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Fig. 5. Presentation of the study area indicating the principal settlements and catchments (in blue letters).

Figure 5 Presentation of the study area indicating the principal settlements and catchments (in blue letters).

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Provided for non-commercial research and education use. Not for reproduction, distribution or commercial use.
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Provided for non-commercial research and education use. Not for reproduction, distribution or commercial use.
Fig. 1. Coupled consideration of slope angle and upslope area for debris flow modeling (Horton et al., 2008). Below an upslope area of 2.5 km?, the slope angle to start the movement is rising with decreasing area, while above 2.5 km/ it is assumed to be con- stant at 15°. Reasons for this approach are the difference in water and sediment availability.
Fig. 1. Coupled consideration of slope angle and upslope area for debris flow modeling (Horton et al., 2008). Below an upslope area of 2.5 km?, the slope angle to start the movement is rising with decreasing area, while above 2.5 km/ it is assumed to be con- stant at 15°. Reasons for this approach are the difference in water and sediment availability.
Confusion matrix according to Begueria (2006). Either the area [m7] or the area propor- tion [%] can be depicted in the cells.
Confusion matrix according to Begueria (2006). Either the area [m7] or the area propor- tion [%] can be depicted in the cells.
Fig. 2. Flow chart of the analysis scheme for rock fall, shallow landslides, debris flows, avalanches, and floods. On the basis of the DEM and optionally land use and lithology (in dar! gray boxes on the left side), a multitude of derivatives (such as slope, planar curvature or flow accumulation; medium gray boxes) are computed. They form the input for the model. (light gray boxes with rounded edges) by means of which first the source areas are identified and second the runout is modeled (dashed lines).
Fig. 2. Flow chart of the analysis scheme for rock fall, shallow landslides, debris flows, avalanches, and floods. On the basis of the DEM and optionally land use and lithology (in dar! gray boxes on the left side), a multitude of derivatives (such as slope, planar curvature or flow accumulation; medium gray boxes) are computed. They form the input for the model. (light gray boxes with rounded edges) by means of which first the source areas are identified and second the runout is modeled (dashed lines).
Fig. 3. Flow chart of the MultiRISK modeling tool. Because of the large number of single steps (Fig. 2) and the time- consuming and error-prone performance of a multihazard analysis, the whole procedure was automated in the software called MultiRISK Modeling Tool. MultiRISK is programmed in Python accessing ArcGIS 9.x toolboxes and offers a graphical user interface for the straightfor- ward operation. The single models are implemented either by activa- tion of external software (e.g., the Matlab-programmed stand-alone software Flow-R), direct inclusion (e.g., the ArcGIS toolbox Flood- Area) or programming in Python on the basis of ArcGIS tools of the ArcToolbox (e.g., the source identification method after Maggioni, 2004). The user is guided through the single steps of the three main components of the modeling tool (Fig. 3): the process modeling, the process model validation, and the exposure analysis. If for one or sev- eral processes an analysis has already been carried out, the tool offers the upload of this project and the subsequent performance of any of the three further steps (see bended arrows in Fig. 3). After having fin- ished the multihazard exposure analysis, the preparation of the Mul- tiRISK visualization can be directly launched to view the results thereafter.
Fig. 3. Flow chart of the MultiRISK modeling tool. Because of the large number of single steps (Fig. 2) and the time- consuming and error-prone performance of a multihazard analysis, the whole procedure was automated in the software called MultiRISK Modeling Tool. MultiRISK is programmed in Python accessing ArcGIS 9.x toolboxes and offers a graphical user interface for the straightfor- ward operation. The single models are implemented either by activa- tion of external software (e.g., the Matlab-programmed stand-alone software Flow-R), direct inclusion (e.g., the ArcGIS toolbox Flood- Area) or programming in Python on the basis of ArcGIS tools of the ArcToolbox (e.g., the source identification method after Maggioni, 2004). The user is guided through the single steps of the three main components of the modeling tool (Fig. 3): the process modeling, the process model validation, and the exposure analysis. If for one or sev- eral processes an analysis has already been carried out, the tool offers the upload of this project and the subsequent performance of any of the three further steps (see bended arrows in Fig. 3). After having fin- ished the multihazard exposure analysis, the preparation of the Mul- tiRISK visualization can be directly launched to view the results thereafter.
Fig. 4. Screenshot of the visualization tool. The following descriptions refer to the red numbers in the graphic: (1) Layers tree, managed by the user. According to predefined options layers can be switched on, off, overlain, etc. (2) Tabs for query, printing, and online guide. (3) Map area and key map visualization. (4) Tools for cartographic interaction as zoom in/ out, spatial query, etc. (5) Scale and map size customization. (6) Tabs to access the different interactive maps.
Fig. 4. Screenshot of the visualization tool. The following descriptions refer to the red numbers in the graphic: (1) Layers tree, managed by the user. According to predefined options layers can be switched on, off, overlain, etc. (2) Tabs for query, printing, and online guide. (3) Map area and key map visualization. (4) Tools for cartographic interaction as zoom in/ out, spatial query, etc. (5) Scale and map size customization. (6) Tabs to access the different interactive maps.
Summary of the parameters to be defined in the software for the modeling of the different hazards and the selection made for the case study. Table 2
Summary of the parameters to be defined in the software for the modeling of the different hazards and the selection made for the case study. Table 2
Overview over the analysis results for the processes debris flows (DF), rock fall (RF), shallow landslides (SL), avalanches (AV) and floods (FL) (TP — true positives, FP — false negatives, FN — false negatives, and TN — true negatives).
Overview over the analysis results for the processes debris flows (DF), rock fall (RF), shallow landslides (SL), avalanches (AV) and floods (FL) (TP — true positives, FP — false negatives, FN — false negatives, and TN — true negatives).
Fig. 6. Examples for the presentation of the hazard modeling results in the MultiRISK visualization tool (note: the brown colors in the lower graphic refer to the number of hazards in each pixel).
Fig. 6. Examples for the presentation of the hazard modeling results in the MultiRISK visualization tool (note: the brown colors in the lower graphic refer to the number of hazards in each pixel).
Presentation of the proportions of TP, FN, and FP as well as the quality indicators sen- sitivity (SY) and positive prediction power (PPP) of the modeling results. Table 4
Presentation of the proportions of TP, FN, and FP as well as the quality indicators sen- sitivity (SY) and positive prediction power (PPP) of the modeling results. Table 4
Fig. 7. Maps of the validation result. Summarizing, the analysis of the Barcelonnette case study using the software tool MultiRISK proved much more comfortable, user- friendly, and much less error-prone than the separate modeling of all single steps; previous attempts to step-by-step perform the analy- sis enabled the authors to come to this conclusion. By means of a lit- erature review complemented by advice from experts and statistical analyses, a worst-case parameterization followed by hazard model validation and exposure assessment could be carried out. Thus, it can be concluded that the MultiRISK tool leads to a higher efficiency in susceptibility and exposure modeling for multihazards so far. How- ever, due to the partly very low PPP values, which stands for a high overestimation of the modeling result, the effectiveness in the case study shows quite some potential for optimization. Nevertheless, a considerable advantage is that long time- and resource-consuming data acquisition has been avoided. However, the applicability and usefulness of such an approach for a very first approximation of an unknown area has to be examined in future studies together with stakeholders.
Fig. 7. Maps of the validation result. Summarizing, the analysis of the Barcelonnette case study using the software tool MultiRISK proved much more comfortable, user- friendly, and much less error-prone than the separate modeling of all single steps; previous attempts to step-by-step perform the analy- sis enabled the authors to come to this conclusion. By means of a lit- erature review complemented by advice from experts and statistical analyses, a worst-case parameterization followed by hazard model validation and exposure assessment could be carried out. Thus, it can be concluded that the MultiRISK tool leads to a higher efficiency in susceptibility and exposure modeling for multihazards so far. How- ever, due to the partly very low PPP values, which stands for a high overestimation of the modeling result, the effectiveness in the case study shows quite some potential for optimization. Nevertheless, a considerable advantage is that long time- and resource-consuming data acquisition has been avoided. However, the applicability and usefulness of such an approach for a very first approximation of an unknown area has to be examined in future studies together with stakeholders.
M.S. Kappes et al. / Geomorphology 151-152 (2012) 139-155 Fig. 8. Screenshot of the visualization of exposed elements in the MultiRISK visualization tool — exposure to debris flows
M.S. Kappes et al. / Geomorphology 151-152 (2012) 139-155 Fig. 8. Screenshot of the visualization of exposed elements in the MultiRISK visualization tool — exposure to debris flows
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