# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 2 Materials and Methods
### Data Collection  
Data collection was performed as a part of the TRANSPOND (Transboundary Monitoring and Information System for Radioactive Contamination in the Event of Natural Hazards) project. The collected data included input data for the Scoops3D (i.e., DEM, soil parameters, and pore water pressure ratio) and nine environmental variables for the relationship analysis (i.e., elevation, slope, aspect, land cover, soil type, annual precipitation, distance to faults, roads, and streams). The collected input data were used to perform the simulation of Scoops3D under five earthquake scenarios (i.e., moderate, strong, severe, violent, and extreme). The limit equilibrium method was employed in order to predefine the potential failure surface and investigate the equilibrium state of the predefined failure surface. As the outputs of Scoops3D, two landslide metrics (i.e., LSI and VOL) were generated. These two landslide metrics and nine environmental variables were then used to perform the relationship analysis with the _RandomForest_ program, in order to determine the relative importance of the environmental variables in structuring the spatial patterns of the landslide metrics. The scheme for the data process, modelling approaches, and the relationship analysis is shown in Figure 2.  
The DEM data were obtained from the SRTM (shuttle radar topography mission) dataset (resolution: 30 m; www.usgs.gov; accessed on 24 November 2020) and further downscaled into a resolution of 10 m using the bilinear resampling approach in the ArcGIS (geographic information system) software V10.7. This DEM raster data were then converted into the ASCII (American Standard Code for Information Interchange) grid format, as required by Scoops3D. The soil parameters were obtained from the Scientific Engineering Centre \"GEOPRI-BOR\" (SECG) in Kyrgyzstan. In this dataset, six soil layers were measured _in situ_ in the Mailuu-Suu catchment. The measured parameters included layer depth, cohesion, angle of internal friction, and specific gravity. The averaged values were used to represent the overall conditions of soil properties in the Mailuu-Suu catchment, and they were 3.7 m, 17.7 kPa, 30.3\({}^{\circ}\), and 19.7 kN/m\({}^{3}\), respectively (Table 1). The detailed measurement method of the soil parameters is available in [PERSON] and [PERSON] et al. [56; 59]. Scoops3D includes two triggering factors, namely the pore water pressure caused by groundwater [60] and the earthquake loading [12; 61]. To reflect the effect of pore water pressure, the \(R_{u}\) coefficient was employed. This value refers to the pore water pressure ratio and ranges between 0 and 1. The calculation of the \(R_{u}\) coefficient is based on Equation (1):  
\[R_{u}=\frac{u}{\gamma z} \tag{1}\]  
where \(R_{u}\) is the pore water pressure ratio, \(u\) is the pore water pressure, \(\gamma\) is the specific gravity of the soil, and \(z\) is the depth below ground. For the specific gravity and depth, the above-mentioned soil parameters were used. According to the suggestion from SECG, the pore water pressure was defined as 10 kPa \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Layer**} & **Depth** & **Cohesion** & **Angle of Internal** & **Specific Gravity** & \(R_{u}\) \\  & **(m)** & **(kPa)** & **Friction (\({}^{\circ}\))** & **(kN/m\({}^{3}\))** & **Coefficient** \\ \hline L01 & 4.0 & 11.6 & 36.0 & 19.2 & 0.13 \\ L02 & 5.2 & 11.3 & 32.3 & 19.2 & 0.10 \\ L03 & 4.0 & 8.8 & 31.0 & 20.0 & 0.12 \\ L04 & 2.0 & 40.0 & 23.0 & 19.1 & 0.26 \\ L05 & 2.5 & - & - & 21.0 & 0.19 \\ L06 & 4.3 & 16.7 & 29.0 & 19.6 & 0.12 \\ Average & 3.7 & 17.7 & 30.3 & 19.7 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Input soil parameters for the simulations of Scoops3D in the Mailuu-Suu catchment (adapted from [PERSON] et al. [56]). Figure 2: The scheme for the data process and modelling approaches.