The evolution of Risk from Landslides: Concepts and Applications for Communities in New Zealand

<p>The aim of this research is to identify temporal changes of risk from landsliding for several locations in New Zealand (the Western Hutt Hills, close to Wellington; Te Arai, close to Gisborne; Mt.Cook/Aoraki Village, South Island). While risk analysis usually targets a particular point in time, this research includes several five-year intervals (based on census years) starting in 1981 until 2006. The scale of this analysis is the community level. Risk is not expressed as an absolute level of loss, for example a dollar value or the number of fatalities. Risk is rather considered as the probability and extent of adverse effects on a community inferred from landsliding. As such, risk is relative: the aim is to quantify risk for a community relative to another point in time, and relative to other communities. In addition, the degree to which risk levels vary between communities is quantified. The objectives of the risk analysis are to: 1. establish landslide hazard, i.e. the frequency and magnitude of landsliding for each location, 2. develop an index of social vulnerability per census year and community, 3. develop an index of social resilience per census year and community, 4. combine 1.-3. and, together with exposure ('elements at risk'), determine risk from landsliding for each community through time.</p>

The evolution of Risk from Landslides: Concepts and Applications for Communities in New Zealand

<p>The aim of this research is to identify temporal changes of risk from landsliding for several locations in New Zealand (the Western Hutt Hills, close to Wellington; Te Arai, close to Gisborne; Mt.Cook/Aoraki Village, South Island). While risk analysis usually targets a particular point in time, this research includes several five-year intervals (based on census years) starting in 1981 until 2006. The scale of this analysis is the community level. Risk is not expressed as an absolute level of loss, for example a dollar value or the number of fatalities. Risk is rather considered as the probability and extent of adverse effects on a community inferred from landsliding. As such, risk is relative: the aim is to quantify risk for a community relative to another point in time, and relative to other communities. In addition, the degree to which risk levels vary between communities is quantified. The objectives of the risk analysis are to: 1. establish landslide hazard, i.e. the frequency and magnitude of landsliding for each location, 2. develop an index of social vulnerability per census year and community, 3. develop an index of social resilience per census year and community, 4. combine 1.-3. and, together with exposure ('elements at risk'), determine risk from landsliding for each community through time.</p>

Dynamic process and failure mechanism of rammed building structure subject to debris flow

&lt;p&gt;The failure mechanism of building structure is important for quantitatively assessing vulnerability of elements at risk, which is a critical step in risk assessment of debris flow. Scholars have recently made great processes in the researches on debris flow hazard effects and vulnerability of elements at risk. Statistical analysis methods have widely used to analyze field survey data and build vulnerability functions. Based on numerical simulation and model experiment, structural dynamic response process was analyzed to evaluate structure vulnerability. However, due to the lack of quantitative relationship between the debris flow hazard-forming mechanism and the dynamic response of building structure, it is essential to analyze the dynamic response characteristics and process of building structure subject to debris flow, which would play an important guiding role in disaster prevention and disaster mitigation.&lt;/p&gt;&lt;p&gt;Through hazard field investigation, the failure modes of rammed earth building caused by debris flow were summarized as burying, scouring and impact. Figure 1 shows the debris flow hazard in Jiende Gully, Liangshan. In addition, by using the finite element analysis method, the structure model of rammed earth building was established to simulate to the impact process of debris flow on the structure. During the dynamic failure process of rammed earth building shown in Figure 2, the failure types of building wall impacted by the debris flow mainly presented at crushed failure of the impact point, tensile failure of the inside wall and shear failure of the corner. Then debris flow destroyed the gable wall, rushed into the room, and broke the doorway, which resulted in damage of the longitudinal wall. Moreover, the response characteristics and failure mechanism of rammed earth buildings under the impact of debris flow further show that the integrity of rammed earth building is poor and the development of cracks cuts off the propagation path of stress, which effectively protects other walls. The transform-shape locations of the rammed earth building including were initially destroyed at the points of the wall foundation, corners of wall and the points impacted by big rocks of debris flow. Therefore, the reinforced measures on the locations where stress suddenly changes, such as wall foundations and wall corners should be paid more attention to protect rammed structure of buildings.&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.451b729a870062696011161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=2ed88a397ba9c221f12dfdaaa040b3d2&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot;&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.84a0a2aa870068796011161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=b7eec20b0a3f0afb5c82791d9e72d449&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot;&gt;&lt;/p&gt;

Updating of existing vulnerability curves with data from recent events in the European Alps

&lt;p&gt;Because effects of climate change and an increase in elements at risk in many mountain areas, loss increased throughout Europe. Yet, factors influencing loss, i.e. physical vulnerability of elements at risk, have gained less attention to date. Here, vulnerability is defined as the degree of loss resulting from the hazard impact on the building envelope. Recent studies have focused on evaluating vulnerability to dynamic flooding using proxies from case studies and based on empirical ex-post approaches (Papathoma-K&amp;#246;hle et al., 2011; Papathoma-K&amp;#246;hle et al., 2017; Fuchs et al., 2019a). However, the transferability of resulting vulnerability functions or curves to other case studies and, therefore, the ability of such models to actually predict future losses, is limited.&lt;/p&gt;&lt;p&gt;Existing vulnerability curves for the expression of the physical vulnerability of buildings to dynamic flooding in the alpine space are associated with a large number of uncertainties. The updating of the existing curves with data from recent events is necessary in order to make existing curves more reliable. In the present study damage data from three torrential events in Italy (Campolongo, Province of Trento, 2010; Braies, Province of Bolzano, 2017; Rotian river creek, Province of Trento, 2018) are used to update existing curves that have been developed for similar settlement types and similar hazard events in the Austrian Alps. At first a new vulnerability curve is developed only for the new study sites and is being compared with existing vulnerability curves in the Austrian Alps. As a second step the new data are fed to the existing vulnerability models (Fuchs et al., 2019b) in order to update them. Preliminary results are presented.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References&lt;/p&gt;&lt;p&gt;Fuchs, S., Keiler, M., Ortlepp, R., Schinke, R., and Papathoma-K&amp;#246;hle, M.: Recent advances in vulnerability assessment for the built environment exposed to torrential hazards: challenges and the way forward, Journal of Hydrology, 575, 587-595, https://doi.org/10.1016/j.jhydrol.2019.05.067, 2019a.&lt;/p&gt;&lt;p&gt;Fuchs, S., Heiser, M., Schl&amp;#246;gl, M., Zischg, A., Papathoma-K&amp;#246;hle, M., and Keiler, M.: Short communication: A model to predict flood loss in mountain areas, Environmental Modelling and Software, 117, 176-180, https://doi.org/10.1016/j.envsoft.2019.03.026, 2019b.&lt;/p&gt;&lt;p&gt;Papathoma-K&amp;#246;hle, M., Kappes, M., Keiler, M., and Glade, T.: Physical vulnerability assessment for alpine hazards: state of the art and future needs, Natural Hazards, 58, 645-680, https://doi.org/10.1007/s11069-010-9632-4, 2011.&lt;/p&gt;&lt;p&gt;Papathoma-K&amp;#246;hle, M., Gems, B., Sturm, M., and Fuchs, S.: Matrices, curves and indicators: a review of approaches to assess physical vulnerability to debris flows, Earth-Science Reviews, 171, 272-288, https://doi.org/10.1016/j.earscirev.2017.06.007, 2017.&lt;/p&gt;

Semi-automated regional classification of slow rock slope deformations integrating kinematics, activity and spatial complexity

&lt;p&gt;Slow rock slope deformations are widespread in alpine environments. They affect giant volumes and evolve over thousands of years by progressive failure, resulting in long-term slow movements threatening infrastructures and potential evolution into massive collapses. In the alpine sector of Lombardia (Italian Central Alps), 208 mapped slow rock slope deformations affect a total area exceeding 580 km&lt;sup&gt;2&lt;/sup&gt; and interact with a variety of elements at risk including settlements, hydroelectric facilities and lifelines characterized by different vulnerability to both slow and progressive deformations. In this context, a systematic, reliable and cost-effective approach is required to classify slow rock slope deformations on the regional scale for landplanning, prioritization and analysis of interactions with elements at risk, depending on their style of activity, including not only mean deformation rate, but also their kinematics and spatial complexity. In this work, we implemented a toolbox that integrates different approaches to classify a large dataset of slow rock slope deformations in discrete groups, according to the deformation style and morpho-structural expression of individuals, mapped on regional scale and characterized through remote sensing techniques. The landslide dataset used in this study was obtained by a &amp;#8220;semi-detail&amp;#8221; geomorphological and morpho-structural mapping on aerial imagery and DEM, performed on regional scale yet including local-scale information (e.g. tectonic lineaments, morpho-structures, landforms, nested deep-seated landslides) and a full set of geological and morphometric attributes. To characterize landslide activity, we use Persistent-Scatterer Interferometry (PSI) data, including PS-InSAR&lt;sup&gt;TM&lt;/sup&gt; and SqueeSAR&lt;sup&gt;TM&lt;/sup&gt; acquired by different sensors (ERS, Radarsat, Sentinel 1A/B) over different time periods from 1992 to 2017. Since Line-of-Sight velocity of point like data can hamper a correct evaluation of both landslide kinematics and deformation rates, for each phenomenon we automatically selected the most complete PSI datasets. From these, through a 2DSAR decomposition procedure, we derived 2D velocity components and computed the magnitude and orientation of the 2D total displacement vector T. &amp;#160;We then applied a supervised machine learning procedure to automatically classify the kinematics of each landslide (i.e. translational, roto-translational, rotational) depending on the statistical distribution of the T vector orientation. As the evaluation of a representative landslide mean deformation rate is strongly affected by spatial heterogeneity and landslide mass segmentation, we implemented an original peak analysis of the velocity distribution in each landslide to calculate a modal velocity of the main body and automatically outline nested sectors with differential displacement rates. Finally, we classified landslides in types, representative of different styles of activity and potential interaction with elements at risk, by combining PSI analysis results with geological, morpho-structural and morphometric variables in a multivariate statistical analysis framework including sequential Principal Component and K-medoids Cluster Analysis. The entire analysis workflow runs in a semi-automated way through a set of GIS and Matlab&lt;sup&gt;TM&lt;/sup&gt; tools. Our procedure can be applied to different large landslide datasets, providing a fast and cost-effective support to landslide classification, risk analysis, landplanning and prioritization of local-scale studies aimed at granting safety and infrastructure integrity.&lt;/p&gt;

AN INDICATOR-BASED APPROACH FOR MICRO-SCALE PHYSICAL FLOOD VULNERABILITY MAPPING

Map representation of vulnerability is a crucial step in evaluating flood impact and all vulnerability indicators that are the final product of risk assessment. So far, in flood risk assessment, this is probably the weakest link. Flood risk mapping suffers from inequality in the level of development in presenting the different components: where exposure and hazard modelling and mapping is well developed and advanced, while vulnerability analysis and mapping are underdeveloped. Therefore, the objective of this paper is to discuss a newly developed GIS-based approach on micro-scale flood vulnerability mapping of physical elements at risk using an indicator-based method. Micro-scale flood vulnerability is used to eliminate flood vulnerability in an area with a high probability of occurrences. The approach is suitable for cost-benefit analysis of structures protection measures. At micro-scale flood vulnerability mapping, it is more suitable to adopt indicator-based vulnerability assessment methods. Because it provides an opportunity for incorporating all the factors and characteristics of elements at risk that contribute to generating their flood vulnerability. Likewise, a considerable amount of studies argue that vulnerability assessment and its representation on maps should focus on the identification of variables that influence the vulnerability of an element at risk. Flood vulnerability mapping at micro-scale provides critical information for the decision-makers on why specific infrastructures are susceptible more than the others. Moreover, assessing and managing flood risk is crucial in order to reduce the loss and adapt to the combined effects of rapid urbanization and climate changes.

An Evaluation of Tsunami Hazard Modeling in Gunungkidul Coastal Area using UAV Photogrammetry and GIS. Case Study: Drini Coastal Area

In recent years, Tourism activities in Gunungkidul Coastal Area rapidly increased. A large number of tourists visiting the coast considered as elements at risk that are exposed to tsunami hazards. Disaster infrastructures provided by the government e.g. hazard maps, evacuation routes, and locations for assembly points are inadequate. The tsunami inundation models provided by the government are based on national topographic maps (RBI), resulting in inaccurate models. DEM generation using UAV Photogrammetry produces high spatial resolution data that results in more accurate tsunami inundation model. The results of the model using UAV photogrammetry are also capable of producing several inundation scenarios and determine the safe areas that can be used for temporary evacuation sites. The use of UAV photogrammetry for tsunami inundation models provides many advantages including low cost and accurate model results. Evaluation of hazard maps and assembly points using UAV Photogrammetry modeling lead to more effective and less time-consuming on the evacuation process.

Upgrading of an index-oriented methodology for consequence analysis of natural hazards: application to the Upper Guil catchment (southern French Alps)

Vulnerability is a complex concept involving a variety of disciplines from both the physical and socio-economic sciences. Currently, two opposite trends exist: the physical approach in which vulnerability is analysed as a sum of potential impacts on elements at risk and the social approach in which vulnerability is mostly viewed as a combination of socio-economic variables determining people's ability to anticipate, cope with and recover from a catastrophic event. Finding a way to combine these two approaches is a key issue for a global vulnerability assessment. In this paper we propose to combine elements from these two approaches through the analysis of the potential consequences of a high-magnitude flood event (recurrence interval (RI) > 100 years) on human and material stakeholders. To perform our analysis, we choose to upgrade an existing index, the Potential Damage Index (PDI; Puissant et al., 2013), by including social criteria. The PDI was originally developed to assess the physical consequences of hazards on the elements at risk (people, building and lands). It is based on the calculation of three sub-indices representing different types of direct and indirect consequences: physical injury consequences (PIC), structural and functional consequences (SFC), indirect functional consequences (IC). Here, we propose to add a fourth sub-index representing the social consequences. This new sub-index, called social consequences (SC) is obtained by combining criteria derived from INSEE French census data and a risk-perception survey conducted in the field. By combining the four indices (PIC, SFC, IC and SC), we managed to create a new index called the Potential Consequences Index (PCI). The new PCI was tested on the Upper Guil catchment to assess the consequences of a high-magnitude flood event (RI > 100 years). Results of the PDI were compared with the PCI and show significant differences. The upgrade to the PDI method provided us with many inputs. The introduction of elements from social vulnerability added an extra dimension to the total consequence map. It allowed us to qualify the potential physical consequences (physical injury, structural and functional consequences) on elements at risk by considering the global resilience of local communities.