# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 2 Materials and Methods
### Data Collection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Magnitude**} & \multirow{2}{*}{**Acceleration (g)**} & **Frequency of** & **Perceived** & **Potential Damage** \\  & & **Occurrence Per Year** & **Shaking** & **Potential Damage** \\ \hline
1.0–1.9 & \(<\)0.0017 & Several million & Not felt & None \\
2.0–3.9 & 0.0017–0.039 & 100,000 to 1 million & Weak & No damage or very rarely damage \\
4.0–4.9 & 0.039–0.092 & 10,000 to 15,000 & Light & Minor damage possible \\
**5.0–5.9** & **0.092**–0.18 & 1000 to 1500 & Moderate & Severe damage to poorly constructed buildings. \\  & & & Zero to slight damage to all other buildings. \\  & & & Damage to a few well-built structures. \\
**6.0–6.9** & **0.18**–0.34 & 100 to 150 & Strong & Earthquake-resistant structures survive with slight to moderate damage. Poorly designed structures \\  & & & & Image to most buildings, partially or completely \\
**7.0–7.9** & **0.34**–0.65 & 10 to 20 & Severe & collapse or severe damage. Well-designed \\  & & & & structures are likely to receive damage. \\  & & & & Major damage to buildings, structures likely to be \\
**8.0–8.9** & **0.65**–1.24 & About 1 & Violent & destroyed. Moderate to heavy damage to study or \\
**9.0** and & & & & earthquake-resistant buildings. \\ greater & \(>\)**1.24** & 1 per 10 to 50 years & Extreme & At or near total destruction—severe damage or \\  & & & collapse to all buildings. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the earthquake magnitude and the corresponding peak ground acceleration, frequency of occurrence, perceived shaking and potential damage (adapted from www.usgs.gov; accessed on 22 March 2021). The selected magnitudes and their accelerations were marked in bold and italic. classes were used (i.e., some types were merged into one type): forest (assigned to the value of 8 for the following analysis), mosaic vegetation (assigned value: 7), grassland (assigned value: 6), mosaic cropland/vegetation (assigned value: 5), cropland (assigned value: 4), sparse vegetation (assigned value: 3), water/snow (assigned value: 2), and bare areas (assigned value: 1). Third, the soil data were extracted from the digital soil map of the world ([[https://worldmap.harvard.edu](https://worldmap.harvard.edu)]([https://worldmap.harvard.edu](https://worldmap.harvard.edu)); accessed on 20 February 2021). Five out of 106 soil types are found in the Mailuu-Suu catchment, and they are Lithosols Humic Cambisols (code: I-Bh-2c, assigned value: 5), Lithosols Chronic Cambisols Humic Cambisols (code: I-Bc-Bh-c, assigned value: 4), Lithosols Chronic Cambisols (code: I-Bc-2c, assigned value: 3), Lithosols Calcic Xerosols (code: I-Xk-2c, assigned value: 2), and Calcic Xerosols (code: Xk4-2 ab, assigned value: 1). Fourth, the annual precipitation data were obtained from WorldClim V2.1 (www.worldclim.org; accessed on 20 February 2021) with a resolution of 1 km [68]. These data were further downscaled into 10 m using the bilinear resampling approach in the ArcGIS. In this study, the average value of the reference period (i.e., 1970-2000) was used as the annual precipitation. Based on the amount, the precipitation was classified into four classes: low (278-450 mm), moderate (450-600 mm), high (600-700 mm), and very high (>700 mm). Fifth, three proximity variables, including distance to faults, roads, and streams were selected. The localities of the faults were obtained from \"Active Tectonics of the Northern Tien Shan\" ([[http://activetectonics.asu.edu/N_tien_shan/N_tien_shan_data.html](http://activetectonics.asu.edu/N_tien_shan/N_tien_shan_data.html)]([http://activetectonics.asu.edu/N_tien_shan/N_tien_shan_data.html](http://activetectonics.asu.edu/N_tien_shan/N_tien_shan_data.html)); accessed on 20 February 2021). The roads were obtained from HOTOSM (Humanitarian OpenStreetMap, www.hotosm.org; accessed on 20 February 2021), and stream networks were generated using the DEM data and the hydrologic analysis of ArcGIS.