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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 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]

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]

Computer-based coupled soilscape-landform evolution models provide an avenue for estimating the erosion potential of a landform over geological timescales. They also provide tools to understand soil-landform relationships in the context of both field and modelled pedogenesis processes. In this study, we used the State Space Soil Production and Assessment Model (SSSPAM) and CAESAR-Lisflood landform evolution model to simulate the evolution of a natural catchment where soil erosion rates have been closely monitored over the last 20 years. Both models predict comparable erosion rates to that of field measurements and produce similar catchment scale geomorphological patterns. Rock content data measured in the field along two transects were compared with SSSPAM simulations at the same representative points. The results showed that SSSPAM produced similar rock content variation along the respective transects. The findings demonstrate 'SSSPAM's ability to predict catchment scale erosion and surface soil distribution. The insights demonstrated here provide confidence in the SSSPAM model for Soilscape predictions for natural, agricultural and post-mining landform assessment. [Display omitted] • SSSPAM and CAESAR-Lisflood models were used to simulate the evolution of a natural catchment. • Both models produced catchment erosion rates comparable with field observations. • SSSPAM reproduced surface rock content variation on 2 transects observed in the field. • SSSPAM also revealed a possible relationship between area, slope and soil grading. • This study demonstrates SSSPAM's ability to predict erosion and surface soil distribution. [ABSTRACT FROM AUTHOR]

The Sediment Transport and Erosion Prediction (STEP) model is methodically described in this paper. The STEP model was implemented at a 3.47 km × 3.47 km grid resolution in the Kalisapi sub-watershed, Central Java, Indonesia. It was calibrated and validated in the period of 2003–2008 and 2010–2014 respectively. The result shows that the STEP model functions satisfactorily at monthly and annual time window in the calibration and validation periods. In both periods, the STEP model is able to produce fruitful annual sediment transport rates with R2 values of 0.71 and 0.9 in the calibration and validation period respectively. Moreover, the STEP model exhibits more equitable spatial variability than that of the widely known USLE model. Correspondingly, it can be surmised that the STEP model is capable of simulating spatial and temporal variability of sediment transport from a watershed at monthly and annual time scales. • STEP model is developed to simulate soil erosion, deposition and sediment transport processes in time and space. • STEP model has been successfully implemented, calibrated and validated in the Kalisapi sub-watershed, Indonesia. • Calibrating STEP model to fit the observation is straightforward as it only needs to modify two model parameters. • STEP model source code and input data for the study area are freely available in the Bitbucket repository service. [ABSTRACT FROM AUTHOR]

Predicting coastal erosion requires an accurate morphodynamic model. XBeach has been widely adopted to simulate storm-induced coastal erosion. Because of the large number of model input parameters and their uncertainties, sensitivity analysis is a crucial step toward reliable results. This requires running a large number of computationally expensive simulations. Here, we adopt a computationally cost-effective approach based on the Non-Intrusive Polynomial Chaos Expansion method to quantify the sensitivity of XBeach to its input parameters. The method is applied to the coastal erosion event caused by Hurricane Sandy along the barrier islands of New Jersey in the United States. The results show a spatial variation of the model sensitivity with increasing boundary conditions. Parameters' interaction is found to decrease as the magnitude of boundary conditions increases, causing a reduction in the nonlinearity of the model behavior, and consequently leading to a reduction in the uncertainty of the model output. • Sensitivity of XBeach model to its input parameters is investigated. • Sensitivity is quantified using Non-Intrusive Polynomial Chaos Expansions method. • Effect of interactions among input parameters on model behavior is quantified. • Effects of boundary conditions and site characteristics on sensitivity are quantified. [ABSTRACT FROM AUTHOR]

A new modeling platform was developed to simulate spatially distributed sediment production and transport in agricultural landscapes. The sediment production at the grid scale was computed using the stand-alone Water Erosion Prediction Project-Hillslope Erosion code, while an advection-dispersion equation represented sediment transport. The model's performance was tested in the Water Resources and Erosion watersheds at the Oklahoma and Central Plains Agricultural Research Center, El Reno, OK, USA. Results showed that this modeling approach could capture the complex behavior of sediment under different management practices in the watersheds. The modeling framework provides mechanistic processes to simulate the fate and transport of sediment across the watershed at spatio-temporal scales that are useful in assessing the environmental impacts of management schemes. Therefore, this study is expected to help advance the current approaches to estimating soil erosion by bridging scale differences to capture the large-scale effects of small-scale soil erosion processes. • A new modeling framework was developed for estimating soil erosion. • Sediment production is evaluated in fully distributed settings. • Different sizes of sediment are considered in simulations. • The model provides mechanistic processes in simulating sediment transport. • The model is able to capture changes in land and management practices. [ABSTRACT FROM AUTHOR]

In rivers, coastal seas and transitional waters, sediment transport processes involve a variety of interacting factors that dynamically vary over time and space. The flow dynamics within these highly heterogeneous natural systems influences the spatial patterns of erosion and deposition of the bed sediments, which in turn shapes and conditions the bottom morphology. By taking advantage of the established modelling framework in both two- and three dimensions for unstructured meshes proposed within the Telemac-Mascaret system, the new module Gaia provides a code structure for solving sediment transport and morphological evolution problems. By a clear treatment of sedimentary processes that happen in the water column, in the bed structure, and at the water–bed interface, Gaia efficiently manages the spatial and temporal variability of sediment size classes, properties and transport modes for two- and three dimensions. In addition, this module can easily be expanded and customised to particular requirements by modifying user-friendly, easy-to-read, and well-documented FORTRAN-90 subroutines. • A new sediment transport and morphological evolution module GAIA, is proposed for the TELEMAC-MASCARET system. It aims at replacing the existing module SISYPHE. • Its generalised framework enables any combination of multiple size classes for both non-cohesive and cohesive sediment to be modelled simultaneously. • The full compatibility between 2D and 3D sediment transport processes is ensured. [ABSTRACT FROM AUTHOR]

Sand ripples are geomorphic features on the seafloor that affect bottom boundary layer dynamics including wave attenuation and sediment transport. We present a new equilibrium ripple predictor using a machine learning approach that outputs a probability distribution of wave-generated equilibrium wavelengths and statistics including an estimate of ripple height, the most probable ripple wavelength, and sediment and flow parameterizations. The Bayesian Optimal Model System (BOMS) is an ensemble machine learning system that combines two machine learning algorithms and two deterministic empirical ripple predictors with a Bayesian meta-learner to produce probabilistic wave-generated equilibrium ripple wavelength estimates in sandy locations. A ten-fold cross validation of BOMS resulted in an adjusted R-squared value of 0.93 and an average root mean square error (RMSE) of 8.0 cm. During both cross validation and testing on three unique field datasets, BOMS provided more accurate wavelength predictions than each individual base model and other common ripple predictors. • Developed and validated a probabilistic equilibrium ripple predictor. • Utilized a stacked generalization ensemble machine learning technique. • Predictions of ripple wavelength resulted in an error of 8.0 cm and an R 2 of 0.93. [ABSTRACT FROM AUTHOR]

The flow routing module in several large-scale erosion and sediment transport models generally adopts simplifications that ignore important physical aspects of river dynamics. In this study we compare for the first time the results generated by a large-scale erosion and sediment transport model, using two different flow routing approaches: a combined Saint-Venant and Muskingum-Cunge method and the Inertial flow routing. For this assessment, simulations are performed with the MGB-SED model. We evaluate sediment characteristics such as suspended sediment load and concentration, floodplain effects, and the flux of suspended sediment within the Madeira River basin. The simulation with the inertial approach presents satisfactory results, similar to those achieved with the combined Saint-Venant approach for lowland rivers and the Muskingum-Cunge method for steeper rivers. Also, Inertial module is capable to represent the complex processes of the basin, but with easier preprocessing and numerical features, thus promising expanded capabilities for large-scale simulations. • Evaluation of the relative weights of the Saint-Venant equation terms. • Large-scale sediment model using inertial flow routing method. • The average percentage that is represented by the advective inertial term is 0.15%. • Similar results were achieved with full Saint-Venant equations for lowland rivers. [ABSTRACT FROM AUTHOR]

Modelling river physical processes is of critical importance for flood protection, river management and restoration of riverine environments. Developments in algorithms and computational power have led to a wider spread of river simulation tools. However, the use of two-dimensional models can still be hindered by complexity in the setup and the high computational costs. Here we present the freeware basement version 3, a flexible tool for two-dimensional river simulations that bundles solvers for hydrodynamic, morphodynamic and scalar advection-diffusion processes. basement leverages different computational platforms (multi-core CPUs and graphics processing units GPUs) to enable the simulation of large domains and long-term river processes. The adoption of a fully costless workflow and a light GUI facilitate its broad utilization. We test its robustness and efficiency in a selection of benchmarks. Results confirm that basement could be an efficient and versatile tool for research, engineering practice and education in river modelling. • Basement v3 simulates different 2D river physical processes. • A suite of test cases proves the tool's robustness and validity. • Modularization and parallelization enable efficient large domain applications. • Multiple computational backends readily available. • Graphical User Interface available for simulation setup. [ABSTRACT FROM AUTHOR]