Hatono and Yoshimura Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00368-6(2020) 7:59

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Development of a global sediment
dynamics model
Misako Hatono1,2*

and Kei Yoshimura3

Abstract
Sediment dynamics play an important role in various aspects of earth system modeling. In this study, we developed
a global sediment dynamics model that considers suspended sediment and bedload at short timescales. We
validated suspended sediment from four observation stations in the Amazon River basin and over 60 observation
stations from around the world based on a variable criteria such as availability of data samples. Our model was able
to effectively reproduce seasonality and spatial distribution of suspended sediment flow. However, our global
estimate of approximately 4 Bt/a was significantly lower than previous estimates; therefore, we discuss potential
causes of this discrepancy, including target time period and discrepancies with previous extrapolated methods. Our
newly developed sediment dynamics model could provide a better understanding of global sediment transfer andcontributes to various related research fields such as coastal modeling and natural disasters.
Keywords: Global sediment dynamics, Suspended sediment flow, Amazon River basin, Oceania

1 Introduction
Rivers play a large role in material transport as well as in
the water cycle. Sediments flowing through rivers carryvarious nutrients and are thus vital in the transport ofvarious materials such as carbon and nitrogen from land
to the oceans (Walling 2006). Movement of sediments in
the rivers can be affected by a wide range of factors suchas dam construction and major flood events. Construction of large dams such as the Madeira Hydroelectric
Complex resulted in a 30% decrease of fine suspended
sediment concentration (Rivera et al. 2019), while Three
Gorges Reservoir led to a 98.7% decrease of multi-year
mean annual sediment deposition in a downstream lake
(Zhou et al. 2016). Walling (2006) stated rivers such as
Rio Magdalena and Fly River saw significant increases in
sediment flow due to human activities such as land
clearance and mining. Kao and Milliman (2008) stated a* Correspondence: misakohatono@hiroshima-u.ac.jp
1
School of Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba,
Sendai, Miyagi 980-8579, Japan
2
Graduate School of Advanced Science and Engineering, Hiroshima
University, 1-4-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima 739-8527,
Japan
Full list of author information is available at the end of the article

river in Taiwan increased its sediment flow significantly when an intense typhoon hit the river basin,
while Horowitz (2010) showed a sharp decrease insuspended sediment concentration after a large floodin the Mississippi River basin which may be because
large amounts of bedload storage was removed due tothe flood. These changes in sediment flow could be
amplified as flood frequency and intensity have been
predicted to increase with climate change (Hirabayashi
et al. 2013).
Sediment-related studies using various methods such asmodeling, observations and remote sensing including thosementioned above have long been conducted on a relatively
local scale, focusing on a specific river basin or shoreline(Blum and Roberts 2009; Shrestha et al. 2013; Yu et al.
2013; Park and Latrubesse 2014). This is partly because
sediment movement and its governing factors depend
largely on regional characteristics such as river discharge,
basin area, lithology, and anthropogenic activities (Milliman
and Farnsworth 2011). To account for contributing factors
in a given basin, many regional models are calibrated using
observation data to accurately represent the spatial andtemporal characteristics of sediment flow (e.g., Bourgoin
et al. 2007; Rostamian et al. 2008; Cohen et al. 2013). The© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

lack of sufficient observation data regarding sediments
worldwide limits the potential target area of these regionalmodels. It is said less than 10% of sediment flow to theoceans is measured (Cohen et al. 2013), whereas river discharge is measured in over 70% of rivers flowing to theoceans (Fekete and Vörösmarty 2007).
A robust understanding of sediment dynamics atlarger scales which would allow for intra-basin comparisons (e.g., Cohen et al. 2014) may compensate for thelack of detailed regional studies in various parts of theworld. Previous studies have collected observation dataworldwide and analyzed the relationship between
sediment-related variables and regional characteristics(e.g., Holeman 1968; Milliman and Farnsworth 2011).
Panin (2004) compiled 30 global estimates of sedimentflow to the oceans using previous studies from 1950 andafterwards. Although the estimates contain uncertainty,
recent estimates in their database have been in the rangeof 13.5–22 Bt/a. Nearly all of the estimates in Panin(2004) and subsequent studies (e.g., Milliman and Farnsworth 2011; Syvitski 2011) are based on limited observations and a simple extrapolation based on few variables,such as basin area and geological parameters. Due tolack of data availability, several of these studies used estimates from a single river basin, the Mississippi River, toextrapolate to global estimates proportionate to landarea (e.g., Gilluly 1955; Mackenzie and Garrels 1966).
With the increase of available observation data worldwide, Holeman (1968) was one of the first studies included in Panin (2004) to compile annual suspended
sediment flow from various studies in all continents.
They conducted simple extrapolation to the global estimate by assuming it is proportionate to the drainage
area of rivers in their database which ranged from 1500
to 6,000,000 km2. With this approach, continuous efforts
to compile up-to-date observation data from various research organizations worldwide must be conducted toachieve observation-based global estimates that reflectchanges in sediment flow over time.
Since achieving sufficient observations at larger scales
using homogenous measuring techniques can be extremely difficult (Mouyen et al. 2018), numerical simulations are often used. Previous studies attempted toestimate long-term suspended sediment flow or yieldusing several key parameters. Syvitski and Milliman(2007) developed the BQART model which estimates
the fluvial sediment flux from a basin as a function ofparameters such as geological and human factors, riverdischarge, basin area, maximum relief, and basin averaged temperature. Pelletier (2012) developed a modelthat estimates long-term sediment flow and yield at the
global scale at 5 arc min spatial resolution. They considered sediment detachment in hillslopes and used the
Rouse number to determine whether the sediment isPage 2 of 15

routed to the downstream pixel. There have also been
several studies aimed to incorporate sediment processes
into Earth System Model frameworks at shorter time periods (e.g., Naipal et al. 2016; Tan et al. 2018), but nonethat explicitly estimates the movement of both suspended sediment and bedload. Cohen et al. (2013) developed WBMsed, which is one of the few distributed
global-scale sediment models to estimate suspended
sediment flow at a daily timescale. Although their simulations showed good accuracy in the long-term averageof global river basins and daily fluctuation of rivers in
the USA, the main limitation of WBMsed is that it uses
the Psi model (Morehead et al. 2003) to estimate thedaily fluctuation from long-term average sediment flow
and does not explicitly model sediment dynamics atdaily timescales.
In this study, we aim to overcome this limitation andcalculate various aspects of sediment transport in rivers
including bedload at short timescales. The following sections describe our newly developed global sediment dynamics model and validation results using a new global
sediment-related observation database. We also conducted sensitivity analysis on multiple parameters within
our model and investigated discrepancies in global sediment flow estimates between our model simulations andpreviously reported values such as those included in
Panin (2004).

2 Methods/experimental
2.1 Model framework

Our global sediment dynamics model was developed
within a framework called Integrated Land Simulator(ILS; Nitta et al. submitted), which consists mainly of aland surface model MATSIRO (Takata et al. 2003), ariver inundation model CaMa-Flood (Yamazaki et al.
2011, 2013), and a coupler Jcup (Arakawa et al. 2011).
As Jcup can exchange variables and communicate between different grid coordinates, this framework makes
it easier to use various forcing and boundary data and tocombine different models. In this study, only CaMaFlood and Jcup are used.
Sediment dynamics are incorporated in a global physically based river routing model known as CaMa-Flood,
which calculates river hydrodynamics including floodplain inundation—using river runoff from land surface
models as the input. Each grid consists of a unit catchment represented by subgrid topography parameters.
The model uses the local inertial equation proposed by
Bates et al. (2010) to calculate river discharge. A cumulative distribution function of the high-resolution elevation
pixels included in each unit catchment is used to distinguish the elevation of every 10% of the floodplain inorder to implicitly calculate the inundated area. The
floodplain elevation profile in each grid is achieved by

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

sorting high-resolution elevation data from SRTM30.
The river channel dimensions such as river width andriver bed area used in sediment dynamics processes in
the following subsection are also derived from theboundary data of CaMa-Flood. Details on the channel
dimensions and the floodplain elevation profile are mentioned in Yamazaki et al. (2011). It should be noted theterm “floodplain” in this study refers to all areas in eachgrid cell excluding the river channel, regardless ofwhether there is flooding or not. Notably, the sediment
dynamics in this study are only incorporated into theriver channel.
2.2 Sediment dynamics

Suspended sediment is sediment transported mainly by
river flow, and bedload is sediment transported by tumbling and rolling near the river bed. Figure 1a provides aschematic in each grid of the sediment dynamics model.
Each grid contains storage for suspended sediment andbedload which gets inflow from upstream grids and outflows to a downstream grid. Exchange between the suspended sediment and bedload storages are conducted bysuspension and deposition. Sediment erosion is estimated to be used as input from the land.
The parameters included in the following processes
were calibrated by trial and error using the suspended
sediment transport measured by observation stations in
the Amazon River basin (mainly Obidos, unless statedotherwise), unless stated otherwise. We chose the Amazon River basin because our observation database included stations relatively near the river mouth, which
would be important when discussing suspended sediment flow to the oceans. Also, there were multiple observation stations within the basin which would allow
for comparison of different characteristics within a basin.
The sensitivity analysis for some of the calibrated parameters is discussed in the results section.
Sediment erosion ers [m3/km2/h] is given by

Page 3 of 15

ð1Þ

ers ¼ βθc r dwhere θ is the slope [deg], r is precipitation [mm/h], andβ, c, d are the constant coefficients (Sunada and Hasegawa 1994). β, c, and d were calibrated to 0.1, 1, and 2
respectively. The slope and sediment erosion are calculated for every 10% of the floodplain elevation profile,and the aggregated sediment erosion is used as input tothe rivers in each grid. Figure 1b shows a schematic of
the slopes estimated from the floodplain elevation profilein CaMa-Flood. The elevation for every tenth percentileof the floodplain in each grid is prepared as the boundary data for CaMa-Flood. The slope for every tenth percentile of the floodplain θi [deg] is estimated as
180− 1 Ei − Ei − 1θi ¼ tanð2ÞB10πwhere Ei is the elevation [m] of the tenth percentile iand B10 is the width for every tenth percentile [m] of thefloodplain. E0 is set to 0 since the floodplain elevation
profile is defined as the height above the river channel.
The adopted fundamental equations for the following
processes follow those of Hamaguchi et al. (2012). The
processes involved in outflowing suspended sedimentfrom any given grid are suspended sediment flow and0
deposition. Suspended sediment flow Qs and deposition
DEP′ can be written as0

Qs ¼ CQ ¼

0

Ss
Q
S

DEP ¼ w f AC ¼ w f A

ð3Þ

Ss
S

ð4Þ

where C is the suspended sediment concentration [m3/m3] Ss is the suspended sediment storage [m3], S is theriver water storage [m3], Q is the river discharge [m3/s],

Fig. 1 Schematic of a sediment dynamics in each grid and b estimated slopes of the floodplain. Ss, Sb, Qs, and Qb are suspended sedimentstorage, bedload storage, suspended sediment flow, and bedload flow, respectively. SSP, DEP, and ers are suspension, deposition, and sedimenterosion, respectively. E0 and E10 are the elevation [m] of river channel and last tenth percentile, respectively. B10 is the width for every tenth
percentile [m] of the floodplain

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

wf is the setting velocity calculated from Rubey (1933),
and A is the river bed area [m2]. River bed area is calculated by multiplying river width and length used in
CaMa-Flood. Following Liston et al. (1994), we assumed
that sediment outflow is proportional to sediment storage to avoid unstable calculations when the storage is
exceeded by outflow from a certain grid. The suspended
sediment outflow from a grid is the sum of suspended
sediment flow and deposition. From Eqs. (3) and (4), theoutflowing suspended storage change ΔSos can be written asdSos¼ − aq þ ap S sdtΔS os ¼ 1 − e − ðaq þap ÞΔt S s

ð5Þ

ð6Þ

w A
where aq ¼ QS and ap ¼ Sf . By assuming that the storagechange can be proportionally attributed to each of thefactors, suspended sediment flow Qs [m3/s] and deposition DEP [m3/s] is estimated in our model as
Qs ¼

aq ΔS osaq þ aq Δt

DEP ¼

ap ΔS osaq þ ap Δt

ð7Þ
ð8Þ

It should be noted the unit of suspended sedimentflow is converted to mass (e.g., tons/day) in the analysis
by assuming the specific gravity of sediment is 2.65.
The outflowing bedload from a given grid includes bed0
load flow and suspension. Bedload flow Qb (Ashida andMichiue 1972) and suspension SSP′ can be written as
qﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ 
τ c
uc
0
3
Qb ¼ 17B sgD3 τ e 2 1 −
1−
ð9Þ
τ
u
0

SSP ¼ qsu A

ð10Þ

where B is the river width [m], s is the specific gravity, D
is the sediment particle diameter [m], u∗ is the shear vel2
ocity, u∗c is the critical shear velocity, τ  ¼ usgd
is the critical shear stress, and qsu is suspending velocity estimatedfrom Itakura (1984). Unlike suspended sediment outflow, the amount of storage is not a limiting factor in
Eqs. (9) and (10). Therefore, we implemented a layer of
bedload that interacts with the suspended sediment,
known as an exchange layer. The volume of the exchange layer Se [m3] is given byS e ¼ minðhb A; S b Þð11Þ

where hb is the thickness of the exchange layer [m] andSb is the total volume of bedload storage [m3]. We set

Page 4 of 15

the thickness of the exchange layer to 0.05 mm which isthe center size of the grain size classification. We assume that the sediment in the exchange layer is theupper limit of outflowing bedload at each time step andapplied the exchange layer to bedload storage ratio tothe original estimated values from Eqs. (9) and (10).
Therefore, the outflowing change in exchange layer storage can be written asdSe¼ − bq þ bs S e
dtΔS e ¼ 1 − e − ðbq þbs ÞΔt S e
0
Qb

ð12Þ
ð13Þ

0

where bq ¼ Sb and bs ¼ SSP
S b . The equations above can be
broken down to derive bedload flow Qb [m3/s] and suspension SSP [m3/s] as
Qb ¼

bq ΔS bbq þ bs Δt

ð14Þ

bs ΔS bbq þ bs Δt

ð15Þ

SSP ¼

The above-mentioned processes are calculated foreach sediment type within each grid. Temporal changes
in storage of each sediment type is given by
S tþΔt¼ S tsi þ
siupstream
X

Qtsik Δt − Qtsi Δtkþ SSPti Δt − DEP ti Δt þ ersti A f¼ S tdi þS tþΔtdi

upstream
X

Δt
3600

ð16Þ

Qtbik Δt − Qtbi Δtkþ DEPti Δt − SSP ti Δtð17Þ

where t is time, Δt is the time step, k is the index indicating each upstream grid for the targeted grid, i indicates the sediment type, and Af is floodplain area [km2].
Nihei et al. (2005) stated that suspended sedimentconcentration becomes vertically uniform during risingwater periods, whereas near-bed suspended sedimentconcentrations exceed surface concentration at peaks
and during decreasing water periods. We implemented ascheme to change the suspension and deposition coinciding with flow velocity change. Changes in the flow regimes are defined as
Rising : vt ≥vt − 1 þ vt − 2þV2

Peak=receding : vt <vt − 1 þ vt − 2
þV
2

ð18Þ
ð19Þ

where vt is the flow velocity [m/s] at time t and V is aconstant value, which is set to 0.0005 m/s. We assume(2020) 7:59

Hatono and Yoshimura Progress in Earth and Planetary Sciencethat suspension increases when water is rising, and define bs in Eqs. (12)–(15) as
bs ¼ α

ssp
sd

ð20Þ

where α is a constant value set to 10. When water ispeaking or receding, we assume the suspended sedimentconcentration to have a vertical profile and apply an exponential function. The near-bed suspended sedimentconcentration is given by
6ZC
1 − e − 6Z

ð21Þ

wf
κu

ð22Þ

Cb ¼
Z¼

and is substituted into C of Eq. (4).
2.3 Boundary data and experimental settings

The developed sediment dynamics model uses precipitation and river runoff as input. We used precipitationfrom a global meteorological forcing dataset (Kim et al.
2009), which disaggregated observation based precipitation using reanalysis data time series, and obtained runoff from an off-line simulation of MATSIRO using Kim
et al. (2009) as forcing data. The original forcing data
was gridded at 1° spatial resolution with daily temporal
resolution. Although there are various precipitation
datasets, we used Kim et al. (2009) in order to maintainconsistency with the river runoff data.
The initial value of bedload storage is the limit of river
bed degradation. Implementing a constant value for all
grids does not reflect regional characteristics, therefore
we used data of depth to bedrock, as described by
Shangguan et al. (2017). This dataset is made by combining various data such as publicly available soil profiles,
borehole logs, and lithological maps using machine learning methods. The originally downloaded 1 km resolution
was interpolated to match that of our experimental settings, 0.5° and multiplied by the grid area to determine theinitial value of bedload storage. Grids having invalid
values, for reasons such as differences in land-sea boundaries, were assigned the mean value of all grids.
Variously sized sediments are both mixed in the river
bed and suspended in the river. We used three representative sizes for sand, silt, and clay to depict the mixedsized sediments (Table 1). The sediment dynamics
Table 1 Representative diameter of three sediment sizes
Representative diameter [mm]Size range [mm]

Sand

1

0.05 < D < 2

Silt

0.025

0.002 < D < 0.05

Clay

0.001

D < 0.002

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explained in the previous section are calculated individually for each sediment size, and we used the total suspended sediment flow of all three sediment sizes tovalidate our model outputs. We used sand, silt, and clay
fractions from Hengl et al. (2014) to obtain the gridded
distribution of each sediment size. The data from Hengl
et al. (2014) represent the percentage of each sedimenttype in the land surface. We aggregated the original 1
km spatial resolution to match that of our experimental
settings. For grids with no valid data for these three sediment sizes (e.g., Sahara Desert), we assumed that all
three sizes existed evenly. These distributions were set
as the distribution of sediment erosion at each time stepand the initial value of bedload storage. Although thegrain size distribution can be different between the land
surface and bedload, we used this distribution for the
initial value of bedload storage since the size distributionof bedload storage is extremely difficult to obtain at the
global scale.
The river and sediment dynamics model was calculated at 0.5° spatial resolution with a unit catchment
assigned to each grid. The temporal resolution wasdetermined to fit the Courant-Friedrichs-Lewy condition, which was approximately 15 min. The results inthe following sections show daily and monthly averaged values. The initial water and suspended sedimentstorage is set as 0. Calculation was conducted for theperiod 1979–2000. The first 2 years were used as
spin-up, and we analyzed a total of 20 years from1981 to 2000. The control experiments that used parameter values stated in the previous section are referred to as CTL.

2.4 Observation data

Although many observations have been conducted for
sediment-related research, most of them are conducted
at a regional- or basin- scale; these observation data havenot been compiled in a global database. We were able togather sediment-related observation data from multiple
sources to compile a global database of over 4000 observation stations worldwide. From the validation criteria in
the following subsection, we validated suspended sediment flow and concentration using data from the Global
Environmental Monitoring System (GEMS; United Nations Environment Programme 2018), Hydrology andGeochemistry of the Amazon Basin (HYBAM; http://
www.ore-hybam.org), National Water Data Archive (Environment Canada; https://wateroffice.ec.gc.ca/) and theUnited States Geological Survey Sediment Data Portal(https://cida.usgs.gov/sediment/). River discharge wasvalidated using data from the Global Runoff Data Centre(GRDC). Discharge data for most stations in this study
were available at daily timesteps.

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

2.5 Selection of validation sites

3 Results

All available data was utilized for the global scale validation. The accuracy of hydrological regimes such as
river discharge is very important as sediment dynamics are calculated using these variables. Therefore, we
included the number of data samples of river discharge as a criteria to select stations for sediment validation sites. In order to have enough samples toestimate the accuracy and account for seasonality, weused stations that had at least 365 and 12 samples
within 1981 to 2000 of valid river discharge and sediment data, respectively. The difference in number ofsamples is because our river discharge data was mostly
in daily timescales and many of our sediment data wasin monthly timescales. The selected stations also
should have overlapping periods of available river discharge and sediment data. Since our model simulations are conducted on a relatively coarse 0.5°spatial
resolution, we used stations that were located in areas
with an upstream area of over 10,000 km2. Sixty-one
observation sites meeting each of the abovementioned criteria were selected for validation. The
correlation coefficient of simulated river discharge at
all selected stations was significant at the 99% level.
The minimum and maximum numbers of sediment
samples at an observation station within 1981 to 2000
were 12 and 7305, respectively. We are actively collecting more observation data which would lead to anincrease in validation sites for our developed model.

3.1 Amazon River

Page 6 of 15

Data from four stations in the Amazon River basin—
Serrinha, Porto Velho, Manacapuru and Obidos—werevalidated using data from HYBAM. Observed suspended sediment concentration and river discharge
were used to calculate the observed suspended sediment flow. Figure 2e shows the location of each of thefour stations. Serrinha and Porto Velho are locatedalong tributaries of the Amazon River, Negro River,and Madeira River, respectively. Table 2 shows the coordinates, upstream area, and annual mean river discharge at the four stations. The observed mean annual
suspended sediment flow at each station was 4, 310,
250, and 360 Mt/a, respectively. These values are estimated by linearly interpolating obtained observation
values, which are three samples per month at most, to
get the annual temporal distribution. The modeledmean annual suspended sediment flow at each station
was approximately 30, 90, 230, and 410 Mt/a, respectively. Figure 2a–d shows the time series of the observation and simulated daily suspended sediment flow [103t/day] of four observation stations in the Amazon Riverbasin. This indicates that the values for the furthest station upstream, Serrinha, are largely overestimated. Thisis because although Negro River, the tributary which
Serrinha is located, is known to have significantly lower
sediment transport because it drains cratonic areas andrainforests (Latrubesse et al. 2005), our simulation

Fig. 2 Daily suspended sediment flow [103 tons/day] at a Serrinha, b Porto Velho, c Manacapuru, and d Obidos. Black circle indicates observation,
blue line indicates CTL simulation. e Basin map. The background color indicates upstream area [103 km2]. Gray grids indicate grids whose
upstream area is smaller than 50,000 km2

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

Page 7 of 15

Table 2 Summary of validation stations in the Amazon River basin
Coordinates (lon, lat)

Upstream area [103 km2]

Mean annual river discharge [m3/s]

Serrinha(− 64.83, − 0.48)300

18,000

Porto Velho(− 63.92, − 8.74)

1000

20,000Manacapuru(− 60.61, − 3.31)

2200

100,000

Obidos(− 55.51, − 1.95)

4700

180,000

Station namecurrently does not take into account geological features
when estimating sediment erosion. Furthermore, theseasonal irregularities seen in the observation values require further investigation including observation uncertainty. Porto Velho showed large underestimation, but
the seasonality was well represented. At Manacapuru
and Obidos, both the fluctuation range and seasonality
are well represented. The correlation coefficient forsuspended sediment flow at Serrinha, Porto Velho,
Manacapuru, and Obidos were 0.10, 0.49, 0.41, and0.66, respectively. Partly due to our assumption thatsuspension increases in rising waters, our simulations
have sharp daily fluctuations which are similar to thesimulation results shown in Cohen et al. (2013).
Figure 3 shows the monthly breakdown of annual suspended sediment flow at each station. The width of eachmonth is proportional to the monthly to annual ratio of
suspended sediment flow. HYBAM had only up to threesamples per month, so they were averaged for themonthly representative values. Due to the irregularmonthly fluctuation in Serrinha, a clear peak is notpresent in the observation values. The remaining threestations showed one peak from winter to early spring,which are effectively simulated. Manacapuru and Obidos
show a relatively similar monthly distribution. Although
the model captures the characteristics of the distribution

where sediment flow in January to April is larger compared to those in July to October, our model simulation
does not have a distinct difference compared to the observation. Porto Velho, where more than half of the annual flow was concentrated in January to April, wasdistributed differently from the other stations in boththe observation and model simulation. This characteristic is more obvious in the observation, where these 4months account for almost 80% of the annual flow. The
Madeira River, in which Porto Velho is located, experiences its flooding season from February to April
and has widely fluctuating river discharge. Largeamounts of sediment enter the river from the Andes
during the flooding season, causing large concentrations in the earlier months of the year. Because of
low river discharge in the upper basin grids in themodel simulation, the high volume of sediment produced in mountainous areas cannot be delivered
downstream, leading to a smaller proportion of sediment flow during the flooding season.
Our model simulation was able to capture the seasonality and peak timings of observation stations in the
Amazon River basin which is one of the largest sourcesof suspended sediment flow to the oceans. This shows
our global scale model has the capability to be used forindividual basins.

Fig. 3 Monthly breakdown of annual suspended sediment flow. a Serrinha, b Porto Velho, c Manacapuru, d Obidos. Left column indicatesobservation, right column indicates CTL simulation

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

3.2 Global scale

Figure 4 shows the correlation coefficients of suspended
sediment flow at 61 validation stations. Circles and triangles indicate stations where correlation coefficientsare significant and insignificant, respectively, at the 95%
significance level. Of the 61 validation stations worldwide, 55 stations had correlation coefficients that weresignificant at the 95% significance level and 20 stations
had a positive Nash-Sutcliffe efficiency (NSE; Nash andSutcliffe 1970). The high number of significant correlation
coefficients indicates that the seasonality of suspended
sediment flow is simulated well at our selected stations,but the peak values and/or fluctuation ranges may stillneed improvement since NSE, which is sensitive to observed and modeled means and variances (Legates andMcCabe 1999), are relatively lower. The R2 of NSE estimated for river discharge and suspended sediment flow
individually at 61 stations was 0.23 (p < 0.01), which suggests accuracy of seasonality of river discharge does have asomewhat weak correlation with the accuracy of suspended sediment flow.
Figure 5 shows the month with the maximum suspended
sediment flow. Forty-four out of the 61 validation stations
that have at least 1 year with observation data in all 12months were selected. Twenty-two stations had the same
maximum month and the others were mostly within a 2month bias. The exceptions were Serrinha in the Amazon
River basin, a station along the Red River, 05OC001, a
station along the Little Colorado River, 09402000, and astation along the Ohio River, 03294500. The observed maximums for Serrinha, 05OC001, 09402000, and 03294500
were February, April, February, and August, respectively,
whereas the simulated maximums were May, July, June andJanuary, respectively. Stations at high latitudes, like those in
Russia and Canada, tend to have a peak at around May andJune (Fig. 6a, b). Stations located in Mexico and the
Mekong River basin, which have similar latitudes, peaked
in July to September, later than the other selected stations

Page 8 of 15(Fig. 6c, d). These characteristics are also apparent during
the peak month of river discharge. Most of the stations
showed a 0–2-month lag between river discharge andsuspended sediment flow, which were present in the model
simulations as well. Serrinha and Porto Velho in the
Amazon River had longer lags of 3–4 months, which were
not apparent in our model simulations.
The temporal trend of annual suspended sedimentflow in CTL for global rivers with basin areas larger than
10,000 km2 were assessed. The number of rivers that increased, decreased, or was stable over the 20 years was
58, 72, and 1022 rivers, respectively. The increasing anddecreasing trends were significant at the 95% level. The
distribution of trends were similar to the results in Walling and Fang (2003) which used 145 rivers in North
America, Europe, and Asia. The temporal trend of thetotal suspended sediment flow to the oceans globally
showed a decreasing trend.
Figure 7 shows the annual suspended sediment flow
at river mouths for (a) rivers listed in Milliman andFarnsworth (2011) (hereafter referred to as MF11) and(b) 20 years average of CTL simulation. River mouths
included in MF11 are shown in Fig. 7b. Rivers having
a modeled annual discharge below 1 m3/s are excluded
in consideration of other rivers, mainly in the Oceania
Region, that were simulated poorly due to the coarse
resolution of the simulation and forcing data. Similar
magnitudes of suspended sediment flow were simulated for regions like the Amazon and norther Eurasia,
but those for southern and eastern Asia and western
North America were underestimated, overall. MF11
has values from various studies so the uncertainty ofthe values in the literature must also be assessed in
the future. For example, the Yellow River is among thelargest suspended sediment supplies with 470 Mt/a;however, this value was calculated from only 5 sampling surveys from 1984 to 1986 (Zhang et al. 1994). It
should also be noted that the data included in MF11

Fig. 4 Correlation coefficient of suspended sediment flow. Circles and triangles indicate significance and insignificance at the 95%
level, respectively

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

Page 9 of 15

Fig. 5 Month with the maximum suspended sediment flow. a Observation and b CTL simulation

are collected using various sampling techniques and sampling periods, so this may be a source of discrepancies between our results and MF11. The following section will
discuss the legitimacy of using previous global estimatesas the standard for validation. Figure 8 shows a directcomparison of MF11 and CTL simulation. The colors ofeach plot indicate the river basin area. The correlation and
NSE were 0.88 and 0.57, respectively, indicating that themodel simulations effectively captured spatial characteristics of global distribution. The total suspended sedimentflow to the oceans from the plotted rivers of MF11 andmodel results were approximately 6.7 Bt/a and 2.8 Bt/a,
respectively.

The amount of global suspended sediment transport
from land to oceans has been reported in numerous
studies, and recent estimates are approximately 13.5~22
Bt/a (Panin 2004). Panin (2004) states some studies haveincluded suspended sediment flow due to anthropogenic
effects in their estimates. The natural volume of sediment transport is approximately 6~12.6 Bt/a, and theanthropogenic sediment transport after the Anthropocene is estimated to be approximately 6.7~12 Bt/a. Ourmodel estimates of approximately 4.0 Bt/a and appear to
be of the same magnitude as the “natural” transport
amount although it is still largely underestimated. Since
our sediment dynamics model does not include

Fig. 6 Averaged monthly suspended sediment flow [103 tons/day]. a Lena River, b Liard River, c Panuco River, and d Mekong River. Solid blueand dashed black lines indicate CTL simulation and observation data, respectively

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

Page 10 of 15

Fig. 7 Annual suspended sediment flow [Mt/a]. a Milliman and Farnsworth (2011) and b CTL simulation

anthropogenic effects such as reservoirs and land usechanges, we consider our estimates to be comparable tothe “natural” transport amount.
Our model simulations were able to simulate the seasonality well at observation stations worldwide. Although our global suspended sediment flow to theoceans were significantly smaller compared to previous
studies, we were able to capture the spatial distributionof sediments well. The following subsection introduces
sensitivity analysis to some of our empirical parametersin order to assess the key factors to changes in suspended sediment flow.

3.3 Sensitivity analysis

Here, we checked the sensitivity to the empirical parameters considered in several processes in our model. The
considered parameters and their CTL simulation andsensitivity experiment values are shown in Table 3. Figure 9 shows the suspended sediment flow at the Obidos
station in the Amazon River basin. The solid and dashedblue lines indicate smaller and larger parameter values,
respectively. The black solid line indicates CTL results
and the black circles indicate HYBAM observation data.
Increasing the erosion, exchange layer, and suspension
parameters and decreasing the rising water parameter

Fig. 8 Scatter plot of annual suspended sediment flow [Mt/a] between MF11 and CTL. The colors of each plot indicate the river basin area. The
solid and dashed line indicate the trend and 1:1 line, respectively

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

Table 3 Parameter values for CTL and sensitivity simulations
Related process Variable/equation CTL

Sensitivity simulation

Erosionβ in Eq. (1)0.1

Exchange layer

hb in Eq. (11)0.00005 0.0005, 0.000005

Rising waters

V in Eqs. (18), (19)0.00050.005, 0.00005

Suspension

α in Eq. (20)10100, 1

1, 0.01

lead to increased suspended sediment flow. The suspended sediment flow increases drastically when theerosion and exchange layer parameters are increased
because these parameters directly increase the suspended sediment storage in each grid. Increasing thesuspension parameter and decreasing the rising waterparameter only slightly increases the suspended sediment flow because they affect only the suspended
sediment in rising waters. Changing the parameters in
the opposite way decreases the flow only slightly except for the sediment yield parameter, which reduced
the suspended sediment flow to approximately half ofCTL. These results indicate that sediment enteringrivers because of land erosion affects the amount ofsuspended sediment flow in the Amazon River basin
more than sediment being suspended from the bedload. The other three stations in the Amazon Riverbasin used for validation had similar trends as well.
These characteristics are different depending on basincharacteristics and will be assessed globally in futurestudies.

Page 11 of 15

The 20-year average of global suspended sedimentflow from the river mouths from each sensitivity experiment ranged from 1.6 to 24 Bt/a. The lowest and highest
estimates were when the sediment erosion parameter
was decreased and increased, respectively. The trend ofchange due to increasing and decreasing the remaining
three parameters were also similar to the changes described above for the Amazon River basin. When comparing the sensitivity at the continental scale, all of thecontinents except Africa were most sensitive to the erosion parameter by far, with the suspended sediment flow
to the oceans ranging from approximately 0.1 to 8.3
times that in CTL. In Africa, the sensitivity to the erosion parameter was relatively smaller compared to othercontinents. From Eq. (1), the sensitivity to β of the erosion parameter can also be interpreted as sensitivity to
the slope, which is a variable with some uncertainty
since accurate representation of terrain slope at the global scale is difficult to obtain. Although these settings
are not feasible for the Obidos station, we can ascertain
the fluctuation range of global suspended sediment flow
estimates from previous studies by adjusting the parameters with large uncertainty and relying on basin characteristics. This suggests a uniform value globally is notenough and parameters that sufficiently reflect various
basin characteristics are necessary. Revising our parameters to enable consideration of regional characteristics
without heavy calibration using observation data will bepart of our future works.

Fig. 9 Daily suspended sediment flow [103 t/a] at Obidos station, as estimated from sensitivity experiments for a erosion, b exchange layer, crising waters, and d suspension. Black circles and solid lines indicate observation and CTL simulations, respectively. Solid and dashed blue lines
indicate smaller and larger parameter values, respectively

(2020) 7:59

Hatono and Yoshimura Progress in Earth and Planetary Science

Page 12 of 15

Table 4 Statistics of simulations excluding sediment dynamics processes at Obidos
Sediment erosion

Suspension

Deposition

Correlation coefficient

0.69

0.99

0.99

1.00

Average annual suspended sediment flow change[Mt/a]

− 350

− 150

+ 1400+ 1 × 10−4

4 Discussion
All of the considered sediment dynamics processes in ourmodel have an impact on the suspended sediment flow.
We assessed the significance of each process to the suspended sediment flow by conducting simulations that exclude each of the following processes: sediment erosion,
suspension, deposition, and bedload flow. The experimental settings are the same with CTL. Table 4 shows thechange in average annual suspended sediment flow andcorrelation coefficient of each simulation at Obidos in the
Amazon River basin. In order to assess the change
compared to CTL, these statistics are estimated using suspended sediment flow of CTL as “observation.” Thechange in average annual suspended sediment flow andcorrelation coefficient indicate change of the magnitude
and seasonality of suspended sediment flow by excluding
a certain process, respectively. Excluding bedload flow did
not have a significant impact on the magnitude or seasonality of suspended sediment flow which is reasonable since
it does not directly affect the suspended sediment storage.
Also, bedload flow at Obidos station was several orders ofmagnitude smaller than suspended sediment flow and theother components in our simulation, which may be one of
the reasons for the negligible impact. This indicates suspended sediment is the dominant source of sedimenttransport in rivers in our simulations. This finding is inagreement with previous studies (e.g., Milliman and
Meade 1983). We found that the only simulation that had
noticeable impact on the seasonality was when we excluded sediment erosion. This may be because while theother variables along with suspended sediment flow are
more or less influenced by hydraulic variables in the river,estimation of sediment erosion does not share any variables with that of suspended sediment flow. Improving
our sediment erosion process could lead to an improvement in the seasonality of suspended sediment flow in our
simulations. The change of average annual suspended
sediment flow was largest when deposition was excluded.
This indicates that deposition is the key factor to theamount of suspended sediment flow in our simulations at
Obidos. The global suspended sediment to the oceans
when excluding sediment erosion, suspension, deposition,and bedload flow were 1.4, 2.7, 15.7, and 4.0 Bt/a, respectively. The relative magnitude of the change in the global
scale was similar to that of Obidos. These findings fromthis analysis offer insight to the significance of eachconsidered process and how to improve our model
simulations.

Bedload flow

Cohen et al. (2013) conducted validation on theirsimulation using WBMsed at the global scale using
long-term average data from Syvitski and Milliman(2007) and at the regional scale for 11 USGS stations.
They used 95 rivers for the global scale validation thatwere selected based on basin area and its consistency
with the model’s river network. By using the same criteria, we selected 403 rivers in MF11. The R2 of global
long-term average suspended sediment flow in WBMsed
and our simulation was 0.66 and 0.82, respectively. Theirsimulations reported an overall underestimation for the
95 rivers, whereas our simulation did not have a clear trend.
Both models showed underestimation in similar regions(e.g., East Asia, Mediterranean basin and northwestern
North America). As for the regional scale, a USGS station
along Illinois River, 05586100, was included in both ourstudy and Cohen et al. (2013). Table 5 shows R2 of daily
river discharge and suspended sediment flow at this station
for WBMsed and our CTL simulation. It should be noted
their validation period was from 1997 to 2007, and also that
their simulations for the USA was conducted at a 0.1°
spatial resolution. Our sediment seasonality had better accuracy compared to WBMsed which may be due to better
accuracy in river discharge since their daily fluctuation of
sediment flow relies heavily on daily fluctuation of river discharge. It seems our simulation underestimates suspended
sediment flows during peak discharge periods compared to
WBMsed, but a more detailed comparison may be necessary as WBMsed seems to overestimates peak river discharge compared to our model. Model intercomparison
using same forcing data will be part of future works.
As shown in the previous section, suspended sedimentfrom land to the oceans is largely underestimated incomparison with the global database MF11. The previous global estimates are based on a compilation of numerous observational data that was extrapolated to fit
the total area of a specific target area. Table 6 compares
our CTL results of suspended sediment transport tooceans for different continents with those of MF11,(Syvitski et al. (2005) and Holeman (1968)). The suspended sediment flow in all of the continents except
Table 5 R2 of daily discharge and sediment flow for WBMsed
and CTL
Discharge

Sediment

WBMsed (Cohen et al. 2013)

0.48

0.35

CTL

0.56

0.53

(2020) 7:59

Hatono and Yoshimura Progress in Earth and Planetary Science

Table 6 Comparison between previous estimates and CTL ofcontinental-scale suspended sediment flow [Bt/a]
MF11

Syvitski et al.
(2005)

Holeman(1968)CTL

North America

1.9

1.9

1.8

0.3

South America

2.3

2.5

1.1

1.1

Africa

1.5

0.8

0.5

0.4

Europe

0.85

0.7

0.4

0.1

Asia

12.55

6.8

14.5

1.9

Total

19.1

12.6

18.2

3.9

Total (excluding Asia)

6.55

5.8

3.7

2.0

Asia are larger in MF11 compared to Holeman (1968),
which is partly due to the increase in number of data
samples. It should be noted that Holeman (1968)’s estimates for Asia do not include Oceania due to lack ofobservation data. Our CTL results in South America,
Africa, and Europe are relatively similar to the estimates by Holeman (1968). The underestimation in
North America comes from the underestimation ofMississippi River basin and northwestern region of
North America. The main source of the large underestimation in our global suspended sediment flow estimates is Asia, specifically Oceania. MF11 states that
half of the suspended sediment from Asia comes fromthe Oceania islands, which account for 6800 Mt/a injust a few percent of the Earth’s total land surface area.
While our model results show obvious underestimations for some continents, there are also discrepancies
in the previous studies. By comparing our model resultswith global long-term estimates and regional timeseries data, we consider the following as the two maincauses of underestimation.

Page 13 of 15

First, the previous global estimates are based on various observational studies that do not necessarily matchthe target period in this study. For example, the suspended sediment flow of the Amazon River in MF11,1200 Mt/a, is based on estimates by Meade et al. (1985).
Previous studies published in different decades (e.g.,
Holeman 1968; Meade et al. 1979) gave different estimates ranging between approximately 400 Mt/a and1000 Mt/a. To estimate the recent amount of suspended
sediment flow, we obtained daily data by applying linear
interpolation temporally to the HYBAM observation
data from 1996 to 2000, consisting of approximately 3
samples per month. The results showed that the meanannual suspended sediment flow for the Obidos stationin the Amazon was approximately 360 Mt/a, which arecloser to estimates by Holeman (1968). Our CTL estimate was approximately 420 Mt/a. The reason for thisdecrease in the observed values is outside the scope of
this study because numerous factors may cause theamount of suspended sediment flow to change. However, our analysis indicates that the sources of previous
global estimates do not necessarily represent the target
period of this study.
The second factor focuses on discrepancies within theextrapolated results. Due to the lack of sufficient observation data on suspended sediment flow at the global
scale, almost all of the previous global estimates reliedon extrapolating existing observation data by consideringfactors such as basin area and geological features. As
shown in Table 6, most of the underestimation is from
Asia. The total suspended sediment flow from the East
Indies islands in CTL was 600 Mt/a. Thirteen rivers withdata stored in MF11 were chosen for direct comparison.
The only threshold was modeled annual mean riverFig. 10 Comparison of suspended sediment flow [Mt/a] between MF11 and OCN simulation a MF11 b OCN, and c scatter plot

Hatono and Yoshimura Progress in Earth and Planetary Science(2020) 7:59

discharge is larger than 1 m3/s. To discuss the regional
characteristics and directly compare them with previous
estimates, we calibrated the parameters to improve theaccuracy for the selected river basins (hereafter referred
to as OCN). The calibrated parameters are those included in calculating sediment erosion as Oceania is said
to have a significantly high sediment yield (Milliman andFarnsworth 2011). Hereafter, we focus on comparing
MF11 and OCN. Fig. 10 shows the distribution of suspended sediment flow in MF11 and OCN. The total suspended sediment flow of the 13 rivers for MF11, OCN,
and CTL are approximately 290 Mt/a, 250 Mt/a, and 80
Mt/a, respectively. The root mean square error of OCN
and CTL are 9 Mt/a and 25 Mt/a, respectively. The NSEof OCN and CTL is 0.88 and 0.09, respectively, whichindicates the calibrated simulations of the suspended
sediment flow of these rivers are significantly improved.
Although the 13 recorded rivers are simulated well, thetotal suspended sediment flow for the entire Oceania region in MF11 and OCN are significantly different at
6800 Mt/a and 1900 Mt/a, respectively. Although ourexperimental settings, such as spatial resolution, may be
responsible for this large difference, we must be carefulwhen directly comparing previous extrapolated largescale estimates with simulated results.

5 Conclusions
In this study, we developed a global sediment dynamics
model in the framework of ILS. We were able to effectively represent the seasonality and magnitude of suspended sediment flow in numerous larger rivers. Whileindividual rivers were well-represented, the global sumof suspended sediment flow was largely underestimatedcompared to previous global estimates. Two factors may
be responsible for this underestimation: the difference intarget time periods and the discrepancies among extrapolated results. These factors will be further investigatedalong with other improvements, such as considering an
objective method for optimal global parameters andimplementing the sediment dynamics in floodplains.
This study will enable simulation of sediment dynamics
at the global and regional scales for different time periods and will contribute to the understanding of sediment dynamics and its relationship with other earthsystem processes.
Abbreviations
GEMS: Global Environment Monitoring System; GRDC: Global Runoff Data
Centre; HYBAM: Hydrology and Geochemistry of the Amazon Basin;
ILS: Integrated Land Simulator; NSE: Nash-Sutcliffe efficiency
Acknowledgements
We would like to thank the originators of the suspended sediment flow andconcentration data acquired through United Nations Environment GEMS/
Water Progamme, HYBAM, National Water Data Archive, and United States
Geological Survey. We would also like to thank the Global Runoff Data
Centre for providing river discharge data.

Page 14 of 15

Authors’ contributions
MH and KY conceived the topic. MH conducted the simulations and analysis.
Both authors discussed the results and commented on the manuscript. Bothauthors read and approved the final manuscript.
Authors’ information
MH is currently an assistant professor at Hiroshima University. Parts of thisstudy were conducted when MH was a postdoctoral fellow at Tohoku
University. KY is a professor at the Institute of Industrial Science in The
University of Tokyo.
Funding
The sources of funding are: JSPS KAKENHI Grant Numbers JP18J00585,
JP15J10456 and JP16H06291, Integrated Research Program for Advancing
Climate Models (TOUGOU) Grant Number JPMXD0717935457 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanand the Arctic Challenge for Sustainability II (ArCS II) Project (Program Grant
Number JPMXD1420318865) from MEXT, Japan.
Availability of data and materials
The data generated from our models may be available upon request fromthe corresponding author. The observation data used in this study areavailable from each corresponding organization.
Competing interests
The authors declare that they have no competing interest.
Author details
1
School of Engineering, Tohoku University, 6-6-06 Aoba, Aramaki, Aoba,
Sendai, Miyagi 980-8579, Japan. 2Graduate School of Advanced Science andEngineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City,
Hiroshima 739-8527, Japan. 3Institute of Industrial Science, The University ofTokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8574, Japan.
Received: 14 November 2019 Accepted: 31 August 2020

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