Risk Due to Debris Flows

Article

Risk assessment ofsimultaneous debris flowsin mountain townships

Peng CuiCAS Key Lab. of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment,

Chinese Academy of Sciences, China

Qiang ZouCAS Key Lab. of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment,

Chinese Academy of Sciences, China; University of Chinese Academy of Sciences, China; and Southwest

University of Science and Technology, China

Ling-zhi XiangChongqing Jiaotong University, China

Chao ZengCAS Key Lab. of Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment,

Chinese Academy of Sciences, China; and University of Chinese Academy of Sciences, China

AbstractMany mountain towns in China are located on the joint alluvial fans of multiple and adjacent past debris flows,making them vulnerable to large, multiple, and simultaneous debris flows during heavy rainfall. Withoutemergency management planning, such flows, often appearing with interconnecting and chain-reactionprocesses, can lead to extensive loss of life and property. In the Wenchuan earthquake-affected area, suchdisasters are common. We analyzed the compound effects of simultaneous debris flow events, and proposedthree quantitative methods of debris risk assessment based on kinetic energy, flow depth, and inundationdepth. Validated using a field study of actual debris flow disasters, these analyses are useful in determiningthe type, quantity, distribution, economic worth, and susceptibility of hazard-affected objects in a region. Sub-sequently, we established a method to determine the vulnerability of different hazard-affected objects, par-ticularly concerning the susceptibility indexes of buildings or structures. By analyzing the elements underlyinghazard formation conditions, damage potential, and the socio-economic conditions of mountain townships, weproposed a systematic and quantitative method for risk analysis of mountain townships. Finally, the proposedmethod was applied to a case study of Qingping Township, which was affected by 21 simultaneous debris flowstriggered by a 50-year return period precipitation event. The proposed method analyzed the superposition andchain-reaction effects of disasters and divided the affected area of the township into three risk zones. The anal-ysis indicated that the calculated risk zones coincide with the actual distribution and severity of damage in thedebris flow event, which suggests that the risk assessment is consistent with results from the actual disaster.

Corresponding author:Peng Cui, 9#, Section 4, Renminnanlu Road, Chengdu 610041, Sichuan, People’s Republic of China.Email: [email protected]

Progress in Physical Geography37(4) 516–542

ª The Author(s) 2013Reprints and permission:

sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0309133313491445

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Keywordsdebris flow, hazard analysis, risk assessment, vulnerability, Wenchuan earthquake

I Introduction

On 12 May 2008, a devastating mega-earthquake

of magnitude 8.0 struck the Wenchuan area in

northwestern Sichuan Province, China. The

Wenchuan earthquake caused subsequent debris

flows to be more active and occur on a larger

scale than in previous years (Cui et al., 2010;

You et al., 2010). Large-scale and simultaneous

debris flows caused by heavy rainfall com-

monly occur in such earthquake-prone areas.

Some large mountain towns in western China

are located on the alluvial fans of several adja-

cent debris flow catchments, and their similar

geological and geomorphological conditions

can cause simultaneous debris flows to develop

in these gullies during periods of locally heavy

rainfall. Moreover, these simultaneous debris

flows often lead to a complex process of damage

accompanied by multiple hazards, including

direct dynamic impact destruction, debris accu-

mulation, and subsequent damage induced

by lifeline destruction and chain-reaction disas-

ters that occur due to river blockages. The

destruction of Qingping Town, Yingxiu Town,

and Longchi Town by simultaneous debris

flows in August 2010 represents typical exam-

ples of this phenomenon (Table 1). Therefore,

it is vital to develop an accurate risk evaluation

of the effects of multiple, simultaneous, or con-

terminous debris flows on mountain towns.

Debris flow risk assessment plays a crucial role

in disaster prevention and mitigation. In recent

years, several studies have examined hazard and

risk assessment of debris flows (Hurlimann

et al., 2006; Jakob and Hungr, 2005). Generally,

debris flow risk analysis focuses on two scales:

regional studies and on-site or local-scale

studies. Debris flow risk analysis on a regional

scale provides a risk awareness of potential

regional hazards, and may satisfy the needs of

macroscopic disaster mitigation. This scale of

analysis is predominantly used in combination

with methods such as geographic information

systems (GIS) (Huggel et al., 2003), statistical

analysis (Mark and Ellen, 1995; Rickenmann,

1999; Cannon et al., 2010), simple dynamic

approaches (Archetti and Lamberti, 2003;

Gret-Regamey and Straub, 2006), and interpre-

tation of aerial photographs or satellite images

(Bisson et al., 2005; Pradhan, 2010). Detailed

on-site studies at a local scale instead focus on

analyzing the damage processes, destruction

range, and potential losses from debris flows.

Scientists have explored various methods and

models to develop accurate risk assessment for a

single debris flow. Liu and Mo (2003) proposed

a debris flow risk evaluation model based on 14

impact factors, establishing five risk levels.

They related debris flow hazard (H) to an appro-

priate number of variables, including magni-

tude, frequency, watershed area, the length of

the main channel, relative height of watershed,

incision density, and the percentage of the

unstable channel bed. The overall vulnerability

(V) was estimated by means of empirical func-

tions dependent on population and the total

amount of property. This was the first major

study on debris flow risk assessment in China,

but the damage process and the resistance of

hazard-affected objects on different locations

of alluvial fans were not taken into account.

Calvo and Savi (2009) proposed a method for

formal risk analysis in debris flow prone areas.

In their method, a Monte Carlo procedure was

applied to quantify debris flow hazards, and

three different vulnerability functions were

introduced to analyze a case of damaged build-

ings. Based on an analysis of the hazards of deb-

ris flows and the individual vulnerability and

value of hazard-affected objects, Gentile et al.

Cui et al. 517

(2008) evaluated four risk degrees of debris

flows and implemented a risk mapping model.

However, the aforementioned studies focused

on analyzing the risk caused by a single debris

flow. Current risk analysis methods for a single

debris flow normally do not consider the com-

pound disaster effects of multiple debris flows.

As is well known, natural disasters involve var-

ious interacting components of natural systems;

ignoring such interconnections, and their

mutual effect(s) on the different types of risks

within a single area, can lead to underestimation

of the synergy produced by the joint functions of

several processes (Kappes et al., 2010; Marzoc-

chi et al., 2012; Perles-Roselloy et al., 2010).

Although hazard and vulnerability analysis

methods are well established for risk assessment

of single natural disasters, assessment of the

compound hazards of simultaneous disasters

poses a variety of challenges due to their widely

different characteristics (Kappes et al., 2010).

To date, some scientists have conducted pre-

liminary research on multi-hazard risk assess-

ment and mapping. Carrasco et al. (2003)

applied the Bayes conditioning probabilistic

method and GIS techniques to establish hazard

zoning in the Jerte Valley, Spain, which is sub-

ject to frequent landslides and floods. Consider-

ing six primary hazards, including shoreline

erosion, riverine flooding, storms, landslides,

seismicity and volcanism, and man-made struc-

tures, De Pippo et al. (2008) adopted a semi-

quantitative method to quantify, rank, and map

the distribution of hazards along the northern

Campanian coastal zone in Italy. Perles-

Roselloy et al. (2010) proposed different meth-

ods for producing multi-hazard maps, including

a synthetic-hazard map, an aggregate-hazard

map, a stability map, and an accumulated risk

map. These studies improve knowledge of risk

assessment for multiple hazards, notwithstanding

that they focused on various hazards on an

Table 1. Examples of typical simultaneous debris flows in earthquake-affected areas.

Date Location Description

13–14 August2010

Qingping,MianzhuCounty

Debris flows, triggered by locally intensive rainfall, simultaneouslyappeared in 21 catchments around Qingping Township, causing 12deaths. The debris (about 400 million m3) blocked the Mianyuan Riverand silted an area 3.5 km in length and 400–500 m wide. The debrisraised the riverbed over 5 m, to a maximum thickness of 13 m in someareas. Furthermore, the debris flow destroyed 479 houses recentlyreconstructed after being destroyed in the Wenchuan earthquake.

13–14 August2010

Longchi,DujiangyanCounty

Debris flows occurred in 43 catchments around the town of Longchi inDujiangyan County, destroying or seriously silting the buildings alongthe nearby watercourse. In total, the debris flows damaged 182houses, destroyed more than 311 ha of farmland and 3.35 km ofroads, and killed 2682 poultry.

13–14 August2010

Yingxiu,WenchuanCounty

Following heavy rain, a debris flow occurred in Hongchun Gully alongthe upper reaches of the Min River, blocking water flow in the river.Eventually, the backwater of the barrier lake submerged the Yingxiuhydropower station, the Shaohuoping Bridge (a suspension bridgeupstream of the debris barrier), and national road G213.Furthermore, the debris forced the main course of the Min River toshift to the right, flooding Yingxiu town. The township had just beenreconstructed on the first river terrace after the Wenchuanearthquake, and the new residential buildings and water supplyfacilities were seriously damaged.

518 Progress in Physical Geography 37(4)

individual basis. However, each type of hazard dis-

plays unique damage characteristics due to idio-

graphic, dynamic, and movement processes.

Consequently, it is difficult to consider the

compound and chain-reaction effects based on

dynamic processes of each hazard in a syn-

thetic-risk assessment for multiple hazards.

Although some semi-quantitative hazard and risk

analysis methods are well established for several

natural hazards (Granger et al., 1999; Marzocchi

et al., 2009; De Pippo et al., 2008), few methods

are available for analyzing the simultaneous

occurrence of multiple debris flows. Therefore,

a method to quantitatively evaluate the risks of

simultaneous debris flows in mountain townships

is necessary in order to develop appropriate risk

management strategies.

This paper explores a new risk analysis

method for mountain towns subject to a group

of large-scale and simultaneously occurring

debris flows, by focusing on analysis of the

superposition effect and of the chain reaction

triggered by multiple debris flows. We propose

a set of hazard evaluation indicators based on

this method to describe the resulting damage.

Finally, we apply the proposed method to con-

duct a case study of Qingping Township, which

was significantly damaged by debris flows on

13 August 2010.

II Hazard characteristics ofsimultaneous debris flows inmountain townships

1 Simultaneous occurrence of debris flows

In China, many mountain towns are located on

the joint alluvial fans of several adjacent debris

flow catchments. According to investigation

and statistical analysis, more than 1000 such

towns are in Western China. Table 2 lists some

typical cities and towns in the region affected

by several adjacent debris flows. Dongchuan

Township resides just within the debris flow

hazardous area (Figure 1). Due to the similar

geological and geomorphological conditions

of these adjacent debris flow gullies, debris

flows triggered by regional rainfall can simulta-

neously occur in a group in these gullies and

result in interconnecting and superpositioned

disasters. This characteristic of debris flows is

particularly evident in areas that have condi-

tions of steep terrain and abundant loose debris.

For instance, a field investigation shows that a

recorded individual rainfall of 96.3 mm and a

rainfall intensity of 77.3 mm in 40 minutes dur-

ing the evening of 7 August 2010 triggered

simultaneous debris flows in Sanyanyu and

Luojiayu Ravines. The Sanyanyu and Luojiayu

debris flows had peak discharges of 1485 m3/s

and 390 m3/s, respectively, and the resultant

flows destroyed the urban area situated on the

alluvial fans, caused 1765 deaths and blocked

the Bailong River, which resulted in inundation

of the township (Table 2). This case is typical

for interconnecting and superposition disasters

of multiple debris flows. During the rainstorm

of 13–14 August 2010, 43 debris flows broke

out in the area of Longchi Township of Dujiang-

yan County, and debris flows appeared in 21

watersheds around the town of Qingping in

Mianzhu County. Both towns suffered heavy

losses from the simultaneous occurrence of

debris flows.

2 Hazard formation conditions ofdebris flows

Due to limiting terrain conditions, it is difficult

to find a flat and open area upon which to build

towns in many mountainous areas. The joint

debris flow alluvial fans in alpine gorge regions

generally have advantageous conditions such as

a gentle slope and thus convenient traffic and

water infrastructure. Thus, these regions

become preferred sites for town construction.

As is well known, old deposits from all sizes

of debris flows gradually form these fans. So,

owing to the quasi-periodicity of debris flow

development (Chen, 2002), large-scale debris

Cui et al. 519

Tab

le2.C

itie

san

dto

wns

affe

cted

by

adja

cent

deb

ris

flow

s.

No.

Nam

eof

city

or

tow

nPro

vince

Loca

tion

Num

ber

of

deb

ris

flow

sN

ame

ofdeb

ris

flow

catc

hm

ents

Des

crip

tion

1La

nzh

ou

(cap

ital

ofG

ansu

Pro

vince

)

Gan

suE103.5

1,N

36.0

415

Hongs

huiG

ully

,W

uquan

shan

Gully

,H

uan

gyu

Gully

,Jia

nG

ully

,et

c.

On

7A

ugu

st1978,1

4deb

ris

flow

ssi

multan

eousl

ybro

keout

inth

eX

ujia

wan

dis

tric

tofLa

nzh

ou

city

,ca

usi

ng

tens

ofdea

ths

and

loss

esofm

ore

than

3m

illio

nyu

an.In

additio

n,a

deb

ris

flow

inH

ongs

huiG

ully

on

20

July

1964

kille

d157

peo

ple

.2

Zhouqu

Gan

suE104.3

8,N

33.8

13

Luojia

yuG

ully

,San

yanyu

Gully

,D

azai

Gully

Tw

osi

multan

eous

deb

ris

flow

socc

urr

edin

the

Sanya

nyu

and

Luojia

yuca

tchm

ents

on

8A

ugu

st2010,ru

inin

gZ

houqu

Tow

nsh

ip(incl

udin

gth

ree

villa

ges)

,ca

usi

ng

1765

dea

ths,

and

leav

ing

22,6

67

oth

ers

hom

eles

s.T

he

dum

ped

deb

ris

blo

cked

the

Bai

long

Riv

eran

dfo

rmed

abar

rier

lake

,whic

hin

undat

edhal

fof

Zhouqu

Tow

nsh

ipfo

rove

r20

day

s.T

he

haz

ard

des

troye

d4321

house

san

d21

build

ings

.3

Zhuan

glan

gG

ansu

E106.0

6,N

35.2

03

Wen

jiaG

ully

,Sh

ijia

Gully

,Li

jiazu

iG

ully

On

27

Apri

l,deb

ris

flow

ssi

multan

eousl

ybro

keoutin

Wen

jiaG

ully

,Sh

ijia

Gully

,and

Lijia

zuiG

ully

.The

cata

stro

phe

buri

edm

ore

than

4000

house

s,ki

lled

800

peo

ple

and

2600

poultry

,an

dto

tally

des

troye

d670

ha

offa

rmla

nd.

4B

eich

uan

Sich

uan

E104.4

4,N

31.8

93

Wei

jiaG

ully

,Xiji

aG

ully

,H

uas

hib

anG

ully

Deb

ris

flow

sin

Wei

jiaG

ully

,X

ijia

Gully

,an

dH

uas

hib

anG

ully

around

Bei

chuan

Tow

nsh

ipw

ere

sim

ultan

eousl

ytr

igge

red

by

inte

nse

rain

fall

on

24

Septe

mber

2008,s

iltin

ghal

foft

he

tow

nsh

ip,

bury

ing

eart

hquak

ere

mai

ns,

and

causi

ng

21

dea

ths

and

enorm

ous

pro

per

tylo

ss.

5Jiu

zhai

gou

Sich

uan

E104.2

3,N

33.2

74

Guan

mia

oG

ully

,B

ala

Gully

,C

uoji

Gully

,H

oush

anG

ully

Duri

ng

hea

vyra

infa

llon

18

July

1984,deb

ris

flow

ssi

multan

eousl

yocc

urr

edin

Guan

mia

oG

ully

,Bal

aG

ully

,and

Cuoji

Gully

,cau

sing

35

dea

ths

and

loss

esofm

ore

than

54.4

3m

illio

nyu

an.

6So

ngp

anSi

chuan

E103.6

1,N

32.6

45

Shan

gyao

Gully

,So

ngl

inG

ully

,D

ongy

uG

ully

,Z

hongz

hao

Gully

,T

apin

gG

ully

In1988,deb

ris

flow

sin

Shan

gyao

Gully

des

troye

d34

house

san

dm

ore

than

8.7

ha

offa

rmla

nd.T

he

deb

ris

flow

ssu

rroundin

gSo

ngp

anT

ow

nsh

ipth

reat

ened

the

safe

tyofm

ore

than

2025

peo

ple

asw

ella

shav

ing

the

pote

ntial

for

anen

orm

ous

amountof

pro

per

tydam

age.

7H

uay

ing

Sich

uan

E106.4

4,N

30.2

64

Jianzi

Gully

,C

uoji

Gully

,Pia

nya

nzi

Gully

,G

uan

yin

Gully

Deb

ris

flow

ssi

multan

eousl

yocc

urr

edin

Jianzi

Gully

,C

uoji

Gully

,Pia

nya

nzi

,and

Guan

yin

Gully

on

2Ju

ly1986.T

his

even

tre

sulted

info

ur

dea

ths,

nin

ebad

lyw

ounded

peo

ple

,an

dlo

sses

of20

mill

ion

yuan

.8

Yaa

n(c

apital

of

Yaa

npre

fect

ure

)

Sich

uan

E102.9

7,N

29.9

72

Luw

ang

Gully

,G

anxiG

ully

Adea

thto

llof16

9pe

rsons

resu

lted

from

sim

ulta

neousl

yocc

urri

ng

deb

risflo

wsin

Luw

ang

Gul

lyan

dG

anxi

Gul

lyon

2N

ove

mbe

r19

79.

One

stre

etofY

a’an

city

was

ruin

ed,r

esul

ting

inve

ryhe

avy

loss

es.

(con

tinue

d)

520

Tab

le2.(c

ontinued

)

No.

Nam

eof

city

or

tow

nPro

vince

Loca

tion

Num

ber

of

deb

ris

flow

sN

ame

ofdeb

ris

flow

catc

hm

ents

Des

crip

tion

9N

ingn

anSi

chuan

E102.7

6,N

27.0

73

Yan

gjuan

Gully

,Y

ingy

ang

Gully

,Sh

enjia

Gully

In1954,a

deb

ris

flow

inY

angj

uan

Gully

des

troye

dm

ore

than

10

house

san

dm

ore

than

6.8

ha

offa

rmla

nd.A

seco

nd

even

tin

the

sam

eye

ar,w

hic

hhad

deb

ris

flow

socc

urr

ing

sim

ultan

eousl

yin

Yin

gyan

gG

ully

and

Shen

jiaG

ully

,rav

aged

one

bri

dge

on

Hei

shui

Riv

eran

ddes

troye

d11

house

san

dm

ore

than

20

ha

off

arm

land.

Sim

ultan

eous

adja

cent

deb

ris

flow

sin

Yan

gjuan

Gully

and

Yin

gyan

gG

ully

des

troye

da

tota

lof14

house

san

d12

ha

of

farm

land

duri

ng

5–27

June

1983.

10

Mae

rkan

g(c

apital

ofA

ba

Tib

etA

uto

nom

ous

pre

fect

ure

)

Sich

uan

E102.2

2,N

31.9

2M

ore

than

10

Dal

angz

uG

ully

,N

aris

ichaz

uG

ully

Min

jingz

hongd

uiG

ully

Sanjia

cun

Gully

,C

ham

uqia

oG

ully

,etc

.

Deb

ris

flow

sbro

keout

inm

ultip

legu

llies

in1949,1

953,a

nd

1957,

resp

ective

ly,ca

usi

ng

hea

vylo

sses

.D

ebri

sflo

ws

thre

aten

edth

ese

curi

tyan

dpro

per

tyofm

ore

than

1300

peo

ple

.

11

Jinch

uan

Sich

uan

E102.0

3N

31.4

82

Bab

uli

Gully

,Cai

jiaG

ully

Jinch

uan

Tow

nsh

iphas

suffer

edfr

om

deb

ris

flow

sm

any

tim

es.A

deb

ris

flow

on

22

June

1926

kille

d17

peo

ple

and

des

troye

dnin

em

ills.

Deb

ris

flow

son

4Ju

ly1976

was

hed

away

asm

allf

acto

ryan

dfiv

ehouse

s,an

ddeb

ris

flow

son

22

July

1980

seri

ousl

ydam

aged

ahosp

ital

and

afo

od-p

roce

ssin

gpla

nt.

12

Dongc

huan

Yunnan

E103.1

2,N

26.0

66

Shiy

ang

Gully

,N

ilagu

Gully

,Sh

enG

ully

,Z

hugu

osh

iG

ully

,T

ianba

Gully

and

lalih

eG

ully

In1964,si

multan

eousl

yocc

urr

ing

deb

ris

flow

sin

Shen

Gully

and

Shiy

ang

Gully

resu

lted

inei

ght

dea

ths

and

the

des

truct

ion

of20

ha

offa

rmla

nd.

13

Qia

ojia

Yunnan

E102.9

2,N

26.9

08

Shuili

anG

ully

,La

osh

uG

ully

,Sh

ihuiy

aoG

ully

,B

ainiG

ully

,Fu

xin

gG

ully

,G

aolia

ngd

iG

ully

,H

einiG

ully

and

Xifa

nG

ully

Sim

ultan

eousl

yocc

urr

ing

deb

ris

flow

sin

Shuili

anG

ully

,Bai

niG

ully

,Fu

xin

gG

ully

,G

aolia

ngd

iG

ully

,an

dH

einiG

ully

duri

ng

24–27

Augu

st1980

des

troye

d71

house

s,m

ore

than

171

ha

off

arm

land,

and

800

mofr

oad

.More

than

2000

tofg

rain

was

lost

due

toth

eev

ent.

521

flows can break out at the same location in the

future under appropriate conditions. Once this

happens, the town on the fans will suffer serious

damage. In the Wenchuan earthquake affected

area, a large number of landslides triggered by

earthquakes provided abundant loose material

for debris flows. Although the debris flows in

some gullies had not broken out for a long time,

after the Wenchuan earthquake debris flows

became more active (Cui et al., 2011a; Hu

et al., 2010; Liu et al., 2010). Moreover, some

gullies changed into debris flow gullies, wherein

if the intensity of rainfall reaches a critical

amount it triggers the loose material in those

channels to form debris flows. Compared with

debris flows before the Wenchuan earthquake,

the magnitude and frequency of debris flows after

the earthquake have increased, to the extent that it

has become a major hazard for mountain towns

located on debris flow fans (Cui et al., 2011a). For

instance, because of locally intensive rainfall on

24 September 2008, debris flows simultaneously

occurred in Weijia Gully, Xijia Gully, and

Huashiban Gully around Beichuan Township

(Figure2). According to field investigations, the

cross-sectional peak discharge reached 225

m3/s. This scale of debris flow is much larger than

what occurred before the earthquake as the result

of similar rainfall intensity. The giant debris flow

silted Qushan Town, the old seat of Beichuan

County, which is regarded as a reserved earth-

quake relic, and caused 21 deaths and enormous

property loss (Figure 3; Table 2).

3 Hazard characteristics of simultaneousdebris flows

As shown in Figure 4, when a mountain town

suffers from multiple debris flows there are par-

ticular characteristics to the interaction effects

of the hazards:

1. Compound and overlapping disasters can

result due to simultaneous debris flows.

Many sites in a town may be hit by inter-

connecting disasters, or one location can

Figure 1. Dongchuan Township, located on the joint alluvial fan of six debris gullies, repeatedly suffered fromdebris flows before it was controlled (SPOT-5 image). In 1964, debris flows simultaneously broke out in ShenGully and Shiyang Gully. The catastrophe resulted in eight deaths and the destruction of 300 ha of farmland.


suffer various successive ones. For

instance, two different potential debris

flows overlap point A in Figure 4, while

point B is affected by a debris flow and

can be subsequently inundated by flood-

ing due to an upstream dam failure. In

another scenario, debris flow may silt up

the river channel and shift its course,

inducing a riverbed change that leads

to a cross-section reduction and a rise

in water level that can eventually flood

river terraces and floodplains (point C in

Figure 4).

2. Multiple hazards can result in a chain reac-

tion of disasters due to the initial debris

flow. Not only can the debris flow move

directly through the town and cause impact

and silting damages for the township,

but large-magnitude flows can also block

rivers and lead to an upstream inundation

hazard. When the makeshift dam col-

lapses, it can result in flood disasters

Figure 2. The spatial distribution of debris flows of Xijia Gully, Huashiban Gully, and Weijia Gully. BeichuanTownship is located in the dangerous zone of the three debris flows.Source: National Administration of Surveying, Mapping, and Geoinfomation.

Figure 3. The debris flow activity and its disastrous results in Qushan Town of Beichuan County after theearthquake. (A) Qushan Town after the earthquake (before debris flow). (B) Qushan Town after the debrisflow event on 24 September 2010. The debris flow ruined and buried many buildings.

Cui et al. 523

downstream. The above successive disas-

ters form a disaster chain called ‘debris

flow–barrier lake–flood’.

3. The third characteristic is the continua-

tion of the disaster. Simultaneously

occurring large-magnitude debris flows

are likely to cause huge compound cata-

strophes and disaster chains. Those inter-

connecting and superposition hazards

often expand spatially and continue

successively.

The debris flow in Hongchun Gully is a typical

example of these three hazard characteristics. The

debris flow occurred on 14 August 2012, deliver-

ing a huge amount of debris that blocked the

watercourse of the Min River. The impounded

water behind the debris barrier submerged Ying-

xiu Hydropower station, Shaohuoping Bridge,

and national road 213 upstream of the barrier. The

dumped debris forced the main channel of the

river to shift to the right past the barrier, causing

flooding in the brand new Yingxiu Township,

reconstructed after the Wenchuan earthquake

(Figure 5). This compound and interconnecting

disaster caused extremely heavy losses (Table 1).

At present, the threat of large-magnitude

and simultaneous debris flows confronts many

mountain towns. However, previous studies

have paid little attention to the issues of chain

reactions, continuity, and the overlapping

characteristics of potential disasters when ana-

lyzing the risk to these mountain townships,

and there has been little research analyzing the

urban hazards of debris flows occurring in a

group after the Wenchuan earthquake. There-

fore, it is important to explore a new method

to solve this issue.

III Risk analysis methods formountain townships suffering fromsimultaneous debris flows

1 Method for hazard analysis

In order to analyze the hazard characteristics of

debris flows occurring in a group around moun-

tain townships, we propose a systematic and

quantitative hazard analysis method supported

by numerical simulation of debris flow move-

ment and flood analysis. The proposed model

is expressed as:

D ¼ De þ Dh þ Di þ Df ð1Þ

Here, D is the total hazard degree, De is the

hazard caused by the impact force of the debris

flow indexed to the maximum kinetic energy

value in each grid during the whole debris flow

movement process, Dh is the hazard caused by

debris flow silting indexed to flow depth, Di is

the inundating hazard of the barrier lake

indexed to the inundated backwater depth, and

Df is the dam-failure flood hazard indexed to

the highest water level of the flooding.

Figure 4. Hazard characteristics for a mountaintownship due to debris flows occurring in a group.


Figure 5. Disaster chain induced by the debris flow that occurred in Hongchun Gully. The debris flow on 14August 2010 destroyed and blocked national road G213, delivered debris, and formed a huge barrier in thechannel of Min River which pushed the main stream toward the right bank, and then caused a flood disaster inthe newly reconstructed Yingxiu Township.Source: Sichuan Bureau of Surveying, Mapping, and Geoinfomation.

Cui et al. 525

a Numerical approach – debris flow processes.Flow velocity is a key parameter for identifying

the impact force of a debris flow, while the flow

depth can reflect the silting hazard (Kienholz,

1999; O’Brien et al., 1993; Rickenmann,

2001; Wei et al., 2006). When discussing debris

flow deposits, the debris flow motion equation

includes three important variables: mud depth,

the x-velocity component, and the y-velocity

component:

Du

Dt¼ gSsx � gSfx

Dv

Dt¼ gSsy � gSfy

ð2Þ

Here, u and v are x-component and y-compo-

nent velocities respectively (m/s), g is accelera-

tion due to gravity(m/s2), Ssx is the bottom slope

of the deposition area in the x-direction, Ssy is

the bottom slope of the deposition area in the

y-direction, Sfx is the friction gradient of

the debris flow in the x-direction and Sfy is the

friction gradient of the debris flow in the

y-direction.

According to O’Brien et al. (1993), Sfx and Sfy

can be calculated using:

Sfx ¼�B

�mhsgn uð Þ þ 2�Bu

�mh2þ kcu

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2 þ v2p

gh

Sfy ¼�B

�mhsgn vð Þ þ 2�Bv

�mh2þ kcv

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiu2 þ v2p

gh

ð3Þ

where �B is the yield stress (N/m2), �m is

the density of the debris flow (t/m3); h is the

flow-depth (m), �B is the viscous coefficient

(N.s/m2), and kc is the roughness coefficient.

The model treats debris flow masses as

aggregates of many small particles, each of

which has its own mass and velocity. To solve

equation (2) numerically, Hu and Wei (2005)

improved the particle model originally devel-

oped by Wang et al. (1997), while Cui et al.

(2011c) discussed the method and approxi-

mated the debris flow movement by using the

forward difference for each particle. The

difference equations can thus be expressed as:

unþ1k � un

k

�t¼ gSn;k

sx � gSn;kfx

vnþ1k � vn

k

�t¼ gSn;k

sy � gSn;kfy

ð4Þ

where unþ1k , vnþ1

k are the values of u and v for the

k-th particle at time nþ1, respectively, and unk ,

vnk , Sn;k

sx , Sn;ksy , S

n;kfx , and S

n;kfy are the values of u,

v, Ssx, Ssy, Sfx, and Sfy for the k-th particle at time

n.

Particle movement can be traced using the

MAC (marker-and-cell) computational tech-

nique (Hu and Wei, 2005). A digital elevation

model (DEM) grid of the real topography of the

debris flow gully is generated using GIS, and

provides division of the computational cells.

Therefore, each grid has a value for flow depth,

velocity, and elevation.

b Methods for determining the impact force and thesilting depth of debris flow. We can simulate the

debris flow movement process in an alluvial

area by adopting the numerical approach in sec-

tion III.1.a, using the DEM of the studied area to

identify the spatial distributions of velocity and

flow depth for each grid square during the

movement process. In order to determine silting

damage, we adopted the maximum flow depth

of the debris flow to index the silting hazard.

The flow depth of each grid (i, j) can be calcu-

lated using the following formula:

Dh ¼ max hð Þ ¼ Nni;j�V

Að5Þ

where Nni,j is the number of particles on grid

(i, j) at time n, �V is the average volume of each

particle (m3), A is the grid area (m2), h is the

flow depth (m), and Dh is the silting degree or

maximum deposit depth (m). Generally, larger

values of Dh represent a more serious hazard.

In order to easily calculate the value of Dh, we

simplify the process of overlapping deposits of

joint and opposite debris flows as the accumula-

tion of sequential debris flows. Therefore, we


can identify the deposition range and the thick-

ness of each debris flow in the same coordinate

system, and obtain the total thickness of deposi-

tion by summing the thickness of each overlap-

ping part.

In order to characterize quantitatively the larg-

est impact hazard at each grid position, we index

the impact force and damage capability of a deb-

ris flow by applying the maximum kinetic

energy. This process allows us to calculate the

impact degree of each grid square for the entire

dynamic debris flow process. The simulation

starts from the gully mouth (at the top of the

alluvial fan) and is implemented by determining:

De ¼ A �maxt>0½ u2 þ v2� �

h��

u ¼ 1

Nnij

XNnij

k¼1

uk

v ¼ 1

Nnij

XNnij

k¼1

vk

ð6Þ

where De is the maximum value of the kinetic

energy of the debris flow (N�m), u and v are the

velocities (m/s) in the x and y directions, respec-

tively, h is the flow depth (m), � is the density of

the debris flow (t/m3), and A is the grid area

(m2), Nni,j is the number of particles on grid

(i, j) at time n, uk, vk are the velocities (m/s) for

the k-th particle on grid (i, j), respectively.

c Method for hazard analysis of a debris flow barrierlake. In general, flow depth is an important

element for identifying the indirect hazard of

dam-failure floods and river-blocking back-

waters. In order to obtain the distribution of

submerged range and depth in mountain towns,

we consider two steps: (1) calculating the

reservoir storage of the barrier lake; and (2)

analyzing the process of the dam-failure flood

based on the DEM.

The possibility of river blockage caused by a

debris flow can be calculated for a given sever-

ity of rainfall. Cui’s previous experiments

proposed the following fundamental discrimi-

nant in order to identify whether a debris flow

has the capacity to block the main channel of

a river (Cui et al., 2006):

CM ¼1:189ð1� cos �Þ2 þ 3:677�B=�M

� 1n QM�M=QB�Bð Þ � 12:132ð7Þ

CF ¼ 0:883ð1� cos �Þ2 þ 2:587�B=�M

� 1n QM=QBð Þ � 8:572ð8Þ

where CM is the momentum criterion of the river

blockage, CF is the discharge criterion of the river

blockage, QM and QB are the flow discharges

of stream flow in the main channel and the

debris flow in the tributary (m3/s), respectively,

�M and�B are the maximum velocities of the river

flow and the debris flow (m/s), respectively, �B is

the debris flow density in the tributary (t/m3), �M

is the water density in the main channel (t/m3), and�is the intersection angle between the main channel

and the tributary (�). If CM � 12.132 or CF �8.572, the debris flow is likely to block the river

channel.

There are two methods for calculating the

height of the barrier dam: a numerical simula-

tion and an empirical formula. Cui (Cui et al.,

2011c) suggested a method to calculate the

debris flow process for a given rainfall condi-

tion. By combining the peak discharge (Qp)

obtained by field investigation and theoretical

calculation, the flow depth distributions at

each grid square during the movement process

and final silting range can be obtained by the

simulation method mentioned in section

III.1.a. The deposit depth at the river channel

calculated by equation (5) indicates the height

of the debris barrier dam. On the other hand, if

we know the flow duration of the debris flow,

we can calculate the total runoff using the

empirical formula:

Vc ¼19

72TQp ð9Þ

where Vc is the total runoff of the debris flow

(m3) and T is the flow duration time of the debris

Cui et al. 527

flow (s). The height (H) of the debris barrier

dam can be estimated by:

H ¼ 2Vc=L BU þ BDð Þ ð10Þ

where L is the length of the barrier dam in the

flow direction of the debris flow (m), BU is

the top width of the barrier dam (m), and BD is

the bottom width of the barrier dam (m).

Consequently, the submerging hazard degree

(Di) of the barrier lake, characterized by the

inundated depth, can be calculated as:

Di ¼ H0 þ Hð Þ � Hi ð11Þ

where H0 is the bottom elevation of the barrier

dam (m); H is the height of the debris barrier

dam (m); Hi is the elevation of the selected point

in the area of the town (m); and Di is the inun-

dated depth for the selected point in town (m).

d Method for hazard analysis of dam-failure floods.The hazard degree (Df) of a dam-failure flood is

acquired by calculating flood discharge and water

level. We select the worst-case scenario of a com-

plete collapse of the barrier dam to calculate the

magnitude of the dam-failure flood in order to

maximize the value of the potential risk. If the

dam-failure flood goes beyond the designed flow

capacity of anti-flood engineering, the mountai-

nous town may be in risk. The inundated depth and

areacanbecalculatedusing the town’s topographi-

cal data and Schoklitsch’s formula (Schoklitsch,

1948) to calculate peak discharge at the dam site:

Qmax ¼8

27

ffiffiffigp Bu

b

� �14

bH32 ð12Þ

where Qmax is the maximum discharge at the dam

site (m3/s), BU is the width at the top of the barrier

dam (m), b is the width of the sluice channel (m),

and g is gravitational acceleration (9.8 m/s2).

The maximum discharge at each cross-section

of the river downstream of the dam site is:

QLM ¼W

WQmaxþ L

VK

ð13Þ

where QLM is the largest flood discharge at each

cross-section (m3/s), L is the distance from the

dam site to the river cross-section (m), W is the

water storage of the barrier lake (m3), Qmax is

the largest flood discharge at the dam site

(m3/s), and VK is an empirical coefficient equal

to 7.15 in mountainous areas, 4.76 in hilly areas,

and 3.13 in the plains (Li, 2006).

To calculate the discharge and water level of

a dam-failure flood at each cross-section, we

use equations (12) and (13), and the terrain sur-

rounding the river. Using these results and the

township’s DEM, the inundated area (Af) and its

flood depth (Df) is determined by:

Af ¼ nðQLM � Q0Þ�R2=3I1=2 ð14Þ

Df ¼ nðQLM � Q0Þ�R2=3I1=2Bi ð15Þ

where Q0 is the flow capacity of each selected

river cross-section (m3/s), R is the hydraulic

radius (m), I is the hydraulic slope, Bi is the

width of the river cross-section (m), and n is

roughness of river channel.

After identifying all the information concern-

ing potential debris flow and flood inundation

damage, the distribution of the debris flow’s

destructive power, the debris flow depth, and

the dam-failure flood depth can be calculated

using GIS spatial analysis functions. This pro-

cess thus provides a complete hazard zoning for

mountain townships. In general, the collapse of

an upstream debris dam often occurs after debris

flow has deposited debris in the township. In

order to calculate the inundation range of the

upstream dam-failure flood, we start with the

method proposed by Cui and Hu (Cui et al.,

2011c; Hu and Wei, 2005) to calculate the range

and depth of the debris flow deposit within the

urban area, then input the changed topographic

conditions of the silted township as a new

variable for calculating the inundation range and

flood depth of the dam-failure flood from

upstream. If one location suffers from several

hazards (e.g. impact force, silt buildup, and flood

inundation), the largest hazard value is selected


to express the hazard degree in one hazard pro-

cess, and then the sum of each individual largest

value of different hazard processes will represent

the total degree of hazard for that location.

2 Method for vulnerability analysis

Scientists with different scientific backgrounds

have a different understanding regarding the

definition of vulnerability (Hufschmidt, 2011;

Papathoma-Kohle et al., 2012). Social scientists

focus more on the characteristics of the society,

relating vulnerability only to the social context

(Bohle and Glade, 2007). On the other hand,

natural scientists and engineers often describe

vulnerability as the degree of loss to an element

at risk (Totschnig et al., 2011; UNDRO, 1984).

In addition, some scientists think there is neither

a common definition for vulnerability nor a

standard methodology for vulnerability assess-

ment (Alcantara-Ayala and Goudie, 2010;

Papathoma-Kohle et al., 2011).

In this paper, we primarily analyze the resis-

tance of hazard-affected objects for vulnerability

assessment of the debris flow. Moreover, there

are five main aspects taken into consideration:

1. identifying the types of hazard-affected

objects;

2. summing the quantity and distribution of

all kinds of hazard-affected objects;

3. establishing the vulnerability assessment

model;

4. calculating vulnerability degree;

5. zoning sub-vulnerability areas.

Through analyzing the spatial and spectral char-

acteristics of the hazard-affected objects in

remote sensing images, we identified the shape,

size, image, shadow, and texture of the hazard-

affected objects. Then we applied the Chinese

National Land Survey Technique Rules (TDT

1014-2007) as criteria for determining the types

of hazard-affected objects, and we used field

investigation for verification. We extracted the

types, quantity, and spatial distribution of the

hazard-affected objects from remote sensing

images of the area surrounding the township,

which provides the data for calculating degree

of vulnerability. Degree of vulnerability is

related to the economic value and the susceptibil-

ity index of hazard-affected objects, defined by:

V ¼ V uð Þ � C ð16Þ

where V is the vulnerability degree, V(u) is the

economic value of a type of hazard-affected

object, and C is the susceptibility value of the

corresponding object. V(u) is calculated by

multiplying the unit price P of a type of hazard-

affected object by its size (area or length) N:

V uð Þ ¼ P � N ð17Þ

The susceptibility (C) indicates the degree of

resistance of the hazard-affected object against

a debris flow. Values for the variable range from

0 to 1, with larger susceptibility values indicating

a more vulnerable hazard-affected object. The

structure and material of a hazard-affected object

determine its susceptibility and damage mode.

Furthermore, the location of a hazard-affected

object in a debris flow gully also influences its

potential damage level. In the main area of the

debris flow gully, hazard-affected objects often

suffer from impact damage, while objects located

in outlying areas of the debris flow fan mainly

suffer from sediment damage. However, most

sediment-affected objects cannot be recovered

or reused either; although the objects maintain

their structure, their function is hard to recover

because of the change in terrain. Thus, the pro-

posed method provides a susceptibility calcula-

tion for each hazard-affected object.

For hazard-affected objects sedimented by

debris flow, the susceptibility index (CD) can

be indexed according to the ratio of the debris

flow depth to construction height:

CD ¼hD

HC

ð18Þ

Where hD is the debris flow depth (m) and HC is

the construction height (m), such as the height

Cui et al. 529

of a bridge or house. When hD

HC� 1, CD will

have a value of 1 as the building or structure

is completely buried.

The susceptibility index (CI) varies with

respect to hazard-affected objects impacted by

debris flows such as building structures or con-

structions. To quantitatively determine the sus-

ceptibility value of different constructions

needs a large number of failure trials or experi-

ments. Moreover, a significant number of real

structure damage survey cases are needed to

confirm the index value (CI). For our method,

we classify all prone buildings or structures in

a debris flow area. In this paper, we carried out

an actual investigation of a disaster situation in

western China, analyzing the data of destroyed

building or structures to obtain the susceptibility

indexes in Table 3 and the probability values for

different building structures.

3 Risk assessment method of debris flows

The definition of disaster risk proposed by

the UN Department of Humanitarian Affairs

(UNDHA, 1992) quantifies risk as:

R ¼ D� V ð19Þ

where R is the risk level, D is the hazard level,

and V is the vulnerability level.

a Process of risk analysis. We use the following

steps to carry out a risk analysis of debris flows

for mountain townships (Figure 6). First, we

build a spatial data set of the debris flow basin

that includes DEM data. Next, we ascertain the

rheological properties and kinematic para-

meters of the debris flow. From this informa-

tion, we can calculate the hazard degree by

applying the hazard analysis method described

above. After identifying the types, quantity, and

distribution of hazard-affected objects, we can

then calculate the susceptibility of the hazard-

affected objects by combining field survey data

with the described vulnerability analysis

method. Finally, the evaluation formula pro-

vides the degree of risk, from which we can

assign risk levels and risk zonation.

Table 3. Susceptibility indexes of buildings or structures.

Types ofstructures

Susceptibilitygrades

Susceptibilityvalues Characteristics*

Adobeconstruction

V 0.9*1.0 Small-scale debris flows can entirely destroy this type ofstructure.

Timber structure IV 0.8*0.9 Small-scale or medium-scale debris flows can seriouslydamage this type of structure.

Brick-woodstructure

III 0.5*0.8 Small-scale or medium-scale debris flows can partiallydestroy this type of structure.

Brick-concretestructure

II 0.2*0.5 Small-scale or medium-scale debris flows do not generallyaffect this type of structure, but it can be partiallydestroyed by large-scale debris flow.

Steel reinforcedconcretestructure

I 0.1*0.2 This type of structure is not generally affected in small-scale or medium-scale debris flows, but it can be par-tially destroyed by a devastating debris flow of hugemagnitude.

*According to the Specification of Geological Investigation for Debris Flow Stabilization (DZ/T 0220-2006), four gradesof debris-flow magnitude are classified by the total runoff as small-scale debris flow for the total runoff less than1�104 m3, medium-scale debris flow for between 1�104 m3 * 10�104 m3, large-scale debris flow for between10�104 m3 * 100�104 m3, and mega debris flow for larger than 100�104 m3.


b Data normalization method. According to equa-

tion (19), the degree of risk is the product of the

hazard degree and the degree of vulnerability.

Since the dimensions of the hazard index and

the vulnerability index are different, we must

normalize the hazard and vulnerability data.

We normalize the indexes by adopting the

following approach:

H0

i ¼Hi

Hmax

;V0

i ¼Vi

Vmax

ð20Þ

where H0i indicates the normalized value of the

hazard degree, Hi is the initial hazard index,

Hmax is the maximum hazard index, V0i indicates

the normalized value of the vulnerability

degree, Vi is the initial vulnerability index, and

Vmax is the maximum vulnerability index.

The determination of Hmax and Vmax is signif-

icant for risk assessment. In general, the values

of Hmax and Vmax are determined by referring to

losses of past events and typical case studies.

The hazard value of the largest disaster in the

study area is regarded as Hmax, and a similar

process can be used to determine the value Vmax.

c Grading method of evaluation results. When

implementing a risk evaluation, the evaluation

indicators are divided into several grades to

describe the degree of hazard. The indicator

values are evenly partitioned between the max-

imum and minimum values; or, based on thresh-

olds provided by experts, may be divided into

three levels of high, medium, and low (Hu and

Wei, 2005; Rickenmann, 2001; Wei et al.,

2006). We automatically divide the evaluation

indicators such as hazard degree, vulnerability

degree, and risk degree into three grades by

applying a probabilistic method using ArcGIS

and the following condition:

M rð Þ þ i� 1ð ÞV rð Þ < r < M rð Þ þ iV rð Þð21Þ

Figure 6. Flow chart of the risk assessment process for debris flows.

Cui et al. 531

where M(r) and V(r) are the mean value and var-

iance of the evaluation indicators, respectively

(where i is the number of grades). If the value of

grid r falls in the range of [M(r), M(r)þ V(r)], the

indicator is regarded as medium level. In the same

way, the range >M(r) þ V(r) belongs to the high

category, and the range <M(r) identifies the low

category. Note that if more grades are required, i

can be assigned a larger number such as 4 or 5. This

method generates reasonable results and avoids

the negative effects caused by abnormal data.

d Risk mapping. Two kinds of hazards must be

determined to develop a debris flow risk map for

mountain townships: the impact and sediment

hazards directly resulting from the debris flow

and the indirect hazards – such as dam-failure

floods and backwater inundation – induced by

a debris flow barrier lake. Features such as the

distribution of risk degree, levels of risk, and

location of the risk zone are key factors in risk

mapping. Applying GIS-based multi-map alge-

bra analysis (David, 2001) using ArcGIS 9.3,

we can calculate the distribution of the degree

of risk based on the results of the hazard and vul-

nerability analyses. Using the calculated data and

grading method described above, we establish

the three risk levels of high, medium, and low

risk, combining grids belonging to the same level

of risk and applying different colors to represent

each of the different levels. We can then use this

data to construct a map of risk zonation for debris

flows by constructing polygons in ArcGIS.

IV Case study: risk assessment forQingping Township

1 Background of the debris flow

The topography of Qingping Township is char-

acterized by high mountains with steep slopes

that average over 25�, while the slopes in the

upper reaches of the valley or in the debris

source areas reach 35�–45�. The longitudinal

profiles of the gully channels typically range

between 105% and 400%. Therefore, the terrain

conditions in the region are especially condu-

cive to debris flow formation.

Qingping Town is located at the front of the

Longmenshan fault belt, an active geological

tectonic structure. The Qingping-Baiyunshan

fault, which is a branch of the Longmenshan

fault, stretches through the township from

northwest to southeast. The rock strata in the

region are heavily fragmented due to active tec-

tonic activity caused by the Longmenshan fault.

The exposed strata are mainly from the Triassic,

Permian, Devonian, Cambrian, and Sinian

Periods. Shale, mudstone, sandstone, limestone,

and interbedded soft and hard rocks dominate

the lithology of the area. Furthermore, the rup-

ture of 5.12 Wenchuan earthquake extended

from southwest to northeast and passed through

Qingping Township. The earthquake intensity

was identified as XI degree in the Qingping

area. The earthquake triggered a large number

of landslides in the region and engendered abun-

dant unconsolidated soil for future debris flows.

In Wenjia Gully, a gigantic landslide provided

6�107 m3 of loose fragmented material accu-

mulation, which became a rich source for a

subsequent debris flow (Xu, 2010).

Qingping Township frequently suffers rain-

storms due to the influence of the monsoon in the

region, and the intensive rainfall generated in these

stormsis themajor triggeringfactorofdebrisflows.

After the Wenchuan earthquake, it was common

for large-scale debris flows to occur simul-

taneously during highly intensive rainfalls. For

instance, 21 debris flows occurred around Qingp-

ing Township during the rainstorm from 18:00 of

12 August to 4:00 of 13 August 2010 (i.e. the ‘8-

13’ event), when 230 mm of precipitation fell

within 10 hours (Cui et al., 2011b) (Figure 7).

2 The ‘8-13’ debris flow disaster

Duringthisheavyrainfall,21debrisflowsoccurred

simultaneously and ruined Qingping Township

(Table 1; Figure 8). The damage that resulted from

the catastrophe can be classified into three


categories. The first is the damage from the debris

flows, such as those in Luojia Gully, Dongzi Gully,

Wawa Gully, Linjia Gully, and Taiyang Gully,

which directly destroyed or silted structures. The

second category is damage from the debris flows

that partly blocked the river channel and caused

flooding, suchas thedebris flows inShaoyaoGully

and Zoumaling Gully. The most serious category

is the debris flow in Wenjia Gully that entirely

blocked the Mianyuan River, causing a dam-

failure flood disaster for Qingping Township.

3 Risk calculation

a Deposition range simulation. Through field sur-

vey data and visits with local people, we obtained

the basic parameters of the debris flows: length

of main channel, area of catchment, channel

slope, roughness coefficient of each channel,

and particle size (Table 4; Figure 9). We then

used these results and the measured grain-size

distribution (Figure 9) to calculate the other

necessary parameters of each debris flow. Equa-

tion (22) (Chen et al., 2003) gives the densities

of the debris flows, while equations (23) and

(24) (Wu et al., 1993) provide the viscous coef-

ficients and yield stresses of each debris flow.

We calculated the peak discharges of the debris

flows using equations (25) and (26). We then

simulated the debris flow motion process on the

alluvial area with the support of a DEM with a

5�5 m grid and the basic parameters listed in

Table 4.

� ¼� 1:32� 103x7 � 5:13� 102x6 þ 8:91x5

� 55x4 þ 34:6x3 � 67x2 þ 12:5xþ 1:55

ð22Þ

Figure 7. Distribution of the debris flows that occurred simultaneously during a rainstorm on 13 August2010. The clustered debris flows hit Qingping Township heavily.

Cui et al. 533

� ¼ 0:1�

1:635

� 27:0

; � � 2:0 t=m3� �

� ¼ 0:1�

0:60

� 4:42

; � � 2:0 t=m3� � ð23Þ

� ¼ 0:1�

1:394

� 7:41

; � � 2:2 t=m3� �

� ¼ 0:1�

2:096

� 45:0

; � � 2:2 t=m3� � ð24Þ

QP ¼ ð1þ ’Þ � QB � DU ð25Þ

’ ¼ � � �!ð Þ= � � �Sð Þ ð26Þ

In the above equations, � is the density of the

debris flow (t/m3), x is the percentage of clay

content in the resulting deposit, � is the viscous

coefficient of the debris flow, � is the yield

stress of the debris flow, QP is the peak dis-

charge of the debris flow (m3/s), QB is the peak

discharge of the water flow (m3/s), ’ is correc-

tion coefficient of the peak discharge of the

debris flow, DU is the blocking coefficient of the

debris flow channel, �! is the water density

(t/m3), and �s is the density of the solid compo-

nents in the debris flow (t/m3).

The simulated debris flow generates the spa-

tial distribution of the velocity and flow depth

(Figure 10a) at each grid in the alluvial area,

with the deposition range provided by support-

ing GIS-based data conversion analysis using

ArcGIS 9.3. The simulated results show that

the debris flows in Luojia Gully, Wawa Gully,

Dongzi Gully, Linjia Gully, and Taiyang Gully

directly destroy buildings and bury roads, and

the debris flows in Wenjia Gully and Zoumal-

ing Gully obviously block the Mianyuan River

and damage roads, farmland, and houses along

riverbanks. Using equation (12), we calculated

the maximum flood discharge at the dam site,

and the maximum discharge of river cross-

section downstream of the barrier using equa-

tion (13). We then combined these results with

equations (14) and (15) by using ArcEngine

and C# to get the inundated area and flood

depth of each grid, as shown in Figure 10b.

b Hazard degree calculation. According to the

simulation results above, each component De,

Dh, Di, Df can be acquired through the hazard

analysis method in subsection III.1. Then, by

applying the normalization method from equa-

tion (20), the hazard degree (D) in the alluvial

area is calculated by applying equation (1), with

division values of 0.6 and 3.0 as given by

Figure 8. Scenario of the debris flow disaster in Qingping Township on 13 August 2010.Source: Land and Resources Department of Sichuan Province.


Figure 9. Grain-size distribution of the debris flow deposit in Qingping Township.

Table 4. Basic parameters of numerical simulation for debris flows.

ParameterLinjiaGully

TaiyangGully

WenjiaGully

ZoumalingGully

LuojiaGully

DongziGully

WawaGully

Basic parametersof gullies

Area of catchment(km2)

1.09 0.40 7.68 5.46 1.30 0.20 0.56

Length of mainchannel (km)

1.51 0.83 4.38 3.10 1.10 0.48 0.92

Average channelslope (%)

250.41 484.34 322.66 183.13 465.98 566.02 367.19

Rheologicalparameters

Channel roughnesscoefficient

0.44 0.41 0.44 0.44 0.44 0.41 0.44

Density (t/m3) 2.10 2.10 2.10 2.15 2.15 2.10 2.10Viscous coefficient

(N.s/m2)2.08 2.08 2.08 2.48 2.48 2.08 2.08

Yield stress (N/m2) 86.09 86.09 86.09 162.50 162.50 86.09 86.09Kinematic

parametersTime-interval (s) 0.20 0.20 0.20 0.20 0.20 0.20 0.20Peak discharge (m3/s) 233 103 1177 774 323 62 130

Cui et al. 535

Figure 10. Calculated results and risk map of Qingping Township. (a) Distribution of flow depth in the debris-flow affected area. (b) Distribution of flow depth of the dam-breaking flood. (c) Hazard zonation map. (d) Vul-nerability zonation map. (e) Distributionof riskdegree in QingpingTownship. (f) Riskmap of QingpingTownship.


equation (21). The resulting three hazard zones

are the high hazard zone (>3.0), the medium

hazard zone (3.0*0.6), and the low hazard zone

(<0.6), as shown in Figure 10c. The high hazard

area accounts for 1,150,000 m2 and 45.9% of the

total hazardous area. This high hazard area is

located in the debris mainstream area, located

along main roads and Mianyuan River. This

region is so flat that it is also the most dangerous

area for potential inundation from a dam-failure

flood. The medium hazard area is 574,000 m2

in size, or 22.9% of the total area. The region is

located around the mainstream area where build-

ings and transport facilities are liable to suffer

from both silting and inundation damage. The

low hazard zone is 780,000 m2, accounting for

31.2% of the total area, and is mostly located in

the higher elevations of Qingping Township

where the hazard degree is relatively small and

potential damage from debris flows and a dam-

failure flood is minimal.

c Vulnerability degree calculation. Using the vul-

nerability analysis method in section III.2, the

hazard-affected objects were classified into four

categories based on the different characteristics

of the geographic elements in the area surround-

ing the township: buildings, roads, cropland,

and grassland. After identifying the shape, size,

image, shadow, and texture of each type of

hazard-affected object from 0.5 m resolution

aerial panchromatic images, the quantity of the

objects was determined.

In order to calculate the vulnerability degree,

we had to determine the integrated economic

value and susceptibility of the hazard-affected

objects. The value of V was determined for the

area surrounding the various hazard-affected

objects by applying the analytical tools avail-

able with ArcGIS, which can calculate area and

perimeter for each polygon. The economic value

of each hazard-affected object was obtained from

local government documents and on-site

Figure 10. Continued.

Cui et al. 537

investigation. Using these data, the integrated

economic value (V) of each hazard-affected

object is produced by unit price multiplying its

amount (area or length). To obtain the suscept-

ibility C of each hazard-affected object, we per-

formed a susceptibility analysis of the building

structure by applying equation (18) and adopting

Table 3 for an approximate value, using the med-

ian value for simplification. Finally, the vulner-

ability degree of each hazard-affected object

was calculated through equation (16). By apply-

ing the normalization and grading method from

equation (20) and formula (21), the vulnerability

was then graded into three levels: low, medium,

and high vulnerability.

Analysis of the vulnerability, given in Figure

10d, shows that the high vulnerability area occu-

pies 904,000 m2, or 36.1% of the total hazard area,

located in the deposition area of the debris flow

and a 100–200 m wide urban area along the town’s

main roads. Residential buildings, streets, infra-

structure, and civil facilities dominate this area.

The medium vulnerability area covers 861,000

m2, or 34.4% of the total hazard area, and is

located in the higher elevations and both sides of

the suburban roads. The main hazard-affected

objects in this area are roads, residential houses,

cropland, and the nearby grassland. The low vul-

nerability area covers 739,000 m2, which accounts

for 29.5% of the total area, and is located in the

watercourse areas and marginal urban areas. In

this zone, the hazard-affected objects are mainly

cropland, bottomland, and suburban roads.

d Risk degree calculation. The hazard value and

vulnerability value were first normalized using

equation (20). Then, using the normalized results

of the hazard degree and the vulnerability degree,

we calculated the risk degree using equation (19),

the results of which can be seen in Figure 10e.

4 Validation of calculation results

For the giant disaster in Qingping Township, the

risk degree is divided into three levels of high,

medium, and low risk, with risk gradation

threshold values of 0.35 and 0.04 as calculated

by equation (21). Using these threshold values,

we can divide the debris flow affected area of

Qingping Township into three risk zones: high,

medium, and low. A risk zoning map built

according to the risk mapping method is illu-

strated in Figure 10f. Statistical analysis shows

that the high-risk zone accounts for 33.4% of the

total hazard area, or 837,000 m2, while the

medium-risk zone is 792,000 m2 (31.6%) and

the low-risk zone is 875,000 m2 (35.0%). On the

other hand, through analyzing of the real quan-

tity of damaged objects for the ‘8-13’ debris flow

disaster in Qingping Township, the statistical

results in Table 5 show that the calculated risk

zones are in agreement with the actual distribu-

tion and damage severity experienced in the

respective hazardous areas. This validation indi-

cates that the risk assessment results for Qingp-

ing Township are consistent with results from

the actual ‘8-13’ debris flow disaster of 2010, and

suggests that this risk analysis method can

describe the compound and chain-reaction disas-

ter of multiple debris flows. Thus, this method is

suitable for providing debris flow risk analysis

and risk management of mountain townships.

V Discussion and conclusion

Many mountain towns in China are located on

joint alluvial fans formed by several adjacent

debris flows. Due to the similar geological and

geomorphological conditions, debris flows may

simultaneously occur in more than one of these

catchments during periods of heavy rainfall,

often causing multiple, overlapping, and inter-

connecting damage processes that lead to

serious disasters. We highlighted and analyzed

this previously unattended issue based on a

study of disaster data, with which we character-

ized the simultaneous debris flows in mountain

townships, using parameters such as compound,

interconnection, superposition, and chain-

reaction effects. Large-magnitude debris flows


Tab

le5.St

atis

tics

ofth

ehaz

ard-a

ffec

ted

obje

cts

inth

ree

risk

zones

inQ

ingp

ing

tow

nsh

ip.

Haz

ard-

affe

cted

obje

cts

Hig

h-r

isk

zone

Med

ium

-ris

kzo

ne

Low

-ris

kzo

ne

Tota

l

Sim

ula

ted

dam

aged

valu

e

Act

ual

dam

aged

valu

e

Dev

iation

per

centa

ge(%

)

Sim

ula

ted

dam

aged

valu

e

Act

ual

dam

aged

valu

e

Dev

iation

per

centa

ge(%

)

Sim

ula

ted

dam

aged

valu

e

Act

ual

dam

aged

valu

e

Dev

iation

per

centa

ge(%

)

Sim

ula

ted

dam

aged

valu

e

Act

ual

dam

aged

valu

e

Dev

iation

per

centa

ge(%

)

House

s(m

2)

135,0

00

134,0

00

0.7

25,0

00

26,0

00

–3.8

5700

6000

–5.0

165,7

00

166,0

00

–0.2

Road

s(m

)6900

6800

1.5

1000

900

11.1

400

400

0.0

8300

8100

2.5

Cro

pla

nd

(m2)

498,0

00

495,0

00

0.6

66,0

00

63,0

00

4.8

12,3

00

12,7

00

–3.1

576,3

00

570,7

00

1.0

Fore

stan

dgr

assl

and

(m2)

112,0

00

107,0

00

4.7

35,0

00

36,0

00

–2.8

14,0

00

15,6

00

–10.3

161,0

00

158,6

00

1.5

539

generally lead to compound hazards for moun-

tain townships, including direct impacts and

silting destruction by the debris flow itself, and

indirect damage from backwater inundation

and dam-failure floods induced by debris flow

barrier lakes created in nearby rivers. In addi-

tion, these chain-reaction effects induce these

disasters to expand spatially and continue suc-

cessively due to a positive feedback effect.

Reasonable and quantitative assessment of

the risks of multiple debris flows is complex and

beyond the ability of existing risk analysis

methods based on single debris flow. Accord-

ingly, we established indicators to describe

compound disasters and chain-reaction effects,

and provided a quantitative method for analyz-

ing the hazards associated with simultaneous

debris flows in mountain townships. The capa-

cities for impact damage, silting damage, and

flooding damage were quantified using the

maximum values of kinetic energy, flow depth,

and inundated depth, respectively.

In recent studies on vulnerability, scientists

have made great progress in producing vulner-

ability curves or functions (Fuchs et al., 2007,

2012; Jakob and Hungr, 2005; Papathoma-Kohle

et al., 2012; Totschnig et al., 2011). In this paper,

we further considered the spatial distribution and

resistance of hazard-affected objects, and devel-

oped a method to determine vulnerability of

various hazard-affected objects, with particular

emphasis on the susceptibility indexes of build-

ings or structures. In the proposed method, five

steps are adopted to analyze the vulnerability of

different hazard-affected objects. The necessary

information about the potential hazard-affected

objects, in terms of type, quantity, and distribu-

tion, can be extracted from panchromatic, high-

resolution (0.5 m) aerial images.

With the support of a debris flow movement

numerical simulation, flood analysis, remote sen-

sing (RS), and GIS techniques, we developed a

systematic and quantitative method of risk

assessment for mountain towns. Finally, this

method was applied to the case study of the

‘8-13’ debris flow disaster in Qingping Town

in 2010, whereby the calculated risk assessment

was consistent with the actual effects of the

disaster. The successful validation suggests that

this method can be applied in risk analysis and

risk management of mountain townships

exposed to multiple debris flows.

Our research has some limitations. In the

hazard analysis process, some empirical equa-

tions such as equation (9) and equations (22)–

(24) were applied to calculate debris flow

hazard parameters. Although these equations

were often used in previous studies, more accu-

rate tests are necessary in order to further

improve the precision of those empirical equa-

tions or to develop theoretical models. For

instance, equation (22) of the debris flow den-

sity calculation was established based on over

100 tests and investigated data sets; however,

there exists an opportunity for improvement

by replacing the current polynomial equation

with a theoretical model based on dimensional

analysis. Compared to objects of construction

and properties in mountain townships, human

beings represent a mobile object whose vulner-

ability it is difficult to calculate accurately.

Accordingly, we did not consider the loss of life

in our appraisal of vulnerability. The mobility of

human beings as a hazard-affected object pro-

duces new challenges for vulnerability analysis.

Therefore, it is vital to develop a solution to

this issue in future vulnerability analysis of

debris flows in mountain townships.

Acknowledgements

The authors thank George Malanson and the other

two anonymous reviewers of this paper for their

detailed remarks and helpful discussions.

Funding

This research was supported by the National Basic

Research Program of China (973 Program) (Grant

No. 2011CB409902; 2008CB425802) and the Nati-

onal Nature Science Foundation of China (Grant

No. 41030742).


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