2. Towards a real-time landslide early warning strategy in Hong Kong Qiming Zhou and Junyi Huang

3. Landslide Hazard in Hong Kong Mass movement of rock, debris or earth down a slope, which can be triggered by various external stimuli, considered as one of the most damaging disaster in the world. Palacky University, Olomouc, Czech Republic, 18-22 November, 20132 Lam Tin, Kowloon (1982)

4. Palacky University, Olomouc, Czech Republic, 18-22 November, 20133 Man-made slope failure Natural terrain slope failure Encroachment of built environment and increasing risk of landslide Landslide Hazard in Hong Kong

5. Palacky University, Olomouc, Czech Republic, 18-22 November, 20134 Influence from environmental variables rainfall-runoff process Real-time early warning system Geotechnical/ statistical model Geotechnical/ statistical model scale-adaptive physical/empirical model Methodology Landslide susceptibility mapping: –A quantitative or qualitative assessment of the classification, volume (or area), and spatial distribution of landslides which may potentially occur in an area.

6. Research Framework Palacky University, Olomouc, Czech Republic, 18-22 November, 20135 Study site selection and reconnaissance field investigation Spatial data acquisition and specification Hydrological ground data collection and rainfall/runoff analysis Surface/sub-surface water discharge analysis The development of landslide susceptibility and risk analysis model Field tests and rainfall-runoff simulation experiment Computer platform implementation System calibration and evaluation

7. Historical landslide inventory (ENTLI database from CEDD) Environmental parameters Elevation (terrain slope and aspect, etc.) Vegetation Index (NDVI) Lithology (1:20,000 geology map) Distance to fault line Distance to major stream Land cover Landslide triggering factors and its consequence Rainfall gauge data (archive, real time and forecast) Service run-off Soil hydorlogy Risk analysis Tertiary Planning Unit (TPU) census data 2011 Transportation network Tracts in conservation parks Palacky University, Olomouc, Czech Republic, 18-22 November, 20136 Landslide Susceptibility Analysis

8. Palacky University, Olomouc, Czech Republic, 18-22 November, 20137 Landslide occurrence record (2000-2008), elevation and slope of Lantau Island, Hong Kong Digital Elevation Model (DEM) and its derivatives (slope, aspect, curvature, etc.) Landslide susceptibility Analysis

9. Palacky University, Olomouc, Czech Republic, 18-22 November, 20138 Vegetation cover rate Normalized Difference Vegetation Index (NDVI) and Major River in Lantau Island, Hong Kong Landslide Susceptibility Analysis

10. Palacky University, Olomouc, Czech Republic, 18-22 November, 20139 LSI = Fr elevation + Fr NDVI + Fr slope + Fr aspect + Fr fault distance + Fr river distance + Fr lithology LSI: Landslide Susceptibility Index Fr: Frequency ratio of each causative factors Frequency ratio model analysis VariablesClassValueType Pixels in domain Pixel % Landslide occurrence points Landslide occurrence points% Frequency ratio (Fr) Elevation (m) 120 - 69 Continuous 46,05030.122248.430.28 269 - 14331,08920.3342816.110.79 3143 - 22025,38716.6060722.851.38 4220 - 29717,75011.6163423.862.06 5297 - 38213,2678.6841815.731.81 6382 - 4779,2076.021927.231.20 7477 -5825,2253.421244.671.37 8582 - 7023,4512.26271.020.45 9702 - 9201,4790.9730.110.12 Classification Pixel in each category and percentage VariablesClassValueType Pixels in domain Pixel % Landslide occurrence points Landslide occurrence points% Frequency ratio (Fr) Distance to fault (km) 10 - 0.62 Continuous 18,33231.061,54858.171.87 20.62 - 1.2012,47821.1485332.061.52 31.20 -1.788,11813.752188.190.60 41.78 - 2.366,24010.57361.350.13 52.36 - 2.955,0158.5060.230.03 62.95-3.524,5887.7700.00 73.52-3.834,2517.2000.00 Landslide Susceptibility Analysis

11. Palacky University, Olomouc, Czech Republic, 18-22 November, 201310 Landslide susceptibility mapping result based on frequency ratio method Landslide Susceptibility Analysis

12. Multi-scale DEM 11 30 50 90 125 m (a)(b)(c) (d)(e) Degree of Importance Palacky University, Olomouc, Czech Republic, 18-22 November, 2013

13. The separation of DEM and hydrologic model Palacky University, Olomouc, Czech Republic, 18-22 November, 201312 SystematicRandomStratified random Source sampling schema

14. The flow vector on a triangular facet Palacky University, Olomouc, Czech Republic, 18-22 November, 201313

15. The slope and aspect of a triangular facet Palacky University, Olomouc, Czech Republic, 18-22 November, 201314 P 2 (x 2, y 2, z 2 ) P 3 (x 3, y 3, z 3 ) P 1 (x 1, y 1, z 1 )

16. The flow direction of each source point Palacky University, Olomouc, Czech Republic, 18-22 November, 201315

17. Flow path tracking Palacky University, Olomouc, Czech Republic, 18-22 November, 201316

18. The flow path set Palacky University, Olomouc, Czech Republic, 18-22 November, 201317

19. The topology of the flow path network Palacky University, Olomouc, Czech Republic, 18-22 November, 201318 Node IDX (m)Y (m)Z (m) … 61540230640727621169.52 … 61640233840727151129.89 … 61740235940726831115.94 … ………… … Line ID Start node End node Slope length (m) velocity (m/s) … 213615616 20217.5= v(…) 214616617 15135.1= v(…) 215617618 1032.4= v(…) ……… ………… Node table Line table v = f(r, s, n)

20. 19 A B The flow path network Palacky University, Olomouc, Czech Republic, 18-22 November, 2013

21. Digital terrain model Palacky University, Olomouc, Czech Republic, 18-22 November, 201320

22. t P t P t P t x y Spatial-temporal rainfall interpolationStratified Random Sampling Rainfall simulator Palacky University, Olomouc, Czech Republic, 18-22 November, 201321

23. Rainfall event simulation 22 t = 9st = 127st = 402s t = 734st = 938st = 1120s

24. The flow generation at the source Palacky University, Olomouc, Czech Republic, 18-22 November, 201323 R = runoff; P = rainfall; E = evaporation; C = interception; I = infiltration Ground observation Remote sensing Soil and infiltration Ground observation

25. 24 From Manning Fomular: v = velocity (m/s) R = hydraulic radius (m) S = hydraulic slope n = Manning roughness coefficient L = flow path length (m) We have: Velocity and time Palacky University, Olomouc, Czech Republic, 18-22 November, 2013

26. Runoff generation and flow simulation DTM: Based on S-DEM method to generate dynamic TIN Simulated rainfall event: 20 minutes 12mm uneven rainfall event Other environmental factors were not considered. 25Palacky University, Olomouc, Czech Republic, 18-22 November, 2013

27. t = 9st = 127st = 402s t = 734st = 938st = 1120s 0 - 0.27 m 3 /s 0.27 – 0.54 m 3 /s 0.54 – 2.7 m 3 /s > 2.7 m 3 /s Palacky University, Olomouc, Czech Republic, 18-22 November, 201326 Rainfall-runoff modelling

28. Palacky University, Olomouc, Czech Republic, 18-22 November, 201327

29. Mapping the detail areas potentially affected by or susceptible to landslides in a timely manner in order to mitigate/prevent the related risk, and compare with/improves the previous model(s) Integration of an interdisciplinary approach by integrating the geotechnical statistic methods and hydrological physical/empirical rainfall-runoff models Big data geography with time-critical natural disaster monitoring or forecasting Palacky University, Olomouc, Czech Republic, 18-22 November, 201328 Research significance

30. Thanks you for listening! Interested in studying in Hong Kong or China? Contact us! qiming@hkbu.edu.hk Palacky University, Olomouc, Czech Republic, 18-22 November, 201329