The Brahmaputra River (BR) is a heavily braided river, due to various intricate paths, high discharge variability and bank erodibility, as well as multi-channel features, which, in turn, cause huge energy dissipation. The river also experiences anastomosing planform changes in response to seasonal water and sediment waves, resulting in a morphology with extreme complexity. The purpose of this study was to provide detailed and quantitative insights into the properties of planform complexity and dynamics of channel patterns that can complement previous studies. This was achieved by investigating the applicability of the anastomosing classification on the Brahmaputra river’s planform, and computing disorder/unpredictability and complexity of fluctuations using the notion of entropy and uniformity of energy conversion rate by the channels, by means of a power spectral density approach. In addition, we also evaluated their correlation with discharge as a dynamic imprint of river systems on alluvial landscapes, in order to test the hypothesis that river flow may be responsible for the development of anastomosing planforms. The analysis suggests that higher discharge values could lead to less complex planform and less fluctuations on the alluvial landscape, as compared to lower discharge values. The proposed framework has significant potential to assist in understanding the response of complex alluvial planform under flow dynamics for the BR and other similar systems.

The Brahmaputra is one of the largest rivers in the world, ranking fifth in average discharge. As a result, it is heavily braided with various intricate paths in order to dissipate its huge energy. Although this river is normally classed as a braided river, it has recently been classified as an anastomosing river due to its multi-channel features over alluvial plains. Additionally, the Brahmaputra river’s morphology is random in nature as a result of its high flow variability and bank erodibility. Its anastomosing planform changes in response to seasonal water and sediment waves, resulting in a morphology that is extremely complex. The purpose of this study is to examine the Brahmaputra river’s anastomosing planform entropy as a measure of complexity, power spectral density as a measure of fluctuation and their relationship to the energy expenditure as an imprint of flflow rate of river systems on alluvial landscapes.

Dhaka, one of the world’s fastest-growing megacities, faces severe urban flood risks driven by rapid urbanization, inadequate drainage infrastructure, and climate change–induced extreme weather. This study develops an integrated urban flood forecasting system for a densely built-up area of Dhaka using the MIKE + hydrodynamic model. The system links hydrological and hydraulic modules to simulate the complex interactions among surface runoff (pluvial), river overflow (fluvial), drainage networks, and overland flow. Real-time data from rainfall, river gauge, and pump stations are incorporated to improve forecasting precision. Sensitivity analysis identified ten influential parameters, optimized during model calibration. Historical flood events from 2019–2022 were used for calibration and validation, yielding high performance with Nash–Sutcliffe Efficiency (NSE) above 0.70, R² above 0.80 and Mean Absolute Error (MAE) below 0.1 m. The model’s robustness was further verified using major flood events in 2020 2021, 2022, 2024 and 2025 validated against flood marks and community reports and observed data. This advanced forecasting framework offers reliable early warnings and scenario simulations, strengthening decision-making and emergency response for flood-prone urban areas in Dhaka.

Dhaka, one of the fastest-growing megacities, is highly vulnerable to urban flooding due to rapid urbanization, inadequate drainage systems, and climate change-induced extreme weather events. This study describes the development of a comprehensive urban flood forecasting system for Dhaka City using the MIKE+ hydrodynamic model. The system integrates hydrological and hydraulic components, modeling complex interactions among surface runoff (pluvial), riverine overflow (fluvial), drainage networks, and overland flow dynamics. The model utilizes real-time data from river gauges, rainfall stations, and pump stations to enhance forecasting accuracy. Sensitivity analysis identified ten key parameters influencing model performance, which were adjusted during calibration. The model was calibrated and validated using historical flood events from 2019 to 2022, achieving strong performance metrics, including Nash-Sutcliffe Efficiency (NSE) greater than 0.65, Coefficient of Determination (R²) above 0.80, and Mean Absolute Error (MAE) less than 0.1 meters. The system was further tested using significant flood events from 2020 and 2021, cross-validated with flood marks and community-reported observations, ensuring reliability. This advanced urban flood forecasting tool supports decision-making and emergency response by providing early warnings and flood scenarios, enabling proactive flood management for a rapidly urbanizing city like Dhaka.