Landslide Susceptibility Mapping by Using a Geographic Information System (GIS) Along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan
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Nat. Hazards Earth Syst. Sci., 19, 999–1022, 2019 https://doi.org/10.5194/nhess-19-999-2019 © Author(s) 2019. This work is distributed under the Creative Commons Attribution 4.0 License. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway), Pakistan Sajid Ali1,2, Peter Biermanns1, Rashid Haider3, and Klaus Reicherter1 1Neotectonics and Natural Hazards, RWTH Aachen University, Lochnerstr. 4–20, 52056 Aachen, Germany 2Department of Earth Sciences, COMSATS Information Technology, Abbottabad, Pakistan 3Geological Survey of Pakistan, Islamabad, Pakistan Correspondence: Sajid Ali ([email protected]) Received: 12 February 2018 – Discussion started: 5 March 2018 Revised: 25 February 2019 – Accepted: 31 March 2019 – Published: 7 May 2019 Abstract. The Karakoram Highway (KKH) is an important ator characteristics (ROC), yielding a predictive accuracy of route, which connects northern Pakistan with Western China. 72 %, which is rated as satisfactory by previous researchers. Presence of steep slopes, active faults and seismic zones, sheared rock mass, and torrential rainfall make the study area a unique geohazards laboratory. Since its construction, land- slides constitute an appreciable threat, having blocked the 1 Introduction KKH several times. Therefore, landslide susceptibility map- ping was carried out in this study to support highway au- Landslides are a result of different geodynamic processes thorities in maintaining smooth and hazard-free travelling. and represent a momentous type of geohazard, causing eco- Geological and geomorphological data were collected and nomic and social loss by damaging infrastructure and build- processed using a geographic information system (GIS) en- ings (Vallejo and Ferrer, 2011). Landslides are mainly caused vironment. Different conditioning and triggering factors for by conditioning and triggering factors. Conditioning factors landslide occurrences were considered for preparation of the include relief, lithology, geological structure, geomechanical susceptibility map. These factors include lithology, seismic- properties and weathering, whereas precipitation, seismic- ity, rainfall intensity, faults, elevation, slope angle, aspect, ity, change in temperature, and static or dynamic loads are curvature, land cover and hydrology. According to spatial triggering factors. Variations in these factors affect the oc- and statistical analyses, active faults, seismicity and slope currence of landslides. Heterogeneity in lithology influences angle mainly control the spatial distribution of landslides. hydrological and mechanical characteristics of rock mass. Each controlling parameter was assigned a numerical weight Slope morphology (curvature) depends upon lithology and by utilizing the analytic hierarchy process (AHP) method. structure within it. Size and type of mass movement changes Additionally, the weighted overlay method (WOL) was em- with variations in lithology and structures. Some lithologies ployed to determine landslide susceptibility indices. As a re- are more permeable and allow water to infiltrate and to in- sult, the landslide susceptibility map was produced. In the crease the pore water pressure. This increase in pore wa- map, the KKH was subdivided into four different suscepti- ter pressure during rainfall events ultimately affects shear bility zones. Some sections of the highway fall into high to strength of the rock mass and slope stability (Barchi et al., very high susceptibility zones. According to results, active 1993; Cardinali et al., 1994), whereas the less pervious rock faults, slope gradient, seismicity and lithology have a strong masses have low infiltration and high runoff leading to de- influence on landslide events. Credibility of the map was val- bris and mud flows (Dramis et al., 1988; Ellen et al., 1993; idated by landslide density analysis (LDA) and receiver oper- Canuti et al., 1993). Sheared and highly jointed rock masses contain shallow slope failures, whereas rockfalls are concen- Published by Copernicus Publications on behalf of the European Geosciences Union. 1000 S. Ali et al.: Landslide susceptibility mapping by using GIS trated in well-bedded massive rock masses (Hu and Cruden, et al., 2016; Kamp et al., 2008; Kanwal et al., 2016; Sha- 1993). Distance from a tectonic feature has an inverse re- habi and Hashim, 2015; Yalcin, 2008) incorporated statisti- lation with rock fracturing and degree of weathering (Prad- cal techniques (Analytical Hierarchy Approach, AHP, with han et al., 2010). State of weathering and fracturing makes weighted linear combination, WLC, and weighted overlay slopes unstable (Ruff and Czurda, 2008). Slope is an impor- method, WOM) into qualitative methods to provide the iden- tant driving parameter for slope failures in the same geolog- tified factors with a numerical weighting. The combination of ical and climatic setting (Coco and Buccolini, 2015). Shear AHP and weighted overlay method (WOM) was termed as strength decreases with increase in slope. Therefore, land- multiple-criteria decision analysis (MCDA) (Ahmed, 2015; slide density increases with increase in steepness (Pradhan Basharat et al., 2016; Kanwal et al., 2016). AHP is a sim- et al., 2010). Kartiko et al. (2006) found that more than half ple and flexible method to analyse and solve complex prob- of the landslides occur in areas where the slope is greater lems (Saaty, 1987, 1990). It facilitates the estimation of influ- than 25 %. Curvature expresses the shape of the slope. If it is ence that different factors might have on landslide develop- positive, then slope will be upwardly convex and will be con- ment by comparing them in possible pairs in a matrix. This cave in the case of a negative value. The later has the ability approach involves field experience and background knowl- to retain water for longer time leading to increase in pore edge of the researcher. A field campaign along the Karako- water pressure and hence in slope failures (Pradhan et al., ram Highway (KKH) in May 2016 enhanced our knowledge 2010). Assessment of risks related to landslides was a long- about factors controlling landslide events. Previous research term challenge for geologists but was significantly facilitated considered MCDA as a better choice because of its accuracy with the eventual availability of remote sensing (Shahabi and to predict landslide hazard (Ahmed, 2015; Basharat et al., Hashim, 2015). Preparation of landslide inventory maps, ac- 2016; Kamp et al., 2008; Komac, 2006; Park et al., 2013; quisition of geomorphological data (elevation, slope, slope Pourghasemi et al., 2012). MCDA (combination of AHP with curvatures, aspect), hydrological parameters and extraction qualitative approaches) was declared a better option for re- of lineaments from remote sensing products is now compar- gional studies (Soeters and van Westen, 1996). Previous wide atively an easier task. usage in landslide susceptibility mapping, high accuracy, a Landslide susceptibility mapping is the spatial predic- simple process and flexibility according to local variation in tion of landslide occurrence by considering causes of pre- landslide controlling parameters compelled us to choose this vious events (Guzzetti et al., 1999). It largely depends upon model. Furthermore, the use of a geographic information sys- knowledge of slope movement and controlling factors (Yal- tem (GIS) facilitated the extraction of geomorphic and hy- cin, 2008). It has hitherto been carried out by many re- drological parameters required for susceptibility assessment. searchers in order to denominate potential landslide hazard Digital elevation models (DEMs) are commonly processed zones through evaluation of responsible factors (Basharat et in GIS to extract crucial parameters for susceptibility as- al., 2016; Komac, 2006; Lee et al., 2002, 2004; Shahabi and sessment such as elevation, slope, aspect, curvature, water- Hashim, 2015; Süzen and Doyuran, 2004). Preparation of shed, etc. (Ayalew et al., 2004, 2005; Basharat et al., 2016; their maps was based broadly on qualitative and quantita- Ohlmacher and Davis, 2003; Rozos et al., 2011; Shahabi and tive approaches. Early research work (Nilsen et al., 1979) Hashim, 2015; Süzen and Doyuran, 2004). Previously, land- was largely quantitative, utilizing deterministic and statistical slide susceptibility maps of different fragments of northern correlations and regression analysis of landslides and their Pakistan were prepared (Bacha et al., 2018; Basharat et al., controlling factors. In this context, safety factors, calculated 2016; Ahmed et al., 2014; Kamp et al., 2008; Kanwal et al., on the basis of engineering parameters, are adduced to im- 2016; Khan et al., 2018; Rahim et al., 2018) (Table 1). They ply deterministic methods. In more recent works, statistical used a single model-based method, except two (Ahmed et methods are favoured, attempting to draw correlations be- al., 2014; Bacha et al., 2018) compared performances of two tween spatial distribution of landslides and their controlling different models. Ahmed et al. (2014), Kanwal et al. (2016) factors. Among these are the analytical hierarchy process, bi- and Rahim et al. (2018) used a regional geological map variate and multi-variate methods, logistic regression neural (1 : 500 000) to produce a landslide susceptibility map of the networks, fuzzy logic, etc. (Basharat et al., 2016; Guzzetti et Upper Indus basin, whereas inventories were based on pub- al.,