Rocinha (Rio de Janeiro, Brazil) is the largest favela in South America. It is located on a steep mountain slope in the Tijuca National Park with a population of over 160,000 living in poor environmental, health and hygiene conditions. The geomorphological and urban setting of Rocinha makes it vulnerable to natural hazards, with the greatest risk posed by flooding or landslides, compromising the precarious balance between ecosystem services (ES) provision and human well-being. The paper aims to assess and map ES provision in a context where available data to identify areas vulnerable to natural risks is limited. The ES analyses were adapted to the specific socio-economic and environmental context of favelas, which are characterised by dense, continuously built-up environment and a high proportion of impervious surfaces, leading to deteriorating environmental, health and hygiene conditions over time. The ES-based approach was pivotal for setting out strategies—including nature-based solutions (NBS)—to mitigate disaster risk and increase local resilience. These strategies were selected taking into account the context of Rocinha and feasibility. The research shows that ES assessment can be the starting point for projects based on NBS with a view to increasing resilience even in environmentally critical contexts.

Climate change impacts urban areas with greater frequency and exposes continental cities located on floodplains to extreme cloudbursts events. This scenario requires developing specific flooding vulnerability mitigation strategies that improve local knowledge of flood-prone areas at the urban scale and supersede the traditional hazard approach based on the classification of riverine buffers. Moreover, decision-makers need to adopt performance-based strategies for contrasting climate changes and increasing the resilience of the system. This research develops the recent Flooding Risk Mitigation model of InVEST (Integrated Evaluation of Ecosystem Services and Trade-off), where cloudburst vulnerability results from the soil’s hydrological conductivity. It is based on the assumption that during cloudburst events, all saturated soils have the potential for flooding, regardless of the distance to rivers or channels, causing damage and, in the worst cases, victims. The model’s output gives the run-off retention index evaluated in the catchment area of Turin (Italy) and its neighborhoods. We evaluated the outcome to gain specific insight into potential land use adaptation strategies. The index is the first experimental biophysical assessment developed in this area, and it could prove useful in the revision process of the general town plan underway.

Purpose: The spatial development of urban areas affects the characteristics of landscape as well as people’s aesthetic perception of it. Specifically, sprawl results in an urban morphology which is diametrically opposed to the compact city model and which assumes several kinds of patterns: for example “striped”, “ribbon” or “leapfrogged” urban development. Assessing urban morphology in spatial terms is crucial to urban policy, while landscape metrics are the key to a comprehensive understanding of different urban development patterns. The purpose of this paper to design and test an urban morphology indicator (UMI) for the Lombardy Regional Landscape Plan. Design/methodology/approach: The paper describes an UMI that can be used to identify the heterogeneity of built-up patterns according to urban porosity, fragmentation and patch shape. This UMI is a result of Esri ArcGIS 10.3 “grouping analysis” which works by applying a spatial statistical metric for clustering geometries in a given geographical area. Findings: Morphological analysis was used in regional urban development policies with a view to minimising impact on surrounding ecosystems and preserving the natural environment and landscape. It defines 28 different urban morphology patterns in the region, which are divided into systems, polarities and urbanised units. Originality/value: The proposed methodology differs from those traditionally used in qualitative/descriptive landscape planning and supports the identification of morphological features with quantitative statistical and spatial data, allowing a fine-scale assessment of complex metrics.