Abstract. Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in high-hazard areas increases the associated risks. Early warning systems (EWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study aims to develop a cost-effective method for low-cost and easy-to-use EWS instrumentalization in landslide-prone areas identified based on data-driven methods. We exemplify this approach in the landslide-prone city of Medellín, Colombia. We introduce a workflow to enable decision-makers to balance financial costs and the potential to protect exposed populations. To achieve this, we first mapped city-level landslide susceptibility using data on hazard levels, landslide inventories, geological and topographic factors using a random-forest model. We then combine the landslide susceptibility map with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of EWS-monitoring sensors at the selected sites, using lessons learned from a pilot EWS in Bello Oriente, a neighbourhood in Medellín. Our study estimates that EWS monitoring sensors could be installed in several landslide-prone areas in the city of Medellín with a budget ranging from €5 to €41 per person (roughly COP 23,000 to 209,000), improving the resilience over 190,000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, they are a social group of very high vulnerability. We provide recommendations for stakeholders on where to proceed with EWS instrumentalization based on five different cost-effective scenarios. Finally, we discuss the limitations, challenges, and opportunities for the successful implementation of an EWS. This approach enables decision-makers to prioritize EWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods.
Abstract. Due to climate change and growing urbanization, fatalities from landslides are rising worldwide, and thus solutions for people at risk are needed. This is especially the case for the Andean cities which are often expanding into the steep slopes surrounding them. In Medellín, Colombia, a combination of landslide-prone dunite rock and steep slopes in the east of the city creates a high-hazard scenario for about 87,000 residents, most of whom live in informal settlements. We developed a landslide early warning system (LEWS) which can be applied in such semi-urban situations. The LEWS consists mainly of a measurement system of horizontal and vertical sensor lines across the slope and autonomous point-sensors in between these lines. All parts of the LEWS, from hazard assessment to the monitoring system and the reaction capacity, are supported by extensive activities together with the local community to gain trust and create synergies. This also includes local authorities, agencies and NGO's. To test such a system, a prototype has been installed in a neighborhood in Medellín in 2020–2022. The experiences of this installation resulted in a framework for LEWS's of this kind which we have compiled on a wiki-page to facilitate replication by people in other parts of the world. Hopefully, this can stimulate a lively exchange between researchers and other stakeholders who want to use, modify and replicate our system.
Fatalities from landslides are rising worldwide, especially in cities in mountainous regions, which often expand into the steep slopes surrounding them. For residents, often those living in poor neighborhoods and informal settlements, integrated landslide early warning systems (LEWS) can be a viable solution, if they are affordable and easily replicable. We developed a LEWS in Medellín, Colombia, which can be applied in such semi-urban situations. All the components of the LEWS, from hazard and risk assessment, to the monitoring system and the reaction capacity, were developed with and supported by all local stakeholders, including local authorities, agencies, NGO’s, and especially the local community, in order to build trust. It was well integrated into the social structure of the neighborhood, while still delivering precise and dense deformation and trigger measurements. A prototype was built and installed in a neighborhood in Medellín in 2022, comprising a dense network of line and point measurements and gateways. The first data from the measurement system are now available and allow us to define initial thresholds, while more data are being collected to allow for automatic early warning in the future. All the newly developed knowledge, from sensor hardware and software to installation manuals, has been compiled on a wiki-page, to facilitate replication by people in other parts of the world. According to our experience of the installation, we give recommendations for the implementation of LEWSs in similar areas, which can hopefully stimulate a lively exchange between researchers and other stakeholders who want to use, modify, and replicate our system.
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