Spatiotemporal evolution of drought and flood events in the Amazon: An approach based on complex network theory

The Amazon Basin exhibits high hydrological variability and has experienced, in recent decades, an increase in the frequency, intensity, and duration of extreme drought and flood events, with significant impacts on ecosystems, water availability, and socioeconomic activities. Understanding not only the isolated occurrence of these events but also their spatiotemporal evolution and interconnections across different regions of the basin remains a major scientific challenge. In this context, this study proposes an approach based on the Standardized Precipitation–Evapotranspiration Index (SPEI) and complex network theory to investigate the spatiotemporal dynamics of droughts and floods in the Amazon. The analysis is based on monthly time series of precipitation and actual evapotranspiration for 43 Amazonian catchments obtained from the CAMELS-BR dataset. Precipitation is represented using data from the CHIRPS product, while actual evapotranspiration is derived from the GLEAM and ERA5-Land datasets. From the climatic water balance, the SPEI accumulated over a 12-month timescale (SPEI-12) is computed, allowing the characterization of medium- to long-term hydrological anomalies. Drought and flood events are identified using widely adopted thresholds in the literature, and additional attributes such as duration, accumulated intensity, and recovery rate are derived to assess the severity and persistence of hydrological extremes. In the subsequent stage, the SPEI time series are analyzed comparatively to quantify similarities in hydrological behavior among catchments and to identify common patterns in the temporal evolution of drought and flood events. These relationships are incorporated into the construction of dynamic graphs, in which each catchment is represented as a node and the connections reflect hydrological proximity among the time series and extreme events characteristics. The temporal analysis of the graphs enables the investigation of how connectivity among catchments reorganizes during dry and wet periods, as well as the identification of regional groupings and catchments that play structurally important roles in the Amazonian hydrometeorological network. By integrating the characterization of hydrological extremes with dynamic network modeling, this study provides an innovative framework for interpreting the complexity of hydrological variability in the Amazon. The expected results contribute to advancing the understanding of the spatiotemporal evolution of droughts and floods and provide support for the development of monitoring, forecasting, and risk management strategies in one of the regions most vulnerable to climate change.