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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]

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]

Identifying the erosion processes contributing to increased basin fine sediment yield is important for reducing downstream impacts on aquatic ecosystems. However, erosion rates are spatially variable, and much eroded sediment is stored within river basins and not delivered downstream. A spatially distributed sediment budget model is described that assesses the primary sources (hillslope soil erosion, gully and riverbank erosion) and sinks (floodplain and reservoir deposition) of fine sediment for each link in a river network. The model performance is evaluated in a 17,000-km2 basin in south-east Australia using measured suspended sediment yields from eight catchments within the basin, each 100–700km2 in area. Spatial variations within the basin in yield and area-specific yield were reliably predicted. Observed yields and area-specific yields varied by 17-fold and 15-fold respectively between the catchments, while predictions were generally within a factor of 2 of observations. Model efficiency at predicting variations in area-specific yield was good outside forested areas (0.58), and performance was weakly sensitive to parameter values. Yields from forested areas were under-predicted, and reducing the predicted influence of riparian vegetation on bank erosion improved model performance in those areas. The model provided more accurate and higher resolution predictions than catchment area interpolation of measured yields from neighbouring river basins. The model is suitable for guiding the targeting of remediation measures within river basins to reduce downstream sediment yields. [Copyright &y& Elsevier]

A coastal area morphodynamics model was developed which is capable of modelling both short-term and long-term bathymetric changes in coastal areas. The model is composed of three main modules: wave transformation module, hydrodynamic module and sediment transport and morphology module. The three main modules run simultaneously where the waves and coastal currents information are passed to the sediment transport module, which calculates the sediment transport fluxes and the corresponding bathymetric changes. The model''s output is linked through dynamic plotting libraries, which produces contour plots, vector plots and animation files for the waves, currents, sediment transport fluxes and bathymetry. The model''s output compares very well with other commercial models for the case of an offshore breakwater over a sloping beach. [Copyright &y& Elsevier]

The prediction and control of river sediment yield (SY) are critical but challenging tasks. Erosion and sediment transfer in river catchments are controlled by different processes, whose relative importance varies in space and time. We thus put forward that SY can be estimated more efficiently by using explicitly the information contained in the similarity within groups. To test this hypothesis, we developed a novel Bayesian hierarchical model, applied it to a sample of heterogeneous river catchments and compared its fixed-effects and mixed-effects performance incorporating different group levels, namely gauges, rivers, basins and clusters of catchments. With a parsimonious linear model consisting of four variables (specific and extreme discharge, elevation and retention coefficient), we achieved good performance criteria in the calibration (NSE: 0.79–0.85) and in the cross-validation for temporal and spatial prediction (NSE: 0.71 and 0.72, respectively). These results support the promising potential of this technique. • We develop a Bayesian hierarchical framework to model river sediment yield (BaHSYM). • For spatial predictions, we combine BaHSYM with clustering of catchments. • Robust predictions can be achieved with a parsimonious linear model. • The mixed-effects model performs largely better than ordinary linear regression. [ABSTRACT FROM AUTHOR]