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Road structures undergo a series of chemical and physical processes once they are put into service. This phenomenon results from the action of the load and the influence of the environment, which causes their progressive deterioration. In order to mitigate the risk of progressive deterioration and guarantee their stability and durability, various maintenance tasks are required, including visual inspections. The Intelligent Bridge Management System of Colombia (SIGP) includes visual inspection as one of its modules. The system has been designed based on state-of-the-art criteria and national experience with relevant damages and bridge collapses. This paper presents the visual inspection methodology, which includes several stages such as a classification scale, condition index, evaluation areas, damage catalog, and evaluation criteria. In addition, a digital application has been developed to facilitate real-time data collection during field inspections using mobile devices, which can be uploaded directly to the system database hosted in the cloud. The results from the inspection of bridges of different typologies and years of construction are presented, as well as general inspection results from 150 bridges in Colombia. The relevance, comprehensiveness, and accuracy of the inspection are supported by a damage catalog, which allows the identification of intervention needs and reduces the bias of the collected data. [ABSTRACT FROM AUTHOR]

This paper explores the scope for upscaling and transnational transfer of participatory landslide risk management strategies for informal settlements in Latin America. Drawing on lessons from transdisciplinary action-research in three neighbourhoods in Medellín, Colombia, and one in São Paulo, Brazil, the paper discusses how bottom-up approaches were developed to co-produce landslide risk management in both Global South cities, in a way that optimises the collaboration between communities and relevant governmental bodies. The analysis focuses on mitigation and 'agreement-seeking', from the perspectives of scale, power and knowledge, which help understand the parallels between co-production of landslide risk management and co-production of urban services. Two key conclusions are that landslide risk management should be built into neighbourhood upgrading and management, and that both community and the state have stronger roles to play in landslide risk management from their respective capacities. The paper also reflects on the role of academia in enabling co-production of landslide risk management through engaging with local communities. [ABSTRACT FROM AUTHOR]

Estimating the likelihood of landslides has proven to be critical for development and protection of infrastructure (e.g. pipelines, roads) and urban settlements. Currently, for regional studies of landslide susceptibility only qualitative or statistical evaluations are possible due to the large spatial variability of geological properties, topography, rainfall patterns, etc. In this paper, we explore an alternative to these approaches using data-driven methodologies to determine landslide susceptibility. We give special attention to the use of geographical information systems, machine learning and statistical techniques to build landslide susceptibility maps. These methods have input as fourteen key causative factors that might influence landslides occurrence. Additionally, feature extraction and feature selection are performed to evaluate if dimensionality reduction increases the prediction accuracy of the machine learning models. The models were compared using a case study in the Eastern Cordillera of Colombia, where the best performing model achieved a predictive performance of 93.07 % . [ABSTRACT FROM AUTHOR]

Introduction-- In Colombia, periods of intense rainfall are frequently associated with landslides. Since these processes are one of the phenomena that cause the most economic losses worldwide, it is necessary to study and model rainfall to establish its relationship with instability, using GIS tools and specialized software for this purpose. Objective-- Model the precipitation corresponding to the period from 2010 to 2019, for the municipality of Santa María in the department of Boyacá, in order to determine the effect of rainfall as a trigger of the mass movements presented during that time. The object of the study focused on the generation of the hydrological model of rainfall between the years 2010-2019, for the municipality of Santa María, to determine the effect of rain as a trigger for the mass movements presented during that time. Methodology-- Rainfall can facilitate the displacement of material, either by reducing resistance or increasing pore pressure. This paper presents a rainfall model developed from GIS tools, a quantitative multitemporal analysis and a probabilistic distribution method of the precipitation triggering factor, to determine periods of exceedance and non-exceedance of the rainfall threshold. Results-- From the rainfall models for the period 2010-2019 and their correlation with the mass movement inventory, the relationship between the rainfall trigger and landslides could be evidenced. Conclusions-- It is evident within the study area the incidence of precipitation and climatic factors in the increased possibility of occurrence of landslides. The model generated can be an input for the generation of the hazard map and municipal risk management. [ABSTRACT FROM AUTHOR]

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 landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed. [ABSTRACT FROM AUTHOR]

Landslides are often deadly natural events. Steep slopes and certain loose soil types are predestined areas for them. Moreover, in the context of climate change, extreme weather events such as heavy rainfall, which often trigger landslides, are becoming even more likely. While all this is well known, it, therefore, stands to reason that this knowledge will lead to the avoidance of these risks. On the other hand, however, there are highly dynamic urbanization processes that often overtake formal urban planning processes by rising population figures and areal expansion. In the course of these processes, economically deprived population groups often have no other option than to informally build on high-risk areas. Against these backgrounds, we systematically examine in this study how these risks develop over a 24-year period from 1994 to 2018 taking into account three time steps, with respect to the citywide exposure and in particular with respect to different social groups. For this purpose, we use heterogeneous input data from remote sensing, landslide hazard maps, and census data. Our case study is the city of Medellín in Colombia. We develop and apply a set of methods integrating the heterogenous data sets to map, quantify and monitor exposure and social vulnerability at a fine spatial granularity. Our results document first of all the highly dynamic growth in total population and urban areas. However, our results reveal that the city's expansion is socially unevenly distributed. People of higher vulnerability proxied by informal settlements are found to settle in considerably higher shares of areas exposed to landslides. This study proposes a methodological setup that allows for monitoring exposure and social vulnerability over long time spans at a fine spatial resolution, allows to bring inequality into the spotlight, and provides decision-makers with better information to develop socially responsible policies. [ABSTRACT FROM AUTHOR]

Landslides typify one of the most hazardous natural phenomena fostering economic and even human losses worldwide. Several countries like Colombia, in South America, are hotspots for fatal landslides. In this contribution, we thoroughly reviewed four available databases, articles, grey literature and web resources, in order to build up a new catalogue of fatal landslides in Colombia. We gathered a catalogue of 2351 individual fatal landslides which caused about 37,959 deaths. Of these, we found 11 fatal events in historical times (pre-twentieth century). In modern times (1912–2020), we analysed landslides' spatial and temporal distribution, finding that in central-western Colombia, particularly in the departments of Caldas, Risaralda, Quindío and Antioquia, these kinds of events are more frequent. Upward trends in these areas and a nationwide increase in the number of events in the last 20 years suggest that fatal landslides are far from being effectively mitigated. Our findings also show a strong correlation between the climate variability phenomenon known as El Niño Southern Oscillation (ENSO) and fatal landslides, particularly during those years when strong La Niña (cold phase of ENSO) events occur. Despite rainfall being the most common trigger for fatal landslides, we observed an increasing trend in anthropogenically related events in the last decade. Finally, we obtained multiple socio-economic indices and ran a statistical analysis at the departmental level in order to assess whether impoverished and vulnerable people are more affected by fatal landslides. We propose that in most cases, departments with low income, high levels of corruption and inequality are usually more affected. [ABSTRACT FROM AUTHOR]

In tropical regions, deep convective systems are associated with extreme rainfall storms that usually detonate flash floods and landslides in the Andean Colombian region. Several studies have used satellite data to address the structure and formation of tropical convective storms. However, there is a local gap in the characterization, which is essential for a better understanding of flash floods and preparedness, filling a gap in a region with scarce information regarding extreme events. In this work, we assess the deep convective storms in a mountainous region of Colombia using meteorological radar observations between 2014 and 2017. We start by identifying convective and stratiform formations. We refine the convective identification by classifying convective systems into enveloped (contained in a stratiform system) and unenveloped (not contained). Then, we analyze the systems' temporal and spatial distributions and contrast them with the watersheds' features. According to our results, unenveloped convective systems have higher reflectivity and hence higher rainfall intensities. Moreover, they also have a well-defined spatial and temporal distribution and are likely to occur in watersheds with elevation gradients of around 2000 m and an aspect contrary to the wind direction. Our assessment of the convective storms is of significant value for the hydrologic community working on flash floods. Moreover, the spatiotemporal description is highly relevant for stakeholders and future local analysis. [ABSTRACT FROM AUTHOR]

An increasing frequency of extreme atmospheric events is challenging our basic knowledge about the resilience mechanisms that mediate the response of small mountainous watersheds (SMW) to landslides, including production of water-derived ecosystem services (WES). We hypothesized that the demand for WES increases the connectivity between lowland and upland regions, and decreases the heterogeneity of SMW. Focusing on four watersheds in the Central Andes of Colombia and combining "site-specific knowledge", historic land cover maps (1970s and 1980s), and open, analysis-ready remotely sensed data (GLAD Landsat ARD; 1990–2000), we addressed three questions. Over roughly 120 years, the site-specific data revealed an increasing demand for diverse WES, as well as variation among the watersheds in the supply of WES. At watershed-scales, variation in the water balances—a surrogate for water-derived ES flows—exhibited complex relationships with forest cover. Fractional forest cover (pi) and forest aggregation (AIi) varied between the historic and current data sets, but in general showed non-linear relationships with elevation and slope. In the current data set (1990–2000), differences in the number of significant, linear models explaining variation in pi with time, suggest that slope may play a more important role than elevation in land cover change. We found ample evidence for a combined effect of slope and elevation on the two land cover metrics, which would be consistent with strategies directed to mitigate site-specific landslide-associated risks. Overall, our work shows strong feedbacks between lowland and upland areas, raising questions about the sustainable production of WES. [ABSTRACT FROM AUTHOR]

Landslides are natural hazards that represent a huge economic burden and cause the loss of human life around the world. In countries such as Colombia, the mass movement events that cause the highest number of deaths and economic losses are often related to river or stream flooding caused by landslides in basins. Therefore, it is necessary to develop tools that estimate and assess landslide risk in such areas. This study presents a methodology to assess the risk associated with landslides in streams or river basins. The hazard posed by landslides is evaluated considering probabilistic methods that include the effects of rainfall and earthquakes. In addition, this study assesses the probability of a sliding mass reaching riverbeds and the probability of riverbed obstruction. Vulnerability is then estimated using impact curves based on the obstruction height. Finally, risk is estimated as the probability that economic losses occur along the riverbed. This methodology is based on probability methods, such as the first-order second-moment (FOSM) method, and the punctual estimates method (PEM). The methodology was applied in the La Liboriana River basin, in the municipality of Salgar in the northwestern Colombian Andes. On May 18, 2015, this mountainous and tropical area suffered a flash flood caused by landslides in the basin, which killed more than 100 inhabitants and caused infrastructure damage and significant economic losses. The results suggest that the proposed method coherently assesses the hazard posed by landslides and that the expected losses are comparable with the records from previous events. [ABSTRACT FROM AUTHOR]