International Journal of Disaster Risk Reduction 41 (2019) 101326 Available online 9 September 2019 2212-4209/© 2019 Published by Elsevier Ltd. Mapping characteristics of at-risk population to disasters in the context of Brazilian early warning system Regina C� elia dos Santos Alval� a a, * , Mariane Carvalho de Assis Dias a, ** , Silvia Midori Saito a , Cl� audio Stenner b , Cayo Franco b , Pilar Amadeu b , Julia Ribeiro b , Rodrigo Amorim Souza de Moraes Santana b , Carlos Afonso Nobre c a Coordination of Research and Development, National Center for Monitoring and Early Warning of Natural Disasters - CEMADEN, 500 Estrada Doutor Altino Bondensan, Distrito de Eug^ enio de Melo, S~ ao Jos� e dos Campos, SP, Brazil b Coordination of Geography, Brazilian Institute of Geography and Statistics - IBGE, 500 República do Chile, Rio de Janeiro, RJ, Brazil c Institute of Advanced Studies, University of S~ ao Paulo, S~ ao Paulo, SP, Brazil A R T I C L E INFO Keywords: Brazil Disaster risk areas Early warning system (EWS) Exposure indicators Vulnerability ABSTRACT Due to the increasing rise of climate-related disasters in the world, knowledge of risk, monitoring and early warning, dissemination and communication, and disaster preparedness have become demanded. Early Warning Systems (EWS) have been proposed as a strategy for reducing the vulnerability of populations living in at-risk areas. A current challenge in knowledge of risk and disaster preparedness is the inclusion of sociodemo- graphic characteristics of the population in EWS. In order to contribute for an initial comprehension of the di- mensions of vulnerability in Brazil, the aim of this paper is to understand the conditions of at-risk populations at an intra-urban scale and the potential application in the Brazilian EWS (BEWS). Through an integration of de- mographic data and landslide and food risk mappings of 825 municipalities historically affected by disasters in Brazilian territory, an estimation of 8,266,566 people and 2,470,506 households was achieved. This result in- dicates that for every 100 inhabitants, 9 lived in disaster risk areas in Brazil. A novel database containing sociodemographic and infrastructure basic services data is available for specifc analysis of those who are exposed to disasters. These data, associated with hazard forecast, are essential for effective early warnings, which allow actions focusing the reduction of human losses. The knowledge of at risk population in Brazil may contribute to the identifcation of the more critical areas that require priority response actions, such as the ones with more presence of elderly, children and a higher concentration of residents in households without sanitation, which are indicators of vulnerability. 1. Introduction Considering the fact that disasters affect all continents, several countries have invested in the implementation of actions focused on disaster risk reduction (DRR). These efforts have prioritized actions that are consistent with those recommended by the Hyogo Framework for Action (2005–2015) and the Sendai Framework for Disaster Risk Reduction (2015–2030). Among these actions, Early Warning Systems (EWS) constitute an effcient strategy to collect and analyze data in real time, and to help provide early warnings to subsidize mitigation re- sponses to lessen the impacts of disaster on human life and property. Thus, among the non-structural mitigation measures that are available for reducing human losses ensuing from disasters, EWS are signifcant in assisting authorities in DRR [20]. Some studies have highlighted the effciency of EWS in reducing not only mortality risk [95] but also economic impacts [72,79]. The term EWS is defned as the set of ca- pacities that are necessary to generate and disseminate timely and meaningful warning information to enable individuals, communities, and organizations that are threatened by a hazard to prepare and to act appropriately and within suffcient time to reduce the possibility of harm or loss [89]. An effective end-to-end and people-centered EWS comprises four main interrelated components: knowledge of risk, monitoring and early warning, dissemination and communication, and preparation [9,88,90]. A failure in one component or the lack of * Corresponding author. ** Corresponding author. E-mail addresses: regina.alvala@cemaden.gov.br, rcalvala@gmail.com (R.C. dos Santos Alval� a). Contents lists available at ScienceDirect International Journal of Disaster Risk Reduction journal homepage: http://www.elsevier.com/locate/ijdrr https://doi.org/10.1016/j.ijdrr.2019.101326 Received 2 May 2019; Received in revised form 5 September 2019; Accepted 6 September 2019