E3S Web of Conferences 415, 02011 (2023)
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https://doi.org/10.1051/e3sconf/202341502011

Numerical Simulation Reflecting Buildings in Area Damaged byDebris flow
Bae-Dong Kang1, Kye-Won Jun2*, Ho-Jin Lee3, Chang-Deok Jang4 and Hang-Il Jo5
1Granduate School of Disaster Prevention, Kangwon National University, S.Korea
2 Granduate School of Disaster Prevention, Kangwon National University, S.Korea
3School of Civil Engineering, Chungbuk National University, S.Korea
4CND, Samcheok-si, Gangwon-do, S.Korea
5Granduate School of Disaster Prevention, Kangwon National University, S.Korea

Abstract. More than 80% of average annual precipitation in South Korea occurs between June and
September owing to heavy rainfall and typhoons in summer, and its land is vulnerable to mountain disasters(landslides and debris flow) as 63% of it is mountainous areas. In this study, an area damaged by debris flowin Wondeok-eup, Samcheok-si, Gangwon-do, Korea under the influence of Typhoon Mitag in 2019 wassurveyed and numerical modeling was performed. Topographic data were created using the 5m grid DEM
derived through the field survey data and GIS technique as well as the building data of the damaged area,and debris flow modeling was performed using the Hyper KANAKO model. A comparison with theinundation trace map showed that the simulation results based on topographic data that reflected buildings
exhibited similar flow patterns and characteristics to the actual damage.

1 Introduction
Global climate change is causing larger natural disasters.
South Korea is vulnerable to mountain disasters, such as
landslides and debris flow, due to heavy rainfall andtyphoon-induced rainfall in summer each year because
mountainous areas represent 63% of its land.
Representative damage cases include the mountaindisasters (landslides and debris flow) caused by
Typhoon Rusa in 2002, Typhoon Maemi in 2003,
localized heavy rainfall in 2011, and Typhoon Mitag in
2019, which caused human casualties and property
damage. Studies have been conducted worldwide topredict and reproduce areas vulnerable to mountaindisasters using GIS and numerical models to reducesuch damage.
Numerical models to analyze the flow characteristics ofdebris flow, among mountain disasters, include FLO-2D
developed by O’Brien et al. (1993)[1] (Peng et al. 2013;Kim 2012)[2, 3], Debris 2D developed by Liu andHuang (2006)[4] (Wu et al. 2013)[5], KANAKO Modeldeveloped by Nakatani et al. (2008; 2012)[6, 7] (Kim2017, Lim 2017; Jang 2019)[8, 9, 10], andRAMMS(WSL-SLF). In this study, topographic datathat reflected buildings in an area damaged by debris
flow were created using the Hyper KANAKO model, anumerical model developed in Japan, and the flow
characteristics of debris flow were analyzed by applying
the data to the model.

2 Theory of Numerical Model
The Hyper KANAKO (Naktani et al., 2012)[7] model isa general-purpose debris flow numerical model that
improved user convenience and graphical user interface(GUI) based on the KANAKO-2D model developed byNakatani et al. (2008)[6]. It is based on the governing
equations of the Takahashi (Takahashi et al., 2001;
Takahashi, 2007)[11, 12] model to analyze debris flow.
The model uses eq. 1 that obtains the erosion/deposition
rate of debris flow by calculating temporal and spatial (xand y directions) changes in flow depth for the total
volume of debris flow, and the continuity equation of eq.
2 that obtains the erosion/deposition rate of debris flowby calculating temporal and spatial (x and y directions)changes in flow depth according to the volumeconcentration of the deposited sediment.
𝜕ℎ+

𝜕𝑡
𝜕𝐶ℎ𝜕𝑡

𝜕𝑢ℎ++

𝜕𝜈ℎ=𝑖

𝜕𝑥
𝜕𝑦
𝜕𝐶ℎ𝑢
𝜕𝐶ℎ𝜈
𝜕𝑥+

𝜕𝑦= 𝑖𝐶∗(1)(2)

where i is erosion/deposition velocity(if i < 0, deposition
and if i >= 0, erosion), h is flow depth, t is time, u is xaxis flow velocity, v is y-axis direction flow velocity, Cis sediment concentration by volume in the debris flow,
C* is sediment concentration by volume in the movable
bed layer.

* Corresponding author: kwjun@kangwon.ac.kr© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0(https://creativecommons.org/licenses/by/4.0/).

E3S Web of Conferences 415, 02011 (2023)
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3.2 Data construction

The momentum equations of debris flow are expressedby eq. 3 and eq. 4. Spatial changes in flow velocity inthe x and y directions can be expressed by the values
obtained by subtracting the resistance force at theboundary from the driving force of debris flow. Inaddition, the riverbed change equation is given by eq. 5.
𝜕𝑢𝜕𝑡𝜕𝜈𝜕𝑡+𝑢+𝑢𝜕𝑢𝜕𝑥𝜕𝜈𝜕𝑥+𝜈+𝜈𝜕𝑢

𝜕𝑦
𝜕𝜈

𝜕𝑦𝜕𝑧
𝜕𝑡

= −𝑔= −𝑔+𝑖 =0

𝜕𝐻

𝜕𝑥
𝜕𝐻
𝜕𝑦

𝜏

− 𝑥−

𝜌ℎ
𝜏𝑦

𝜌ℎ

3.2.1 Topographic data
For the topographic data of the area damaged by debrisflow, a 5m grid DEM was created using the contour linesof the 1:5,000 digital topographic map. Since building
information was in the Shp file format, it was converted
into Raster in the same 5m grid size as DEM andsuperimposed upon DEM(figure 2). The creation of
topographic data through a digital topographic map
involves difficulty in implementing the characteristicsof the coast, drainage channels, and covered rivers.
Therefore, it is deemed necessary to create topographic
data using measuring equipment such as drones and
LiDAR for more accurate modeling.

(3)
(4)
(5)

where 𝑔 is acceleration due to gravity, H is flowelevation (H = h + z), z is bed elevation, ρ is density ofthe fluid, τx and τy are riverbed shearing stresses in thex- and y-axis directions, respectively.

3 Application of Numerical ModelIn this study, a 5m-resolution DEM was created fromthe 1:5,000 digital topographic map provided by theNational Geographic Information Institute using
ArcGIS to analyze the flow characteristics of debris
flow, and it was superimposed onto building
information. The binary conversion software included in
the model was used to convert it into the topographic
data required by the model. Total discharge, peakdischarge, and sediment concentration, which are theinput parameters of the model, were calculated using theempirical formulas presented by the National Institutefor Land and Infrastructure Management (NILIM,2016)[13] in Japan.

Fig. 2. DEM and convert to raster in buildings data

3.2.2 Input parameter
To perform numerical simulation on debris flow,
information on input parameters, i.e., sediment yield,sediment concentration, total discharge, and peakdischarge, is required. The sediment yield, the width,depth, and length of the valley (Table 1) were measured
through a field survey and the equation proposed by
Ikeye (1981)[14] (eq. 6) was used. The sediment yield
was applied to the empirical formulas presented by
NILIM to calculate the sediment concentration (eq. 7),
total discharge (eq. 8), and peak discharge (eq. 9). The
values in a previous study were entered for the unit
weight and internal friction angle of soil and the unit
weight of water (Nakase, 2004)[15]. Table 2 shows theinput parameters for the Hyper KANAKO model.

3.1 Target area
Galnam-ri, Wondeok-eup, Samcheok-si, Gangwon-do
is an area close to the East Sea of Korea. It has a highterrain to the west and low terrain to the east as there isa high mountain range to the west. The watershed has anarea of 0.84 km2, a maximum elevation of 316 m, aminimum elevation of 11.6 m, an average elevation of163 m, and an average slope of 17˚. A debris flow
disaster occurred in the area due to rainfall of up to 110
mm/hr under the influence of Typhoon Mitag in October
2019. 55 houses were buried or inundated and resultedin 111 victims in the downstream area(figure 1).

𝑉 = ∑𝑛𝑖=1(𝐿 × 𝐵 × 𝐷)𝑖𝜌 tan 𝜃𝐶𝑑 = (𝜎−𝜌)(tan(6)(7)

𝑄𝑠𝑝 = 0.01 ∙ ∑ 𝑄

(9)

𝐶 ∙𝑉

∑ 𝑄 = ∗ 𝑑𝑞𝑝
𝐶𝑑∅−tan 𝜃)(8)where V is Sediment yield(m3), L is length of valley(m),B is breadth of valley(m), D is erosion depth of valley(m),ρ is density of the particle(kg/m3), σ is density of thefluid(kg/m3), ø is internal friction angle(°), θ is riverbedgradient(°). and riverbed gradient θ is applied anaverage riverbed gradation 14.9° of basin

Fig. 1. Location of the study area.

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E3S Web of Conferences 415, 02011 (2023)
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Table 1. Estimation of sediment yield through field
investigation
No1
2
3
4
5
6

Length(m)
0.6
2.2
1.7
0.6
3.4
1.9

Depth(m)
13.9
12.9
7.5
10.1
14.0
9.2

Breadth(m)
80339
230
298
452
1,104

Sediment yield
(m3)
667.2
9,620.8
2,932.5
1,805.9
21,515.2
17,724.7

Table 2. Summary of parameters and values used in Hyper
KANAKO
Input parameterσρøθ
CdC*Vdqp
Qsp

Density of the particle [kg/m3]
Density of the fluid [kg/m3]
Internal friction angle [°]Riverbed gradient [°]Sediment concentration by volume
in the debris flow
Sediment concentration by volume
in the movable bed layer
Initial volume [m3]
Maximum discharge rate of thedebris flow [m3/s]

Fig. 4. Result of the simulation (max flow depth). (a) notreflect buildings (b) reflected buildings.

Value2,600
12003514.9

Topographic data that reflected buildings showedsimilar flow patterns to the actual damage. When theinundation trace map was compared with the maximum
flow depth under the two conditions at the same points,
the maximum flow depth was found to be 5.1 m at Site
A and 1.4 m at Site B for topographic data that did notreflect buildings. For topographic data that reflectedbuildings, the maximum flow depth was found to be 7.8m at Site A and 3.1 m at Site B, which were similar to
the results of the inundation trace map (7.0 m and 2.5m)(table 3, figure 5).

0.53
0.654,266
614.334 Result of Numerical Modelling

Table 2. Comparison of maximum flow depth andinundation trace by Point
Site A
Site B(a) Not reflect buildings
5.1 m
1.4 m
(b) Reflected buildings
7.8 m
3.1 m
Height of inundation trace
7.0 m
2.5 m

Figure 4 shows the results of the numerical simulationof debris flow in the downstream area performed using
the topographic data and input parameters constructedin chapter 3. Figure 4(a) demonstrates the maximum
flow depth of debris flow for topographic data that did
not reflect buildings, while Figure 4(b) demonstrates themaximum flow depth of debris flow for topographic
data that reflected buildings. Flow characteristics werecompared for Site A where debris flow expands and Site
B where the flow changes.

Fig. 5. The situation at the time of the damaged and height ofinundation trace. (a) : Site A, (b) : Site B.

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5 Conclusions

Reinforcement in a Forested Catchment, Ph.D.
Dissertation, Kangwon National University (2019)
11. T. Takahashi, Process of occurrence, flow anddeposition of viscous debris flow, in River, coastal
and estuarine morphodynamics (pp. 93-118),
Springer, Berlin, Heidelberg (2001)
12. T. Takahashi, Progress in debris flow modeling. In
Progress in landslide science (pp. 59-77),
Springer, Berlin, Heidelberg (2007)
13. National Institute for Land and InfrastructureManagement (NILIM). Manual of technicalstandard for establishing sabo master plan fordebris flow and driftwood. Technical note ofnational institute for land infrastructure
management, No. 364. Japan (2016)
14. H. Ikeya, A method for designation for area dangerof debris flow (1981)
15. Y. Nakase, M. Fujimoto, S. Egashira, I. Takahiro,
A consideration of run-off prediction by debris
flow, in Conference of Japan Socity of Engineering
Geology 13, pp. 63-64 (2004)

In this study, numerical simulation of debris flow wasperformed using the Hyper KANAKO model amongdebris flow numerical models, and flow characteristics
in an area damaged by debris flow were analyzed under
two topographic conditions. As for the input parametersof the model, sediment yield, peak discharge, andsediment concentration were used based on the fieldsurvey data. When the flow of the actual debris flow wascompared with the damage estimated from theinundation trace map, it was found that topographic datathat reflected buildings showed similar flow patterns
and maximum flow depths. In future research, it isnecessary to determine the validity of the results bycreating precise topographic data and calculating input
parameters using LiDAR and drones, and theapplicability of the modeling results will be presented
by simulating several damaged areas.
This research was supported by the program of ResearchProgramtoSolve
UrgentSafetyIssues(2022M3E9A1095664), through the National Research
Foundation of Korea(NRF), funded by the Korean
government. (Ministry of Science and ICT(MSIT), Ministry of
the Interior and Safety(MOIS))

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