Physical complexity to model morphological changes at a natural channel bend

This study developed a two-dimensional (2-D) depth-averaged model for morphological changes at natural bends by including a secondary flow correction. The model was tested in two laboratory-scale events. A field study was further adopted to demonstrate the capability of the model in predicting bed deformation at natural bends. Further, a series of scenarios with different setups of sediment-related parameters were tested to explore the possibility of a 2-D model to simulate morphological changes at a natural bend, and to investigate how much physical complexity is needed for reliable modeling. The results suggest that a 2-D depth-averaged model can reconstruct the hydrodynamic and morphological features at a bend reasonably provided that the model addresses a secondary flow correction, and reasonably parameterize grain-sizes within a channel in a pragmatic way. The factors, such as sediment transport formula and roughness height, have relatively less significance on the bed change pattern at a bend. The study reveals that the secondary flow effect and grain-size parameterization should be given a first priority among other parameters when modeling bed deformation at a natural bend using a 2-D model

A two-dimensional hydro-morphological model for river hydraulics and morphology with vegetation

This work develops a two-dimensional hydro-morphological model which can be used to simulate river hydraulics and morphology with various vegetation covers. The model system consists of five modules, including a hydrodynamic model, a sediment transport model, a vegetation model, a bank failure model and a bed deformation model. The secondary flow effects are incorporated through additional dispersion terms. The core components of the model system solve the full shallow water equations; this is coupled with a non-equilibrium sediment transport model. The new integrated model system is validated against a number of laboratory-scale test cases and then applied to a natural river. The satisfactory simulation results confirm the model's capability in reproducing both stream hydraulics and channel morphological changes with vegetation. Several hypothetical simulations indicate that the model can be used not only to predict flooding and morphological evolution with vegetation, but also to assess river restoration involving vegetation

Supplementary information files for "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"

Supplementary files for article "SwinFlood: A hybrid CNN-Swin Transformer model for rapid spatiotemporal flood simulation"Deep learning-based flood prediction methods have demonstrated significant potential for rapid simulation and early warning of flood disasters. Existing flood surrogate models typically require developing diverse deep-learning architectures based on multiple flood-driving factors, making it challenging to apply these models to different flood scenarios within a consistent network architecture. The temporal resolution of predicted flood maps is also inherently constrained by input flood-driving factors. This paper conceptualizes flood modeling as the translation from coarse-grid to fine-grid flood maps and proposes a novel spatiotemporal flood simulation method termed SwinFlood. The flood-driving factors are unified into two-dimensional coarse-grid hydrodynamic features and fused with fine-grid static terrain features. Utilizing the Swin Transformer for deep feature extraction, the model ultimately outputs fine-grid flood maps. A multi-level model evaluation approach is implemented to systematically assess the performance of the SwinFlood model at global, local, and pixel levels. The proposed model is applied to a complex urban–rural catchment in the upper reaches of the Shenzhen River. Compared to physics-based models, the results demonstrate that the SwinFlood model effectively captures the spatiotemporal variations of water depth, velocity, and river discharge, achieving a speed-up ratio exceeding 1900. The SwinFlood model outperforms traditional purely CNN-based models with comparable parameters. This study provides an efficient and accurate deep-learning method for real-time flood simulation and management.© The Author(s), CC BY-NC-ND 4.0</p

Physical complexity to model morphological changes at a natural channel bend

This study developed a two-dimensional (2-D) depth-averaged model for morphological changes at natural bends by including a secondary flow correction. The model was tested in two laboratory-scale events. A field study was further adopted to demonstrate the capability of the model in predicting bed deformation at natural bends. Further, a series of scenarios with different setups of sediment-related parameters were tested to explore the possibility of a 2-D model to simulate morphological changes at a natural bend, and to investigate how much physical complexity is needed for reliable modeling. The results suggest that a 2-D depth-averaged model can reconstruct the hydrodynamic and morphological features at a bend reasonably provided that the model addresses a secondary flow correction, and reasonably parameterize grain-sizes within a channel in a pragmatic way. The factors, such as sediment transport formula and roughness height, have relatively less significance on the bed change pattern at a bend. The study reveals that the secondary flow effect and grain-size parameterization should be given a first priority among other parameters when modeling bed deformation at a natural bend using a 2-D model