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The formation and evolution of coastal dunes result from a complex interplay of eco-morphodynamic processes. State-of-the-art models can simulate aeolian transports and morphological dune evolution under certain conditions. However, a model combining these processes for coastal engineering applications was not yet available. This study aims to develop a predictive tool for dune development to inform coastal management decisions and interventions. The aeolian sediment transport model AeoLiS is extended with functionalities that allow for simulations of coastal landforms. The added functionalities include the effect of topographic steering on wind shear, avalanching of steep slopes and vegetation processes in the form of growth and wind shear reduction. The model is validated by simulating four distinct coastal landforms; barchan-, parabolic-, embryo dunes and blowouts. Simulations, based on real-world conditions, replicate the landform formation, migration rates and seasonal variability. • Dune shaping processes are implemented into AeoLiS enabling landform simulations. • Four distinct landform types are simulated utilizing AeoLiS. • AeoLiS replicates accurate landform shapes and dynamics under real-world conditions. • Capabilities added to AeoLiS enable its use for a range of engineering applications. [ABSTRACT FROM AUTHOR]

We present the implementation of a new fully distributed multiclass soil erosion module. The model is based on a 2D finite volume solver (Iber+) for the 2D shallow water equations that computes the overland flow water depths and velocities. From these, the model evaluates the transport of sediment particles due to bed load and suspended load, including rainfall-driven and runoff-driven erosion processes, and using well-established physically-based formulations. The evolution of the mass of sediment particles in the soil layer is computed from a mass conservation equation for each sediment class. The solver is implemented using High Performance Computing techniques that take advantage of the computational capabilities of standard Graphical Processing Units, achieving speed-ups of two orders of magnitude relative to a sequential implementation on the CPU. We show the application and validation of the model at different spatial scales, ranging from laboratory experiments to meso-scale catchments. • A multiclass soil erosion module is implemented in a 2D shallow water model. • Sediment transport is modelled considering rainfall and runoff driven processes. • The GPU parallelization of the code enables its application to complex catchments. [ABSTRACT FROM AUTHOR]

The main purpose of the research was to develop appropriate tools for forecasting the effects of storms. The use of the high-resolution SWAN wave model powered by AROME wind fields to couple it with the SWANOneSed sediment transport model and a model using the Ruggiero formula, predicting the extent of the flooded shore, allowed the development of reliable tools for forecasting the effects of storms. Validation using qualitative analysis showed that the results obtained from the coupled SWANOneSed model are characterized by high consistency in intensification and direction of changes. The model using the Ruggiero formula obtained a high value of the correlation coefficient, i.e. 0.970 and 0.992 for the Satbałtyk-Xbeach model data, based on the Hunt&Mase formula, and the high-resolution Pleiades Neo satellite data, respectively. By combining models from the SWAN family, i.e. the SWAN and SWANOneSed wave model and a model using the Ruggiero formula, it was possible to obtain reliable and compatible tools enabling an uninterrupted and synchronized operational process of generating forecasts of storm effects. [Display omitted] • The coupling of the SWAN & SWANOneSed enables reliable and operational forecasting. • Measured & terrain surveyed data-fed model are consistent with other observed data. • Formula van Rijn reliably reproduces sediment transport in various types of coasts. • The Ruggiero formula is applicable in various parts of the Polish coast. [ABSTRACT FROM AUTHOR]

Based on soil erosion and sediment transport processes, CAMF (Cellular Automata-based heuristic for Minimizing Flow) selects sites for afforestation to minimize sediment influx at a catchment's outlet. CAMF uses a raster representation of the catchment and a steepest ascent hill-climbing optimization heuristic, safeguarding spatial interaction. Its execution time can be prohibitively long for large data-sets. Parallelization results in a speedup of 20 to 24 on 28 cores. We present variants of the optimization method to reduce the number and cost of the iterations. We present a tuning algorithm for the meta-parameters of these variants. The results obtained for two contrasting catchments illustrate that the accelerations reduce the cost by a factor larger than 100, with negligible effect on the afforested cells and magnitude of the sediment reduction. The results indicate that higher levels of spatial interaction have a stronger impact on the accuracy of the results and/or the execution time. • An efficient method to select sites for afforestation to minimize sediment loss. • Modified steepest ascent hill climbing method reduces runtime with minor quality loss. • A parallel implementation achieves nearly optimal speedup on multi-core processors. • Application to two river catchments shows the impact of spatial interaction. [ABSTRACT FROM AUTHOR]

This study emphasizes the importance of accurate calibration in sediment transport models and highlights the transformative role of artificial intelligence (AI), specifically machine learning, in improving accuracy and computational efficiency. Extensive experiments were carried out in the Riba-Roja reservoir, which is located in the northeastern Iberian Peninsula. The accumulated sediment volume (ASV) curve was used to calibrate these experiments. The optimal ASV curve was found to be very close to the experimental data, with only minor differences in upstream areas. The results revealed a consistent rate of sediment transport and settling. Furthermore, the study investigated the capabilities of deep neural networks (DNNs) in predicting ASV curves and observing variable performance. In essence, the study highlights AI's potential for enhancing sediment transport models. • The study underscores the value of AI in enhancing sediment transport model calibration. • The simulations revealed accurate results, with minor discrepancies in upstream regions. • The DNNs, demonstrated reliable predictive capabilities in forecasting ASV curves. • AI, coupled with Monte Carlo method, reduces sediment model computation time. [ABSTRACT FROM AUTHOR]

Literature review reveals that many erosion models have limitations for application to constructed landforms. The new EroCA model aims to address this gap. The model uses established erosion and sediment transport models applied to multiple particle size classes at fine spatial and temporal resolutions, covering both suspended load and bedload. Using data from an experimental plot in Northern Australia, global sensitivity analysis was used to investigate the sources of uncertainty, limitations of the model, and priorities for data collection. Further analysis explored sensitivity to model grid size. The results showed that the Manning roughness, erodibility parameters and particle size are the most important parameters. Results also showed low parameter identifiability and hence the difficulty of calibration, even when using high quality experimental data. EroCA has the capacity to be a powerful landform modelling tool at appropriate scales of application; however ideally its data needs would be considered during monitoring design. • In-depth attempt to develop and assess a high spatial resolution process-based erosion model for a mined landform. • Improved understanding of the effects of various parameters on soil erosion process and modelling. • New insight into the limitations of experimental plot data sets for supporting high resolution erosion models. [ABSTRACT FROM AUTHOR]