## Abstract  
In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of \"exact\". The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The \"exact\" model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action. Keywords:spatial uncertainty; debris flow; susceptibility +
Footnote †: journal:  
Susceptibility, in this context, is a qualitative assessment of potential areas of instability with respect to debris flows. In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known unless a direct field survey is conducted. Landslide inventories are often constructed based on mapping from aerial imagery, media reports, local governmental agencies, witness accounts, and field work by third party sources [5; 14; 15]. Uncertainties are inherent in all spatial data and at all scales [16]; however, when working at a continental scale and in the absence of direct field surveys, the uncertainties are inherently greater [17; 18]. \"Uncertainty exists widely in the natural world, and certainty is conditional and relative\" [19]. When utilizing methods that overlay and correlate multiple datasets each with their own uncertainty, their derivative products, such as susceptibility maps, are prone to error propagation of an unknown magnitude [19]. It is not a matter of adding more or better data, but rather a \"sobering reminder that uncertainty is an irreducible part of sufficiently complex knowledge\" [20], and thus should be thought of as a natural component of, and addressed in, every project. The presence of data uncertainty does not preclude the use of the data, but rather necessitates a methodology for qualitatively or quantitively characterizing and conveying the level of uncertainty and modeling the associated uncertainty for the benefit of the end-users of the hazard model. \"Unlike industrial and other products of material processes, knowledge products do not carry with them the evidence of their own inadequacy and the most critical aspects of the quality of its products are often only testable through their indirect and sometimes remote consequences\" [20]. Our aim is to minimize the societal 'testing' of an inadequately understood susceptibility model. Although there are many factors and attributes associated with debris flow analyses which are prone to uncertainty, such as debris flow location, type, volume/size, setting, predisposing factors, triggers, etc.