Rain triggered landslide hazard analysis
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Rain triggered landslide hazard analysis
- 1. Eng. JSM Fowze Research Associate, GeoInformatics Center Asian Institute of Technology, Thailand Rain-Triggered Landslide Hazard Analysis: A Case Study in Nan Province, Thailand ASEAN Seminar on Utilization of Satellite Images for Disaster Risk Reduction, Miracle Grand Hotel, Bangkok 11 Aug 2010
- 2. Background … a common geological phenomenon in many parts of the world! Landslides receive considerable attention!!! plague facetted This landslide occurred at La Conchita, California, USA, in 2005. Ten people were killed. Courtesy USGS
- 6. Background … 1988 Landslide and Flooding Event: Nov 19-23 Unprecedented and Disastrous! Surat Thani and Nakon Si Thamarat: Hardest hit provinces 373 lives estimated property damage US$ 280 million ( Noppadol et al, 1993) In 2001, a similar event in Petchabun Province: Aug 11 136 lives estimated property damage US$ 5 million ( Yumuang, 1993) ( Tantiwanit, 2007)
- 8. Background … Landslide Events in Thailand since 1970 (Soralump and Chotikasathien, 2007)
- 10. Situation Analysis… Landslide is an on-going and growing concern in Thailand Frequency of greater than 100 million baht events in Thailand (Soralump, 2007)
- 14. Modeling… Infinite Slope Stability Model
- 15. SINMAP is simplified for Infinite Slope Stability Model… Modeling…
- 16. In Hydrological Modeling Infinite Slope Stability Model… Modeling…
- 17. Topographic Wetness Index? Make use of certain assumptions that had been made in the formulation of topographically based wetness index models Infinite Slope Stability Model… Modeling…
- 18. Specific Catchment Area, a : Upslope area per unit contour length (m 2 /m) A landmark development in hydrology Assumptions: 1. Shallow subsurface flow follows topographic gradients Contributing Area to Flow at any pt : a 2. Lateral discharge is in equilibrium with R (m/hr) 3. The capacity for lateral flux is Tsin Uniform k s ) q = Ra Topographic Wetness Index Modeling… Effective Recharge Transmissivity
- 21. Modeling… Data … 2. Multi-Region Cal. Theme: LCLU + Lithology
- 22. Modeling… Data … 2. Multi-Region Cal. Theme: LCLU + Lithology
- 23. Data… Modeling… 2. Multi-Region Cal. Theme with 12 Regions
- 24. Data: Modeling… 3. Geotechnical
- 25. Data: Modeling… 3. Geotechnical… No. Rock Type Specific Gravity Natural Moisture Content, % Plasticity Characteristics USCS Classification Group Bulk Unit Weight, kN/m3 Sat. Unit Weight, kN/m3 LL PL PI Group Symbol Group Name 1 ms-4 2.64 22 25 20 5 CL-ML Sandy CL-ML 16.2 17.2 2 ms-5 2.67 32 68 34 34 MH Sandy MH 15.8 17.1 3 ms-5-3 2.57 35 64 39 25 MH MH with sand 14.5 16.0 4 p-2-1 2.71 29 74 27 47 CH Sandy CH 19.2 19.3 5 q 2.68 14 43 25 18 SC SC with gravel 16.7 17.7 6 t-p 2.69 19 51 37 14 MH Sandy MH 14.4 16.8
- 26. Data: Modeling… 4. Hydrological 100 mm/day rainfall has been used as the threshold for early warning in Thailand by DMR (Calibration) (Scenario Modeling)
- 27. Data: Modeling… 5. Landslide Point Theme
- 28. Modeling… Data: 5. Landslide Point Theme
- 29. SINMAP Model Parameters Modeling… For Calibration Run Region Type h Sat. Density Root Cohesion Soil Cohesion Combined Cohesion Dimensionless Cohesion Friction Angle Hydraulic Conductivity ks Transmitivity T = ks * Ob Recharge R T/R m kg/m 3 kPa kPa kPa deg m/sec cm/day m 2 /day mm/day m 1 F-ms-4 1 1750 7 2.2 9.2 0.536 29 8.00E-04 6912 69.1 100 691 2 F-ms-5 1 1740 7 4.0 11.0 0.644 18 8.00E-04 6912 69.1 100 691 3 F-ms-5-3 1 1629 7 3.2 10.2 0.638 21 8.00E-04 6912 69.1 100 691 4 F-p-2-1 1 1964 7 5.5 12.5 0.649 16 8.00E-04 6912 69.1 100 691 5 F-q 1 1800 7 3.1 10.1 0.572 24 8.00E-04 6912 69.1 100 691 6 F-t-p 1 1708 7 2.7 9.7 0.579 26 8.00E-04 6912 69.1 100 691 7 OTF-ms-4 1 1750 0 2.2 2.2 0.128 29 8.00E-04 6912 69.1 100 691 8 OTF-ms-5 1 1740 0 4.0 4.0 0.234 18 8.00E-04 6912 69.1 100 691 9 OTF-ms-5-3 1 1629 0 3.2 3.2 0.200 21 8.00E-04 6912 69.1 100 691 10 OTF-p-2-1 1 1964 0 5.5 5.5 0.285 16 8.00E-04 6912 69.1 100 691 11 OTF-q 1 1800 0 3.1 3.1 0.176 24 8.00E-04 6912 69.1 100 691 12 OTF-t-p 1 1708 0 2.7 2.7 0.161 26 8.00E-04 6912 69.1 100 691
- 30. Execution: Calibration Modeling… Threshold Rainfall for Early Warning, Land Cover as of 2009
- 31. Execution: Calibration Modeling… SI Hazard Class SI>1.5 Low 1.5>SI>1.25 Medium 1.0>SI>0.0 High Region Low Medium High Total 1 F & ms 4 2 0 0 2 2 F & ms 5 0 1 0 1 3 F & ms 5-3 4 2 0 6 4 F & p 2-1 0 0 0 0 5 F & q 0 0 0 0 6 F & t-p 0 2 0 2 7 OTF & ms 4 0 1 4 5 8 OTF & ms 5 2 4 5 11 9 OTF & ms 5-3 12 14 24 50 10 OTF & p 2-1 0 0 1 1 11 OTF & q 0 0 0 0 12 OTF & t-p 0 2 11 13 Landslides 20 26 45 91 % of Landslides 22 29 49 100 Area 74.2 11.2 11.4 97.2 % Area 76 12 12 100
- 32. Execution: Scenario Modeling Modeling…
- 34. We @ GIC of AIT Vulnerability Analysis Risk Analysis Hazard Analysis
- 35. We @ GIC of AIT Risk Management Disaster Reduction Risk Assessment
- 36. Many Thanks Let us team up www.geoinfo.ait.ac.th
Notas do Editor
- The SINMAP model requires, therefore, 2 variables and 3 parameters as input. The variables ‘theta’ and ‘a’ are implicitly input in the form of a DEM for the model to calculate them for each and every pixel. The three parameters are C, phi, and the ratio R over T. It should be noted, at this point, that the reality is very complex and it would not simply be possible to model a wide range of material and climatic conditions with one single value. The model accounts for this uncertainty and it is so developed to input these parameters by specifying lower bound and upper bound values assuming a uniform probability distribution. Further, the temporal and spatial variability is accounted for by the appropriate choice of a parameter and a variable. The water to soil density ratio r is normally assumed to be 0.5 An inventory of landslide initiation points is also needed as a crucial information for model calibration.