10th Asian Regional Conference of IAEG (2015)

Risk evaluation and debris-flow-induced river blocking probability assessment
along Duwen highway

HAN Mei (1), HU Xiewen(2,3), Hu Tao(4), LIANG Jingxuan(2)(1) School of Mathematics, Southwest Jiaotong University, China

E-mail: han80mei@163.com(2)Faculty of Geosciences and Environment Engineering, Southwest Jiaotong University, China(3)State and local Engineering Laboratory of Spatial Information Technology of High-Speed Rail Operation Security, China(4)College of Environmental and Civil Engineering, Chengdu University of technology, China

Abstract
On July 10th 2013, due to continuous heavy rainfall in Wenchuan earthquake area, manylarge-scale debris flows occurred in Taoguan, Qipan gully, as well as in many others places
along Duwen highway. Residential areas, factory plants and parts of Duwen highway weredestroyed or buried by a large amount of sediments, which also blocked Minjiang river. This
caused a direct economic loss of hundreds of millions of RMB. In order to evaluate the risk
degree of the debris flows scientifically, in this paper, appropriate evaluation factors are
selected to evaluate 6 typical debris flows occurring on 10th July. Grey correlation degree
method and entropy method are applied to obtain the weight value of each factor. A
mathematical model of risk assessment is established to calculate the risk degree of each gully.
For each of the 6 typical debris flow gullies, the evaluating parameters are adjusted accordingly,
and the corresponding risk of blocking Minjiang River is also evaluated by using theriver momentum ratio to the tributary debris momentum. The calculated results agree well withthe actual cases.

Key words: Wenchuan earthquake area, Duwen highway;risk evaluation, river block probability

After Wenchuan earthquake on May 12th2008, thestability of rock soil mass of the shattered mountains is
now dramatically reduced in the earthquake area.
Landslide and debris flow can be easily induced bycontinuous
heavy
rainfall,leadingtodebris-flow-induced river blocking, or even burst offlood. This will threaten the safety of residents along
Minjiang River. Therefore, the risk evaluation on thepossibility of debris-flow-induced river blocking is ofgreat importance for effective prevention and control
over the debris flow.
In the past research, the essence of the risk
assessment has been focusing on the selection andquantization of the evaluation indices and thedetermination of the weight of those indices. For thedetermination of the index weight, several methods are
generally used, such as Analytic Hierarchy Process (Tieet al., 2006), binomial coefficients(Tao, 1982), grey
correlation method(Zou et al., 2003), fuzzy
mathematics method(Su et al., 1993), entropy
method(Tie et al. 2005),
the method of rankcorrelation coefficient(Chen et al.2013), etc.
The research method of debris-flow-induced river
blocking probability includes qualitative analysis(Hu etal. 2009), quantitative calculation(Xu et al.
2002),experimental investigation(Liu et al. 2013),
numerical simulation(Wang et al.2005).
In this paper, according to the features of the 6
main debris flow gullies along Duwen highway inWenchuan earthquake region in July 10th 2013, we use
the grey correlation method combined with entropy
method to calculate the evaluation index. A
mathematics model is established and the risk degreeof the debris flow is evaluated. At the same time,
combining the character of the region, the blocking
probability, i.e., the blocking risk degree of Minjiang
river induced by the debris flow, is also evaluatedaccording to the characteristic of the targeted region.
1. The outbreak circumstance of the debris flow

1

10th Asian Regional Conference of IAEG (2015)

After Wenchuan earthquake on May 12th2008, ahuge amount of loose sediments triggered by theearthquake was distributed in the debris flow valley
along Duwen highway. The relatively steep slope of thegullies is geographically favorable for the movement ofloose solid materials and thus their involvement indebris flow activities. Moreover, frequently occurreddebris-flow-induce driver blocking gives rise to asecondary disaster in the earthquake area.
In 2013, a widespread heavy rainfall, lasting from
6th to 10th July, occurred in Wenchuan earthquake
region. The intensity of this rainfall reached
104mm/24-hour, which is lower than the expected
intensity of “one-in-20 years”. But because the scale of
the rainfall at the upstream is much larger than that at
the entrance of the gully, the actual average scale of therainfall is estimated to be larger than the scale of"one-in-50 years".
Affected by the heavy rainfall, over 16 gullies
experienced the burst of enormous debris flow, causing
tremendous economic losses and casualties. The debris
flows also destroyed intercepting dams and drainage
canals constructed after the “12-May” earthquake. Partof the sediments rushed into Minjiang river, blocked
the watercourse and elevated the riverbed, whichbrought a potential trouble to the traffic, watercourse
and local residents.
Therefore, it is necessary and important to accessthe risk of debris-flow-induced river blocking in areasonable and objective way.

(1) Integrality index of the watershed X1. This
index denotes the ratio of the area to the squared length,reflecting the confluence condition of surface runoff.
Large value of X1 corresponds to large scale of peak
flow, which is favorable to the formation of debris
flows.
(2) The scale of one burst of a debris flow (one in
20 years) X2( 10 4 m 3 ). This value directly reflects thedanger degree of the debris flow. The larger X2 thelarger the potential disaster.
(3) The longitudinal slope X3 (‰), which
represents the initial launching capability of a debris
flow gully. The larger the slope, the faster the gully can
initially launch a debris flow.
(4)The amount of loose material per unit area
X4(104m3/km2), representing the distribution density of
the loose materials in the watershed.
(5) Averaged annual rainfall X5(mm).Rainfall is themain cause of debris flow, therefore, large averaged
annual rainfall could lead to a high possibility oflaunching debris flow.
(6) Blockage coefficient X6 of debris flow. Thiscoefficient denotes the blocking degree of loosematerials in a gully. Large blockage coefficient meansa large amplification of the gully to the debris flow
scale, corresponding to a large potential disaster.
This paper focuses on 6 main debris flow gullies,
including Taoguan and Qipan gully, 6 index mentioned
above are obtained by analyzing topographic maps andpractical out-door investigations. The results are shownin Table1.

2 Selection of the risk assessment index for debrisflow

3

For the selection of the risk assessment index, thefollowing features need to be considered:representative,
independent,
accessibleformeasurements and quantification. In this paper, we use
the following 6 indices:

Establishment of the risk evaluation model

3.1 Grey correlation method
Based on the related theory of grey correlation
method (Deng, 1987), the weight of the 6 indices

Table 1Characteristic parameters of the typical debris flow gullies along Duwen highway on July 10th, 2013
The scale of one
Integrality

burst of a debris

The debris flow

index of theflow (one in 20

gully

watershed

years)

X1

X2
4

3

(10 m )The

The amount of

longitudinalloose material

slope

per unit area

X3

X4

Averagedannual rainfall
X5

(‰)

4

(10 m3/km2)（mm）

Blockagecoefficient

X6

Taoguan

0.249

108

197

32.4

1,253.1

2.4

Mozi

0.33

67

424

87.5

526.3

2.4

Qipan

0.219

133

192

48.3

528.7

3

Huaxi

0.25

12

287

117.8

598.6

3

Chutou

0.274

60

184

50.6

526.3

2

Zhangjping

0.53

9

317

54.2

1,253.1

2.3

2

10th Asian Regional Conference of IAEG (2015)

mentioned above can be determined by the following
steps:(1) Combine associated sequence

factor can be calculated by the above formula.
(6) Weight or factor.
The higher correlation degree, indicating the closer
relation with referenced sequence. The weight isdefined as follows:

X= {X1 , X 2 , X 3 , X 4 , X 5 , X 6 }Xi  {X（, X（,, Xi (6)}
i 1）
i 2）

m

wi   (i ) /   (i )(2) Non-dimensionalization.
Depending on practical problems, the dimension ofdifferent factors varies. Therefore, Transformation ofmean value should be calculated first by using formula(1):

X i' (k )  X i (k ) /1
 X i (k )n k (i ) （1） i ( k )  X 1' ( k )  X i' ( k )(6)Following the above steps, we can calculate the weight

W1 =(0.18，0.153，0.17，0.171，0.168，0.158)（2）(4) Correlation coefficient calculated by maximum
and minimum value.
Which is defined by formula (3): min   max
 i (k )   max（3）

In this formula, the resolution ratio  is taken as 0.5.
(5)Calculation of grey correlation degree
Correlation coefficient is an index describing
correlation degree. Take sequence 1 as reference
sequence. In order to obtain degree of correlation
andbetweencomparative
sequence
Xi
referencesequenceX1, which is defined as formula (4):

 1i 

1 n
  ijn j 1of each evaluation factor:

Here, k denotes the label index of the debris flow
gullies, which ranges from 1 to n, and i represents thelabel index of the indexes from 1 to m.
(3) Sequence of the absolute difference.
The method is as follows:1i (k ) 

(5)

i 1

aij 

xij  min{ xij}i(7)max{ xij} min{ xij}iii  1,2,, m; j  1,2,, n n1 1i (k )n k 13.2 entropy evaluation method
According to the principle of entropy evaluation
method, the evaluation matrix was established, thesteps of calculating index weight are as follows:(1) Standardizing the indicators. Indicators include
positive indicators and negative indicators, 6 risk
assessment factors chosen in this paper are all positive
indicators, which mean that the danger of debris flow is
proportional to the indicators. The normalization
formula can be written as:（4）

The normalized matrix is denoted as R  (aij )n  m .
(2) Calculate the eigen weight pijof the ith index
below the jth evaluate indicators as shown informula (8):Then take one of the surplus sequences in turn to bereference sequence, then the grey correlation degreepij （  ij ）between all comparative factor and analytical

aij
m

a
i 1

（8）
ij

Table 2Classification of evaluation index and assignment
grade

X1

X2

X3

X4

X5

X6

value

1

≤0.1

≤1

≤50

≤20

≤100

≤1

0

2

(0.1)-0.2(1)-10(50)-100(20)-50(100)-500(1)-1.5

0.3

3(0.2)-(0.3)(10)-100(100)-(200)(50)-(100)(500)-(1000)(1.5)-(2.5)

0.7

4

≥0.3

≥100

≥200

≥100

≥1000

≥2.5

1

3

10th Asian Regional Conference of IAEG (2015)

The debris flow gullies are generally of high danger
on the whole under current condition, which isconsistent with the actual condition in spot.

(3) Entropy Ej ofthe jth index is calculated asshown below:

Ej  

1 m
 pij  ln pij
ln m i 1（9）Compared to flood, debris flow is fluid with higher
density and viscosity and it contains large amount ofhuge rocks. After fluid into the river, density current
was formed with the river fluid because of thedifference from the river in density, Scoured by theriver, the debris flow was partly taken away, partly
stays in the channel which causes congestion. The maincause of river blocking includes intersection angle
between the debris blow gully and the river, the flowrate of the debris flow ratio to that of the river, densityof the debris flow, lasting period, solid matter quantityof the debris flow and grain composition etc.
The probability of blocking river is namely the
influence degree of the main river by the debris flow.If
the energy of the debris flow is greater than that of themain river, then the probability of blocking river ishigh. The component in the main direction of riverwidth of tributary stream power ratio to that of themain river is taken as the risk degree of blocking riverby the debris flow(Xu et al.2002).

In order to gurantee Ej ranges from 0 to 1, lnpij was
defined to be 0 when pijequals to 0, then 1-Ej is taken
as utility value.
(4) Calculate the weight of the jth index
usingentropy method, formula is as follows:

wj 

1 Ei
m

m   Ei

（10）

i 1

Following the above steps, the weight of the indicescan be calculated by Matlab programming:

W2 =(0.171,0.058,0.238,0.120,0.322,0.091)
3.3 The comprehensive evaluation model andsimulations
Based on linear weighting method, the risk
assessment model of the debris flow gully isestablished in this paper. The risk degree of each debris
flow gullies is calculated. We first combine these two
sets of weights, then classify and assign the 6 evaluate
indices according to Table 2 (value in the brackets
means it is not included in this degree). Finally wesubstitute the combination weighting into the risk
assessment model, as shown in formula (11):m

P(i )  W x

(11)

W *  uW 1  (1  u )W 2

(12)

j 1

Here,

* '
j ij

 s qJ ssin  ,  90 wQJ wK 
 qJ
 s s [1  sin(  90 )],  90  wQJ w

calculated value of risk degree.
Take u as a half, then the risk degree and risk gradeof each debris flow are calculated using the above
evaluation model. The results are shown in Table 3:

4.1 The calculation of the density of the debris flow
After the "12-May" earthquake, a large amount ofloose solid material with sufficient provenance was
piled up in the debris flow gullies in research area. Thiskind of debris flow is usually viscous. The density ofthe debris flow determines the sediment concentrationof the debris flow, which is available by formulationand look-up table method comprehensively. The
equation of formulation method is as follows:

Table 3Classification of risk assessmentclassification

range of each

grade

description

grade

1
2

mild dangerous
intermediate
dangerous≤0.35
0.35-0.60

3severely dangerous

0.60-0.85

4extremely dangerous

≥0.85

(13)

Here, K is the influence degree on the main river by
the debris flow, that is, blocking risk level; q and Q is
the peak discharge of the debris flow and riverrespectively;  s ,  w is the density of the tributary andthe main river respectively; Js, Jw describes thelongitudinal slope of the tributary and the main riverrespectively;  denotes the intersection angle of thetributary between the main river.
The parameters calculation process is as follows.

xij' is the assignment result, P(i) is thedanger

4 Debris-flow-induced river blocking probablity

s 

4

Gs
V

(14)

10th Asian Regional Conference of IAEG (2015)

Here  s is the density of the debris flow; Gs is the
prepared mud weight with the unit t; V

is the3prepared mud volume with the unit m .
4.2 Peak discharge of debris flow calculation
Peak discharge of the debris flow is the key factor
of debirs-flow-induced river blocking. With larger peakdischarge, the quantity of sediment entering into themain river per unit time becomes bigger, while theprobability of river blocking is higher. The peakdischarge of debris flow can be obtained by rain-floodcorrection or by morphological investigation. Whenusing rain-flood correction, the peak discharge of thedebris flow and rainstorm are considered to occur at thesame time. The formula is as follows:

QC  (1   )QB  DC

(15)

In this equation, Qc is the peak discharge of thedebris flow with rainfall frequency of P. QB is thedesign discharge of the flood triggered by rainstorm
with rainfall frequency of P. DCis the blockage
coefficient of the debris flow.  is the mud correction
coefficient , the formula is as follows:  ( C   W ) /( H   C )(16)

4.3 Confluence angle and gradientIntersection angle is generally regarded as the key
factor for debirs-flow-induced river blocking, which
dominates the effect of debris flow on the main river.
The slope is an important index reflecting the debris
flow gully and main river energy release, which can beobtained by remote sensing images.
4.4 Water flow of Minjiang river
The average water flow of Minjiang river isranging from 168 to 268m3 per second, in flood period,
it is about 1890m3 per second. The debris flows occur
successively in the gullies, leading to the river blocking.
Using Formula 12 with the above parameter values, therisk of river blocking is calculated, as shown in Table4.

4.5 Conclusions of debris-flow-induce driver
blocking probability
In the calculations of river blocking probability, theparameters for the main river and the branch were
adjusted accordingly by considering the specificcircumstance of each gully. Taking the critical index
K=10, we calculated the blocking probability of
Minjiang river by the debris flow. The calculated
results agree with the practical case occurring on 10th
July 2013.
At the entrance of Taoguan gully, huge number ofindustrial plants and relatively small gully slope makeTaoguan gully an ideal place to buffer and store debris
flow. Moreover, the debris flow at the upstream
blocked Minjiang river, this caused a lift of the river
bed at the entrance of Taoguan gully, leading to a fasterwater flow, thus diluting the debris flow. Therefore, thedebris flow at Taoguan gully did not contribute to theblocking of Minjiang river.
The debris flow occurring at Huaxi gully (600meters
away at the downstream of Mozi gully)prior to theoneat Mozi gully,blocked Minjiang river and induced
water flowing backwards. This reduced the velocity of
the water flow of Minjiang river. In addition, highway
bridge piers tend to hinder the movement of debris
flow as well. Finally, Mingjiang river was seriously
blocked with water level lifted by 7 meters, resulting in
a complete disruption of G213 and Duwen highway.
For Qipan gully, even with alluvial fanbeing
3300meters long, the burst of materials in debris flow
could not be effectively directed to Minjiang river due
to a low longitudinal slope of merely 73‰. Over
two-thirds of Mingjiang surface was occupied by thedebris flow.
After the occurrence of debris flow at Huaxi gully,
huge amount of loose materials was washed out andcarried to the region between the entrance of the gully
and Minjiang river bed. This lifted the river bed close
to Dunwen highway. The third bridge in Caopo and thetraffic was disrupted. The crashed bridge fell down andlied vertically beneath the bridge of Dunwen highway,
leading to a complete blocking of Minjiang river. This
accelerated the formation of Minjiang river blocking by

Table 4 Risk level and risk grade of different debris flow gullies
the debris flow gully

Taoguan

Mozi

Qipan

Huaxi

ChutouZhangjiaping

Risk degree

0.808

0.814

0.711

0.842

0.7

0.845

Risk grade

severely

severely

severely

severely

severely

severely

Blocking river risk degree

6.1

19.3

17.3

20.1

8.4

18.4

River blocked or not

no

yes

yes

yes

no

yes

5

10th Asian Regional Conference of IAEG (2015)

debris flow and dammed lakes.
For the debris flow at Chutou gully, one burst ofdebris flow contains solid materials of about 50000
square and over half of the watercourse was occupied.
The mainstream was squeezed to the left and carriedaway by the upstream flow. The river blocking was notformed.
The upstream rainfall intensity of Zhangjiaping
gully is relatively low, the discharge of Minjiang riveris later than the debris flow at Zhangjiaping gully, thedeposit body caused clogging of Minjiang river andoccupied 80 percent of the watercourse width.

Mechanics, Vol.34, No. 5, pp. 1409-1415.
Deng Julong. (1987): Basic Method of Grey Systems.
Wuhan: Huazhong University of Technology Press.
Hu Xiewen,Lv Xiaoping, Huang Runqiu, et al.(2009 ):Developmental features and evaluation of blocking
dangers of Dashui ditch debris flow in Tangjiashan
dammed lake, Chinese Journal of Rock Mechanics
and Engineering, Vol. 28, No. 4, pp.850-858.
LiuCuirong, Yao Lingkan, Du Cui, et al. (2013):
Experimental research on criteria anddisaster-forming patterns of damminglarge river bydebris flow in earthquake disaster areas. China civilengineering journal, Vol. 46, No. 1, pp.146-152.
Liu Xilin, Tang Chuan. (1955): Danger Assessment on
Debris Flow, Beijing:Science Press.
Su Jingyu, ZhouXiyuan, Fan Shuirong.(1993): A Fuzzy
set evaluation method of hazard degree of debris flow,
Journal of Natural Disasters, Vol.2, No. 2, pp.83-90.
Tao Huacheng.(1982):A method of dealing withmulti-objective decision problem, Journal of Systems
Science and Information, Vol. 2, No. 2.
TieYongbo, Tang Chuan. (2005):Information
entropy-based hazard assessment of debris flow gully,
Journal of Catastorphology, Vol. 20, No. 4, pp. 7-12.
TieYongbo, Tang Chuan. (2006):Application of AHP
in single debris flow risk assessment, The Chinese
Journal ofGeological Hazard And Control, Vol. 17,
No. 4, pp.80-84.
Wang Qin, Yao Lingkan, He Ping, et al. (2005): Latticeboltzmann simulation of debris flow ingoing primaryriver. Journal of natural disasters, Vol. 14, No. 3,
pp.29-33.
Xu Yongnian．KuangShangfu，Huang Yonsjian.etal.(2002):Hazard-decidingindices for convergence ofdebris flow. Journal of NaturalDisasters，Vol. 11, No.
3, pp.33-38．
Zhuang Jianqi,GeYonggang, Chen Xingchang.(2012):
Assessment of river block probability by debris flow
along Duwen highway after Wenchuan earthquake.
Journal of engineering geology. Vol. 20, No. 2,
pp.195-203.
Zou Xiang, Cui Peng, Wei Fangqiang.(2003):
Application of Grey-correlation Method to
ActivityEvaluation of Debris Flow, Journal of
Mountian Science,Vol. 21, No. 3,pp.360-364.

5 Conclusions
Based on geologic investigations on 6 typicaldebris flow gullies and the analysis of the characteristic
parameters. the risk evaluation and probability of river
blocking were studied. The conclusions are as follows:(1) According to the characteristics of the debris
flows in earthquake area, 6 risk evaluation indices were
determined: integrality index of the watershed, rush
scale of the debris flow once a time, the longitudinalslope, loose material reserves in unit, Annual mean
rainfall, blockage coefficient separately. Among these,
the blockage coefficient gives full consideration to thedam break phenomena and the amplification effectcaused by the debris flow gullies in Wenchuan
earthquake disaster region afterwards.
(2) Combining grey correlation method withentropy value method, the calculated risk degree of the
6 debris flow gullies agrees well with the real case.
(3) The tributary stream power ratio to that of themain river is taken as the risk degree of river blocking
by the debris flow. The particular situation of eachgully is analyzed, including the rainfall, slope, flowrate of the main river and the blocking effect of plants
and piers. The calculated river blocking probability is
consistent with the practical situation.
References
Chen Pengyu, QiaoJingshun, Peng Zuwu, etc.
(2013):Screening of debris flow risk factors and risk
evaluation based on rank correlation, Rock and Soil

6