## 4 Results
### Impact of Factor Uncertainties on Susceptibility Results

Table 8: AUC and factor contributions (gain) for each of the five MaxEnt models. The \"Random\" model was developed merely as a view on the integrity of the \"Original\" model, in comparison. So, it is noteworthy that the \"Original\" model does not perform as well as a model built on randomly generated locations. Although the \"Exact\" model has verified locations and the highest AUC, results cannot be used to represent the European continent due to the statistically small number of such events (five) and their localized distribution, Figure 7, hence the \"Exact\" model is not further considered. For precipitation, soil type, soil thickness, topsoil percent clay, and landform factors, there is concurrence in the factor classes associated with susceptibility among the four models, within a reasonable margin. There are significant differences for fault density, landcover, climate, elevation, drainage, depth to bedrock, and aridity. In most of these cases there is reasonable agreement in three of the four models, Table 9.  
Figure 6: Debris flow susceptibility maps. (**A**). “Original”, (**B**). “93 Random”, (**C**). “LTE 5 km”, (**D**). “LTE 1 km”. Warmer colors represent higher susceptibilities. Base maps are from ArcGIS®, the intellectual property of Esri, used herein under license. Copyright © Esri. Figure 7: Distribution of the five European debris flow events with a location accuracy of “exact”. Base map is from ArcGIS\({}^{\text{0}}\), the intellectual property of Esri, used herein under license. Copyright © Esri. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor/Model** & **“Original”** & **“93 Random”** & **“LTE 5 km”** & **“LTE 1 km”** \\ \hline _precipitation (monthly average mm 1970–2000)_ & _275–300_ & _300–325_ & _260–280_ & _275–300_ \\ \hline
**fault density (km/sq km)** & 0.02–0.14 & 0.01–0.02 & 0.13–0.14 & 0.01–0.14 \\ \hline _soil type_ & _Gleysol_ & _Flewisol, Gleysol_ & _Fluvisol, Gleysol_ & _Gleysol_ \\ \hline
**landcover** & urban & urban & urban & sparse vegetation \\ \hline
**Climate (Köppen-Geiger)** & Dfa—hot summer & BSk—semi arid & Csa—Mediterranean & \\  & humid continental climate & steppe & and Cfc—subpolar & \\  & & oceanic climate & \\ \hline
**lithology** & unconsolidated & intermediate volcanic & unconsolidated & \\  & sedimentary (su) & (vi) & sedimentary (su) & \\ \hline _soil thickness (m)_ & _0–2.5_ & _0–1_ & _0–2_ & _0–2.5_ \\ \hline _landform_ & _Plains on sedimentary_ & _Humid plains on sedimentary lithology_ & _Plains on sedimentary lithology_ & _Plains in alpine system_ \\ \hline
**elevation (m)** & 3250–3500 & 3200–3500 & 0–500 & 3250–5000 \\ \hline
** drainage** & “Very poor” & “Imperfectly” & “Imperfectly” & “Moderately well” \\ \hline _topsoil percentclay_ & _5–18_ & _20_ & _20_ & _0–22_ \\ \hline
**depth to bedrock (cm)** & 0 & 114 & 0 & 0–2000 \\ \hline
**aridity (dimensionless index)** & \(\sim\)1300 (Arid) & \(\sim\)1300 (Arid) & \(\geq\)60,000 (Humid) & \(\sim\)15,000 (Humid) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of factor classes with highest significance to susceptibility in four models. _Italicized_ factors are those with closest agreement. Next, three pairwise comparison maps were produced with pixel differencing of the susceptibility values. The first comparison is the \"Original\" model minus \"93 Random\", the second comparison is the \"Original\" model minus \"LTE 5 km\", and the third is \"Original\" minus \"LTE 1 km\", Figure 8A-C, respectively. The results of each map are displayed with a ten-break Natural Break (Jenks) classification. Figure 8D provides an enlarged view of the susceptibility differences for the two sample events 560 and 6381.