Caracterização do índice de anomalia de chuva (IAC) da Bacia Hidrográfica do Rio Itapicuru, estado da Bahia, a partir da base de dados Brazilian Daily Weather Gridded Data (BR-DWGD)

Abstract

Extreme rainfall events are episodic climatic fluctuations in precipitation patterns that are associated with the occurrence of natural disasters worldwide and have significant impacts on various sectors of society. Forecasting these events based on the monitoring of climate variables is essential for the development of decision-making systems and public policies to mitigate environmental impacts by government agencies, especially in regions susceptible to periods of drought and consequent water shortages, such as the northeastern semi-arid region. The watersheds in this region of the country—which serve as key territorial units for water resource management—are particularly vulnerable to disruptions in their biological processes, exacerbated by the current climate change scenario. A prime example is the Itapicuru River basin, one of the most important water reservoirs in the state of Bahia, which encompasses the Central Tucano sub-basin. In this context, drought monitoring indices are used by researchers to provide a spatiotemporal overview of the frequency of rainfall anomalies based on a historical series of precipitation data provided by the Brazilian climate monitoring network, such as the Rainfall Anomaly Index (IAC), which is categorized using annual rainfall totals and is easy to manipulate mathematically. Due to limitations in spatial coverage, these indices can be calculated using information provided by gridded databases, which are modeled based on interpolations from neighboring rain gauge stations at high spatial resolution. For Brazil, the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset was developed, which stores daily climate variables over a 65-year time span (1961–2025) and has a spatial resolution of 0.1° × 0.1°. Thus, the objective of this study was to characterize the IAC of the Itapicuru River watershed using the BR-DWGD database, in order to analyze the behavior of rainfall anomalies and their temporal association with ENSO (El Niño-Southern Oscillation) climate oscillations. The index proved capable, in practical terms, of providing an overview of rainfall anomalies for the analyzed data series. Furthermore, it was observed that ENSO exerts a reasonable influence on the occurrence of precipitation anomalies, but this influence is not sufficient to explain extreme events. As a direction for future studies, we suggest investigating the relationship with other climate oscillations that occurred during the analyzed period and using other monitoring indices to enhance the robustness of the analyses.