An prediction model for post-
earthquake debris flow runout zones in
the Wenchuan earthquake area
State Key Laboratory of Geohazard Prevention, Chengdu
University of Technology
ZHU JingZHU Jing
1. Introduction1. Introduction
◆ Debris flows - one of the most dangerous geomorphologic processes that occur in mountainous areas.
◆ The May 12, 2008 Wenchuan earthquake happened three years ago. The heavy rain events have induced massive debris flows.
◆ With the ability to rapidly erode and transport large amounts of material, debris flows have the potential for massive destruction and may be the most hazardous consequence of earthquake-related erosion.
◆ The main objective of the present study was to develop a feasible and verifiable approach to hazard assessment of potential debris flow runout zones on the fans in the Wenchuan earthquake area.
地震前影像
地震后影像
Debris flow event on Sep. 24, 2008 in Beichuan city
2008年 6 月 12日 2008年 9 月 24日
Photograph showing the source areas, channels, and depositional
fans of Shenjia gully debris flow.
a b c
DF16
DF15
RS Imagery from three different data shows the landslide and debris flow development near Beichuan city
◆ These debris flows in the Wenchuan earthquake
area showed an increase in flow volume and
discharge, causing debris flow runout zones to be
much larger than usual.
For this reason, existing methods for the prediction of
the characteristics of runout zones of debris flows
were not applicable for the debris flows in the
earthquake-affected region.
General considerations
◆ In addition, existing empirical models for prediction
of the runout distance on the fans rely on input
parameters that are often difficult to estimate,
including volume, velocity, and frictional factors..
General considerations
Recent catastrophic debris flows indicate a need to improve our understanding of erosion processes following the violent earthquake, and a demand for predictive models that provide critical information on the location and magnitude of these potential disasters.
Therefore, it was necessary to develop a new model for future risk management in the reconstruction areas. We hope to develop a model to estimate debris flow runout zones from easily measurable topographic parameters and sediment supply in a drainage basin.
Our study focused on an area situated in the northeast of the earthquake's epicentre.
46 debris flow gullies with well-defined debris flow fans were chosen and investigated in the study area.
Variables that could potentially influence debris flow runout
zones were measured and used in multiple regression
analysis. Variables were divided into two main categories:
catchment characteristics and the volume of sediment
supply.
Catchment morphological characteristics was
measured in ArcGIS using 25-meter digital elevation
models (DEMs). The runout zones on the fans were
extracted from the SPOT5 images (October 14, 2008)
Data sources
The catchment area, A, is the area enclosed by the
ridge defined by the highest elevations
surrounding the stream.
The catchment relief, H, reflects the gradient of the
channel in a basin and is determined by
dividing the change in elevation between the
top of the debris flow scar and the beginning of
the debris flow deposit.
Data sources
The maximum runout distance, Lf , is defined as the
length between the onset of deposition point and the lowest point of the deposit on the alluvial fan. This variable is assumed for deposition generally occurring outside the channel and downstream of the fan apex.
The maximum runout lateral width, Bf, is defined as the width of the debris flow lateral spreading in the depositional zone.
Data sources
H
Lf
Bf
Landslide scar
Depositional zone
Basin divide
Parameters measured in the debris flow catchments
DF1DF2
DF18
DF6
DF11
Volume of sediment supply (VL)
The volume of a landslide, VL, is generally estimated
by multiplying the area covered by the deposit by an
estimated average thickness.
To determine the volume of sediment supply in
debris flow source areas, the post-earthquake aerial
photographs taken on May 18, 2008, with a
resolution of 0.3 m, could be used to identify the
landslide deposit regions at least 10 m2 in size.
Data sources
Forty-nine landslides of different scales in the Weijia
gully and Sujia Gully catchments were selected as
the samples for the determination of landslide
thickness.
t =1.432 Ln( SL )-4.985
Where :
t average landslide thickness (m)
SL landslide area (m2)
Data sources
Empirical relationship between the landslide surface area and
the landslide thickness in the debris flow catchments
Model generation
A multiple regression technique was used to develop an
empirical model for the determination of the characteristics of
debris flow runout zones.
Lf = 0.36A0.06+0.03(VL∙H)0.54-0.18
Bf = 0.40A0.08+0.04(VL∙H)0.35-0.23 Where:
Lf is the predicted maximum runout distance (km)
Bf is the maximum width (km)
VL is the volume of removable sediment in the catchment (106m3)
A is the surface of the debris flow catchment area (km2)
H is the catchment internal relief (km)
Hongchun gully
Shaofang gully
Xiaojia gully
Wangyimiao gully
Mozi gully
Low- altitude aerial photo taken on Augest 15, 2010 showing some debris flow fans produced by the catastrophic event near Yingxiu town on August 14, 2010.
0
0. 1
0. 2
0. 3
0. 4
0. 5
0 0. 1 0. 2 0. 3 0. 4 0. 5Observed (km)
Pred
icte
d (
km)
Maximum runout distanceMaximum lateral width
Validation
To validate the reliability of the statistical model, an independent test of the regression models with 17 catchments yielded generally good results and met the requirements for determination of debris flows runout zones in the Wenchuan earthquake areas.
The major advantage of the empirical relationship was its
simplicity. The only necessary input data were the
topographic parameters and the loose sediment supply. In
contrast, several potential limitations should be considered as
following:
--The main limitations are related to the DEM resolution and
to the estimate of the landslide debris volume.
--The multiple regression was established based on the
limited datasets of surveyed debris flows without taking into
account the specific catchment characteristics that may
influence runout behaviours.
Discussion
--The multiple regression does not account for event volume,
velocity, material properties, or fan morphology, so other,
more rigorous, analyses may be required to provide more
accurate estimates of runout zones.
In spite of the limitations mentioned above, test results
showed that the proposed method could be of potential utility
for practical applications in the Wenchuan earthquake area
and other similar seismic areas where its suitability can be
demonstrated through validation and experience.
Discussion
To enhance the accuracy of prediction, a high priority is the
better understanding and description of depositional
characteristics and runout behaviour of debris flows.
More observations about debris flow events will allow a
refinement of the empirical methods.
In addition, site-specific investigations and knowledge of the
history and magnitude of debris flow events still remains the
most important basis for prediction of any debris flow runout
zone, regardless of the approach used.
Discussion
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