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 (marcan.graﬃn@etu.toulouse-inp.fr)
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
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 .
Sandy beaches in low-lying coastal areas are currently undergoing particularly dramatic erosion that is destabilizing
coastal socio-ecosystems . 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 . However, the magnitude of the retreat
and the importance of other factors driving beach changes are currently being debated , 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 :
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.  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  while deltas, and
estuaries are more likely to accrete. At the same time, the sea level rise will accelerate 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. Given that satellite observations are
now abundant[17, 18], such global studies can be performed. Luijendik et al. 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 , there is still a need to develop
numerical models based on observations and physical processes. 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.
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 ), 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). 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  (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 (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. 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, 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. 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., 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. as a proxy. After having automatically extracted the global spatial
distribution of beaches, Luijendijk et al. 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., 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. 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.. 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). 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 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.
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, 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, although this underestimates the potential for coastal resilience due to
sediment availability. Local subsidence due to sediment compaction following freshwater pumping can reach several metres like
in Japan or Indonesia, 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. 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 will increase the sediment transport potential of rivers as temperature directly aﬀects the capacity of
the river to erode the bed. The intensiﬁcation (rarefaction) of precipitation in temperate (arid) zones will lead to an
increase (decrease) in Qriver . 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.
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  Local examples have shown that sandy coasts were rapidly reconstructed after river dam removal . 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. 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. 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
However, all these site-speciﬁc mitigation cases have neglected the sediment imbalance that results from larger scale, often
regional, sediment redistribution. 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 . 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). 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 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. 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, 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. 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(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. Lis determined from the lit_cl_smj categories
in the GLiM database.
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. 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. on the basis of a 3.75◦longitude 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, 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
Because our model focuses on sand, we consider that only 35% of the total riverine sediment input is sand reaching the
coastal zone 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. 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
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.
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, and the resulting value is
lc= 8.5mwhich is approximately equal to the average value of the DoC at the global scale. 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:
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.
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., 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 and our calculated ∆Q.
Symbol Condition Description N R
A - Entire dataset 11161 -0.024
B Sandy Sandy coastlines 3916 0.064
Cξ < 0.9all the transects where obs correlates with |∆Q|6498 0.381
D Sandy & ξ < 0.9Sandy coastlines 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
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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.
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.
The authors declare no competing interests.
12 The paper is a non-peer reviewed preprint draft submitted to EarthArXiv.