ALBERT

Description: The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1,230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns resulting in different landslide types. In this study, we focus on modelling the spatial pattern of landslide susceptibility based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, we investigate the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models, on the susceptibility model performances using a pixel-based logistic regression approach. Topographic data is extracted from different the digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereo-photogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs generally outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. On the regional level we investigate the effect of distinguishing landslide types between shallow and deep-seated landslides. The separation of landslide types allows to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as topographic wetness, tangent curvature and total rainfall depth. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.

Description: The inhabited zone of the Ugandan Rwenzori Mountains is affected by landslides, frequently causing loss of life, damage to infrastructure and loss of livelihood. This area of ca. 1230 km2 is characterized by contrasting geomorphologic, climatic and lithological patterns, resulting in different landslide types. In this study, the spatial pattern of landslide susceptibility is investigated based on an extensive field inventory constructed for five representative areas within the region (153 km2) and containing over 450 landslides. To achieve a reliable susceptibility assessment, the effects of (1) using different topographic data sources and spatial resolutions and (2) changing the scale of assessment by comparing local and regional susceptibility models on the susceptibility model performances are investigated using a pixel-based logistic regression approach. Topographic data are extracted from different digital elevation models (DEMs) based on radar interferometry (SRTM and TanDEM-X) and optical stereophotogrammetry (ASTER DEM). Susceptibility models using the radar-based DEMs tend to outperform the ones using the ASTER DEM. The model spatial resolution is varied between 10, 20, 30 and 90 m. The optimal resolution depends on the location of the investigated area within the region but the lowest model resolution (90 m) rarely yields the best model performances while the highest model resolution (10 m) never results in significant increases in performance compared to the 20 m resolution. Models built for the local case studies generally have similar or better performances than the regional model and better reflect site-specific controlling factors. At the regional level the effect of distinguishing landslide types between shallow and deep-seated landslides is investigated. The separation of landslide types allows us to improve model performances for the prediction of deep-seated landslides and to better understand factors influencing the occurrence of shallow landslides such as tangent curvature and total rainfall. Finally, the landslide susceptibility assessment is overlaid with a population density map in order to identify potential landslide risk hotspots, which could direct research and policy action towards reduced landslide risk in this under-researched, landslide-prone region.

Description: Development of hazard maps is one of the measures promoted by the international community to reduce risk. Hazard maps provide information about the probability of given areas to be affected by one or several hazards. As such they are useful tools to evaluate risk and support the development of safe policies. So far studies combining hazard mapping with accessibility to services are few. In hazardous environments, accessibility of the population to strategic infrastructure is important because emergency services and goods will principally be offered at or provided from these locations. If a road segment is blocked by a hazard, accessibility to services may be affected, or worse, people may be completely disconnected from specific services. The importance of each road segment in the transport network as a connecting element enabling access to relevant services is therefore critical information for the authorities. In this study, we propose a new application of hazard mapping which aims to define the importance of each road segment in the accessibility to services, taking in account the probability of being affected by a hazard. By iteratively removing one segment after the other from the road network, changes in accessibility to critical infrastructure are evaluated. Two metrics of road segment importance considering the population affected and the hazard probability are calculated for each segment: a road accessibility risk metric and a users' path vulnerability metric. Visualization of these road metrics is a useful way of valuing hazard maps and may help to support discussions about the development of new infrastructure, road capacity increase and maintenance of existing infrastructures, and evacuation procedures.

Description: Development of hazard maps is one of the measures promoted by the international community to reduce risk. Hazard maps provide information about the probability of given areas to be affected by one or several hazards. As such they are useful tools to evaluate risk and support the development of safe policies. So far studies combining hazard mapping with accessibility to services are few. In hazardous environments, accessibility of the population to strategic infrastructure is important because emergency services and goods will principally be offered at or provided from these locations. If a road segment is blocked by a hazard, accessibility to services may be affected, or worse, people may be completely disconnected from specific services. The importance of each road segment in the transport network as a connecting element enabling access to relevant services is therefore critical information for the authorities. In this study, we propose a new application of hazard mapping which aims to define the importance of each road segment in the accessibility to services, taking into account the probability of being affected by a hazard. By iteratively removing one segment after the other from the road network, changes in accessibility to critical infrastructure are evaluated. Two metrics of road segment importance considering the population affected and the hazard probability are calculated for each segment: a road accessibility risk metric and a users' path vulnerability metric. Visualization of these road metrics is a useful way of valuing hazard maps and may help to support discussions about the development of new infrastructure, road capacity increase and maintenance of existing infrastructures, and evacuation procedures.