## 5 Discussion  
Identifying specific factor classes that have a dominant association with past debris flow events is essential in preparing debris flow susceptibility maps across an area of study [28] at any scale. The impact of locational uncertainty in accurately selecting those predisposing factor classes varies depending on the scale at which one is performing the analysis, the scale of lateral change of the factor itself, the extent of location uncertainty of the debris flow events being used, and the risk associated with an incorrect model [21; 22]. A total of 88.8% of this study's Europe debris flow inventory has a locational uncertainty  
Figure 8: Model susceptibility differences. (**A**). Pixel difference between “Original” and “93 Random” models. (**B**). Pixel difference between “Original” and “LTE 5 km” models. (**C**). Pixel difference between “Original” and “LTE 1 km” models. Warmer colors represent areas of higher susceptibility values in “Original” model. (**D**). Map 9B zoomed into area around the Italian Alps for visual enhancement of susceptibility disparities. Base maps are from ArcGIS®, the intellectual property of Esri, used herein under license. Copyright© Esri. from 1 to 50 km, with 6% defined as \"unknown\". A total of 82.5% of the complete inventory (all continents, all landslide types) have a locational uncertainty from 1 to 250 km, with 4.9% unknown. Noteworthy differences arise in factor and factor class determinations and susceptibility results among five MaxEnt models, each using data with different location uncertainties. Within their buffered areas, 35% of the original inventory of debris flow events were found to have from two to seven different soil types, an average of 5.8 different precipitation values, and fault densities with high values from 1.75 to 4.2 times the low values within a buffered event. The \"Original\" model with the most uncertainty, and the \"Exact\" model with no uncertainty, provide end limits on a spectrum for model comparisons. Of the five models, \"Original\" model has the largest Very High susceptibility classification area (Figure 6A and Table 9), five times larger than the \"Exact\" model, and the lowest AUC (0.891). The \"93 Random\" model has a slightly higher AUC (0.893) than the \"Original\" model, and an area of Very High susceptibility three times the size of the \"Exact\" model. The better performance of a random model adds to the questionability of the original dataset as satisfactory input to susceptibility modeling. \"LTE 1 km\" (24 points with good distribution) has the smallest Very High susceptibility area (Figure 6D and Table 9), and the second highest AUC (0.921). The \"LTE 5 km\" model, with 55 points well distributed across the continent, has an intermediate AUC (0.896) and an intermediate areal coverage of Very High susceptibility (Figure 6C and Table 10), two times larger than the \"Exact\" model. Due to the small number of points in the \"Exact\" model (five) and localized distribution, this model is not further considered as a viable model. Commonly, 2D model uncertainty visualization methods rely on gray scale gradations; hue, saturation, intensity coloration; fill grain density; and crispness versus fuzziness of classes, to convey model uncertainty [43,44]. These representations do not lend themselves to complex displays at large geographic extents, such as continental debris flow susceptibility. As an enhanced view on susceptibility model choices and with an attempt to better convey the 'uncertainties' to model users, mean and standard deviation overlay maps were produced based on a combination of the two best models, \"LTE 5 km\" and \"LTE 1 km\" (Figure 9), and with the \"Original\" and \"LTE 5 km\" models (Figure 10). The former may favor the error of omission, while the latter may favor the error of commission. The better model for further investigation is dependent on the intended use.