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Analysis of Sinmap in Landslide Susceptibility Mapping of Coonoor Watershed, Nilgiris, India

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Analysis of Sinmap in Landslide Susceptibility Mapping of Coonoor Watershed, Nilgiris, India Analysis of Sinmap in Landslide Susceptibility Mapping of Coonoor Watershed, Nilgiris, India Subbarayan Saravanan ( [email protected] ) National Institute of Technology Tiruchirappalli https://orcid.org/0000-0003-4085-1195 Jacinth Jennifer Jesudasan National Institute of Technology Tiruchirapalli Leelambar Singh National Institute of Technology Tiruchirapalli Abijith Devanantham National institute of technology Tiruchirapalli Research Keywords: Coonoor, GIS, Landslide Susceptibility map, SINMAP, Shallow landslide. Posted Date: August 27th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-63451/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 ANALYSIS OF SINMAP IN LANDSLIDE SUSCEPTIBILITY MAPPING OF COONOOR 2 WATERSHED, NILGIRIS, INDIA 3 4 ABSTRACT 5 Landslide susceptibility mapping is crucial for risk management in mountainous Nilgiris district, Tamil Nadu, India. 6 In this study, the spatially distributed Stability INdex MAPping (SINMAP) model was used to develop a landslide 7 susceptibility model for Coonoor watershed, which is based on a steady-state hydrologic model coupled with an 8 infinite-slope stability equation. Shuttle Radar Topography Mission (SRTM) digital elevation model was utilized to 9 compute the slope and other topographic parameters. In-situ data collection and laboratory tests were executed to 10 estimate the hydro-geotechnical parameters. A detailed field study was conducted, and 35 samples were taken from 11 the landslide locations to perform laboratory tests to find the geotechnical parameters. Also, an inventory landslide 12 map was generated to evaluate the model performance. Statistical results of SINMAP show that, under the fully 13 saturated condition, 58.96% of the region was found to be stable, 16.79% moderately stable, 8.72% quasi-stable and 14 15.24% unstable. The Area under the Curve (AUC) value of the predicted result is 72.3% which infer that the 15 underlying predictors perform good in classifying the outcome. Thus, the considered parameters in the model have 16 played its part well in the classification of the region based on its stability. 17 Keywords: Coonoor; GIS; Landslide Susceptibility map; SINMAP; Shallow landslide. 18 1. INTRODUCTION 19 Last few decades have faced worst diasaters, viz., floods, landslides, earthquakes, tsunamis and hurricanes, which 20 prone a threat to human lives and property. Overall, landslides alone represents 9% of the natural diasaters which have 21 happened worldwide during the 1990’s and this trend is likely to carry on in upcoming decades with the increase in 22 population and urbanization along with the drastic changes in the climate pattern (Schuster 1996). 23 The profound increase in the world’s population leads to drastic climate change and disastrous events. Landslides are 24 said to be one of the protuberant catastrophes around the world, most likely to occur in hilly terrain. However, 25 landslides are claimed to be the natural processes that are responsible for the channel maintenance, landscape 26 formation, evolution, and sediment supply; it also serves as a mechanism to release sediment from the slopes of hills 27 to the stream and rivers (Cendrero & Dramis 1996; Michel et al. 2014; Petley 2012). Among various geological risks, 28 landslides instigate more human loss and damages that costs billions of dollars. Few measures to mitigate these losses 29 include the generation of maps that implies the landslide susceptibility, danger and risk. Over latest years, geographical 30 information systems (GIS) is gaining its potential for spatial data management, analysis and manipulation. The Indian 31 hilly terrains in the Himalayas, North, East and the Western Ghats region are extremely susceptible to landslides 32 (Mandal & Mandal 2018). The occurrence of landslide is triggered by natural and human activities (Haigh & Rawat 33 2012). The causal factors of landslides may broadly be classified under geological causes, morphological causes and 34 social causes (Mandal & Maiti 2015). The strength of the terrain materials, their weathering property, presence of 35 fractures and fissures, permeability and stiffness of the rocks fall under the geological causes. (Seshagiri Rao 2020). 36 The tectonic and volcanic reflexes, erosion activity, type of vegetation, thawing and freezing activity are clubbed 37 under the morphological causes. The excavation, construction, irrigation, deforestation, water leakage and mining 38 from utilities are categorized under the human causes (Maina-Gichaba et al. 2013; Shit et al. 2015). 39 With the invention of remote sensing and GIS techniques, the disaster monitoring and management sectors have 40 benefitted extensively. The mapping of landslide susceptibility zones has been done widely worldwide using 41 numerous techniques and methodologies. The zone mapping demarcates the spatial extent, which is likely to landslide 42 occurrence. The concerned region prone to landslides though triggered by external factors, the terrain properties and 43 characteristics also influence the susceptibility of the region. Thereby, making it necessary to consider the triggering 44 factors and causative parameters of the terrain.(Jennifer et al. 2020; Wang et al. 2014) 45 The characteristics of catchments and its sediment yield and its relationship with the occurrence of landslides have 46 accelerated the need for study based on hydrological, physically-based and spatially-distributed models (Bathurst et 47 al. 2005; Michel et al. 2014). The hydrological and stability models coupled with DEM in the GIS platform are utilized 48 to calculate the factor of safety (FOS), which facilitates the analysis of various potential circumstances or rainfall 49 events (Corominas & Moya, 2008; Michel et al. 2014). There exist several stability models such as GEOtop-FS, 50 SHALSTAB, CHASM, SHETRAN, SUSHI, TRIGRS, and SINMAP (Safaei et al. 2011). There are very few methods 51 that adopt the terrain characteristics such as the geology of the bedrock, soil type, thickness and cohesive nature, land- 52 use/ land cover, vegetation type including the drainage of the terrain and the precipitation in the region. Among these, 53 the SINMAP (Stability INdex MAPping) (Pack et al. 1998) adopts probabilistic approach that utilizes the geomorphic, 54 hydrological and geotechnical features. SINMAP is an extension designed for ArcView GIS SINMAP, which is 55 extensively applied in assessing the shallow transitional land sliding phenomena influenced by the shallow 56 groundwater convergence. The SINMAP approach is applicable to the shallow translational landsliding phenomena 57 and is not functional in the case of analyzing deep-seated instability. The field information is essential for calibration, 58 and the data required includes the soil and climate properties which are vastly inconstant in space and time. The 59 precision of the output is greatly reliant on the digital elevation model (DEM) and the software relies on grid-based 60 sata structures. The output stability indices are interpreted in terms of relative hazard, which could be assisted for 61 reconnaissance-level and detailed mapping, given that the input data are reliable and precise (Pack et al. 1998). 62 The current study is based on the the analysis on the landslide vulnerable zones in the Nilgiris district of Tamil Nadu, 63 India, which is a disaster prone region with a history of significant landslide occurrence. A notable event is one which 64 occurred in Novemeber 2009, when the landslide befell at over three hundred locations in the region causing severe 65 damages to the people and society leaving nearly fifty people dead and hundreds of people homeless (Ganapathy et 66 al. 2010; Uvaraj and Neelakantan 2018). Numerous landslides were reported within five days from 10th to 15th of 67 November, 2019. After 1978, this event has been declared as the major disaster in the district. Ooty, Coonoor and 68 Kotagiri are the regions profoundly affected by landslides in the district. 69 The application of the SINMAP model over the region prone to shallow landslides triggered by rainfall has been 70 implemented by various researchers in their study to predict the spatial distribution of the unstable zones (Zaitchik 71 and Van 2003; Zaitchik et al. 2003; Calcaterra et al. 2004; Lan et al. 2004; Meisina and Scarabelli 2007). 72 73 2. STUDY AREA DESCRIPTION 74 The study area covers a part of Coonoor river watershed in the Nilgiris district, mountainous terrain in the North- 75 Western part of Tamil Nadu, India. The Coonoor watershed has a catchment area of 45.4 km2 and extends from 76 latitude 11°20¢00¢¢ N to 11°28¢00¢¢ N and longitude 76°40¢05¢¢ E to 76°49¢40¢¢ E in the Nilgiris district (Fig 1). The 77 watershed predominantly consists of forest (38.7%), tea estates (14.7%), cropland (11%), settlement (19.2%), and 78 scrub land (29.2%) (Saravanan et al. 2019). The elevation of the terrain ranges from 1557 to 2638 meters above the 79 sea level. The basin has four distinct seasons, South-West monsoon (SW) and North-East monsoon (NE) ) from June 80 to September, and October to December respectively, and the winter and summer season from January to February 81 from March to May respectively. The average annual rainfall ranges from 1100 to 1600 mm/year, while most of the 82 precipitation is predominant during the NE monsoon period. The depth of the soil usually varies from one to three 83 feet and that of the sub-soil from 3 to 5 m. The soil gets saturated even during the low-intensity rains and water flush 84 through the fissures rapidly. The geology is represented by the Charnickite group which comprises of two-pyroxene 85 granulite, charnockite, banded quartz-magnetite quartzite and thin pink quartzo-felspathic granulite outcropping or 86 covered by forest-cum-grassland (Guru et al., 2017). The rocks are metamorphic in nature and various geomorphic 87 features comprised in this region are debris slope, denudational hill, denudational slope and plateau. The economy of 88 this region depends highly on the cultivation of tea plantations, The temperature of the district ranges between 18℃ 89 to 28℃ during summer and between 0℃ to 16℃ during winter.
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