Rehabitar la Montaña: strategies and processes for sustainable communities in the mountainous periphery of Medellín
Thousands of poor, marginal communities’ residents die due to landslide events every year. The quick urbanization of mountainous areas, combined with an increased frequency of landslide events induced by heavy rains due to climate change, is leading to increased deaths year after year, and the problem will only get worse. Landscape Architects, trained to analyze and intervene in situations with complex natural and social territorial dynamics, are in position to make positive contributions to mitigating risks in such situations. A collaborative effort between a team of landscape architects from Germany and Urbanists in Medellin Colombia is carrying out a four-phased research and implementation proposal to anticipate and mitigate risk in low-income settlements on Medellin's urban periphery. They propose five pilot projects to test risk mitigation strategies through monitoring and early warning systems, drainage improvements, urban agriculture, slope forestation, and developing sites with the supplied services.
Global patterns of loss of life from landslides
Global loss of life from landslides is poorly quantifi ed. A global data set of fatalities from nonseismically triggered landslides that resulted in loss of life between A.D. 2004 and 2010 permits for the fi rst time proper quantifi cation of impacts and spatial distributions. In total, 2620 fatal landslides were recorded worldwide during the 7 yr period of the study, causing a total of 32,322 recorded fatalities. These total numbers of landslides and victims are an order of magnitude greater than other data sets have indicated, but analysis of the data suggests that it may still slightly underestimate the true human costs. The majority of human losses occur in Asia, especially along the Himalayan Arc and in China. This geographical concentration dominates the annual landslide cycle, which peaks in the Northern Hemisphere summer months. Finally, numbers of fatalities per event show a fat-tailed power law distribution, with the density of landslides being moderately correlated with the population density on a national basis.
The monitoring and prediction of mining subsidence in the Amaga, Angelopolis, Venecia and Bolombolo Regions, Antioquia, Colombia
Abstract Amaga, Angelopolis, Venecia and Bolombolo are small towns located in Antioquia, in the Central Cordillera of the Colombian Andes. Mining has been practised in this region for a period of at least 100 years. This mining has mainly been small-scale, poorly mechanised and restricted to shallow room and pillar workings. Recently, the semi-mechanisation of some mines has enabled coal to be extracted using longwall mining methods. However, this has resulted in subsidence that has caused severe damage to structures, residential property, and agricultural land, and also induced landslides. In the British Isles, there are several reliable methods that can be used to predict the likelihood and magnitude of mining subsidence. The British Coal Corporation and the University of Nottingham have developed one such method, the “Subsidence With Influence Function Technique (SWIFT).” Based on mining subsidence observations undertaken in the coalfields of Britain over a period of approximately 50 years. The SWIFT program was used to predict the magnitude of subsidence, above a longwall panel, at the Industrial Hullera mine in Colombia. The results were then compared with subsidence profiles obtained from precise levelling and field monitoring. In each case, the SWIFT program overestimated the magnitude of mining subsidence by 0.17–0.20 m. However, the morphology of the subsidence profile, area-of-influence and location of maximum subsidence were similar. This overestimation of the predicted subsidence was attributed to the occurrence of strong, igneous rocks, such as rhyolite sills, in the Colombian coal measures. These strong, competent horizons act as cantilever beams during subsidence, causing bed separation and therefore reducing the magnitude of subsidence. In spite of these differences, mining subsidence can be predicted with a reasonable degree of accuracy and precision using the SWIFT technique, provided the software is calibrated and used in conjunction with local expertise.