Floods and Social Vulnerability: Study on the Xingu River in Altamira / Pa

Abstract

The objective of this research was to classify the social vulnerability in Altamira-PA, considering the occurrence of seasonal floods and the future scenario of stabilization of the water level in the flood quota. The Social Vulnerability Index was determined by using fluviometric station data provided by ANA and socioeconomic variables from IBGE. The results indicate a moderate to low vulnerability that does not reflect the socio-spatial environment of the area, where the alert level of 6 m is recurrently exceeded during the floods of the Xingu river. The Belo Monte hydroelectric power plant will ensure the maintenance of the level of floods, which implies in the demand of urban planning that can reduce the liabilities generated by both the maintenance of wetlands, and for the extensive relocation of people, in addition by studies that assess the influence and consequence of extreme events in the region.

Gazing into the Crystal Ball: A Review of Futures Analysis to Promote Environmental Justice in the UK Water Industry

“…Understanding the impacts of interventions can be complex; the impact within a geographical area is not necessarily homogenous. Franco et al [45] highlight the need to understand vulnerability, in this case social vulnerability to flood risk, at a communityrelevant level of granularity. Similarly, Qiu et al [56] highlight the need to understand impacts on ecosystem services in both spatial and temporal contexts as they can behave in disparate ways.…”

“…A review by the UK Environmental Observation Framework [72] showed that 80% of environmental data collected could not be reused by others due to a lack of coordination and governance, leading to fragmented data sharing. Temporal and spatial granularity is important in assessing the impacts to a water system, in terms of both human and ecosystem effects [45,56], as well as the interpretation of results relating to interventions within the system. Uncertainty regarding regulatory data is relevant to the method proposed by Crossman et al [46], which uses regulatory data as the input to the INCA-P model used in this analysis; however these data have become less robust in the eight years since this method was published [73].…”

IOTFlood: hardware and software platform using internet of things to monitor floods in real time

“…It is estimated that, worldwide, 20 million people suffer annually from the effects of this environmental phenomenon, to include 270 thousand in Brazil (Christofidis et al, 2019). The adverse effects of seasonal floods in riverside communities and in cities bordering river basins, such as deaths, destruction of houses and crops, loss of material goods and long periods of homelessness, are the main social consequences caused by the alteration of the hydrological regime (Cristaldo et al, 2018;Franco et al, 2018).…”