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Discussion papers
https://doi.org/10.5194/essd-2019-71
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-71
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data description paper 27 May 2019

Data description paper | 27 May 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

Global distribution of nearshore slopes with implications for coastal retreat

Panagiotis Athanasiou1,2, Ap van Dongeren1,5, Alessio Giardino1, Michalis Vousdoukas3,4, Sandra Gaytan-Aguilar1, and Roshanka Ranasinghe5,2,1 Panagiotis Athanasiou et al.
  • 1Deltares, Delft, The Netherlands
  • 2Water Engineering and Management, Faculty of Engineering Technology, University of Twente, Enschede, The Netherlands
  • 3European Commission, Joint European Research Centre (JRC), Ispra, Italy
  • 4Department of Marine Sciences, University of the Aegean, Mitilene, Greece
  • 5IHE Delft Institute for Water Education, Delft, The Netherlands

Abstract. Nearshore slope, defined as the cross-shore gradient of the subaqueous profile, is an important input parameter which affects hydrodynamic and morphological coastal processes. It is used in both local and large-scale coastal investigations. However, due to unavailability of data, most studies, especially those that focus on continental or global scales, have historically adopted a uniform nearshore slope. This simplifying assumption could however have far reaching implications for predictions/projections thus obtained. Here, we present the first global dataset of nearshore slopes with a resolution of 1 km at almost 620,000 points along the global coastline. To this end, coastal profiles were constructed using global topo-bathymetric datasets. The results show that the nearshore slopes vary substantially around the world. An assessment of sea level rise (SLR) driven coastline recession (for an arbitrary 0.5 m SLR) with a globally uniform coastal slope of 1:100, as done in previous studies, and with the spatially variable coastal slopes computed herein shows that, on average, the former approach would under-estimate coastline recession by about 40 %, albeit with significant spatial variation. The final dataset has been made publicly available at https://doi.org/10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d.

Panagiotis Athanasiou et al.
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Global distribution of nearshore slopes P. Athanasiou, A. van Dongeren, A. Giardino, M. Vousdoukas, S. Gaytan-Aguilar, and R. Ranasinghe https://doi.org/10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d

Panagiotis Athanasiou et al.
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Short summary
This dataset provides the spatial distribution of nearshore slopes at a resolution of 1km along the global coastline. The calculation was based on available global topo-bathymetric datasets and ocean wave reanalysis. The calculated slopes show skill in capturing the spatial variability of the nearshore slopes when compared against local observations. The importance of this variability is presented with a global coastal retreat assessment for an arbitrary sea level rise scenario.
This dataset provides the spatial distribution of nearshore slopes at a resolution of 1km along...
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