Data integration and visualization framework for high- resolution urban rainfall analysis: a coastal city case study

Climate changes pose increasing challenges for urban watersheds, particularly in densely populated coastal cities, requiring improved monitoring to avoid underestimating precipitation patterns and to enhance predictive accuracy. In this context, the large volume of precipitation data collected daily in the city of Salvador-Brazil highlights the need to develop an autonomous and user-friendly platform accessible to both public managers and citizens. This study presents the development of a framework that integrates historical precipitation data from National Centre for Monitoring and Early Warning of Natural Disasters (CEMADEN) and Salvador Civil Defense (CODESAL), using high-resolution temporal records. The dataset comprises over 10 million rainfall observations collected between 2016 and 2025, with intervals of 5-5120 minutes. Preliminary data processing was performed in Excel and Python Programming, followed by the configuration of technical requirements and visualization design in Power BI. The results showed a rainfall pattern behaviour with a critical zone of short-duration events, characterized by intense peaks at 5 minutes (41.2 mm) and subsequent a inflection zone with gradual growth trends. Over 12 watershed basins, 3 were identified as critical areas in terms of maximal rainfall, concentrating 85 of the 171 neighbourhoods and the largest number of extreme rainfall events among all evaluated stations. These findings facilitate broader public access to technology, support a better understanding of the impacts of intense and spatiotemporal rainfall, particularly in contexts where high-resolution data from dense rain gauge stations pose challenges for analysis and visualization in flood risk management and decision-making.