10th Asian Regional Conference of IAEG (2015)

Survey and analysis the "8.13" debris flows fan in Longchi Area of Dujiangyan
City, Sicuan Province
Qu Yongping, Tang Chuan, Chang Ming
State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection ,Chengdu University of Technology, Chengdu, China
Abstract
The disasters of debris flows occurred more frequently in the earthquake zone after the 2008 Wenchuan
tectonic earthquake. And the growth of perniciousness of debris flow as a geometric progression, the accumulation
characteristics of debris flow directly responded to the damage degree. On August 13, 2010, debris flow disasters
were broke out of more than 50 gullies in the study area, which initiated by strong rainfall, and destroyed thebuildings and roads in earthquake reconstruction area. According to the field observation of 36 debris flow gullies
and statistical analysis between deposits features and drainage basin characteristics, establish functional equation
as deposits thickness, deposits width, run out distance, deposits gradient. On this basis, a geometric statisticalmodel of debris flow fan was established by the characteristics of fan accumulated characteristics as Engineering
Grade-increase Debris Flow. The validation showed that the established model was suitable for the prediction of
the debris-flow accumulation characteristics in the Wenchuan earthquake area. The results of the study will help
authorities select safe sites for rehabilitation and relocation in the future and can also be used as an important basis
for debris-flow risk management.
Keywords: Debris flow fan, Deposits features, Statistical model, Dujiangyan city1.

reserves, but also controlled by the most fundamental
factor as the topographic features of alluvial fans andthe physical properties of debris flow. In this paper, 36
debris flows were investigated as shown in Fig.2, andtheir fans relative complete, found that the posteriorborder longitudinal slope was greater than anteriorborder longitudinal slope in the accumulated area ofthe debris flows, and statistical model was establishedby the data of 36 debris flows, to provide references
for risk assessment and management for debris flow inquake affected area.

Introduction

Since the "5.12" Wenchuan Earthquake,numerous debris flows were triggered in the central
quake-affected area, particular in the rainy season.
Because of the strong earthquake, the geologicalenvironment more vulnerable to landslides (Tolga etal, 2011), and triggered more than 56 000 landslides in
the central part of the area of Wenchuan earthquakes(Dai et al, 2011), the substantial volumes of landslides
for debris flow can be generated (Alexander, 1989).
The hazard scope of debris flow fans is directly
determined by its accumulated characteristics (Liu etal, 1995). The formed of the deposits fan link between
landscapes and channel basin, because large volumesof sediment can be transported from hill-slopes tovalleys (Webb et al, 1988; Benda, 1990; Eaton et al,2003). There is many ways to study the sedimentarycharacteristics of debris flows, such as the statisticalanalysis (Tang et al, 2012, 2014), numericalsimulation (Tang et al, 2011) and test model (Liu et al,1995).
The study area is located in the “5.12” Wenchuan
Earthquake region. On August 13, 2010, a total of 52
debris flows was triggered by continuous heavy
rainfall. To above research, deposits zones ofearthquake-caused debris flow not only controlled by
the drainage area, watershed elevation, source2.

Study area

The study area, located at upstream of MinjiangRiver, which belonged to Dujiangyan City of Sichuan
Province, and the landforms belonged to the highland
and ravine. The tributary distributed along both sidesof the Longxi River like as the dendritic. The exposed
lithology included granite, limestone and dolerite(Fig.1). Accompanying intensive structure activities,
especially the causative fault of "5.12" Earthquake as
Yingxiu-Wenchuan fault passed through the Bayigully, lead to the bedrock exposed, rocks crushed, andformed a lot of unconsolidated deposits of Quaternary
and widely distributed as in Fig.2, which providing a
lot of sources for "8.13" debris flow.

1

10th Asian Regional Conference of IAEG (2015)

Fig.1 Lithology figure of study area.

Fig.2 Distribution and provenances in study area

2010, the thundershowers began, and rainfall intensity
suddenly increased at 14:00 of August 13, 2010, thenfreshet induced the debris flows at 16:00. The rainfall
intensity of 24mm/h recorded when debris flow
occurred, and peak intensity of 75mm/h recorded
during that time period. The pre total rainfall
precipitation is about 30mm before debris flow
occurring, cumulative rainfall for debris flow of 150
mm in 3h was recorded from 14:00 until 17:50(Fig.4).

The study area belongs to the humid monsoon
region, and located in the subtropical seasons area.
Seen from the Fig.3, and found that the rainfall mostly
concentrated from June to September, and about 83%
of the annual precipitation falls.
Rainfall characteristic is one of the most importanttriggering factors for debris flows (Shieh et al, 2009;
Chang et al, 2009), and heavy rainstorms occurred On
13 August 2010, initiating debris flows in Longchi ofearthquake affected area (Q et al, 2012). According todata from recording Dujiangyan station, on August 4,

Fig.3 Average precipitation in the Longchi area3.

Fig.4 Hourly and cumulative rainfall for the 13 August, 2010

Data analysis

The most important characteristics for debris
flow run-out zones on alluvial fans is the maximum2

10th Asian Regional Conference of IAEG (2015)

characteristics.
Field investigations of 36 debris flow fans instudied area, found that the run-out volume of debris
flow positively related to maximum run-out distance,
maximum run-out width and the maximum profile
thickness. Due to the similar topography, lithologicaland geological structure, which lead the material
composition and particle size, and the geometriccharacteristics of deposit fans to similar (Fig.5).

run-out distance, the maximum cumulate width, theprofile thickness. And the influenced parameters foraccumulated characteristics of debris flow, such as thedebris-flow volume, the flow velocity, the cumulated
slope, the solid material properties, and the landslide
deposit volumes to be studied (Chuan et al, 2012).
According 22 typical debris flow gullies in studied
area by previous researched (Ding, 2011), and throughfield measurement of the deposit slope and profile
thickness of the fans, to get the data of accumulated（a）Debris fans of Changheba gully.

（b）Debris fans of Zhichang gully.

（c）Debris fans of Shuida gully.

Fig. 5 Typical debris flow fans in the study area

and run-out volumes of debris flow was positive
correlation, and the average value of the landslide
deposit volumes transformed into debris flow run-out
volumes was 0.57, and the fluctuation amplitude of
conversion rate of landslide deposit volumes torun-out volumes was 0.29~1.

3.1 Landslide deposit volumes
The occurred possibility and broken-out
frequency of debris flow directly affected by theconditions of landslide deposits and precipitation,
and watershed features, et al. Through Spss software
to statistical analyses 22 debris flow gullies, foundthat the empirical relationship between landslide
deposit volumes and debris flow run-out volumes
was positive correlation, according to the relative
coefficient of R2 and significant coefficient of P, therelationship between landslide deposit volumes andrun-out volumes shown as below (Fig.6):

3.2 Elevation different
The elevation different of catchments provided thepotential energy for landslide starting. According to
the regression analyzed of correlation and significance,
the regression equation between the elevationdifference of catchment and the debris flow run-out
volumes established as exponential distribution(Fig.7):

Fig.6 Empirical relationship between the landslide volumes
and run-out volumes

V=3.916W

1.068

2,R =0.72&P=7. 41×10-7(1)

Where V is debris flow run-out volumes (m3); W islandslide deposit volumes (m3); R is the correlation
coefficient; P is the significant coefficient.
According to the field data statistical analyzed, therelationship between the landslide deposit volumes

Fig.7 Empirical relationship between the landslide volumes
and elevation difference
2.408

V=H

+0.001,R2=0.8&P=4.55×10-8(2)

Where H is elevation difference of catchments (m).

3

10th Asian Regional Conference of IAEG (2015)

is elevation different of catchments (m); J is channel
average longitudinal gradient.

3.3 Channel longitudinal slope
According to the field investigation and statisticalanalysis, found that the relationship between channel
average longitudinal gradient and accumulation
characteristics on run-out zone was positive
correlation. And the average of specific value forchannel average longitudinal gradient to elevationdifferent is 3.637, the fluctuation amplitude of ratio
about 0.791~9.583. The average of specific value forchannel longitudinal slope to maximum lateral width
was 3.997, the fluctuation amplitude was about 1.522~11.153.
All the above analysis as known, run-out volumesof debris flow related to the landslide deposit volumes,
elevation different and channel longitudinal slope.
Moreover, the result shows that the run-out volumes
determined run-out distance, lateral width and profile
thickness of debris flow on the alluvial fans. Througha great deal of statistical analysis, the regression
equation as follows:
V=0.014W+7.074H+2218.94J-3658.95,
R2=0.92&P=0.009(3)
Where W is landslide deposit volumes for debris
flow (m3); V is run-out volumes of debris flow (m3); H

4.

Run-out zones characteristics

In the process of depositing of debris flow on the
alluvial fans, the run-out distance, lateral width andprofile thickness controlled by watershed of gullies
characteristics and fluid properties characteristics. In
order to analyze the geometry features of the debris
flow fans, by statistics deposited parameters of 36
debris flow ditches, such as outlet width, max lateralwidth, length of fans, fans slope.
4.1 Debris flow fan slope
Up to now, the shape of debris flow fans of the 3Dmodel universal unified, the fan slope of posteriorborder slightly steeper than anterior border, generally
speaking the anterior border slope of the deposit fansonly a few degrees (Liu et al, 1992). In the conditionsof lowly fan slope, the sine value approximately
equaled to tangent value, namely sinα≈tanα, α1 isposterior border slope, α2 is anterior border slope, α3is alluvial fan slope.

Fig.8 Empirical relationship of deposit slope

The run-out zones mainly consists of run out
distance, lateral width and thickness. When the debris
flow stopping and silting on the alluvial fan area, theaccumulation characteristics mainly due to theoriginal slope reduced along gullies in accumulationarea, and unconstrained conditions of the lateral on
alluvial fan, leading to the kinetic energy of debris
flow dissipated quickly along the flow direction, andthe potential energy of fluid along vertical flow
direction dissipated quicker than the flow direction.
By the analysis of regression, discovered that therelationship between run-out characteristics andlandslide deposit volumes was nonlinear as follows(Fig.9):By the regression analysis(Fig.8)，the posteriorborder slope, anterior border slope and alluvial fan
slope met the conditions as following:sin  1  sin  30.699 , R 2  0.98& P  0.0001 
sin  2  sin  30.839 , R 2  0.99&P  0.0001

(4)

Where symbols mean is as the same as above.
From 36 debris flows fans data, known that theslope of fans satisfied relation as α1＞α2, α1＞α3, andα2＞α3. Further, the average specific value for sinα2 tosinα3 was about 1.54, and the fluctuation amplitude ofratio about 1.07~2.67; The average specific value forsinα1 to α2 was about 2.55, and the fluctuation
amplitude of ratio ranged 2.08~3.70; The average
specific value for sinα2 to sinα3 was about 1.69, andthe fluctuation amplitude of ratio ranged 1.39 ~1.93.

Lmax 113.86V 3  21.665V 2  2.689V, R2  0.94&P  0.0001  (5)Dmax  239.334V 3 82.022V 2  7.964W, R2  0.89&P  0.0001
h  8.459V 3  2.605V 2  0.222V, R2  0.96&P  0.0001


4.2Run-out characteristics

4

10th Asian Regional Conference of IAEG (2015)

Fig.9 Empirical relationship of deposit features.

Seen the Fig.10, according to the correlation andsignificance, shown that the relationship between
run-out characteristics and landslide deposit volumes
as follows:

Lmax  1.038W 3  0.024W 2  9.538105W, R2  0.7&P  0.00015 (6)
Dmax  7.661W 3  0.07W 2 1.71104W, R2  0.76&P  0.0002 h  0.267W 3  0.03W 2  6.305104W, R2  0.7&P  0.00011 Where W is landslide deposit volumes (m3), Lmax is
max run out distance (m); h is profile thickness (m);Fig.10 Empirical relationship between deposit features and landslide deposit volumes

fluctuation amplitude of 0.52~0.7. The relationshipbetween the run out distance and the max profile
thickness was positive correlation, the ratio of the max
run-out distance and the max profile thickness was
22.53, and the fluctuation amplitude of 12.56 ~40.33.

According to the statistical of field survey data
(Fig.11), found that the length of debris flow fansatisfied the relation as L3 ≈L1+L2, and L2 > L1.
Further, the average value of L2/L1 was 1.46, and thefluctuation amplitude of the ratio about 1.07~1.98.
The average value of L2/L3 was 0.59, and theFig.11 Empirical relationship of deposit length

The relationship between run out distance andlength of debris flow fan analyzed, namely contained
the length of posterior border to max lateral width,
and the length of anterior border to max lateral width,
satisfying the following conditions:L3  L m ax 0.99 , R 2  0.98& P  0.0001 
L1  L3 0.81 , R 2  0.99& P  0.0001 
L 2  L3 0.892 , R 2  0.99& P  0.0001 (7)

Moreover, through the regression analysis ofprofile thickness and landslide deposit volumes in the5

10th Asian Regional Conference of IAEG (2015)

gullies, for one classification: non man-madecondition that the terrain feature of accumulation area
has non effects of human engineering, leading tooutlet slope of gullies steeper than alluvial fan slope,and the speed of alluvial fan decaying more thanoutlet (Fig.12-(a)); For the other classification:
man-made condition that the gullies outlet or alluvial
fan influenced by artificial factors, such asengineering construction (highway, embankment),
making the slope of terrain suddenly increased (Fig.
12- (b)). Based on the field observation and statistics,
the debris flow model in the study area as Fig.12- (b)study area, found that relationship as follows:
V=352.28h3-85.03h2+6.1h-359.94,
R2=0.88&P=0.0017(8)

4.3 Debris flow deposit fan modelThrough the field survey of debris flow depositionon the alluvial fan, found that the shape of fan was
similar. On the basis of previous research shown thatthe deposition of debris flow occurred below 10°(TANG et al, 2011). On the other hand, theaccumulation characteristics obviously affected by theterrain feature of accumulation area of debris flow

Fig.12 Two classifications of deposit fans model

Where L1x is posterior border length of arbitrary
point distance (m); L2x is anterior border length ofarbitrary point distance (m).
By the above study shown, the geometric shape ofdebris flow fan mainly due to the accumulation area
feature, fluid properties, and the debris flow run out
model obtained in the study area in the following
Tab.1:

By statistic, the deposit fan model of 36 debris
flow gullies in the study area alike the Fig.12- (a), andthe lateral width of any position along the flow
direction related to the length of posterior border L1,
length of anterior border L2, outlet width a, and therelationship as follows:
D1x  a  0.28L1x , D2 x  a  0.28L1  0.08L2 x(9)Tab.1 Prediction model of the study area
Function equation
3

2

-6

V=0.014W +0.0004W -1.3×10 W

R2

Parameter ratio

Ratio range

0.73

W/V

2-3.5

2

0.94

V/Lmax74.9-210+0.001

0.8

V/H

1.12-9.77

sinα1=sinα30.699099

sinα1/ sinα32.08-3.70

sinα2=sinα30.839

3

Lmax=113.86V -21.665V +2.689V
2.408

V=H

0.99

sinα2/ sinα31.39-1.93

0.81

1

L3/ L1

2.05-3.6

0.892

1

L3/ L2

0.52-0.7

h=8.459V -2.605V +0.222V

0.96

V/h

70794.65-329765.

V=0.014W+7.074H+2218.94J-3658.95

0.92

L1= L3
L2= L33

2

6

10th Asian Regional Conference of IAEG (2015)

5.

background and moving mechanism of debris flow is
closely. According to the prediction model of Longchi
area, contrast to run out characteristic of Shaofang
gully, Modao gully, Sanshengong gully, Chamagudao
gully, Maliu gully, Hongchun gully, Xioajia gully, theevaluation result as following (Tab.2):

Validation of prediction model

In order to verify the practicability of deposit model,
this paper took the debris flow gullies in Wenchuan
earthquake area for example, owing to in the central
part of the area of Wenchuan "5.12" earthquake zone,
which leading to the lithology is similar, geographicalposition closed, structure alike, namely the occurringTab.2 Validation result of prediction model in the earthquake zone
Gully Name

Actual run out Predict run out

Amplitude range /mError range

distance /mdistance /mShaofang

70

113.57113.58-182.37

Modao

357

305.13

Sanshengong

47

Chamagudao

Actual lateral Predict lateral

Amplitude range

Error range

width /mwidth /m

/m

0-1.6

250

247.31132.37-247.31

0.01-0.47

176.07-604.32

0.51-0.69

193

238.12176.07-604.32

0.08-2.58

22.02

12.59-43.21

0-0.7339

18.79

12.59-43.21

0-3.0672

144.01

83.27-258.80

0-2.9680

112.2083.27-112.20

0-3.11

Maliu

271

294.32

169.42-581.48

0-1.15264

230.57169.42-581.48

0-3.33

Hongchun

390

393．75226.69-777.78

0-0.99350

300

226.62-777.78

0-1.22

Xioajia

170

334.37192.24-660.49

0.13-2.88

260

254.76

192.45-660.49

0-1.54section thickness and profile width diffusion rate andthe accumulation volumes of debris flow fans, watershed elevation positively associated with the channel
average longitudinal gradient was inverse correlation.
Contrast to the fan data as known in Wenchuan area,
the error of prediction model for debris flow run-out
zone was in the range, and otherness of individualdebris flows owing to the strongly effected of moving
environment and spatial relations of tributary outlet
and the main stream, so that the research about thecharacteristics of blocking river is particularly
important in the future.

According to the results of prediction and verified
in the table, contrast predicted results of run-out
distance and width to field measured data, found thatthe error of predicted results to field results was not
great, the error range of debris flow run-out distance
was 0~2.96, the error range of run out width was
0~3.33.
6.

Conclusion and Discussion

According to the study of group debris flows in the
study area of Longchi area, field investigation andstatistical analysis the characteristic of debris flow,
found that the accumulation characteristics andparticle properties is similar. Based on the statistics,
prediction model for run-out characteristic on the
alluvial fan created.
(1). The field investigation found that the deposits
fan slopes of 36 debris flows in study the area issimilar, and the slope mainly concentrated in 5°~9°,
the average value of slope about 7°. The longitudinalslope of posterior border of debris flow fan
concentrated in 0.05~0.16, and the average was about
0.119; The longitudinal slope of anterior border ofdebris flow fans ranged from 0.05 to 0.15, and theslope of average was 0.083; The longitudinal slope ofdebris flow fan was about 0.033~0.07, and theaverage value was 0.047.
(2). In the study area, the debris flow run-out
volumes, run-out distances and width, profile
thickness related to the landslide deposit volumes,
elevation difference of catchments and the channel
longitudinal slope. According to the geometry andmaterials of debris flow, the statistic model ofdebris-flow fans established, the lateral width, theReferences
Alexander D (1989), Urban landslides. Progress in PhysicalGeography 13: 157-191
Benda L (1990), The influence of debris flows on channel sandvalley floors in the Oregon Coast Range, U.S.A. Earth Surf
Proc Land 15:457–466
Chuan Tang, Jing Zhu, Ming Chang, et al (2012), An
empiricalestatistical model for predicting debris-flow runout
zones in the Wenchuan earthquake area. QuaternaryInternational 250:63-73.
CHANG Ming, TANG Chuan, ZHANG Dandan (2014), Debris
Flow Susceptibility Assessment Using a Probabilistic
Approach: A Case Study in the Longchi Area, Sichuan
Province, China. J. Mt. Sci. 11(4): 1001-1014
Chuan Tang, Jing Zhu, Ming Chang, et al (2012), An
empirical-statistical model for predicting debris-flow runout
zones in the Wenchuan earthquake area. QuaternaryInternational 250:63-73.
Chang FJ, Chiang YM, Lee WS (2009), Investigating the impactof the Chi–Chi earthquake on the occurrence of debris flows
using

7

artificial

neuralnetworks.

Hydrol

Process23:

10th Asian Regional Conference of IAEG (2015)2728-2736.

Earthquake in central Taiwan, China. International Journal ofDing Jun (2011), Research on Magnitude and Run-out Distance

Sediment Research 24(2): 177-188.

of Debris-flows in Strong Earthquake Area. Chengdu

Q. Xu, S. Zhang, W. L. Li, et al(2012), The 13 August 2010

University of Technology. (in chinese)

catastrophic debris

flows

after

the 2008

Wenchuan

Dai FC, Xu C, Yao X, et al. (2011), Spatial distribution ofearthquake, China. Nat. Hazards Earth Syst. Sci 12:201-216.

landslides triggered by the 2008 Ms 8.0 Wenchuan

TANG Chuan, Li Weile, Ding Jun et al (2011), Field

earthquakes, China. Journal of Asian Earth Sciences 40 (4):Investigztion and Research on Giant Debris Flow on August883-895.

14,

Eaton SL, Morgan MA, Kochel RC, Howard AD (2003), Role ofdebris flows in long-term landscape denudation in the central
Xilin, Tang

Chuan

(1995),

DebrisinYingxiu

Town,Epicenterof

WenchuanGeoscience 36(1)：172-180.(in chinese)

Appalachians of Virginia. Geology 31:339-342
Liu

2010

Earthquake.Earth Science-Journal of China University of
Tolga Gorum, Xuanmei Fan, Cees J. van Westen, et al (2011),

flow hazard

Distribution

analysis .Science Press. (in chinese)patternofearthquake-induced

landslides

triggered by the 12 May 2008 Wenchuan earthquake.

Liu xilin, Tang Chuan,Zhang Songlin et al (1992), Geological

Geomorphology 133:152-167.

Hazards to Agriculture in Jiangxi Province and Their Control.

Webb RH, Pringle PT, Reneau SL, Rink GR (1988), Monument

The Chinese Journal of Geological Hazard and Control（4）：Creek debris flow, 1984: implications for formation of rapids

32-40 (in chinese)on the Colorado River in Grand Canyon National Park.

Shieh CL, Chen YS, Tsai YJ, Wu JH (2009), Variability inGeology 16:50-54.

rainfall threshold for debris flow after the Chi–Chi

8