Debris flows, and landslides in general, are worldwide catastrophic phenomena. As world population and urbanization grow in magnitude and geographic coverage, the need exists to extend focus, research, and modeling to a continental and global scale. Although debris flow behavior and parameters are local phenomena, sound generalizations can be applied to debris flow susceptibility analyses at larger geographic extents based on these criteria. The focus of this research is to develop a global debris flow susceptibility map by modeling at both a continental scale for all continents and by a single global model and determine whether a global model adequately represents each continent. Probability Density, Conditional Probability, Certainty Factor, Frequency Ratio, and Maximum Entropy statistical models were developed and evaluated for best model performance using fourteen environmental factors generally accepted as the most appropriate debris flow predisposing factors. Global models and models for each continent were then developed and evaluated against verification data. The comparative analysis demonstrates that a single global model performs comparably or better than individual continental models for a majority of the continents, resulting in a debris flow susceptibility map of the world useful for in-depth research, international planning, and future debris flow susceptibility modeling and determining societal impacts.

This paper presents a multidisciplinary approach using modern technologies for the analysis and modelling of the debris flow that occurred at Torrent Rochefort (Aosta Valley—Italy) September 2015. A detailed on-site geological and geomorphological study was performed to highlight the main characteristics of the basin, useful for validating and calibrating dynamic simulations. The total mobilized volume was estimated by comparing a pre-event DTM and a post-event DTM generated from an unmanned aerial vehicle. A digital terrain model comparative analysis provided a quantitative estimation of erodible depths in different sectors of the Rochefort basin. Numerical modelling of the event was performed using the continuum mechanics-based code RASH3D that enabled a simulation of the dynamic debris motion on complex topography. The results demonstrate the importance of a detailed geomorphological study for the validation and calibration of numerical results. Finally, some considerations were inferred about the magnitude of unstable debris and the possible consequences on local infrastructures.

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.

<p>There are many types and degrees of uncertainty associated with spatial data and processes. </p><p>There are many factors and attributes associated with debris flow analyses which are prone to uncertainty.  For simplicity, in this presentation, only two attributes of debris flow events are investigated along with the impact of their uncertainty on the determination of environmental predisposing factors.    These two attributes, critical to debris flow susceptibility analyses, are landslide classification and event location.  The associated predisposing factors studied herein are lithology, soils, climate, ecophysiographic units, topography, hydrology, and tectonics.</p><p>In a landslide susceptibility analysis, landslide event location accuracy is paramount yet often inaccurately known.  Landslide inventories are often constructed based on mapping from aerial imagery, media reports, and field work by third party sources; and in a data-driven approach to debris flow susceptibility analysis the landslide type is important in modeling the relevant predisposing factors distinctive to each landslide type. </p><p>In a study of global debris flow susceptibility an analysis of the impact between known location and a location accuracy offset, and landslide categorization uncertainty demonstrates the impact of uncertainty in defining the appropriate predisposing factors associated with debris flows.</p><p>This analysis is part of a larger debris flow global susceptibility determination which trains on known debris flow events and the predisposing factors associated with them to identify potential areas that may be susceptible to debris flows.  This study looks at the impact/differences that mis-categorization or location uncertainty have on the determination of predisposing factors, along with methods of conveying uncertainty information. </p>

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. Five 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 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 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 of 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.