Approaches to exploring drought using satellite data
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
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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)
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)
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
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)
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.