55 research outputs found
Multi-decadal shoreline change in coastal natural world heritage sites – a global assessment
No full textNatural World Heritage Sites (NWHS), which are of Outstanding Universal Value, are increasingly threatened by natural and anthropogenic pressures. This is especially true for coastal NWHS, which are additionally subject to erosion and flooding. This paper assesses shoreline change from 1984 to 2016 within the boundaries of 67 designated sites, providing a first global consistent assessment of its drivers. It develops a transferable methodology utilising new satellite-derived global shoreline datasets, which are classified based on linearity of change against time and compared with global datasets of geomorphology (topography, land cover, coastal type, and lithology), climate variability and sea-level change. Significant shoreline change is observed on 14% of 52 coastal NWHS shorelines that show the largest recessional and accretive trends (means of -3.4 m yr
-1 and 3.5 m yr
-1, respectively). These rapid shoreline changes are found in low-lying shorelines (<1 m elevation) composed of unconsolidated sediments in vegetated tidal coastal systems (means of -7.7 m yr
-1 and 12.5 m yr
-1), and vegetated tidal deltas at the mouth of large river systems (means of -6.9 m yr
-1 and 11 m yr
-1). Extreme shoreline changes occur as a result of redistribution of sediment driven by a combination of geomorphological conditions with (1) specific natural coastal morphodynamics such as opening of inlets (e.g. Río Plátano Biosphere Reserve) or gradients of alongshore sediment transport (e.g. Namib Sea) and (2) direct or indirect human interferences with natural coastal processes such as sand nourishment (e.g. Wadden Sea) and damming of river sediments upstream of a delta (e.g. Danube Delta). The most stable soft coasts are associated with the protection of coral reef ecosystems (e.g. Great Barrier Reef) which may be degraded/destroyed by climate change or human stress in the future. A positive correlation between shoreline retreat and local relative sea-level change was apparent in the Wadden Sea. However, globally, the effects of contemporary sea-level rise are not apparent for coastal NWHS, but it is a major concern for the future reinforcing the shoreline dynamics already being observed due to other drivers. Hence, future assessments of shoreline change need to account for other drivers of coastal change in addition to sea-level rise projections. In conclusion, extreme multi-decadal linear shoreline trends occur in coastal NWHS and are driven primarily by sediment redistribution. Future exacerbation of these trends may affect heritage values and coastal communities. Thus shoreline change should be considered in future management plans where necessary. This approach provides a consistent method to assess NWHS which can be repeated and help steer future management of these important sites.
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Sandy coastlines under threat of erosion
No full textSandy beaches occupy more than one-third of the global coastline1 and have high socioeconomic value related to recreation, tourism and ecosystem services2. Beaches are the interface between land and ocean, providing coastal protection from marine storms and cyclones3. However the presence of sandy beaches cannot be taken for granted, as they are under constant change, driven by meteorological4,5, geological6 and anthropogenic factors1,7. A substantial proportion of the world’s sandy coastline is already eroding1,7, a situation that could be exacerbated by climate change8,9. Here, we show that ambient trends in shoreline dynamics, combined with coastal recession driven by sea level rise, could result in the near extinction of almost half of the world’s sandy beaches by the end of the century. Moderate GHG emission mitigation could prevent 40% of shoreline retreat. Projected shoreline dynamics are dominated by sea level rise for the majority of sandy beaches, but in certain regions the erosive trend is counteracted by accretive ambient shoreline changes; for example, in the Amazon, East and Southeast Asia and the north tropical Pacific. A substantial proportion of the threatened sandy shorelines are in densely populated areas, underlining the need for the design and implementation of effective adaptive measures.Accepted Author ManuscriptCoastal Engineerin
Morphodynamic acceleration techniques for multi-timescale predictions of complex sandy interventions
No full textThirty one percent (31%) of the world's coastline consists of sandy beaches and dunes that form a natural defense protecting the hinterland from flooding. A common measure to mitigate erosion along sandy beaches is the implementation of sand nourishments. The design and acceptance of such a mitigating measure require information on the expected evolution at time scales from storms to decades. Process-based morphodynamic models are increasingly applied, together with morphodynamic acceleration techniques, to obtain detailed information on this wide scale of ranges. This study shows that techniques for the acceleration of the morphological evolution can have a significant impact on the simulated evolution and dispersion of sandy interventions. A calibrated Delft3D model of the Sand Engine mega-nourishment is applied to compare different acceleration techniques, focusing on accuracy and computational times. Results show that acceleration techniques using representative (schematized) wave conditions are not capable of accurately reproducing the morphological response in the first two years. The best reproduction of the morphological behavior of the first five years is obtained by the brute force simulations. Applying input filtering and a compression factor provides similar accuracy yet with a factor five gain in computational cost. An attractive method for the medium to long time scales, which further reduces computational costs, is a method that uses representative wave conditions based on gross longshore transports, while showing similar results as the benchmark simulation. Erosional behavior is captured well in all considered techniques with variations in volumes of about 1 million m3 after three decades. The spatio-temporal variability of the predicted alongshore and cross-shore distribution of the morphological evolution however have a strong dependency on the selected acceleration technique. A new technique, called 'brute force merged', which incorporates the full variability of the wave climate, provides the optimal combination of phenomenological accuracy and computational efficiency (a factor of 20 faster than the benchmark brute force technique) at both the short and medium to long time scales. This approach, which combines realistic time series and the mormerge technique, provides an attractive and flexible method to efficiently predict the evolution of complex sandy interventions at time scales from hours to decades.Coastal Engineerin
Ultrasoon torsielassen van aluminiumlegeringen: Nauwkeurigheid en sterkte van de lasverbinding
No full textTechnische MateriaalwetenschappenApplied Science
Reply to: Sandy beaches can survive sea-level rise
No full textinfo:eu-repo/semantics/publishedVersio
Short Term Morphological Impact of the Eierlandsedam
No full textAfter decades of coastal erosion in the north-west area of the Island of Texel, the shore-normal `Eierlandse Dam` had been built in 1995. The dam, situated in the outer delta of the Eierlandse Gat tidal inlet, resulted in a remarkable short-term morphological development of the adjacent coast. Four years following completion of the dam, large sedimentation rates were measured on both sides of the dam. On the updrift (south) side, sedimentation was predicted as a result of the blocked alongshore sediment transport. However, on the downdrift (north) side of the dam, no sedimentation was predicted. Today, almost two decades later, it remains unclear which processes contributed to the sediment accumulation on the north side of the dam. The increased functionalities and capabilities of the present-day modelling software enable a re-evaluation of the morphological processes around the dam in particular, and gain insights in the complex short-term morphodynamics in this area. To capture the driving mechanism(s) of the net sediment transport towards the northern area of the Eierlandse dam, the state-of-the-art process-based computational model Delft3D is applied. To investigate the influence of various conditions and processes on the morphological development around the dam, simulations are performed with various boundary conditions, model processes and formulations. After calibration of the model, one-year morphological predictions show large similarities with the observed bed level development at both sides of the dam. The ebb tidal currents seem responsible for the large amounts of sedimentation at the north side of the dam, predominantly during spring tides when flow velocities and tidal excursion increase. The ebb tidal channel `Robbengat` is located along the northern tip of the Island of Texel and curves from the inlet around the Eierlandse dam. The Robbengat channel has been eroding by strong ebb tidal currents since 1985. The eroded sediments of the channel are transported by the flow towards the outer delta. Before the channel curves, the flow is partly deflected towards the northern area of the Eierlandse dam. The flow enters a shallow area and decelerates, resulting in deposition of sediment. This conclusion rejects the conclusions drawn by previous studies regarding the same area, where complex hydrodynamics such as eddy forming and spiral flow in the channel bend were drawn as possible causes of the sedimentation.Hydraulic EngineeringCivil Engineering and Geoscience
Permeability-Depth (K-Z) Database by Ranjram, Gleeson, and Luijendijk (2015).
No full text<p>/\/\/\/A Modest Permeability-Depth Database\/\/\/\</p>
<p>Assembled by Mark Ranjram, MSc Candidate, McGill University, 2014.<br>contact: [email protected]</p>
<p>Reference:<br>Ranjram M, Gleeson T & Luijendijk E (2015) Is the permeability of crystalline rock in the shallow crust related to depth, lithology or tectonic setting? Geofluids, 15,106-119.<br>--------------------------------------------------------------------</p>
<p>The database is provided in its entirety in the excel file "Ranjram_Gleeson_Luijendijk_KZ_Database.xls"</p>
<p>The cleanest representation of the data is provided in the sheet labelled "Main Database."</p>
<p>--------------------------------------------------------------------</p>
<p>The excel file is composed of five Sheets:</p>
<p>1. "Table of Contents"<br>This sheet provides a brief description of each sheet.</p>
<p>2. "Data Grab"<br>This sheet presents the raw data which makes up the database. The information here is roughly presented and should only be used if the user thinks there is an error in the main database (Although contacting the author is probably the best first step to take if an error is apparent).</p>
<p>3. "Methods"<br>This sheet provides a qualitative description of the data assembled from each reference in the main database. A Y/N indicator is provided for each reference describing whether the data are included in the main database (Volcanic rocks are excluded from the main database). Individual depth-permeability data are listed for references which provide a compilation of permeability-depth values. An 'x' under Salinity and Temperature indicates that the reference provides measurements of Hydraulic Conductivity but has no indication of tempearture or salinity with depth. Salinity-depth and temperature-depth relationships for these data are inferred from salinity-depth and temperature-depth relationships provided by studies in similar or nearby regions. Where possible, a "root reference" is provided if the referenced study collected its permeability data from another study.</p>
<p>4. "Main Database"<br>This sheet provides the cleanest presentation of the assembled data and is the main sheet in this file. This sheet indicates the Location, reference, rock type, depth, and permeability information for every point included in our study. All conductivity values are converted to permeability in units of m² and each conversion is described in the sheet. The columns labelled "RESULTS/WORKING VALUES" provide the information analyzed and considered in our study. "k error" describes the range of uncertainty in permeability centred at the "working value" of permeability. "depth error" provides the depth range over which the permeability measurement is taken, with the range centred at the value in the "Depth" column. The "BOUNDED DEPTH DATA" columns explicitly indicate the upper and lower depths at which the corresponding permeability measurement was made.</p>
<p>5. "Jump Off Sheet"<br>This sheet lists the data in the Main Database into sortable columns of Location, Reference, Lithology, permeability-depth values, and maximum depth. This sheet is useful for sorting and extracting desired permeability values. The columns filled in green are the "RESULTS/WORKING VALUES" described in the "Main Database" sheet. The Max Depth column indicates the deepest depth associated with each point, and our study excluded points deeper than 2.5 km.</p>
<p> </p
Shoreline change in coastal Natural World Hertiage Sites
No full textThis repository contains the data associated with the paper: Multi-decadal shoreline change in coastal Natural World Heritage Sites - a global assessment (http://doi.org/10.1088/1748-9326/ab968f).
It contains:
- 2020_transects_data_shorelines_linearity_geomorphology_final.csv: a dataset of transect-based shoreline and geomorphological datasets in 67 coastal Natural World Heritage Sites with available shoreline data.
-2020_transects_data_shorelines_strong_linear_geomorphology_final.csv: a dataset of transect-based strong linear trends (recessional, depositional and stable) and geomorphological datasets in 59 coastal Natural World Heritage Sites with strong linear behaviour.
Shorelines were derived from a global assessment of derived Landsat images (Luijendijk, A. et al.,2018, https://doi.org/10.1038/s41598-018-24630-6, Hagenaars, G. et al., 2018, 10.1016/j.coastaleng.2017.12.011 and Hagenaars, G. et al., 2017, http://resolver.tudelft.nl/uuid:34a0114b-5e39-4b52-9940-3a7e9f5a2982). Conditional and outlier data cleaning were undertaken on the raw shoreline dataset for further analysis.
The geomorphological data have been obtained from the following datasets:
Topography: Global Map DEM (2017) - https://globalmaps.github.io/
Land cover: Global Land Cover by National Mapping Organisations - GLCNMO (Kobayashi, T. et al., 2013, 10.5539/jgg.v9n3p1)
Coastal type: Worldwide Typology of Nearshore Coastal Systems (Dürr, H. H. et al., 2011, https://doi.org/10.1007/s12237-011-9381-y)
Lithology: Global Lithological Map - GliM (Hartmann, J. & Moosdorf, N., 2012, https://doi.org/10.1029/2012GC004370)
For each perpendicular transect to the satellite-derived shorelines, a set of data is provided in the table columns:
transect_id: unique identifier of the transect
Continent: continent of the transect
country_name: country of the transect
Intersect_lon: the longitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).
Intersect_lon: the latitude of the middle point of the transect drawn between the first available shoreline (1984 or more) and the latter available shoreline (2016).
NAME: name of the coastal Natural World Heritage Site
ORIG_NAME: the original name of the coastal Natural World Heritage Site
INT_CRIT: criteria of selection of the coastal Natural World Heritage Site
dt_max: is the latest year for which SDS data points are available (2016)
dt_min: is the first year for which SDS data points are available (starts from 1984)
nb_shorelines: the number of shorelines available from dt_min to dt_max
cor: Pearson’s correlation coefficient (r)
p.value: Pearson’s correlation coefficient (r) p-value
cor_fact: classification of Pearson’s correlation coefficient (r)
0: Non-linear (-0.3 to 0.3).
1: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)
2: Strong linear (less than -0.7 or greater than 0.7)
coeflm: Ordinary Least Square linear regression rate (m yr-1)
std: standard deviation of the Ordinary Least Square linear regression rate (m yr-1)
Mean_DEM_Class: topography
1: 0 ≤ elevation ≤ 1 m (extremely low-lying)
2: 1 < elevation ≤ 10 m (low-lying)
3: 10 < elevation ≤ 50 m (middle)
4: 50 < elevation ≤ 400 m (high)
5: No data (transects without available elevation)
GLIM_categories: lithology
ev: Evaporites
ig: Polar ice and Glaciers
pa: Acid Plutonic Rocks
pb: Basic-Ultrabasic Plutonic Rocks
pi: Intermediate Plutonic Rocks
mt: Metamorphic Rocks
sc: Carbonate Sedimentary Rocks
sm: Mixed Sedimentary Rocks
ss: Siliciclastic Sedimentary Rocks
su: Unconsolidated Sediments
py Pyroclastic
va: Acid Volcanic Rocks
vb: Basic Volcanic Rocks
vi: Intermediate Volcanic Rocks
Land_Cover_Class: land cover
1: Coral reefs
2: Mangroves
3: Marshes
4: Vegetated
5: Non-vegetated
6: Urban areas
Coastal_type_Class_1: coastal types
1: Small deltas
2: Tidal systems
3: Lagoons
4: Fjords and fjärds
5: Large rivers
6: Large rivers with tidal influence
7: Karst-dominated stretches of coasts
8: Arheic (dry areas)
9: Islands
cor_classes_1: classification of Pearson’s correlation coefficient (r)
StrongLin: Strong linear (less than -0.7 or greater than 0.7)
WeakLin: Weak linear ( -0.7 to -0.3 or 0.3 to 0.7)
NonLin: Non-linear (-0.3 to 0.3).
shoreline_trend_classes: Ordinary Least Square linear regression rate (m/y) for strong linear shoreline behaviours
StrongLinSta: stable - shoreline change rate between -0.5 and 0.5 m yr-1
StrongLinAcr: depositional - shoreline change rate >0.5 m yr-1
StrongLinErr: recessional - shoreline change rate <-0.5 m yr-
A global analysis of how human infrastructure squeezes sandy coasts - Scripts & Data
No full text<p><strong>Abstract</strong></p><p>Coastal ecosystems provide vital services, but human disturbances cause massive losses. In particular the nearshore development of infrastructure constrains the space for these ecosystems to adapt to shoreline retreat – called <i>coastal squeeze</i>. Nevertheless, coastal squeeze through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide and show that infrastructure is situated at a median distance of 392 meter from sandy shorelines. 33% of sandy shores harbour less than 100 m of infrastructure-free space, and projections suggest that 23-30% of the infrastructure-free space will be lost by 2100 due to rising sea levels. Additionally, population density and gross domestic product explain 35-39% of observed squeeze variation, emphasizing the increasing pressures imposed as countries develop and populations expand. Although sandy shores are 4-7 times less squeezed in nature reserves, only 16% of world's sandy shores has a protected status. We therefore advocate incorporating protection in spatial planning to safeguard these critical ecosystems.</p><p>==============================================</p><p><strong>Methods</strong></p><p>The analyses rely on the following freely available datasets:</p><ul><li>OpenStreetMap - streets: <a href="https://www.openstreetmap.org/#map=7/52.154/5.295">https://www.openstreetmap.org/#map=7/52.154/5.295</a> </li><li>OpenStreetMap - shoreline: <a href="https://osmdata.openstreetmap.de/data/land-polygons.html">https://osmdata.openstreetmap.de/data/land-polygons.html</a></li><li>Global Urban Footprint: <a href="https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/">https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/</a></li><li>World population: <a href="https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b">https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b</a></li><li>GDP per capita: <a href="https://data.worldbank.org/indicator/NY.GDP.PCAP.CD">https://data.worldbank.org/indicator/NY.GDP.PCAP.CD</a></li><li>Protected areas: <a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</a></li><li>Projected shoreline change: <a href="https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e">https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e</a></li><li>CoastalDEM: <a href="https://www.climatecentral.org/coastaldem-v2.1">https://www.climatecentral.org/coastaldem-v2.1</a></li></ul><p>In addition, we requested the sandy shoreline data from:</p><ul><li>Luijendijk et al. (2018) "The State of the World's Beaches", Scientific Reports 8: 6641; <a href="https://www.nature.com/articles/s41598-018-24630-6">https://www.nature.com/articles/s41598-018-24630-6</a></li></ul><p>The deposited folder contains 4 subfolders based on the separate analyses presented in the paper. In each subfolder you find a Matlab script to run and accompanying datasets to load. A readme file is included, which further explains the scripts and datasets.</p><p><strong>Source data file</strong></p><p>In the data file <i>Source_Data.xlsx</i>, each sheet contains the data for one figure or table of the manuscript.</p>
A parametric study concerning estuary mouth dynamics and inlet closure
No full textWith the development of a process-based model (Delft3D) of the St Lucia Estuary inlet, a first approach is made with regard to the estuary mouth dynamics and closure mechanisms that are observed at St Lucia inlet. The purpose of this thesis is to get a better understanding of the hydrodynamic and morphological behaviour of the St Lucia inlet with the additional effect of the Mfolozi River discharge. The focus in this thesis is on the period after 2001 till present, where the management policy is to let the St Lucia inlet function in its natural state and with the possibility to join with the Mfolozi River. A model with a schematized situation of the estuary with representative inlet geometry is forced with representative waves and tide conditions. Important factors determining the inlet stability such as tidal prism, longshore sediment transport, inlet geometry and river discharge are investigated in this thesis. The Mfolozi River mouth and St Lucia Estuary entrance are situated in a seasonal varying climatic regime with long drought periods with low riverine flows followed by wet periods and cyclonic events. A high energy wave climate in combination with a micro-tidal regime and a high rate of longshore sediment transport are the most important factors of the instability of the St Lucia inlet. According to Bruun (1978) inlets that are classified with a P/M ratio below twenty are found to be unstable and the inlet may be closed by deposition of sediment during a storm event because the tidal prism is relative small. In line with Bruun, the St Lucia inlet can be classified as an unstable inlet with a low P/M ratio of approximately two. Three scenarios were developed with different estuary dimensions; a small, a medium and a large basin. The inlet geometry is the same in the scenarios and each scenario is modelled with five different simulations. The simulations are forced at the boundaries by a varying range of tide and wave conditions. The tide is varied from average to neap and spring tide. The wave height is varied from average to higher and extreme wave heights. Higher waves are responsible for a higher rate of longshore sediment transport and with both varying tide and wave conditions a wide range of P/M ratios are modelled. In addition the influence of a lower D50 was investigated, and the influence of the Mfolozi River was simulated. The results of the simulations show that they are in line with expectations. Small P/M ratios show that inlets are unstable and different closure mechanisms are observed. Similar to what is found in nature regarding the A-P relationship, a decreasing cross-sectional area with a lowering tidal prism, is also found with the Delft3D models which suggest that the model is capable of giving a good representation of the morphodynamics.Hydraulic EngineeringCivil Engineering and Geoscience
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