences and Natural Resourcesmentioning

confidence: 99%

“…Sediment starvation at coastal areas is amplified by mining activities at the river basins, estuaries, and harbors, for navigational and construction purposes [5,11]. Past and current sea level rise is a minor cause of shoreline retreat at sand shores, and probably only contributes 10-15% to actual shoreline retreat [10]. In the face of these pressures and the many activities that have arisen, coastal areas have become highly conflicting areas, where port interests compete with traditional activities (artisanal fishing, agriculture), where the economic interests associated with real estate compete with environmental conservation, where mass tourism competes with the maintenance of the cultural values of locals, where fixed coastal protection works compete with natural landscape values, where extreme sports compete with bathing practices, where industrial activities compete with nature tourism.…”

Section: International Journal Of Environmental Sciences and Natural Resourcesmentioning

confidence: 99%

Coastal Erosion: from Coastal Natural Resources Loss to Territorial Imbalances

Almeida

2021

IJESNR

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This mini-review addresses the Coastal erosion dilemma with which governments seek to deal. The problem stems from coastal attractiveness to society since coastal regions are rich in biodiversity, provide food, transportation, and recreation, appealing to people and economic activities. Consequently, they are becoming densely populated areas which is determinant to the coastal erosion escalation. The growth and diversity of activities located in coastal regions that directly or indirectly depend on them have led to increasing pressures with damaging ecosystems and accelerating coastal erosion. Damming, dredging, inert extraction, fluvial and marine engineering works, and coastal zones' urbanization have all caused profound imbalances in the natural systems. Among other things, these imbalances have reduced the number of sediments arriving at the littoral, contributing to coastal ecosystems' degradation and diminishing their resilience in extreme weather events. On the other hand, if some of those pressures result from societies' development and highly urbanized coastal areas, others are directly related to natural processes potentiated by global warming. In any of these situations, the path drawn is that of leading to degradation and over-utilization, jeopardising coastal natural resources. This paper aims to present some results and details of an ongoing research project on the social-economic and environmental impact of coastal erosion in a Portuguese coastal urban area.

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“…Following the same visualisation, Swart et al (2015) analysed of the influence of internal variability on Arctic sea-ice extent (RCPs-GCMs-realisation) and, more recently, Fernández et al (2019) presented a research work on seasonal precipitation and temperature changes and their dependence on GCMs and RCMs, realisations, emission scenarios or RCPs, and resolution. While studies on projections of coastal impacts and, in particular, of coastal erosion, have shown progress in the quantification of the relative contribution of uncertainty dimensions to the total uncertainty (e.g., Le Cozannet et al, 2019;Athanasiou et al, 2020), they mainly focus on the application of variancebased decomposition methods and mostly limit the top-down approach-related sources of uncertainty considered to RCPs, SLR, and CEMs, and do not provide neither a conception nor a visualisation of the full cascade.…”

Section: Introductionmentioning

confidence: 99%

Visualising the Uncertainty Cascade in Multi-Ensemble Probabilistic Coastal Erosion Projections

et al. 2021

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Future projections of coastal erosion, which are one of the most demanded climate services in coastal areas, are mainly developed using top-down approaches. These approaches consist of undertaking a sequence of steps that include selecting emission or concentration scenarios and climate models, correcting models bias, applying downscaling methods, and implementing coastal erosion models. The information involved in this modelling chain cascades across steps, and so does related uncertainty, which accumulates in the results. Here, we develop long-term multi-ensemble probabilistic coastal erosion projections following the steps of the top-down approach, factorise, decompose and visualise the uncertainty cascade using real data and analyse the contribution of the uncertainty sources (knowledge-based and intrinsic) to the total uncertainty. We find a multi-modal response in long-term erosion estimates and demonstrate that not sampling internal climate variability’s uncertainty sufficiently could lead to a truncated outcomes range, affecting decision-making. Additionally, the noise arising from internal variability (rare outcomes) appears to be an important part of the full range of results, as it turns out that the most extreme shoreline retreat events occur for the simulated chronologies of climate forcing conditions. We conclude that, to capture the full uncertainty, all sources need to be properly sampled considering the climate-related forcing variables involved, the degree of anthropogenic impact and time horizon targeted.

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Uncertainties in projections of sandy beach erosion due to sea level rise: an analysis at the European scale

Cited by 61 publications

References 57 publications

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

Deep uncertainties in shoreline change projections: an extra-probabilistic approach applied to sandy beaches

Coastal Erosion: from Coastal Natural Resources Loss to Territorial Imbalances

Visualising the Uncertainty Cascade in Multi-Ensemble Probabilistic Coastal Erosion Projections

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