1. 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 Jing

2. 1. 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.

3. 地震前影像 地震后影像

4. Debris flow event on Sep. 24, 2008 in Beichuan city

5. 2008 年 6 月 12 日 2008 年 9 月 24 日

8. Photograph showing the source areas, channels, and depositional fans of Shenjia gully debris flow.

9. a b c DF16 DF15 RS Imagery from three different data shows the landslide and debris flow development near Beichuan city

10. ◆ 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

11. ◆ 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

12. 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.

13. 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.

14. 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

15. 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

16. The maximum runout distance, L f ， 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

17. H Lf Bf Landslide scar Depositional zone Basin divide Parameters measured in the debris flow catchments

18. DF1 DF2 DF18 DF6 DF11

20. Volume of sediment supply (V L ) The volume of a landslide, V L, 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 m 2 in size. Data sources

21. 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( S L )-4.985 Where ： taverage landslide thickness (m) S L landslide area (m2) Data sources

22. Empirical relationship between the landslide surface area and the landslide thickness in the debris flow catchments

23. Model generation A multiple regression technique was used to develop an empirical model for the determination of the characteristics of debris flow runout zones. L f = 0.36A 0.06 +0.03(V L ∙H) 0.54 -0.18 B f = 0.40A 0.08 +0.04(V L ∙H) 0.35 -0.23 Where: Lfis the predicted maximum runout distance (km) Bfis the maximum width (km) V L is the volume of removable sediment in the catchment (106m3) Ais the surface of the debris flow catchment area (km2) His the catchment internal relief (km)

24. 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.

25. 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.

26. 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

27. --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

28. 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