A multisource information system in support of early warning systems in Brazil: The REINDESC database

Systematic documentation of disaster events is fundamental for supporting early warning systems (EWS), particularly in establishing critical thresholds and analyzing spatial patterns. Nevertheless, low-severity floods and landslides remain largely underreported because their impacts are limited and often overlooked. This study presents a comprehensive, multiscale, multisource information platform developed to strengthen early warning capacities in Brazil. The database’s central purpose is to address the nationwide absence of systematic records for small-scale hydrogeological hazards. We present the REINDESC database, describe its methodological framework, and highlight its relevance to the Brazilian EWS. Data from multiple sources were compiled comprehensively, and events were systematically classified by magnitude, temporal accuracy, and spatial precision. Between 2016 and 2023, more than 11,000 occurrences were documented, capturing the spatial and temporal patterns of low-magnitude, high-frequency events. During this period, nearly 7 million individuals (6,961,199) were affected by intensive events associated with intermediate- and high-magnitude occurrences. In contrast, almost 2 million people (1,961,322), representing 22% of the total affected population, were affected by extensive events associated with low-magnitude occurrences. Southeastern Brazil was the most affected region, with nearly 3 million residents impacted, of whom 41% experienced extensive-risk events. The findings underscore the cumulative significance of low-magnitude hydrogeological events in Brazil and highlight the need to integrate such occurrences into national risk reduction strategies. Based on this experience in developing collection procedures, we identified several advantages and challenges, limitations, and gaps in consolidating a disaster database. REINDESC demonstrates substantial potential for strengthening EWS by providing a robust, standardized, and spatially explicit record of hydrogeological hazards across the country. Identification and classification of high frequency/low risk events and low frequency/high-risk disasters is a valuable resource for the Brazilian Early Warning System. Highlighting the impacts of both small and large disaster events underscores their importance in regions with a higher frequency of hydrological and geological events. • The REINDESC is an innovative database that catalogues hydrogeological events in Brazil, encompassing not only major disasters but also small-scale floods and landslides. • Given that small-scale events occur frequently, REINDESC emphasizes their significance due to their cumulative impacts. • The database utilizes a wide array of sources, ensuring comprehensive information with regard to accuracy, depth, reliability, and trustworthiness. • REINDESC is designed for rapid data acquisition, providing information on landslides and floods within hours of their occurrence and offering timely updates. • The applications of REINDESC have significantly enhanced Brazil's early warning system, making it essential for assessing and verifying flood and landslide warnings.

Authors

Institutions

Publication Details

Journal
International Journal of Disaster Risk Reduction
Published
2026-03-13
DOI
https://doi.org/10.1016/j.ijdrr.2026.106099
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A multisource information system in support of early warning systems in Brazil: The REINDESC database

Maria Cristina Maciel Lourenço, Rafael Alexandre Ferreira Luiz, Maria das Dôres Medeiros, Rogério Carneiro et al.
International Journal of Disaster Risk Reduction
Data-Driven Disease Surveillance
article

A multisource information system in support of early warning systems in Brazil: The REINDESC database

Maria Cristina Maciel Lourenço, Rafael Alexandre Ferreira Luiz, Maria das Dôres Medeiros, Rogério Carneiro, Vanessa Canavesi, Pedro Ivo Mioni Camarinha, Silvia Midori Saito, Tiago Bernardes, Gustavo Costa Moreira Silva, Jose A. Marengo, Rodrigo Silva da Conceição
article en

Abstract

Systematic documentation of disaster events is fundamental for supporting early warning systems (EWS), particularly in establishing critical thresholds and analyzing spatial patterns. Nevertheless, low-severity floods and landslides remain largely underreported because their impacts are limited and often overlooked. This study presents a comprehensive, multiscale, multisource information platform developed to strengthen early warning capacities in Brazil. The database’s central purpose is to address the nationwide absence of systematic records for small-scale hydrogeological hazards. We present the REINDESC database, describe its methodological framework, and highlight its relevance to the Brazilian EWS. Data from multiple sources were compiled comprehensively, and events were systematically classified by magnitude, temporal accuracy, and spatial precision. Between 2016 and 2023, more than 11,000 occurrences were documented, capturing the spatial and temporal patterns of low-magnitude, high-frequency events. During this period, nearly 7 million individuals (6,961,199) were affected by intensive events associated with intermediate- and high-magnitude occurrences. In contrast, almost 2 million people (1,961,322), representing 22% of the total affected population, were affected by extensive events associated with low-magnitude occurrences. Southeastern Brazil was the most affected region, with nearly 3 million residents impacted, of whom 41% experienced extensive-risk events. The findings underscore the cumulative significance of low-magnitude hydrogeological events in Brazil and highlight the need to integrate such occurrences into national risk reduction strategies. Based on this experience in developing collection procedures, we identified several advantages and challenges, limitations, and gaps in consolidating a disaster database. REINDESC demonstrates substantial potential for strengthening EWS by providing a robust, standardized, and spatially explicit record of hydrogeological hazards across the country. Identification and classification of high frequency/low risk events and low frequency/high-risk disasters is a valuable resource for the Brazilian Early Warning System. Highlighting the impacts of both small and large disaster events underscores their importance in regions with a higher frequency of hydrological and geological events. • The REINDESC is an innovative database that catalogues hydrogeological events in Brazil, encompassing not only major disasters but also small-scale floods and landslides. • Given that small-scale events occur frequently, REINDESC emphasizes their significance due to their cumulative impacts. • The database utilizes a wide array of sources, ensuring comprehensive information with regard to accuracy, depth, reliability, and trustworthiness. • REINDESC is designed for rapid data acquisition, providing information on landslides and floods within hours of their occurrence and offering timely updates. • The applications of REINDESC have significantly enhanced Brazil's early warning system, making it essential for assessing and verifying flood and landslide warnings.

International Journal of Disaster Risk ReductionVol. 137
Centro Nacional de Monitoramento e Alertas de Desastres Naturais (BR), Korea University of International Studies (KR), Universidade Estadual Paulista (Unesp) (BR)
Openalex Percentile: Top 25%
Data-Driven Disease Surveillance
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.