Kido et al.
Progress in Earth and Planetary Science(2023) 10:54
https://doi.org/10.1186/s40645-023-00580-0

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Assessing the impact of climate change
on sediment discharge using a large ensemble
rainfall dataset in Pekerebetsu River basin,
Hokkaido
Riho Kido1, Takuya Inoue1* , Misako Hatono1 and Kazuki Yamanoi2

Abstract
Increased rainfall associated with climate change can increase sediment discharge. The supply of fine sedimentfrom slope failures inhibits bed armoring of mountain rivers and increases sediment discharge to the downstream
reaches. Floods without slope failures lead to bed erosion and armoring and may ultimately decrease sediment
discharge. Thus, it is important to consider sediment discharge from slope failure and bed erosion as factors affecting sediment production. Climate change affects not only the rainfall amount, but also the temporal rainfall pattern;consequently, the pattern affects the sediment production factors and the amount of sediment discharge. However,changes in sediment discharge due to climate change based on sediment production sources have not yet beenclarified. In this study, we statistically analyzed 1200 results simulated using a physics-based sediment runoff model
to assess the impact of changes in temporal rainfall patterns on sediment discharge and sediment production
sources in the Pekerebetsu River Basin. In the simulations, we used the rainfall predicted in d4PDF (Database for policy
decision-making for future climate change), a large ensemble climate simulation database at 5 km and 20 km resolutions. Our results showed that the climate-driven increase in sediment discharge was considerably larger than that of
rainfall. An increase in short-term heavy rainfall increased the supply of fine sediments from slope failure. This resulted
in the suppression of bed armoring and a large increase in sediment discharge. Thus, the increase in sediment discharge is not only caused by an increase in rainfall but also by changes in temporal rainfall patterns and sediment production factors. The sediment discharge calculated for the 20 km resolution climate projection was nearly one orderof magnitude smaller than that for the 5 km resolution. This suggests that the 20 km resolution climate projections
do not adequately represent orographic rainfall in the mountains and thus, do not adequately reproduce extreme
sediment discharge events. An increased sediment supply causes bed aggradation and decreases the river conveyance capacity of the downstream channel. The model developed in this study will contribute to flood risk analysis
and flood control planning for increased rainfall due to climate change.
Keywords Climate change, Sediment discharge, Large-ensemble data, Temporal rainfall pattern, Slope failure,
Channel erosion, Spatial resolution of climate change projections*Correspondence:
Takuya Inoue
inouetakuya@hiroshima-u.ac.jp
1
Graduate School of Advanced Science and Engineering, Hiroshima
University, 1‑4‑1 Kagamiyama, Higashi‑Hiroshima, Hiroshima 739‑8527,
Japan
2
Disaster Prevention Research Institute, Kyoto University, Kyoto 612‑8235,
Japan© The Author(s) 2023. 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 theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence 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 thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

1 Introduction
The recent increase in flooding worldwide has been
attributed to atmospheric factors; however, an oftenforgotten factor is the reduction in river conveyancecapacity associated with sediment deposition (Lane et al.
2007; Sofia and Nikolopoulos 2020; Milan and Schwendel 2021). Changes in river conveyance capacity do notaffect the amount of water that flows through the river
system during a flood, but they affect the probability of
a flood event overtopping the embankments and flood
defenses. Increased sediment supply can contribute tobank erosion in gravel beds (Métivier et al. 2017; Ahmed
et al. 2019; Inoue et al. 2020; Rachelly et al. 2022) andsediment-induced abrasion in bedrock rivers (Sklar andDietrich 2004; Inoue and Nelson 2020; Inoue et al. 2021),
resulting in bridge failures. In addition, an increasing
number of towns located in valleys and fans are affectedby flooding, including debris flows, in countries withsteep mountains such as Japan and Switzerland (Rickenmann and Koschni 2010; Shimizu et al. 2020; Hashimoto
et al. 2020).
Simulations using General Circulation Models (GCMs)
predict increased rainfall intensity in many parts of theworld under most future climate change scenarios, and
it is generally believed that such increased rainfall intensity will lead to increased landslide activity (Crozier
2010; IPCC 2021). However, there is still a high level of
uncertainty due to the errors inherent in scenario-driven
world predictions and the coarse spatial resolution ofcurrently available downscaled predictions (Crozier2010). Recently, model development aimed at simulating large-scale sediment transport in GCMs has become
active (Hatono and Yoshimura 2020; Li et al. 2022; Cohenet al. 2022). Several studies have incorporated global andregional climate models into physics-based slope-stability models to investigate the effects of climate change on
landslides (Ciabatta et al. 2016; Alvioli et al. 2018; Peres
and Cancelliere 2018; Hürlimann et al. 2022). However,it remains unclear how the spatial resolution of climatechange projection models affects sediment runoff.
Landslides are not the only sources of sediment transported to rivers and towns located in valleys and fans. In
a 2016 flood event in Hokkaido, Japan, Miyazaki et al.
(2018) and Furuichi et al. (2018) conducted field observations and found that 70 % of the sediment discharged
from the upper reaches of the Pekerebetsu River, a tributary of the Tokachi River located in eastern Hokkaido,
into the downstream fan originated from the riverbed of
the upstream mountainous river. Higher rainfall increases
the flow rate of mountain rivers, consequently increasing their sediment transport capacity (Hürlimann et al.
2022). In addition, riverbed deformation in mountain
rivers can alter the grain size distribution of sediments

Page 2 of 14

in the riverbed and cause phenomena that affect sediment transport rates, such as armoring (e.g., Pitlick et al.
2008). However, the effects of climate change on the sediment transport capacity and grain size in mountain rivers
remain largely unknown.
In this study, we investigated changes in sediment
discharge due to climate change using a large ensemble
climate simulation database (d4PDF; Mizuta et al 2017;
Ishii and Mori 2020) as input to a physics-based modelthat accounts for landslides, mountainous channel erosion, and grain size changes. We also clarified the effectof the spatial resolution of d4PDF (Yamada et al. 2018) on
sediment discharge. Furthermore, we calculated the sediment discharge derived from each production source anddetermined how changes in temporal rainfall patterns
caused by climate change affect sediment production andrunoff.

2 Methods
2.1 Study area

Our study area was the upper reaches of the Pekerebetsu
River in Hokkaido, Japan (Fig. 1a). The Pekerebetsu
River is a tributary of the Tokachi River that flows into
the Pacific Ocean. The Tokachi River is located in a cold
region (Peel et al. 2007). The annual precipitation averaged from 1996 to 2005 in the Tokachi River Basin wasapproximately 900 mm, which is much less than the average annual precipitation of 1600 mm for other basins inJapan (River Bureau and Ministry of Land, Infrastructure,
Transport, and Tourism 2006). However, in late August
2016, heavy rains due to fronts and typhoons caused
river overflows and landslides, mainly in the TokachiRiver Basin, resulting in extensive damage in many areas(Fig. 1b). Warm moisture carried by typhoons developed
into orographic rainfall near our study site and became
the heaviest rainfall in Hokkaido since observations
began in 1977, exceeding 500 mm in three days centeredin the Hidaka Mountains (Aoki et al. 2018).
The upper reaches of the Pekerebetsu River consist
mainly of granite, which is easily weathered. The periglacial deposits formed by repeated freezing and thawing of
the bedrock during the glacial period were distributed in
the surface layer of the slopes at the bottom of the valley(Miyazaki et al. 2018). As mentioned above, these sediments remain on the slopes and mountainous riverbeds
because there is generally little rainfall in this watershed,
and large amounts of sediment are easily runoff downstream once heavy rains occur. The peak flow discharge
at the downstream end of the study area was 402 m­ 3/s(Hokkaido Torrential Rainfall Hazard Research Team2017), and the total amount of sediment discharged
from the study site was 380,800 ­m3 which was estimatedfrom the difference in LiDAR surveys across this event

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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Fig. 1 Information of study area. a Map of Hokkaido Island (Japan) and location of Pekerebetsu River basin, Hokkaido, Japan. The red areais the study area, the blue line is the Tokachi River, the brown line is the Hidaka Mountains. b Aerial photograph of the flooding including debris
flow at Pekerebetsu River after 2016 heavy rainfall (Taken on September 1, 2016)(Ministry of Land, Infrastructure, Transport and Tourism2017). In addition, Miyazaki et al. (2018) stated that 70%of the sediment was produced from the riverbed.
2.2 Model
2.2.1 Model summary

In this study, we used a numerical model (see Additional
file 1) to assess the effects of climate change on sediment
discharge. This model comprises three physics-based components: First, the sediment production model calculates
the amount of sediment produced by slope failure. Basedon Darcy’s law, slope failure occurs when the amount ofwater in the soil exceeds a threshold. Second, the sedimentsupply model calculates the amount of sediment supplied
from the slopes to the river channels. Sediment is supplied
to a river channel when it exceeds the amount deposited
on a cliff cone. Third, the sediment transport model calculates the bed-load and suspended-load transport rates thatflow through the channel accompanied by the erosion anddeposition of the riverbed. Here, we defined the sediment
discharge as the sum of the bedload and suspended loadtransport rates at the downstream end of the study site. The
bedload and suspended-load transport rates were mainly
controlled by the Shields number (τ∗ = hI/sd ), which
was calculated using the flow depth and velocity froma kinematic wave model, where, h is the water depth, I is
the energy slope, s is the specific gravity of the submerged
sediment, and d is the grain size of the sediment. The flow
discharge required for depth and velocity simulations wasdetermined by the runoff model (Wooding 1965) using

rainfall data and was provided as input to each channel
reach.
2.2.2 Model modification

In this study, we investigated how predicted future climatechange will alter the balance between sediments produced
from slopes and riverbeds. We modified the sedimentmass conservation law using the method proposed byInoue et al. (2020) to track sediments based on production
sources, as follows:(1 − )

nk

∂zbi
1
= i
∂t
B

u1i,j=1

Qb

k

nk

nk

u1i,j=1Qu1i ck
k=1
nk
k=1
nk

u1i,j=2k

u2i,j=1nkk

u1i,j=2

i,j=1

Qi ck

−
k=1

u2i,j=2

Qb

+

nk

i,j=2

Qb

−

k

k=1
nk

u2i,j=2Qu2i ck

+
k=1

nk

i,j=2

Qi ck

−
k=1

nk
Li
Qsup
+
k

k=1

k

Qu2i ck

k=1

k=1
nk

+

i,j=1

Qb
k=1

+
nk

Qu1i ck

+

nk

−

k=1

Qb

+

k

k=1
nk

k=1

+

u2i,j=1

Qb

+

Ri
Qsup
k
k=1

(1)
where  is the porosity of riverbed materials, zb is theriverbed elevation, B is the width, Q is the water discharge,Qbk,ck , Qsupk are, respectively, the bed load
discharge, concentration of the suspended load, and supplied sediment discharge of the material with k grain size.
The superscript indicates the ID of the unit channels;iKido et al. Progress in Earth and Planetary Science(2023) 10:54,u1i , and u2i indicate the unit channeli , and the two
upstream channels connected to it. The superscripts Li
and Ri indicate the ID of the unit slopes located on theleft and right sides of the unit channeli , respectively. j
indicates the identification number of each production
source ( j = 1: riverbed; j = 2: slope).
In our model, we used Ashida and Michiue’s equation(Ashida et al. 1991) and the modified Egiazarov equation(Ashida et al. 1991) to estimate the bedload transport
rate and critical Shields number (i.e., Shields number at
the threshold for sediment motion). In addition, we used
Itakura and Kishi’s equation (1980) and Rubey’s equation(Rubey 1933) to estimate the entrainment and deposition
rates of the suspended loads, respectively.
2.2.3 Parameter input and calibration

We generated geometric input data of slopes and channels from a DEM with 10 m resolution measured by
LiDAR prior to the flood published by the Geospatial
Information Authority of Japan. Miyazaki et al. (2018)
investigated the grain size distribution at terraces andcliff cones that remained uneroded after the 2016 heavy
rainfall (Fig. 2). They investigated the grain size distribution by measuring the triaxial and weight in the field
for gravels larger than 75 mm in grain size and by conducting laboratory tests for sand and gravel smaller than
75 mm in grain size. To calculate the sediment discharge,
we used the average grain size distribution of the terrace deposits for the initial riverbed materials and thatof the cliff cone deposits for the slope failure materials.
In addition, the coefficient of permeability was set to
0.0001 m/s, based on Creager ’s Table (1945), which wasestimated from the grain sizes at which 20 percent of thetotal weight was finer.

Fig. 2 Grainsize distribution of study area observed by Miyazaki et al(2018) after the 2016 heavy rainfall. Black symbols show the averagevalues collected from river terraces which were not eroded
during 2016 heavy rainfall, and gray symbols show the averages value
collected from cliff cones which deposited during 2016 heavy rainfall

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To reproduce observed values during the 2016 heavy
rainfall (see Sect. 2), we calibrated the model parameters (i.e., αc , γ and f ). For the input rainfall data, weused the AMeDAS rainfall data from July 1 to September 5, including the heavy rainfall of 2016. Here, αc is aparameter for setting the channel width based on theregime law (αc = 5) and γ is a parameter that corrects forthe thresholds of slope failure (γ = 0.91). Also, f is theinfiltration rate into the bedrock under the soil layer ( f= 0.01 mm/h) and we set a very small value because thebedrock infiltration is not dominant during heavy rain(see the Additional file 1).
Comparing the calculated results with the observed
results, the peak flow discharge was 397 ­m3/s in the calculated results compared to 401 ­m3/s in the observed
results. The total sediment volume that flowed out of thebasin was 387,000 m­ 3 in the calculated results, compared3
to 380,800 ­m in the observed results. The percentage of
sediment produced from the slope was 33% in the calculated results compared to 30% in the observed results.
Thus, our model accurately captured the flow and sediment discharge observed in the study area.

3 Results
3.1 Rainfall data analysis

To evaluate extreme events that are statistically infrequent, we used a large ensemble data for climate change
in Japan, called d4PDF (Mizuta et al 2017). The Databasefor Policy Decision Making for Future Climate Change(d4PDF) uses a global atmospheric model for historical climate simulation and +4 K future climate simulation with a 60 km resolution and the model for the areasurrounding Japan with a 20 km resolution. In historicalclimate simulations, the sea surface temperature (SST)and greenhouse gas concentrations observed from 1951
to 2010, among others, were prescribed as boundary
conditions. It contains 6000 years of data, with each of
the 100 members calculated from different initial values and 60 years time perturbation. On the other hand,in the +4 K future climate simulation, the global averagetemperature increased by 4 °C compared to that before
the Industrial Revolution in 1850. It contains 5400 yearsof data, with 60 years time perturbation for each of the
90 members that were given 15 perturbations representing observational uncertainties in six different warming
patterns. Thus, this prediction was not made for a specific year, and there was no target period. The d4PDF
of the area around Japan with a 20 km resolution is a
dynamic downscaling of the global model. There were3000 years of data for the historical climate simulationand 5400 years of data for the +4 K future climate simulation (Mizuta et al 2017). We extracted the data from thislarge ensemble dataset. First, a cumulative probability

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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density function based on the Gumbel distribution following Hoshino and Yamada (2023) is calculated using
the annual maximum 3-day precipitation within a single
60-year ensemble member. This was performed for all
members of the historical climate simulation and +4 K
future climate simulation. Then, 10 members with various slopes in the cumulative probability density function
were manually selected for each historical climate simulation and +4 K future climate simulation. This provided
data for 600 years each for the historical climate simulation and the +4 K future climate simulation.
The spatial resolution of the d4PDF is 20 km, which
does not accurately reproduce heavy orographic rainfall. Therefore, we also used rainfall data downscaled toa 5 km resolution by Yamada et al. (2018) as input in ourmodel for comparison. We used the watershed averagefor the 5 km resolution and the rainfall of one grid containing the study area for a 20 km resolution. Because
Yamada et al. (2018) only downscaled 10 days, which
includes the timing of maximum rainfall in each year,
we simulated 10 days’ worth of sediment discharge for
600 years in the historical climate simulation and +4 K
future climate simulation.
We computed the maximum annual rainfall and 10-day
total rainfall return period using the extracted d4PDF
rainfall data. We used a nonparametric method (Cunnane 1978) to calculate the return period.

P=

N + 0.6 − i
N + 0.2(2)

where P is the non-exceedance probability, N is the number of samples, and i is the number of ranks of the samples sorted by the order of magnitude.
The annual maximum rainfall increased by a factor of
about 1.3 to 1.4 for the 20 km resolution and by a factor
of about 1.4 to 1.6 for the 5 km resolution from historical climate simulation to +4 K future climate simulation(Figs. 3b, 3d, and Table 1), while the 10-day total rainfall increased by a factor of about 1.0–1.1 for the 20 km
resolution and by a factor of 1.2–1.3 for the 5 km resolution (Fig. 3a, c, and Table 1). Thus, the annual maximum
rainfall has a larger increase rate due to predicted climatechange compared to the total rainfall, and the increase
rates are larger at the 5 km resolution than those at the
20 km resolution in our study area.
To investigate the effects of temporal rainfall patterns
on sediment runoff, we conducted a cluster analysis on
the d4PDF hyetograph. First, we extracted data 60 h
before and after the peak rainfall with a return period
greater than 2 years and applied normalization so thatthe maximum rainfall was equal to 1. The obtained
rainfall patterns were classified into six clusters using
the k-means method (Macqueen 1967) (Fig. 4). The six

clusters were characterized, as shown in Table 2. Types 1
and 2 had short and long rainfall durations, respectively.
Type F experienced heavy rainfall before the peak, TypeM experienced heavy rainfall around the peak, and Type
L experienced heavy rainfall after the peak. The heat map
in Fig. 5 shows the classification ratio of all rainfall events
that were clustered in the historical and +4 K future climate simulations. The historical climate simulation had
more events with a maximum rainfall of 15 mm/h or less,whereas the +4 K future climate simulation had more
events with larger maximum rainfall. In particular, shortterm rainfall patterns (M-1 and L-1), with a maximum
rainfall of 25 mm/h or more, are increasing.
3.2 Climate change impact

A comparison between Figs. 3 and 6 and Tables 1 and 3shows that the increased rate of sediment discharge atboth resolutions was larger than those of annual maximum rainfall and 10-day total rainfall. Thus, we analyzed
the factors that contributed to the increase in sediment
discharge in detail by focusing on the results from the
5 km resolution where the sediment increase was particularly large. In other words, why did sediment discharge increase 3–5.5 times while precipitation increased
only 1.2–1.6 times and flow discharge increased only
1.6–1.9 times? Because flow discharge is linked to rainfall, the rapid increase in sediment discharge cannot be
explained solely by an increase in stream bed stress due
to increased flow discharge.
The heat map in Fig. 7 shows the proportion of sediment produced from the slope. The vertical and horizontal axes were delimited in the same manner, as shown in
Fig. 5. As mentioned in Sect. 4, climate change increases
the number of short-term rainfall patterns (M-1 and L-1)
with a maximum rainfall of 25 mm/h or more. When the
maximum rainfall was greater than 25 mm/h, the proportion of sediment produced from the slopes was higher inshort-term rainfall patterns. This suggests that when the
maximum rainfall is high, short-term rainfall increases
slope failure.
Figure 8 shows the changes in the proportion of sediment produced from the slope (Pslope) and the average grain size of the exchange layer (Fm) at the end ofeach year. We can see that Fm is smaller when Pslope islarger, and vice versa, indicating that the average grain
size of the exchange layer decreases as the slope failure
increases. When fine sediments are fed into armoredbeds owing to slope failure, the friction angle between
the bed materials decreases, and the critical Shields
number for incipient sediment motion decreases(Egiazaroff 1965). Thus, even if the flow discharge was
the same, the sediment on the riverbed moved more

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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Fig. 3 Return periods for historical and +4 K future climate simulation rainfalls. a Annual maximum rainfall at 20 km resolution, b annual maximum
rainfall at 5 km resolution, c 10-days total rainfall at 20 km resolution, and d 10-days total rainfall at 5 km resolution. Blue and orange dots represent
historical climate simulation and +4 K future climate simulation, respectively

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

Page 7 of 14

Table1 Comparison of rainfall for each return period
Return period

20 km

5 km

Present ­(P20)

Future ­(F20)

Ratio of increase­(F20/P20)

Present ­(P5)

Future ­(F5)

Ratio ofincrease ­(F5/P5)

Annual maximum rainfall (mm/h)
50

17.3

24.3

1.40

33.5

47.7

1.42

20

14.2

20.6

1.45

25.8

41.2

1.60

10

12.3

16.6

1.35

20.0

32.2

1.61

Total rainfall (mm/10 days)
50

213.7

224.9

1.05

353.7

477.3

1.35

20

179.0

201.0

1.12

280.4

365.4

1.30

10

152.5

168.8

1.11

229.4

275.0

1.20

Fig. 4 Rainfall patterns of historical and +4 K future climate simulation data clustered using k-means method. The gray lines represent the rainfall
pattern for each year, and the red line represents the average of all years in each cluster

Table 2 Characteristics of rainfall patterns
Heavy rainfall
Before the peak Around thepeak

After the peak

Rainfall duration
Short

F-1

M-1

L-1

Long

F-2

M-2

L-2

easily. Increased water volume due to climate change
flows over these non-armored beds, causing large
amounts of sediment to run off. This may explain why
the rate of increase in the sediment discharge washigher than the rate of increase in rainfall. A nonlinear
relationship exists between rainfall, flow, and sediment
discharge. Tan et al. (2021) predicted that increased
extreme rainfall in the Mississippi/Atchafalaya River

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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Fig. 5 Existence probability of rainfall. The horizontal axis represents the differences in rainfall patterns based on the clustering results shownin Fig. 4. The vertical axis represents the maximum rainfall by dividing it into three categories: 15 mm/h or less, more than 15 mm/h, and 25 mm/h
or less, and more than 25 mm/h

Fig. 6 Return period of the amount of sediment discharge for 10 days. a Results using 20 km resolution rainfall data, and b Results using 5 km
resolution rainfall data. Blue and orange dots represent historical climate simulation and +4 K future climate simulation, respectively

Basin increases N and P fluxes owing to erosion and willcontinue to increase in the future owing to increased
extreme rainfall events with climate change. Therefore, the rates of increase were different. However, therate of increase in sediment discharge is considerably
higher than the other rates of increase. The supply offine sediments from the slope contributed significantly
to this result.
The average grain size of the exchange layer (Fm)
was affected not only by Pslope but also by the timing of heavy rainfall. The heat map in Fig. 9 shows
the average grain size of each type. For Type 1, there

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Table3 Comparison of the flow and sediment discharge for each return period
Return period

20 km

5 km

Present ­(P20)

Future ­(F20)

Ratio of increase­(F20 /P20)

Present ­(P5)

Future ­(F5)

Ratio ofincrease ­(F5
/P5)

50

77

48

1.60

241

148

1.62

20

56

42

1.31

183

107

1.70

10

47

30

1.57

136

71

1.91

Flow discharge ­(m3/s)

Amount of sediment discharge ­(m3/10 days)
50

5497

9288

1.69

52,107

152,833

2.93

20

2391

5560

2.32

23,96587,747

3.66

10

1124

2218

1.97

7379

40,8305.53

of F-2. When the maximum rainfall is large and there
is heavy rainfall after the peak, even though there is alarge supply of fine sediment from the slope, most of
this is discharged during the event, and the grain size
after the event becomes larger. As shown above, whenthe maximum rainfall was small, the average grain size
decreased with the amount of fine sediment supplied
from the slope; however, when the rainfall was large,
the average grain size increased with the amount of
rainfall after the peak.
3.3 Grid resolution impact
Fig. 7 The proportion of sediment produced from the slope
averaged over all cases for each mesh. The horizontal axis represents
the differences in rainfall patterns based on the clustering results
shown in Fig. 4. The vertical axis represents the maximum rainfall
by dividing it into three categories: 15 mm/h or less, morethan 15 mm/h and 25 mm/h or less, and more than 25 mm/h

were no significant differences between F-1, M-1, andL-1 because of the short rainfall duration, whereas
for Type 2, there were different trends for each rainfall rank. First, when the maximum rainfall was lessthan 15 mm/h, Fm of L-2 decreased. This is because
the Pslope of L-2 was larger than that of F-2 and M-2,
and the supply of fine sediment from the slope waslarge. When the maximum rainfall was between 15
and 25 mm/h, Pslope was almost the same for all types,
but the timing of the heavy rainfall becomes later, Fm
becomes larger. This is because as the timing of theheavy rainfall is delayed, fine sediments are discharged
within the event, and armoring occurs after the event
ends. Finally, when the maximum rainfall was greaterthan 25 mm/h, Fm was larger in L-2 than in F-2,although Pslope of L-2 was significantly larger than that

The 5-km resolution shows annual maximum rainfall and
10-day accumulated rainfall that are 1.5 to 2 times larger
than those of the 20-km resolution (Table 4). However,the calculated sediment runoff from these rainfall eventswas considerably larger, by a factor of 10–20 from 20-km
resolution to 5-km resolution (Table 4). Most of the rainfall events in the 20-km resolution had an annual maximum rainfall of 15 mm/h or less and did not represent
the strong rainfall that was represented in the 5-km resolution (Table 5). Thus, the 20-km resolution may underestimate the amount of sediment discharge because it
cannot represent rainfall strong enough to cause a large
amount of sediment discharge.
In addition, as shown in Fig. 6, in the case of 20 km resolution, the 380,000 ­m3 sediment discharge observed during
heavy rainfall in 2016 was not included in either the historical climate simulation results or the +4 K future climate
simulation results. On the other hand, for the 5 km resolution, the maximum value of the current climate results is
close to that value. Therefore, it is desirable to use rainfall
data with a resolution that can represent strong rainfall toaccurately predict the amount of sediment discharge inmountainous areas where heavy orographic rainfall is themain determinant of sediment runoff.

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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Fig. 8 The annual change over time in the proportion of sediment produced from the slope out of total sediment discharge (Pslope) per year
for each member and the average grain size of the riverbed (Fm) at each end of the year

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

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Fig. 9 The average grain size of the exchange layer averaged over allcases for each mesh. The horizontal axis represents the differencesin rainfall patterns based on the clustering results shown in Fig. 4. The
vertical axis represents the maximum rainfall by dividing it into three
categories: 15 mm/h or less, more than 15 mm/h and 25 mm/h
or less, and more than 25 mm/h

4 Discussion
The most significant aspect of this study was the statistical evaluation of future changes in sediment runoff
using 600 years of projected climate change rainfall foreach historical climate simulation and the +4 K futureclimate simulation. Although there have been previous
studies on future changes in sediment discharge due toclimate change, most of these analyses were based on a
few rainfall patterns. For example, Lane et al. (2008) created three patterns of rainfall increase due to climatechange (frequency, magnitude, and variability) and evaluated the change in discharge for each of these patterns.
They obtained rainfall data for climate change, extending
the observed rainfall in different ways for each rainfall

pattern, based on the ratio of the increase in peak flowin the A2 scenarios for the 2050s and the 2080s. Frequency change indicates that the number of days with
rainfall increased, magnitude change indicates thatrainfall increased equally on rainy days, and variabilitychange indicates that rainfall increased on the three highest rainfall days. Their results showed that the volumeof sediment discharge increased in all scenarios, withthe pattern of increased frequency showing a smaller
rate of increase than the other two patterns. However,in their study, future rainfall was established by processing the actual rainfall using monthly rainfall projections
that were somewhat reliable. Therefore, no analyses havebeen conducted directly using hourly rainfall data fromclimate change projections. That is, it did not adequately
account for the changes in rainfall patterns, as shown in
Figs. 4 and 5. The reason behind this is because there isa problem with the reliability of the deterministic evaluation of sediment discharge by extracting a few patterns
from the one-hour rainfall of climate change projections.
To address this problem, we calculated the sediment discharge using 600 years of rainfall in a +4 K future climate

Table 5 The existence probability per annual maximum rainfall
Annualmaximum
rainfall(mm/h)

20 km

5 km

Present ­(P20) Future ­(F20) Present ­(P5) Future ­(F5)
-15

96.0

86.8

76.5

60.6

15–25

4.0

12.0

17.7

23.0

25-

0.0

1.3

5.8

16.5

Table4 Rainfall and 10-day sediment discharge for each resolution
Return period

20 km
Present ­(P20)5 km

5 km/20 km

Future ­(F20)

Present ­(P5)

Future ­(F5)

Present ­(P5/P20)

Future ­(F5/F20)

Annual maximum rainfall (mm/h)
50

17.3

24.3

33.5

47.7

1.93

1.97

20

14.2

20.6

25.8

41.2

1.82

2.00

10

12.3

16.6

20.0

32.2

1.62

1.94

Total rainfall (mm/10 days)
50

213.7

224.9

353.7

477.3

1.65

2.12

20

179.0

201.0

280.4

365.4

1.57

1.82

10

152.5

168.8

229.4

275.0

1.50

1.63

Amount of sediment discharge ­(m3/10 days)
50

5497

9288

52,107

152,833

9.48

16.45

20

2391

5560

23,96587,747

10.02

15.78

10

1124

2218

7379

40,830

6.56

18.41

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

simulation and a historical climate simulation (1200 years
in total) and treated the results statistically.
Our calculation results indicate that, in both the historical climate simulation and the +4 K future climate
simulation, sediment from the channel bed is a greater
source of production than sediment from the adjacentslope. However, there was a trend toward an increasing proportion of sediment from the slopes in the +4 K
future climate simulation. This is consistent with thetrend observed in the analysis conducted by Lane et al.
(2008) for the Upper Wharfe catchment of Yorkshire
Dales National Park in the U.K. Our results, which uses
1-h rainfall rates, also suggest that the frequency of shortduration rainfall events with high rainfall intensity will
increase in the future, resulting in increased sedimentproduction from slope failure. Jakob and Lambert (2009)
stated that antecedent and short-term rainfall contributed to the temporal development of landslides in British
Columbia, Canada, which supports the predictions of thesoil moisture-dependent slope failure model employed inthis study. However, Biasutti et al. (2016) investigated therelationship between past landslides and rainfall on thewest coast of the USA and stated that the rainfall patternthat causes slope failure depends on the topography andclimate. Therefore, further analysis of the relationshipbetween rainfall patterns and slope failures is necessary.
When the grain size of the slope-failure sediment
was finer than that of the channel bed, as in our study
area, the mean grain size of the bed surface layer
decreased with increasing slope failure. Egiazaroff(1965), Hirano (1971), and Buffington and Montgomery (1999) stated that the critical number of large
gravel sheets decreases as fine sand is deposited on
the riverbed, because the internal friction angle of thesediments (or bed surface roughness) decreases. Similar phenomena have been observed not only in gravel
beds, but also in bedrock beds (Inoue et al. 2014;
Mishra and Inoue 2020). The supply of sediment witha smaller grain size to the riverbed due to slope failure would have increased the transport capacity of thechannel, and thus, the rate of future increase in sediment discharge would have been greater than that of
rainfall.
A comparison of the sediment discharge calculated from rainfall data at 5 km and 20 km resolutions
showed that the sediment discharge calculated fromrainfall at 5 km resolution was significantly higher.
This is because the 20-km resolution does not represent extreme rainfall events of short duration, as
described by Rupa Kumar et al. (2006) and Westra
et al. (2014). The bed shear stress must exceed thecritical shear stress for the sediment in the riverbed to
begin moving. As previously described, slope failure

Page 12 of 14

occurs when the water content of the soil exceeds acertain value. Thus, there is sufficient rainfall for sediment movement to occur, and the 20 km resolutionof the rainfall data does not receive enough rainfall to
exceed that amount, resulting in a very large difference
in sediment discharge when compared at the same
return period.
Finally, we present future challenges of the proposedmodel. In cold regions, such as the Pekerebetsu Riverbasin, which is our study area, and in basins at highelevations, such as those in the Alps, sediment production by freezing and thawing is important, in addition
to rainfall-induced slope failure (e.g., Gruber and Haeberli 2007). However, our model focuses on sedimentproduction during heavy rainfall events and does notconsider sediment production associated with temperature changes such as freezing and thawing. In general,
if the temperature increases due to climate change,
the frequency of freeze–thaw weathering is expected
to decrease; however, it has been pointed out that incold regions, the heat-retaining effect of snow cover
may be lost, and the frequency of freeze–thaw events
may increase (e.g., Rengers et al. 2020). In addition,
increased evapotranspiration and flood frequency
associated with climate change may contribute to bedrock weathering owing to wet-dry weathering (e.g.,
Sumner et al. 2022). It would be interesting to consider
these effects in future studies.

5 Conclusions
Predicting changes in sediment discharge associated with
future climate change is essential for assessing flood risk
due to riverbed aggradation. To accurately predict sediment discharge, it is important to consider the effects ofslope failure in mountainous areas and changes in grain
size in mountain rivers on erosion and sediment transport. In this study, we investigated future changes in sediment production and discharge using a physics-basedmodel that accounts for slope failure, channel erosion,and mixed grain-size sediment.
Our results indicate that future sediment discharge
will be 3–5.5 times larger than the present sediment discharge, even though precipitation has increased only 1.2
1.6 times. Our results also show that the large increase in
sediment discharge was caused by an increase in the frequency of slope failures owing to changes in rainfall patterns and an increase in the sediment transport capacityof mountainous channels by fine-grained sediment supplied from the slopes.
The amount of sediment discharge using 20 km resolution climate data is one order of magnitude smaller
than that using 5 km resolution data, indicating thatthe 20 km resolution data cannot reproduce large-scale

Kido et al. Progress in Earth and Planetary Science(2023) 10:54

sediment discharge, such as the 2016 event. This is probably because climatic data with a 20 km resolution do notadequately reproduce topographical torrential rainfall
in mountainous areas, and extreme events such as slope
failures may not be accurately reproduced.
Abbreviations
LiDAR	Light detection and ranging
DEM	Digital elevation model
SiMHiS	Storm-induced multi-hazard information simulator
d4PDF	Database for policy decision making for future climate change

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​023-​00580-0.
Additional file 1. SiMHiS (Storm Induced Multi-Hazard Information
Simulator).
Acknowledgements
This study used d4PDF data produced under the Earth Simulator Special
Proposal, which was downscaled to a 5 km resolution in collaboration with
Dr. Tomohito Yamada of Hokkaido University and the Hokkaido Regional
Development Bureau. Dr. Riki Iwasaki of Hokkaido University provided nonexceedance probability data of rainfall for each member of the d4PDF dataset.
Author contributions
TI conceived of the study. RK and KY conducted the experiments and drafted
the figures. TI, MH, and KY contributed to the interpretation of the results. TI
and MH supervised this study. RK drafted the manuscript with review, revision,and input from all authors. All the authors approved the final version of themanuscript.
Funding
This work was supported by JSPS KAKENHI (Grant No. 22H01602).
Availability of data and materials
The datasets supporting the conclusions of this study are available at zenodo,
https://​doi.​org/​10.​5281/​zenodo.​75493​49.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 24 January 2023 Accepted: 2 August 2023

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