Multi-temporal relative landslide risk analysis for sustainable development of rapidly growing cities

In the last decades, developing countries have experienced an increase in impact of natural disasters due to both the ongoing climate change and the sustained expansion of urban areas. Intrinsic vulnerability of settlements due to poverty and poor governance, as well as the lack of tools for urban occupation planning and mitigation protocols, have made such impact particularly severe. Cuenca (Ecuador) is a significant example of a city that in the last decades has experienced considerable population growth and an associated increasing of loss due to landslide occurrence. Despite such effects, updated urban planning tools are absent, a condition that suggested an evaluation of multi-temporal relative landslide risk, here presented based on updated data depicting the spatial distribution of landslides and their predisposing factors, as well as population change between 2010 and 2020. In addition, a multi-temporal analysis accounting for risk change between 2010 and 2020 has been carried out. Due to the absence of spatially distributed data about the population, electricity supply contract data have been used as a proxy of the population. Results indicate that current higher relative risk is estimated for municipalities (parroquias) located at the southern sector of the study area (i.e. Turi, Valle, Santa Ana, Tarqui and Paccha). Moreover, the multi-temporal analysis indicates that most municipalities of the city located in the hilly areas that bound the center (i.e. Sayausi, San Joaquin, Tarqui, Valle, Sidcay, Banos, Sidcay, Ricaurte, Paccha and Chiquintad), experiencing sustained population growth, will be exposed to an increased risk with a consistently growing trend. This information is consistent with landslide susceptibility data derived by a machine learning-based analysis that indicate higher susceptibility to landslides in hilly areas surrounding the city center. The obtained relative risk maps can be considered as a useful tool for guiding land-planning, occupation restriction and early warning strategy adoption. The used methodological approach, accounting for landslide susceptibility and population variation through proxy data analysis, has the potential to be applied in a similar context of growing-population cities of low to mid-income countries, where data, usually needed for a comprehensive landslide risk analysis, are only partly available.