PreprintPDF Available
Preprints and early-stage research may not have been peer reviewed yet.

Abstract and Figures

The current retreat of the world's coastline has a profound impact on human activities and ecosystems. The scientific community has primarily focused on the potential impact of sea level rise. At the global scale, the contribution of river sand loads to coastal erosion has been overlooked. Here we present the first global sand pathway model from land to sea. Our model reveals that sand tends to accumulate towards tropical regions. We show that the recent shoreline evolution is significantly controlled by the imbalance in the sand budget, challenging the idea that sea level rise due to climate change is currently the main driver of coastal erosion. Our model highlights that the significant reduction in sand supply due to tens of thousands of river dams and its consequences on coastal erosion could be avoided by an effective sustainable management policy.
Content may be subject to copyright.
On the overlooked impact of river dams on beach erosion
Graffin M.1,2,Regard V.1,Almar R.2,Carretier S.1,Maffre P.3
Correponding author : Marcan Graffin (
The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
The manuscript is currently undergoing peer review for the journal Nature Sustainability.
The current retreat of the world’s coastline has a profound impact on human activities and ecosystems. The scientific
community has primarily focused on the potential impact of sea level rise. At the global scale, the contribution of river
sand loads to coastal erosion has been overlooked. Here we present the first global sand pathway model from land to sea.
Our model reveals that sand tends to accumulate towards tropical regions. We show that the recent shoreline evolution is
significantly controlled by the imbalance in the sand budget, challenging the idea that sea level rise due to climate change
is currently the main driver of coastal erosion. Our model highlights that the significant reduction in sand supply due
to tens of thousands of river dams and its consequences on coastal erosion could be avoided by an effective sustainable
management policy.
Coastal zones are dynamic areas at the interfaces between land and sea. These areas concentrate a large part of the world’s
population as well as rich and rare ecosystems. However, human activity severely affects the fragile equilibrium of these
areas, notably by influencing the climate and ecological continuity, thereby weakening coastal ecosystems while increasing
the exposure of populations to natural hazards [1].
Sandy beaches in low-lying coastal areas are currently undergoing particularly dramatic erosion that is destabilizing
coastal socio-ecosystems [2]. In spite of the economic, cultural and environmental interest of coastal zones, the scientific
literature makes little mention of the global drivers of coastline evolution. It is generally assumed a priori that global
and regional oceanic factors such as sea level rise and changes in wave regimes are the primary drivers of beach evolution
[3, 4, 5, 6, 7], predicting unprecedented future beach retreat and disappearance [8]. However, the magnitude of the retreat
and the importance of other factors driving beach changes are currently being debated [9], in particular oceanographic and
geologic processes over a wide range of spatial and temporal scales, all affected by climate and human factors in diverse ways.
These processes include sea level changes, sediment transport by tides, currents and waves, sediment supply from rivers and
land and offshore loss, and coastal uplift or subsidence, among many others [10]:
Coastline Evolution =f unction(Nearshore Transport,Sources,Sinks,Relative Sea Level) (1)
There is a permanent flow of sediment from watersheds to the ocean, delivered by rivers. Sand grains are deposited at
the coast by rivers, while the finer sediment particles are carried further out to sea. Thus, sand that has been temporarily
deposited in the nearshore area around the river outlet is then resuspended by waves. The one-way longshore transport ends
when the sand permanently deposits in the nearshore system or when it flows into a submarine canyon and permanently
leaves the nearshore system. A nearshore hydrosedimentary cell is a distinct area of coastline where sand enters the ocean
and flows along the coast in a single direction. At the convergence of two successive cells, sand can either accumulate or be
removed from the system. The balance of sand available for beaches is the amount of sand entering the littoral cell minus
the amount leaving. If this sand balance is altered, the beach morphology changes.
1GET (Université de Toulouse/CNRS/IRD/UPS), Toulouse, France
2LEGOS (Université de Toulouse/CNRS/IRD/UPS), Toulouse, France
3University of Berkeley, San Francisco, California, The U.S.A
Recently, Nienhuis et al. [11] highlighted the role of river sediment supply for 1000 deltas worldwide who have been
subjected to land loss due to dam building. The proliferation of river and coastal infrastructures hinders sediment transit
and contributes to depriving beaches located away from outlets of an essential supply of sand [12] while deltas[11],[13] and
estuaries[14] are more likely to accrete. At the same time, the sea level rise will accelerate[15] at an unprecedented rate that
will compound the current problems.
Coastal sand pathways at the global scale have not been addressed by the scientific community as of yet. There is a need
for a comprehensive consideration of sand availability as a driver of global coastlines[16]. Given that satellite observations are
now abundant[17, 18], such global studies can be performed. Luijendik et al.[2] analysed thousands of satellite-based shoreline
data from 1984 to 2016; they found that 31% of the world’s ice-free shoreline are sandy, among which 24% are eroding, 28%
are accreting and 48% are stable. In order to predict sandy coast erosion and accretion [8], there is still a need to develop
numerical models based on observations and physical processes[19]. This is the way forward to improve science-based coastal
management strategies so as to effectively mitigate the effect of the inevitable sea level rise due to climate change as well as
human interventions on the sediment pathway[20].
Here, we investigate how the combination of terrestrial sand supply and sand coastal redistribution drive the coastline
evolution at the global scale. We introduce a new integrated sand pathway model, and we quantify the impact of dams on
modern era coastal sand budgets and the observed coastline retreat.
Our global sand pathway model
A numerical ‘along-coast’ sand transport model has been developed and implemented on a global scale along the coastline
which has been segmented into 50 km-long transects. The sand budget of each one of the 11,161 calculated transects is defined
considering the sand mass conservation equation defined above(Figure 1) calculated nearshore from one end of the transect to
the other and from the coast to the depth of closure. The source term corresponds to the solid discharge by rivers (referred to
then as Qriver )[21], and the sink term corresponds to the cross-shore sand transport towards the deep ocean[22, 23, 24]. Last,
a transfer term corresponds to the wave-induced longshore sand transport (referred then as Qwave)[25]. It is the predominant
factor in the transport of sand along open coasts exposed to waves, reaching its maximum in the surf zone which generally
extends offshore for tens of metres to kilometres. Each of these sediment fluxes relies on different physical mechanisms and
thus requires a large amount of data to be estimated. Qriver is calculated from a calibrated erosion law (BQART formula)
depending on the catchment area, mean annual catchment discharge, average temperature and local slope[26, 27] (Methods).
Qwave is calculated from ocean data such as the wave period, wave orientation relative to the coast and the breaking wave
height [25] (Methods). The cross-shore sand transport is estimated from the local depth of closure. The depth of closure
is the maximum depth of significant seaward cross-shore sediment transport by the waves; it provides information on the
fraction of sand flux leaving the coastal system toward the open ocean[23] (Figure 1).
This model predicts patterns in coastal sand accumulation and removal, i.e. accreted or eroded volumes. We do not predict
sandy coast growth or retreat, strictly speaking, because in order to convert the calculated accumulation/removal volumes into
morphological evolution this would require taking into account the relative sea level changes (e.g. sea level rise, subsidence)
and morphodynamic considerations (e.g. beach profile evolution), which would then add unnecessary uncertainties for the
specific question addressed in this contribution.
Tropical sandy beaches resulting from a global convergence of wave-driven sand
The quantification of Qriver is well documented locally at the outlets of the world’s major rivers[21]. It is then possible to
integrate over large areas (e.g. continent) and on the global scale [21, 27]. Using the BQART formula at the global scale
gives a total annual sediment flux of ΣQriver = 15.1Gt/yr, within the range of current estimates[28, 29], corresponding to
5700 ×106m3/yr - considering ρs= 2650 kg/m3. Spread over all the coastal transects, this gives an average of 1,050,000
tons per year per transect, or 400,000 cubic metres per year per transect.
The time-averaged Qwave obtained from oceanic conditions is an estimate of the potential (i.e. maximum) annual flux of
sediment transport along the coast by waves. On a global scale, the longshore sediment transport is structured into coastal
cells (e.g. mainland, islands) along which sediments flow in the same direction. Changes in sediment supply affect the
entire cell. On average, the longshore sediment transport potential is approximately 155,000 cubic metres (410,000 tonnes)
per year, which is the same order of magnitude as the average Qriver. This similarity means that the longshore transport
capacity can globally convey the river input from their outlet all around the coastal cells, leading to sediment accumulation
at the boundary between two adjacent cells, where longshore sediment transport converges. The global latitudinal means of
Qwave show the tendency of waves in the northern and southern hemispheres to transport sediment southward or northward,
respectively. Around the equator, the reversal of the direction of the longshore sediment transport potential represents a
global convergence zone of these transport potentials. On average, on a global scale, waves induce sediment transport from
higher latitudes to the equatorial zone with notably high sediment deposition in tropical areas where the decrease in wave
2 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
Figure 1: Schematic representation of the model. Schematic diagram of the sand budget at the coast for one transect
(centred on one black ellipse). The influx of sand comes from the river (Qriver ) as well as the wave-induced longshore drift
(Qwave,in ). Conversely, sand is lost by the longshore drift (Qwave,out), or the cross-shore sediment transport fraction (Rcross).
A positive or negative budget is indicative of accretion or erosion at the coast, respectively.
activity is most pronounced. (Figure 2).
Figure 2c illustrates that the relative importance of |Qwave |and Qriv er depends on the latitude. Between 15°N and 5°S,
Qriver dominates over |Qwave |whereas |Qwav e |dominates south of 10°S and north of 35°N.
When Qriver <|Qwav e |, the river sediment supply is easily transported away by Qwave. Sediment deposition, e.g. beach
construction, is expected where |Qwave |diminishes. On the contrary, areas where Qriver >|Qwave |may be dominated by
large river sediment discharge, and locally outweighs Qwave , which means that most of the sediment input is either deposited
near the outfall or it drifts out to sea.
From 25°N to 0°(30°S to 0°), there is a consistent gradient in the mean southward (northward) coastal transport, which
means that the southern (northern) component of coastal transport tends to decrease progressively from 25°N to 0°(30°S to
0°). This gradient of transport capacity leads to a progressive deposition of sediments, especially in tropical areas where this
trend is the most pronounced.
At low latitudes (as well as in enclosed seas), the weak wave regime induces a shallow depth of closure[30], which greatly
limits the size of the nearshore zone. Consequently, sediment is more likely to bypass or drift directly from river outlets into
the open ocean[31]. In our model, this results in low values of Rcross at the equator, which lead to low sand budgets despite
high riverine inputs.
These predictions of the distribution of sandy beaches are consistent with observations made from satellite observations
by Luijendijk et al.[2], and thus provide a first explanation for the increased presence of beaches in tropical zones.
River sand supply as the overlooked driver of the modern era global beach
Unlike relative sea level rise, sand supply is rarely mentioned as being potentially responsible for beach erosion dynamics on
a global scale. However, it appears that many coastal sites in the world are subjected to erosion that extends well beyond the
relative rise in sea level, such as the deltas of large rivers, which are deprived of a significant part of their sediment supply
by dams and irrigation networks[11, 32, 33].
In order to quantify the influence of sand input on beach dynamics and to evaluate the sand distribution model, we
compare our results with observed coastline evolution data. As no sand budget database exists on a global scale, we use
the shoreline evolution data from Luijendijk et al.[2] as a proxy. After having automatically extracted the global spatial
distribution of beaches, Luijendijk et al.[2] analysed the annual evolution trend of the coastline (υobs in m/year) and the
yearly variability of the observations around this trend (obs). Note that from now on, our analysis is restricted to the beach
3 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
Figure 2: A global view of fluvial sediment supply and longshore sediment transport. (a) Global geographic
distributions of the river sediment discharge (Qriver ) and (b) absolute longshore sediment transport potential (|Qwave |),
with arrows indicating the direction of transport and circles showing the major convergence areas. (c) 5°resolution latitudinal
averages of Qriver (red line), absolute Qwave (blue line) and Qwav e (blue polygons - positive for northward oriented, negative
for southward oriented). Black arrows show the direction and intensity of the long-shore sediment transport.
areas contoured by Luijendijk et al.[2], which represents roughly one third of the entire transect dataset.
In order to isolate the role of the imbalance in the sand budget from that of continuous mechanisms such as sea level rise
or subsidence, we focus on obs. This variability corresponds to high frequency events (intra to inter-annual), and therefore
cannot be due to mechanisms acting on decadal time scales such as continuous sea level rise or land subsidence. We postulate
that this variability instead reflects variations in the sediment imbalance at a local scale, due to a competition between Qriver
and Qwave .
For the subset of sandy transects where this assumption seems to be true (see Methods), which represents 64% of the
sandy coastlines of the Luijendijk et al.[2] database, the correlation coefficient between υobs and Qis R= 0.588. The
imbalance of the sand budget thus explains half of the variance of the coastal erosion trends for 64% of the transects provided
by Luijendijk et al.[2]. We observed either no correlation or a weaker correlation when considering the entire dataset and
when rocky and sandy coasts are not differentiated (R= 0.381, Table 1 in Methods).
The significant correlation R= 0.588 between Qand υobs for a large majority of global sandy beaches shows that our
sand budget model captures a first order contribution to the current sandy coastline evolution. This contribution is the local
imbalance of coastal sediment flux. Thus, the river sediment supply cannot be neglected when considering the evolution of
beaches at a global scale.
4 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
Figure 3: Modelled sediment imbalance. Global geographic distributions of the modelled sediment imbalance for sandy
coasts for which the high frequency evolution of the coastline is considered to be potentially dominated by the imbalance of
the sand budget (ξ < 0.9, see Methods).
Influences on terrestrial supply: the impact of river dams
Researchers have long discussed the impact of anthropogenic activities on river sediment supply [27, 34]. Surprisingly, its
effect, although described by authors, has not been taken into general consideration and is overlooked by the community
studying beach dynamics.
In order to assess this anthropogenic effect, we have calculated the global picture for a world in which dams trap all
the incoming sediment and for a pristine world without dams (see Methods). These two models are end-members. The
model with dams is extreme, as the sediment trapping efficiency is quite variable, mainly depending on the water residence
time[35, 36], usually >50% and sometimes up to 100% (example of the Aswan Dam on the Nile[37]). The difference between
these two scenarios is shown in Figure 4. It shows that a dam retention capacity of 100% would reduce the overall Qriver
by half, from 600,000 cubic metres in a pristine world to 270,000 cubic metres per year per transect on average in a world
where dams retain all the sediments, thus depriving the coastal system of half of its sediment supply. Note that these values
consistently bracket the current value estimated by the BQART model (400,000 cubic metres per year per transect). In this
tested scenario, 18% of the sandy transects (n= 454, i.e. approximately 23,000 km of coastline) with a neutral or positive
sand budget become sediment deficient once the dams are taken into account. Although extreme, this scenario illustrates the
potential effect of dams and provides pertinent information on the location of coastal areas affected by sedimentary input
loss (Figure 4). Sediment loss is critical for especially large deltas, which are fragile areas mostly formed from river sediment
supply [11, 38, 39].
The effects of river sediment loss sometimes extend beyond the delta area and become regionally pervasive, as is the case
along the Gulf Coast of the United States[40] where the multiplication of dams has led to a significant reduction in the supply
of sediments from the Mississippi River to the coasts. In the last forty years, this has resulted in some of the highest rates of
erosion in the state of Louisiana, as well hundreds of kilometres further to Texas and Florida[41].
One implication of these results is that a local modification in a catchment can have repercussions on the sandy coast evolution
far away along the continental cell.
Limitations and way forward
Although the sand budget plays a primary role in the evolution of the coastline on a global scale, other phenomena, natural
or not, can influence this evolution on different spatial and temporal scales. The contribution of these phenomena is difficult
to quantify because global databases (e.g. high-resolution offshore topographic data or a subsidence map) are still lacking.
Among these various phenomena, the global rise in sea level over the last 100 years has had a visible impact on beaches,
5 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
Figure 4: Dam-induced relative sediment retention. Global geographic distributions of the percentage of decrease in
Qriver due to the presence of dams in catchments, assuming that dams retain all the sediment coming from upstream. The
blue dots represent transects where the sand budget shifts from positive or neutral to a deficit once the dams are taken into
especially along gently sloping low-lying coasts. With a 25 cm rise in the sea level since 1900[1], the sea has gained up to 15
metres on land for beaches with a slope of 1 degree. For the future, with an accelerating sea level rise, studies predict that half
of the world’s beaches could disappear by 2100[8], although this underestimates the potential for coastal resilience[9] due to
sediment availability. Local subsidence due to sediment compaction following freshwater pumping can reach several metres like
in Japan or Indonesia[42], greatly exceeding any other cause for coastline evolution. Abrupt or transient vertical movement
associated with the seismic cycle along subduction zones is another factor that can be responsible for metric to centimetric
vertical uplift or subsidence over a period of years[43]. The whole of these phenomena are not quantified everywhere and
may explain the remaining variance in the current sandy beach trend υobs that is not explained by Q. There is still consid-
erable unacceptable uncertainty about what the world’s coasts will look like at the end of the century under different scenarios.
Nevertheless, our study constitutes a major breakthrough by providing the first evidence that the current trend in sandy
coastal evolution is also controlled by the local imbalance of sand transport in a predictive way. Sediment supplied by rivers
plays a crucial role in this imbalance and any variation in this supply, caused by dams for example, can affect sediment
redistribution along continental cells. Variations in the river sediment supply are dependent on climate change. The rise
in temperature[44] will increase the sediment transport potential of rivers as temperature directly affects the capacity of
the river to erode the bed[45]. The intensification (rarefaction) of precipitation in temperate (arid) zones will lead to an
increase (decrease) in Qriver [46]. Last, the multiplication of extreme climatic events (i.e. strong droughts, monsoons, etc.)
will make the land vulnerable to erosion and thus influence sediment transport; a monsoon after a strong drought mobilizes
large quantities of sediments. Dams are not the only anthropogenic factor to be considered with regards to the evolution of
Qriver . Land use changes also have a critical role to play in the delivery of river sediment supply to the coast. Urbanization
can hamper sediment transport, particularly through the increase in infrastructure along rivers and coasts. Conversely, defor-
estation and land use in general may also help increase Qriver by increasing the exposure of deforested soils to precipitation
and the erodibility of soils that do not have enough tree roots to provide support[47].
Moving toward coastal zone sand management practices at the sand cell level
Our results show that the main threat for sandy coasts may come from a river sediment supply deficit that will be compounded
by a rise in the sea level in the future. While the inertia of a global mean sea level rise is too large to be reversed in the 21st
century, a sediment unbalance occurs on a local-to-regional scale and can either increase or decrease the coastal impacts of
6 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
rising seas [9] Local examples have shown that sandy coasts were rapidly reconstructed after river dam removal [38]. However,
such actions must be based on conservation and integrated policies, a trade-off at the nexus of sustainable coastal areas.
Past experience has shown that effective, site-specific coastal planning can mitigate beach erosion and result in a stable
coastline; the most prominent example of this is the Dutch coast[48]. While sea level rise results in coastal recession al-
most everywhere around the world, many locations have ambient erosive trends related to human interventions that could
theoretically be avoided by more sustainable coastal and watershed management practices[49, 20, 50]. At the same time,
the magnitude of the projected sea level rise implies unprecedented pressure on our coasts, requiring the development and
implementation of informed and effective adaptation measures. At local scales, human activities can also directly affect the
coastline, both in terms of erosion and accretion. Some countries such as China or the Netherlands are undertaking large-scale
works to gain ground on the sea. Conversely, some countries (China, India, the USA, etc.) use their beaches as sand quarries
to supply the construction industry. Land subsidence due to agriculture, mining, or urban development[13, 51], as well as
coastal infrastructure, can be a dominant factor in coastal evolution[42]. This was recently highlighted by the decision to
move the capital of Indonesia, given the impossibility of sustainably protecting Jakarta, the current capital, from marine
flooding [42].
However, all these site-specific mitigation cases have neglected the sediment imbalance that results from larger scale, often
regional, sediment redistribution[52]. Our study strongly suggests that the most efficient management strategies cannot be
limited to a local scale. Our study highlights that a modification of the sediment supply by a river, which generally traverses
several countries, can have repercussions far away along the coast up to thousands of kilometres (e.g. Namibian coast),
depending on the coastal sediment cell. For example, the Bight of Benin, located in the Gulf of Guinea, West Africa, is
under the influence of sediment supplied by the Volta and Niger rivers, and this sediment is redistributed along the coast.
However, several agriculture and hydro-power dams were constructed on these rivers, as well as deep water harbours, blocking
the transport of sediment downstream. Although some countries are implementing expensive mitigation strategies locally, a
collaborative international effort would certainly deliver more benefits [53, 54] with a reduced cost [55]. This teleconnection
must be considered in coastal management. Although current legislation does not take the integrated analysis of continental
and offshore sources of sediments into account, our study suggests that it is possible to act on the evolution of sandy coasts
controlled by the sediment imbalance, for example by managing the sediment retention by dams or adapting land use policies
at a regional-to-continental scale. Given that the change in sediment outflux from one river may impact the shorelines of
another country, we anticipate that integrated and comprehensive approaches such as the one proposed for the first time in
our study could have consequences both for national coastal management policies and for international legislation.
Coastal transects and their mass budget, Q
For the coastline, we use the Global Self-consistent Hierarchical High-resolution Geography Database (GSHHG version 2.3.6
August 17, 2016)[56]. The GSHHG coastline is segmented into points representing 50-km long entities called transects. The
code used to solve the sand imbalance along the coast is a 1D code linking successive transects along the coast by calculating
the following mass budget:
Q=RcrossQin Qout =Rcr oss(Qriver +Qwav e,in)Qwave,out (2)
where Vis the volume of sediment in m3,Rcross is the cross-shore transport rate, Qriver is the annual fluvial solid dis-
charge into the transect in kg/year, and Qwave,in (respectively Qwave,out ) is the wave-induced longshore sediment trans-
port flux, in kg/year, coming from the previous transect (respectively going to the next transect). Rcross is calculated as
Rcross = 2 ×min(1, DoCi/lc)1, where DoCiis the local depth of closure and lcis a characteristic length.
River sediment supply, Qriver
The annual river sediment discharge, represented by the variable Qr iver, has been calculated using the BQART[45] formula
(see Equation 3). It works for catchments and therefore we used it for every catchment flowing to the ocean, as defined in the
HydroBASIN database[57]. In the HydroBASIN database, small streams that drain directly to the coast are aggregated into
entities of the order of 100 km2(max 500 km2). In the absence of a better solution, we applied BQART on these surfaces,
even if BQART has not been validated in this case.
Qriver =ωBQ0.31A0.5R. max(T , 2) (3)
where ω= 0.0006[45], Qriver is the solid river discharge in Mt/year,Tis the average ambient temperature (°C), Qis the
liquid river discharge (km3/year), Ais the drainage area of the catchment (km2) and Ris the relief (i.e. the difference in
elevation from highest catchment point and its outlet, in km). In addition, B=I L(1 TE)EHaccounts for geological as
7 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
well as human factors[45]. In the formulation of B, I is a modulation from glacier erosion: I= 1 + 0.09Agwhere Agis the
percentage area covered by glaciers. L is a lithological factor usually in the range of 0.5 (low erodibility lithology) to 3 (high
erodibility lithology). TEis the fraction of sediment trapped in lakes, whether natural or anthropogenic; it amounts to 0-1,
with a probable global average of 0.2[45](value used for the calculation). Last, EHis an anthropic factor, and can have one of
three possible values: EH= 0.5for areas with conservative human footprints (density <200 inh./km2, and gross domestic
product per capita>15000 $/yr); EH= 2 for areas with high human footprints (density >200 inh./km2, and gross domestic
product per capita<1000 $/yr); or EH= 1, for areas with low human footprints.
In order to calculate Qriver with the BQART formula, we extracted the values of Q,A,R,T,Ag,population density,
and gross domestic product per capita from the HydroBASIN database[57]. Lis determined from the lit_cl_smj categories
in the GLiM database[58].
In the BQART model, the effect of dams is lumped into the TEparameter, but TEis poorly constrained, and includes
potential sediment storage in plains. The same value was used for all the catchments and corresponds to a worldwide mean of
0.2[45]. In order to better evaluate the impact of dams on the sediment flux in different catchments, we used a model where
the sediment flux can be calculated on every pixel and either summed or partially removed by dams in order to calculate the
sediment outflux to the ocean Qriver . This model was proposed by Maffre et al.[26] on the basis of a 3.75longitude by 1.9
latitude grid of cells:
E=kq0.2s1.3max(T , 2)0.9(4)
where Eis the pixel erosion rate in m/year,kis a constant parameter adjusted to obtain a global sediment outflux of 19 Gt
(as predicted by the BQART model), sis the local slope, qis the run-off (mm/year) and Tis the ambient temperature in
(°C). Erosion rates are summed within catchments in order to predict the sediment flux Qriver at their outlet.
To quantify the impact of dams on Qriver , we used the GOODD dam database[59], which provides the location of the dams
as well as the associated upstream watersheds. We calculated Qriver,dam by masking the area upstream from the dams, so
that it mimics total sediment retention in dam reservoirs assuming that 100%of the sediment is trapped. Overall, Qriver,dam
fits Qriver calculated by using the BQART with a correlation coefficient of R= 0.76. We also calculated a pristine Qr iver,p
assuming a world without dams and using Equation 4, so that we can compare the two situations with respect to the sandy
coast evolution.
Because our model focuses on sand, we consider that only 35% of the total riverine sediment input is sand reaching the
coastal zone[60] and that this sand has a median diametre of d50 = 400µm.
Longshore wave-induced sand transport, Qwav e
The empirical wave-induced longshore sediment transport is based on hydrological and topographic data and calculated using
the Kamphuis formula (Equation 5). We consider a single grain size of d50 = 400µm which corresponds to intermediate-sized
sand, with an underwater beach slope of tan(β) = 0.1[61]. Tpis the peak wave period in s,Hbreak is the wave height at the
breaker line in mand θbreak is the wave angle at the breaker line in degrees. The average wave regime was derived from
Era-Interim (ECMWF) over the 1993-2015 period.
Qwave = 2.33T1.5
50 H2
break sin(2θbreak)0.6(5)
According to the relative angle between the waves and shoreline, transport occurs in either one direction or the other.
A set of consecutive transects where the transport moves in the same direction constitutes a hydrosedimentary cell. Within
a cell, each transect receives sand from the previous transect and supplies sand to the next transect. At each of the two
extremities of the cell, the sand either converges (accretion) or diverges (erosion).
Islands are considered as isolated coastal systems; there is no sand flux from one island to another. The model provides
results in one iteration starting from a hypothetical initial situation where sandy beaches are infinite reservoirs of detached
sand and non-sandy stretches of coast are empty reservoirs. Here, the sandy transects were delineated as in the database of
Luijendijk et al.[2]
Sand loss towards the ocean, Rcross
To take the sand outflux from the coast to the ocean into account, we determined a cross-shore ‘rate’ for each transect. This
rate was developed to model seaward sand sinks, the direct drift of sand from river outlets to the open ocean and wave-induced
8 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
The parameter DoCi/lc, where lcis a characteristic length, controls the value of this rate. It was adjusted to maximize
the correlation between the modelled sand budget Qand the observed erosion trends υobs[2], and the resulting value is
lc= 8.5mwhich is approximately equal to the average value of the DoC at the global scale[30]. A rate close to -1 means
that the system loses twice as much sand locally through cross-shore transport as it receives through riverine and coastal
transport. Conversely, a rate close to 1 means that a negligible part of the input drifts offshore.
obs vs. Qcorrelation: ξdefinition and breaking down the dataset into subsets
After calculating the sand budget on each transect, we applied a sliding average filter with a radius of 5°to the results in
order to smooth them out and to highlight the overall patterns. Points found at latitudes above 50°(north and south) were
excluded as they correspond to areas such as Patagonia or Northern Canada where the model fails to properly represent sand
transport, probably due to the multitude of closely interspaced islands.
The metric ξhas been defined in order to quantify how the high-frequency shoreline evolution is correlated to the sand
budget (Q). We assume that, for an ideal beach constrained only by sand supply, the shoreline evolves linearly with the
sand budget. In other words, high-frequency shoreline evolution (erosion or accretion) is represented by obs. Thus, ξis
defined as the relative difference of |Q|obs from its reference value given by the ratio of global average values |Q|obs;
it is non-dimensional:
ξ=||Q|obs/|Q|obs 1|
Considering the normalized value Q=|Q|/|Q|versus the normalized value
obs =obs/obs ,ξ= 0 means
obs i.e. a perfect linear regression between
obs and Q. When ξis higher, the difference between normalized obs
and |Q|is also higher; this difference follows the rule Q= (1 ±ξ)×
obs. We define a threshold value ξcfor ξin order to
select the transects that do not deviate too much from the perfect linear regression between
obs and Q.ξc= 0.9satisfies
this constraint, without being too restrictive. The threshold value ξc= 0.9also ensures that the variability in the evolution
of the coastline obs is comparable to that expected from the value of Q.
Q[(1 ξc)×
obs ; (1 + ξc)×
obs ; 1.9×
obs]considering ξc= 0.9
Based on the definition of ξand the nature of coastal transects described in Luijendijk et al.[2], we produced the four
subsets of transects defined in Table 1. These subsets are working subsets upon which we test the correlation between the
observed annual coastline evolution trend υobs[2] and our calculated Q.
Symbol Condition Description N R
A - Entire dataset 11161 -0.024
B Sandy Sandy coastlines[2] 3916 0.064
Cξ < 0.9all the transects where obs correlates with |Q|6498 0.381
D Sandy & ξ < 0.9Sandy coastlines[2] where obs correlates with |Q|2506 0.588
Table 1: Subset characteristics, and results of the analysis: N is the total transect number and Rthe correlation coefficient
between υobs and Q.
[1] M. Oppenheimer, B.C. Glavovic, J. Hinkel, R. van de Wal, A.K. Magnan, A. Abd-Elgawad, R. Cai, M. Cifuentes-Jara,
R.M. DeConto, T. Ghosh, J. Hay, F. Isla, B. Marzeion, B. Meyssignac, and Z. Sebesvari. Sea level rise and implications
for low-lying islands, coasts and communities. IPCC Special Report on the Ocean and Cryosphere in Changing Climate,
[2] A. Luijendijk, G. Hagenaars, R. Ranasinghe, F. Baart, G. Donchyts, and Stefan Aarninkhof. The state of the world’s
beaches. Scientific Reports, 2018.
[3] P. Bruun. Sea level rise as cause of shore erosion. Journal of Waterways and Harbor Division, ASCE 88:117–130, 1962.
9 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
[4] R.G Dean. Beach response to sea level change. Ocean Engineering Science, v.9 of The Sea:869–887, 1990.
[5] S.P et al. Leatherman. Sea level rise shown to drive coastal erosion. Transactions of the American Geophysical Union,
81 (6):55–57, 2000.
[6] J.D. Rosati, R.G. Dean, and T.L. Walton. The modified bruun rule extended for landward transport. Marine Geology,
340:71–81, 05 2013.
[7] C.H. Everts. Sea level rise effects. Journal of Waterway, Port, Coastal and Ocean Engineering, American Society of
Civil Engineers, 111 (6):985–999, 1985.
[8] Michalis I. Vousdoukas, Roshanka Ranasinghe, Lorenzo Mentaschi, Theocharis A. Plomaritis, Panagiotis Athanasiou,
Arjen Luijendijk, and Luc Feyen. Sandy coastlines under threat of erosion. Nature Climate Change, 10:260–263, 2020.
[9] J.A.G Cooper, Gerd Masseling, Giovanni Coco, Andrew Short, Bruno Castelle, K. Rogers, Edward J. Anthony, A.N.
Green, J.T. Kelley, O.H. Pilkey, and D.W.T. Jackson. Sandy beaches can survive sea-level rise. Nature Climate Change,
October 2020.
[10] Sean Vitousek, Patrick L. Barnard, and Patrick Limber. Can beaches survive climate change? Journal of Geophysical
Research: Earth Surface, 122(4):1060–1067, 2017.
[11] J.H. Nienhuis, A.D. Ashton, and D.A. et al. Edmonds. Global-scale human impact on delta morphology has led to net
land area gain. Nature, 577:514–518, 2020.
[12] James Syvitski, Scott Peckham, Rachael Hilberman, and Thierry Mulder. Predicting the terrestrial flux of sediment to
the global ocean: a planetary perspective. Sedimentary Geology, 162(1-2):5–24, November 2003.
[13] Patrick Marchesiello, Nguyet Minh Nguyen, Nicolas Gratiot, Hubert Loisel, Edward J. Anthony, Cong San Dinh, Thong
Nguyen, Rafael Almar, and Elodie Kestenare. Erosion of the coastal mekong delta: Assessing natural against man
induced processes. Continental Shelf Research, 2019.
[14] C. B. Craft, E. D. Seneca, and S. W. Broome. Vertical Accretion in Microtidal Regularly and Irregularly Flooded
Estuarine Marshes. Estuarine, Coastal and Shelf Science, 37(4):371–386, October 1993.
[15] Mohsen Taherkhani, Sean Vitousek, Patrick L. Barnard, Neil Frazer, Tiffany R. Anderson, and Charles H. Fletcher.
Sea-level rise exponentially increases coastal flood frequency. Scientific Reports, 10(6466), 2020.
[16] R. Ranasinghe. On the need for a new generation of coastal change models for the 21st century. Sci Rep, 10, 2010, 2020.
[17] Jérôme Benveniste, Anny Cazenave, Stefano Vignudelli, Luciana Fenoglio-Marc, Rashmi Shah, Rafael Almar, Ole An-
dersen, Florence Birol, Pascal Bonnefond, Jérôme Bouffard, Francisco Calafat, Estel Cardellach, Paolo Cipollini, Gonéri
Le Cozannet, Claire Dufau, Maria Joana Fernandes, Frédéric Frappart, James Garrison, Christine Gommenginger, Guoqi
Han, Jacob L. Høyer, Villy Kourafalou, Eric Leuliette, Zhijin Li, Hubert Loisel, Kristine S. Madsen, Marta Marcos,
Angélique Melet, Benoît Meyssignac, Ananda Pascual, Marcello Passaro, Serni Ribó, Remko Scharroo, Y. Tony Song,
Sabrina Speich, John Wilkin, Philip Woodworth, and Guy Wöppelmann. Requirements for a coastal hazards observing
system. Frontiers in Marine Science, 6:348, 2019.
[18] A. Melet, P. Teatini, G. Le Cozannet, C. Jamet, A. Conversi, J. Benveniste, and Rafael Almar. Earth observations for
monitoring marine coastal hazards and their drivers. Surveys in Geophysics, [Early access]:p. [46 p.], 2020.
[19] R. Ranasinghe. On the need for a new generation of coastal change models for the 21st century. Sci Rep, 2020.
[20] T. Tiggeloven, H. de Moel, H. C. Winsemius, D. Eilander, G. Erkens, E. Gebremedhin, A. Diaz Loaiza, S. Kuzma,
T. Luo, C. Iceland, A. Bouwman, J. van Huijstee, W. Ligtvoet, and P. J. Ward. Global-scale benefit–cost analysis of
coastal flood adaptation to different flood risk drivers using structural measures. Natural Hazards and Earth System
Sciences, 20(4):1025–1044, 2020.
[21] John Milliman and Katherine Farnsworth. Runoff, erosion, and delivery to the coastal ocean. River Discharge to the
Coastal Ocean: A Global Synthesis, page 43, 01 2011.
[22] Robert J. Hallermeier. Uses for a calculated limit depth to beach erosion. Coastal Engineering 1978, pages 1493–1512,
[23] Edward J. Anthony and Troels Aagaard. The lower shoreface: Morphodynamics and sediment connectivity with the
upper shoreface and beach. Earth-Science Reviews, 2020.
[24] Nieves G. Valiente, Gerd Masselink, Tim Scott, Daniel Conley, and Robert Jak McCarroll. Role of waves and tides on
depth of closure and potential for headland bypassing. Marine Geology, 407:60–75, 2019.
10 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
[25] Kamphuis. Alonshore sediment transport rate. Coastal and Oc. Engrg., 117:624–640, 1991.
[26] P. Maffre. a. a, a:a, a.
[27] Jaia Syvitski and John Milliman. Geology, geography, and humans battle for dominance over the delivery of fluvial
sediment to the coastal ocean. Journal of Geology, 115, 01 2007.
[28] James Syvitski and Albert Kettner. Sediment flux and the Anthropocene. Philosophical Transactions of the Royal
Society A: Mathematical, Physical and Engineering Sciences, 369(1938):957–975, 2011.
[29] Bernhard Peucker-Ehrenbrink. Land2sea database of river drainage basin sizes, annual water discharges, and suspended
sediment fluxes. Geochemistry, Geophysics, Geosystems, 10(6), June 2009.
[30] Erwin W.J. Bergsma and Rafael Almar. Coastal coverage of esa’ sentinel 2 mission. Advances in Space Research,
65(11):2636 – 2644, 2020.
[31] Jonathan A. Warrick. Littoral sediment from rivers: Patterns, rates and processes of river mouth morphodynamics.
Frontiers in Earth Science, 8:355, 2020.
[32] Liviu Giosan, Stefan Constantinescu, Peter D. Clift, Ali R. Tabrez, Muhammed Danish, and Asif Inam. Recent mor-
phodynamics of the indus delta shore and shelf. Continental Shelf Research, 26(14):1668–1684, 2006.
[33] Cope M. Willis and Gary B. Griggs. Reductions in Fluvial Sediment Discharge by Coastal Dams in California and
Implications for Beach Sustainability. The Journal of Geology, 111(2):167–182, March 2003.
[34] John Milliman and M. Ren. River flux to the sea: impact of human intervention on river systems and adjacent coastal
areas. Impact on coastal habitation, pages 57–83, 1995.
[35] Eric Maneux, Jean Luc Probst, Eric Veyssy, and Henri Etcheber. Assessment of dam trapping efficiency from water
residence time: Application to fluvial sediment transport in the Adour, Dordogne, and Garonne River Basins (France).
Water Resources Research, 37(3):801–811, March 2001.
[36] Guangming Tan, Peng Chen, Jinyun Deng, Quanxi Xu, Rouxin Tang, Zhiyong Feng, and Ran Yi. Review and improve-
ment of conventional models for reservoir sediment trapping efficiency. Heliyon, 5(9):e02458, September 2019.
[37] DE Walling and BW Webb. Erosion and sediment yield: a global overview. IAHS Publications-Series of Proceedings
and Reports-Intern Assoc Hydrological Sciences, 236:3–20, 1996.
[38] J.A. Warrick, A.W. Stevens, and I.M. et al. Miller. World’s largest dam removal reverses coastal erosion. Sci Rep, 2019.
[39] SH Sharaf El Din. Longshore sand transport in the surf zone along the mediterranean egyptian coast. Limnology and
Oceanography, 19(2):182–189, 1974.
[40] L. Mentaschi, M.I. Vousdoukas, and JF. et al. Pekel. Global long-term observations of coastal erosion and accretion. Sci
Rep, 2018.
[41] Robert A. Morton, Tara Miller, and Laura Moore. Historical Shoreline Changes Along the US Gulf of Mexico: A
Summary of Recent Shoreline Comparisons and Analyses. Journal of Coastal Research, 2005(214):704 – 709, 2005.
[42] Anh Cao, Miguel Esteban, Ven Paolo Bruno Valenzuela, Motoharu Onuki, Hiroshi Takagi, Nguyen Danh Thao, and
Nobuyuki Tsuchiya. Future of Asian Deltaic Megacities under sea level rise and land subsidence: current adaptation
pathways for Tokyo, Jakarta, Manila, and Ho Chi Minh City. Current Opinion in Environmental Sustainability, 50:87–97,
June 2021.
[43] M. Farías, G. Vargas, A. Tassara, S. Carretier, S. Baize, D. Melnick, and K. Bataille. Land-level changes produced by
the mw 8.8 2010 chilean earthquake. Science, 2005(329), 2010.
[44] O. Hoegh-Guldberg, D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi,
F. Engelbrecht, J. Guiot, A. Payne S.I. Seneviratne A. Thomas R. Warren Y. Hijioka, S. Mehrotra, and G. Zhou.
Impacts of 1.5ºc global warming on natural and human systems. IPCC Report, 2018.
[45] James P. M. Syvitski and John D. Milliman. Geology, Geography, and Humans Battle for Dominance over the Delivery
of Fluvial Sediment to the Coastal Ocean. The Journal of Geology, 115(1):1–19, January 2007.
[46] P. Greve, L. Gudmundsson, and S.I. Seneviratne. Regional scaling of annual mean precipitation and water availability
with global temperature change. Earth System Dynamics, 9:227–240, 2018.
11 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
[47] J.D. Restrepo, A.J. Kettner, and J.P.M. Syvitski. Recent deforestation causes rapid increase in river sediment load in
the colombian andes. Anthropocene, pages 13–28, 10 2015.
[48] Jan P.M. Mulder, Saskia Hommes, and Erik M. Horstman. Implementation of coastal erosion management in the
netherlands. Ocean & Coastal Management, 54(12):888–897, 2011. Concepts and Science for Coastal Erosion Management
[49] R.L. Morris, A. Boxshall, and S.E. Swearer. Climate-resilient coasts require diverse defence solutions. Nat. Clim. Chang.,
(10):485–487, 2020.
[50] Daniel Lincke and Jochen Hinkel. Coastal migration due to 21st century sea-level rise. Earth’s Future, 9(5), 2021.
[51] Robert J. Nicholls, Daniel Lincke, Jochen Hinkel, Sally Brown, Athanasios T. Vafeidis, Benoit Meyssignac, Susan E.
Hanson, Jan-Ludolf Merkens, and Jiayi Fang. A global analysis of subsidence, relative sea-level change and coastal flood
exposure. Nat. Clim. Chang., 11:338–342, 2021.
[52] Rafael Almar, Elodie Kestenare, J. Reyns, Julien Jouanno, E. J. Anthony, R. Laibi, M. Hemer, Yves Penhoat du, and
R. Ranasinghe. Response of the Bight of Benin (Gulf of Guinea, West Africa) coastline to anthropogenic and natural
forcing : Part 1 : Wave climate variability and impacts on the longshore sediment transport. Continental Shelf Research,
110:48–59, 2015.
[53] B. Alves, D. B. Angnuureng, Pierre Morand, and Rafael Almar. A review on coastal erosion and flooding risks and
best management practices in West Africa : what has been done and should be done. Journal of Coastal Conservation,
24:art. 38 [22 p.], 2020.
[54] Olusegun Dada, Rafael Almar, Pierre Morand, and Frédéric Ménard. Towards West African coastal social-ecosystems
sustainability : Interdisciplinary approaches. Ocean and Coastal Management, May 2021.
[55] Nicolas Rocle, HELENE REY-VALETTE, François Bertrand, Nicolas Becu, Nathalie Long, Cécile Bazart, Didier D.
Vye, Catherine Meur-Ferec, Elise Beck, Marion Amalric, and Nicole Lautrédou-Audouy. Paving the way to coastal
adaptation pathways: An interdisciplinary approach based on territorial archetypes. Environmental Science and Policy,
110:34–45, August 2020.
[56] P. Wessel and W. H. F. Smith. A global self-consistent, hierarchical, high-resolution shoreline database. J. Geophys.
Res., 101, 1996.
[57] B. Lehner and Grill G. Global river hydrography and network routing: baseline data and new approaches to study the
world’s large river systems., 2013.
[58] Jens Hartmann and Nils Moosdorf. The new global lithological map database GLiM: A representation of rock properties
at the Earth surface. Geochemistry, Geophysics, Geosystems, 13(12), 2012.
[59] M. Mulligan, A. van Soesbergen, and L. Saenz. GOODD, a global dataset of more than 38,000 georeferenced dams, 2020.
[60] L. D. Wright and C. A. Nittrouer. Dispersal of river sediments in coastal seas: Six contrasting cases. Estuaries,
18(3):494–508, 1995.
[61] Fabrice Ardhuin and Aron Roland. Coastal wave reflection, directional spread, and seismoacoustic noise sources. Journal
of Geophysical Research: Oceans, 117(C11), 2012.
Author contributions statement
M.G. carried out the study and wrote the initial draft. V.R. calculated the sediment supply from rivers at the global scale.
All authors discussed the results and contributed to the manuscript.
Additional information
Accession codes and data
The raw data that support the findings of this study are already available online. Calculated data (e.g. sediment outfluxes)
are provided as tables. The Matlab Code will be made freely available through gitlab repository.
Competing interests
The authors declare no competing interests.
12 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.
Full-text available
In recent years, Rivers2Morrow ( has initiated research on the morphology of the Dutch rivers Rhine and Meuse. Rivers2Morrow is a research programme financed by Rijkswaterstaat and the Ministry of Infrastructure and Water Management. In this programme, eight PhD-students are studying the response of these river systems to changes in controls due to climate change. In this white paper, their findings, especially with respect to sediment fluxes and sediment management, have been combined with the results in scientific publications on rivers globally to address the highly relevant topic of disturbed sediment dynamics in ‘rivers under pressure’. This paper presents an expert-view on the impacts of climate change and human interventions on sediment fluxes in rivers, globally. We invite the community to comment on our view on ‘rivers under pressure’ and share their thoughts with us.
Full-text available
The coastal system can be regarded as co-evolving socio-economic and ecological systems undergoing intense environmental pressures owing to the mechanisms of change exerted by human activities against a background of natural change. Understanding and managing ecological responses to these changes in the coastal areas require interdisciplinary approaches. Here, we develop a new approach of coastal socio-ecological systems (CSES) based on earlier work on the press-pulse dynamics (PPD) socio-ecological systems. To show the relevance of the modified (mPPD) framework, we applied it to two unique features (mangroves and beach systems) of the western African coastal (WAC) systems. Then, we constructed plausible 21st-century coastal systems scenarios at the coast based on a set of descriptive indicators (population growth, economic development, environmental quality, governance, technological advancement and climate change) for a better understanding and sustainable management planning of WAC systems. We found that different indicators characterizing each scenario will exert different pressures on the WAC systems, under the forms of the long-term press and short-term pulse events. The cross-cutting narratives of the different future scenarios in the face of climate change using the mPPD framework offer valuable insight into the development of WAC management strategies, policies and other agendas. It helps to define the plausible implications of following, or not, a particular management path. The inconsistencies between the aspirations of different resource users and lack of coordination of human activities taking place on land and in the coastal zone, partly due to fragmentation of institutions and weak coastal governance are revealed. In this context, the mPPD-CSES framework can be used to investigate how ecosystems can experience different (intensities of) press as well as different frequencies of the pulse. Thus, its adaptability to construct future coastal vulnerability scenarios adds to its usefulness as a robust and dependable integrated coastal zone management tool.
Full-text available
Rising mean and extreme sea‐levels and induced increased coastal flooding are expected to lead to massive coastal migration if coasts are not protected. Using a wide range of sea‐level rise (SLR) scenarios, socioeconomic pathways and discount rate assumptions, 21st century coastal migration is assessed at global scale assuming local cost‐benefit optimal protection decisions for about 12,000 coastal segments with homogeneous coastal and socioeconomic characteristics. Costs considered include investment and maintenance cost for protection, migration cost in the case of no protection, and expected annual damage to assets by extreme sea‐level events that over‐top existing protection. Robust decisions in favor of protection over all scenarios are found for about 3% of the global coastline, covering 78% of global coastal population and 92% of global coastal floodplain assets. For the remaining 97% of global coastline cumulative 21st century land loss ranges from 60,000 to 415,000 km² and coastal migration ranges from 17 to 72 million people. Big countries with long uninhabited coastlines suffer the biggest land losses. In absolute terms big countries in South and South‐east Asia account for the highest coastal migration, while in relative terms small island nations suffer most. Global cost of 21st century SLR can be lowered by factor two to four if local cost‐benefit decisions also consider, next to protection, coastal migration as an adaptation option.
Full-text available
Sea level rise and land subsidence — induced flooding are projected to have severe impacts on highly populated Asian deltaic cities. These cities are already suffering from frequent floods, though few comparative analyses have been conducted on the similarities and differences of their adaptation approaches. Thus, this study aims to investigate the current adaptation pathways of Asian deltaic cities to flooding induced by slow onset events such as urbanization-induced land subsidence and sea level rise, by looking at Tokyo, Jakarta, Manila, and Ho Chi Minh City as case studies. Evidence from them shows that an engineering approach towards flooding adaptation is shaping the future of Asian deltaic cities. However, emerging challenges question the sustainability of this approach. Recommendations on how to improve current adaptation pathways and direction for future research are also provided.
Full-text available
Climate-induced sea-level rise and vertical land movements, including natural and human-induced subsidence in sedimentary coastal lowlands, combine to change relative sea levels around the world’s coasts. Although this affects local rates of sea-level rise, assessments of the coastal impacts of subsidence are lacking on a global scale. Here, we quantify global-mean relative sea-level rise to be 2.5 mm yr⁻¹ over the past two decades. However, as coastal inhabitants are preferentially located in subsiding locations, they experience an average relative sea-level rise up to four times faster at 7.8 to 9.9 mm yr⁻¹. These results indicate that the impacts and adaptation needs are much higher than reported global sea-level rise measurements suggest. In particular, human-induced subsidence in and surrounding coastal cities can be rapidly reduced with appropriate policy for groundwater utilization and drainage. Such policy would offer substantial and rapid benefits to reduce growth of coastal flood exposure due to relative sea-level rise.
Full-text available
Rivers provide important sediment inputs to many littoral cells, thereby replenishing sand and gravel of beaches around the world. However, there is limited information about the patterns and processes of littoral-grade sediment transfer from rivers into coastal systems. Here I address these information gaps by examining topographic and bathymetric data of river mouths and constructing sediment budgets to characterize time-dependent patterns of onshore, offshore, and alongshore transport. Two river deltas, which differ in their morphology, were used in this study: the Elwha River, Washington, which builds a mixed sediment Gilbert-style delta, and the Santa Clara River, California, which builds a cross-shore dispersed sand delta from hyperpycnal flows. During and after sediment discharge events, both systems exhibited a similar evolution composed of three phases: (i) submarine delta growth during offshore transport of river sediment, (ii) onshore-dominated transport from the submarine delta to a subaerial river mouth berm, and (iii) longshore-dominated transport away from the river mouth following subaerial berm development. Although stage (ii) occurred within days to weeks for the systems studied and was associated with the greatest rates of net erosion and deposition, onshore transport of sediment from submarine deposit to the beach persisted for years following the river discharge event. These morphodynamics were similar to simple equilibrium profile concepts that were modified with an onshore-dominated cross-shore transport rule. Additionally, both study sites revealed that littoral-grade sediment was initially exported to depths beyond the active littoral cell (i.e., below the depth of closure) during the stage (i). Following several years of reworking by coastal processes, bathymetric surveys suggested that 14 and 46% of the original volume of littoral-grade sediment discharged by the Santa Clara and Elwha Rivers, respectively, continued to be below the depth of closure. Combined, this suggests that integration of river sediment into a littoral cell can be a multi-year process and that the full volume of littoral-grade sediment discharged by small rivers may not be integrated into littoral cells because of sand and gravel “losses” to the continental shelf.
Full-text available
The West African coast is vulnerable to natural hazards and human interventions. Although various measures have been taken at different scales, mostly at the local level, there is a need to improve management at the regional level. We examine these actions and possible solutions from different perspectives and provide conclusions and recommendations on the integration of solutions to improve coastal management. From North West Mauritania to across the Gulf of Guinea a system of coastal zoning that can be managed holistically is encouraged. The development of holistic planning is seen as a sustainable approach to management that seeks to link users/processes together rather than focus on a single particular issue and solution. Strengthening, monitoring, promoting the observation network and generalising open data centralisation and exchange for a better understanding of coastal dynamics and pressures is encouraged. There is a need for capacity building, expertise and federative actions. Furthermore, the need to identify and involve not only stakeholders, but also communities and scientists with multilevel inputs. All must agree on coordinated plans to achieve stakeholder objectives, using an approach adapted to the multi-spatial scale (e.g at the scale of sediment cells, integrating from the sources of sediment in river basins to their redistribution along the coast, perturbed by climate changes and anthropic stresses), so that only regional solutions are appropriate and will be effective. These must follow sustainable strategies with a multi-temporal sequenced solution and anticipate changes, or adaptive solutions using solutions in synergy with different time frames as well as managing natural and human systems responsibly. A plan that considers changes in coastal systems and anticipates impacts and adapts plans accordingly will be key.
Full-text available
Coastal zones have large social, economic and environmental values. They are more densely populated than the hinterland and concentrate large economic assets, critical infrastructures and human activities such as tourism, fisheries, navigation. Furthermore, coastal oceans are home to a wealth of living marine resources and very productive ecosystems. Yet, coastal zones are exposed to various natural and anthropogenic hazards. To reduce the risks associated with marine hazards, sustained coastal zone monitoring programs, forecasting and early warning systems are increasingly needed. Earth observations (EO), and in particular satellite remote sensing, provide invaluable information: satellite-borne sensors allow an effective monitoring of the quasi-global ocean, with synoptic views of large areas, good spatial and temporal resolution, and sustained time-series covering several years to decades. However, satellite observations do not always meet the precision required by users, in particular in dynamic coastal zones, characterized by shorter-scale variability. A variety of sensors are used to directly monitor the coastal zone and their observations can also be integrated into numerical models to provide a full 4D monitoring of the ocean and forecasts. Here, we review how EO, and more particularly satellite observations, can monitor coastal hazards and their drivers. These include coastal flooding, shoreline changes, maritime security, marine pollution, water quality, and marine ecology shifts on the one hand, and several physical characteristics (bathymetry, topography, vertical land motion) of coastal zones, meteorological and oceanic (metocean) variables that can act as forcing factors for coastal hazards on the other hand.
The lower shoreface provides the connection between the continental shelf and the shoreline via its onshore transition called the upper shoreface. Lower shorefaces are diverse, and range from bedrock-controlled, through sediment-starved to sediment-rich, siliciclastic, carbonate, low to high wave-energy, microtidal to macrotidal, and are variably affected by storm and wind-driven flows. The lower shoreface can be a repository for deposits of terrestrial origin, and a zone of active carbonate production. It can therefore be an important source of sediment for beaches, dunes, estuaries, and tidal basins. There has been progress in the ability to predict suspended sediment transport under non-breaking and shoaling waves across the lower shoreface. However, high-resolution measurement of sediment transport over unknown seabed configurations with unpredictable bed-level changes under hydrodynamic conditions that are unknown at the outset, and especially involving bedload transport, is still faced with significant challenges. Non-linear interactions between processes contributing to sediment transport render calculations and modelling of transport directions and magnitudes uncertain, and the spatial and temporal scales of transport are much larger than those of the upper shoreface. On the other hand, transport rates and morphological change may be much smaller on the lower shoreface compared to the upper shoreface. Another challenge is the upscaling of short-term measurements to explain the long-term morphological evolution of the lower shoreface. This limited understanding implies that current paradigms of lower shoreface dynamics based on morphological equilibrium and disequilibrium relative to the ocean-forcing conditions may be too simplistic, though possibly appropriate over long timescales (decades to millennia), and modelling work and prediction of change no more than exploratory. Over such long timescales, boundary conditions (sea level, wave climate) are likely to change. Making way forward on these issues is important for understanding the connectivity between the lower shoreface and beach recovery after major storm erosion, and for estimating coastal sediment budgets, short- to long-term coastal change and response to natural and anthropogenic perturbations. At geological timescales, the lower shoreface is a central element in tracking shoreline changes. Progress is needed in measuring sediment transport and upscaling to timescales compatible with lower shoreface change. It is also important to take advantage of on-going rapid progress in seabed and shallow stratigraphic mapping, bed-level changes, including remote-sensing approaches, for a better understanding of lower shoreface morphodynamics and sediment connectivity with the coast. This includes the now routine identification of large subaqueous bedforms, possibly ubiquitous features on the world's continental shelves, their mobility over time, and their potential link with the shoreline. The common relationship between fine sand, dissipative beaches and large aeolian dunes also poses the question of how fine sand is abundantly supplied from the lower shoreface, given the common perception that it is readily swept offshore on beaches. These multi-theme challenges need to be addressed in order to advance our understanding of the lower shoreface and its connectivity with the upper shoreface and beach.
Traditional coastal protection methods that rely on built, hard structures like seawalls may not be effective to keep pace with a changing climate. Nature-based coastal defences based on habitat restoration can be an adaptive coastal protection alternative.