## 2 Materials and Methods
### Input Data  
The landslide inventory database (SOPO) from the Polish National Geological Institute was used for the input data (Figure 1). These data allowed the generation of models and validation datasets. The existing landslide locations and boundaries within the SOPO database were captured using conventional techniques, mostly comprising field reconnaissance, the visual interpretation of aerial photographs, and the analysis of historical data [39]. The landslides within the study area are stored in the SOPO database and were mapped during field work in the years 2010, 2011, 2012, 2013, 2014 and 2015 [35; 37; 38; 40]. Additional mapping was also performed on the basis of topographic maps at a 1:10 000 scale, supported by stereoscopic analyses of aerial photographs and LiDAR data [9].  
Various Landslide Conditioning Factors (LCFs) were generated from the data captured from different sources. Table 1 presents the data sources used to create so-called second-order input data such as the Digital Elevation Model (DEM), geological and soil map etc. The LiDAR data were acquired within the framework of the IT System of the Country Protection (ISOK) project. Almost the entire area of Poland has been scanned for the implementation phase of the extraordinary hazard (mostly water hazard) protection system (ISOK project) [41]. A point cloud with a density of 4-6 points/m\({}^{2}\) was generated, and the calculated Root Mean Square Error was about 0.15 m for the height coordinate [41]. This LiDAR point cloud was subsequently used for DEM generation. To decrease the data volume and discard some of the artefacts from the original DEM, we followed the recommendations of [42; 43; 44] and generated a DEM with a resolution of 2 m. This approach appears to be common among various papers, and many authors have reported that the finest DEM resolution is not always the best choice [42; 43; 44; 45]. Based on the DEM, topographical LCFs were subsequently generated as described in Section 2.4.  
A Sentinel-2A image acquired via the Copernicus Scientific data hub was used to extract the boundary of Roznow Lake. This shape was extracted by the calculation of the Normalized Difference Vegetation Index (NDVI). More specifically, an NDVI value lower than 0 was used to extract the shape of the lake, based on which another LCF was calculated (the lake proximity). Moreover, an agricultural soil map acquired from the geoportal of the Malopolskie vovodeship was used to generate a soil-suitability map. A geological map was also obtained from the Polish National Geological Institute.  
Figure 2: Conceptualization of the applied methodology (SCAI: Seed Cell Area Index).  
As well as the geological map, the soil maps were digitized and stored as vector layers in ArcGIS (version 10.6, ESRI, Redlands, CA, USA). Additionally, a thrust and fault network was generated based on previous work [30] via a digitization process. The road network was acquired from OpenStreetMap (OSM). Because the river network in OSM is very sparse, the stream network was extracted with the DEM, with which the flow accumulation and direction were initially calculated. The subsequent thresholding of the flow accumulation values allowed the extraction of the stream network. Finally, this network was manually refined and converted into the vector layers. Because precipitation is a direct trigger of landslides worldwide, we downloaded the precipitation measurements from four different meteorological stations in this area. We utilized measurements from 2019 due to the changes of the weather conditions caused by the climate change. From one year to another, we observed much more extreme weather phenomenon. Thus, to represent the most real and current rainfall situation, we applied the rainfall measurement from 2019. A precipitation map was generated based on these measurements by inverse distance weighted interpolation. A summary of all the data used in this study is presented in Table 1.