5/3/2018 Susceptibility analysis of landslide due to earthquake due in Gorkha (25th April 2015) Animesh Bahadur Pradhan GIS IN WATER RESOURCES CE 547

Transcription

1 Susceptibility 5/3/2018 analysis of landslide due to earthquake due in Gorkha (25th April 2015) Animesh Bahadur Pradhan GIS IN WATER RESOURCES CE 547

2 Contents 1. Acknowledgement: Motivation and Background Objective: Methodology... 4 i. Factors Considered:... 4 ii. Data... 4 iii. Projection... 4 iv. Methodology/Process Results and Observations Conclusion: Future Work Appendix: pg. 1

3 1. Acknowledgement: I would like to thank Dr. Julie Coonrod for providing me this opportunity and guidance to complete this project. I would also like to thank Smriti Chaulagain and Asifur Rehman for helping me during this project. Finally, I would like to thank my family and friends who encouraged me during this project. Thank you. pg. 2

4 2. Motivation and Background. Nepal lies in one of the most seismically active zones. It lies between Eurasian and Indian plate and main frontal thrust (MFT) runs through the country. This makes the country very prone to earthquake impact. The geological condition does not help this cause either. There is a gradient change from 0 to about 90 Figure 1 A man comparing the valley before (in picture) and after landslide caused by the earthquake in just about 250 km across. During the recent 7.8 M earthquake, many seismic landslides were triggered which caused loss of property and life. One of the main notable landslide and avalanches that were triggered were in Langtang Valley and Everest basecamp which are very popular tourist destination. About 329 people were missing after the seismic landslide in Langtang Valley and at least 22 people were missing in Everest basecamp. This made me think what if these areas were not suitable to begin with or it lies in fault zone. If that was the case, there was high chance that could have been avoided or necessary measures could have been taken to minimize the loss. 3. Objective: The main objective of the project was to identify high risk landslide zone which could be triggered due to earthquake. This identification will also help in locating vulnerable zone and preliminary rescue and relief programs if any of these misfortunate events do take place. pg. 3

5 4. Methodology i. Factors Considered: The factors that were considered for this study were a. Slope b. Geological composition of the rocks c. Distance between from the fault line d. Intensity of the earthquake (severity of earthquake shaking observed) Equal weightage of 25% was given to each of the factors and were regarded as equally important to calculate the susceptibility of earthquake with respect to all four factors. ii. Data The required data were obtained from various sites. DEM data for slope and intensity distribution chart was downloaded from USGS official website. Geological composition of the rocks and fault line were obtained from official website of ICIMOD. iii. Projection Modified UTM Projection was used to project the data and eliminate distortion. As Nepal lies between UTM Zone 44N and 45N, a small modification is to be done to incorporate the exact projection on the earth surface. This is one of the most used projection in Nepal used by Department of Survey, so this projection was selected. False Easting and Northing were selected as and 0 respectively. Central meridian was set up at 84 and scale factor was changed to where all the units were in meter. pg. 4

6 iv. Methodology/Process Figure 2 Slope of Landform in Nepal DEM data obtained was converted into slope form using slope command. This was again reclassified into 9 groups with class from 0-9. Higher slopes were allocated higher numbers and lower slope lower. pg. 5

7 Figure 3 Geological Composition of Nepal Geological data was also converted into raster form and each unique attribute were reclassified where rock with higher strength were given lower numbers and soils were given higher numbers. Figure 4 Intensity Distribution of Earthquake on 25th April 2015 pg. 6

8 Earthquake intensity was also converted into raster format and value that were obtained were used with higher intensity being higher numeric value. The earthquake intensity of Gorkha earthquake of magnitude 7.8 Richer Scale was considered for this analysis. The main reason for considering this event was because it is one of the most devastating earthquake that has struck in Nepal in recent years and data related to that earthquake was readily available in USGS. Fault line data was first converted into raster form and then Euclidean distance was measured with 10 classes with maximum distance was specified was 10km. The nearest one were given higher values when reclassification was done. Raster calculator was used to calculate the total weightage by adding all the values up and multiplying with Then the obtained results were reclassified into 4 categories according to the suitability percentage using reclassify option where 4 classifications were made with suitability of less than 25%, 25-50%, 50-75% and more than 75% were obtained and displayed in the map. Spatial analyst tools such as Euclidean distance, raster calculator, reclassify were used to process raster calculation and other calculation required for the project. Conversion tool was used to convert vector quantity to raster and raster into vector or polygon. 5. Results and Observations The result showed that areas where earthquake had highest intensity had higher chances of landslide. The area near the epicenter of earthquake was also at high risk of the landslide. The areas where there was higher gradient was also observed to have higher chances of landslide due to this earthquake. This could be acceptable because there is a lot of energy near the epicenter of the earthquake which may trigger soil and rock to act as liquid (liquification) and angle higher pg. 7

9 than 45 is unstable in Geotech engineering term. So, this may result in triggering of earthquake in these areas. The output also showed higher chances of landslide in lower region. This is a very confusing because general idea is that there is no landslide in region with low gradient. This output may have occurred since these are the areas which are formed by soft soils with low strength and high classified value that we have given them. Lack of knowledge about setting the weightage factor may have also caused this anomaly. Figure 5 Output Showing Probability of Earthquake in Different Parts of Nepal pg. 8

10 6. Conclusion: Figure 6 Comparison Between Obtained and Real Data The obtained results were compared with the actual landslide that did occur because of this earthquake. And the result showed that prediction of landslide location was obtained with some accuracy. The actual landslide that occurred was mostly in the range greater than 50%. The factors that were considered for analysis were of highly important factor and effective. More factors should be considered, and proper weightage should be set up for accuracy. 7. Future Work. The use of magnitude and depth of the earthquake could be utilized to find out the effect of earthquake at a certain distance which might make the prediction more accurate and predictable. Other factors such as soil formation, ground water and surface water condition, types of land use and modification such as construction of dam and road protection structures may have altered the geological strength. Frequency of landslide occurrence may also help accurate prediction of landslide location. pg. 9

11 Appendix: Figure 7 Map of Nepal with Plates and Fault line Figure 8 Model for Calculation of Output pg. 10

12 Process: i. For Slope: DEM was imported in ArcGIS. Slope Command from Spatial Analyst was used to convert DEM to slope. Then it was reclassified into groups with whole number. Higher values were given to high slope and lower value to lower one. It was supposed that higher slope will have higher chance of falling than lower ones. ii. For Geological data: Geological vector (polygon) data set were converted into raster form using polygon to raster from Conversion Tool. Then it was reclassified using Reclassify function where higher values were given to soils which have low cohesion or unit mass. This resulted in rocks having higher values and soils having low values. iii. Intensity Distribution: Intensity distribution shape file of Gorkha Earthquake of 25 th April 2015 of magnitude 7.8 Richter Scale was obtained from USGS website. Then this vector quantity was converted into raster form using poly to raster tool from Conversion Tool. As the intensity were already in whole number form they were not reclassified. Higher the intensity, higher the energy and shake felt which will initiate landslide more quickly than lower intensity one. iv. Fault line: As scientist believe that areas near the fault line are not strong and is easier for the landmass to move, this will initiate earthquake rather quickly which will cause landslide to occur more frequently in these areas. So Euclidean distance from spatial analyst tool with maximum distance specified to be 10km, was pg. 11

13 used to get Euclidean raster. This raster was then reclassified using reclassify where higher values were allocated to the ones which was near to fault line and so on. We could again reclassify the obtained term in two groups where one group was allocated near 10km distance from fault and other greater than that. For this project, both were considered and the first one with many groups was suitable and was used. Figure 9 Raster Calculator with Formula used Raster Calculator: Each value was given an importance of 0.25 (25%) and was multiplied by 0.25 and added as shown in figure to get a value. Then the value was reclassified into 4 groups using Reclassify into less than 25%, 25%-50%, 50%-75% and 75% and above. pg. 12