In this study, we conducted multiple physical experiments to estimate the efficacy and spatial pattern of erosion by abrading sediment moving through a simple U‐shaped channel bend with erodible bed and banks. The experiments showed that in the bend, lateral abrasion followed a monotonically increasing linear relationship with sediment feed rate. However, vertical incision had a more complex relation with the sediment feed rate, with an initial increase in abrasion as the feed rate increased followed by a decrease in abrasion of the bed as cover effects became dominant at higher feed rates. Bank erosion was large in places where the width and the lateral slope of the point bar were relatively large. On the other hand, in places where the width of the point bar was smaller, the bedrock bed was eroded primarily along the boundary of the point bar, resulting in a bedrock bench near the outer bank.

Results from computational morphodynamics modeling of coupled flow-bed-sediment systems are described for 10 applications as a review of recent advances in the field. Each of these applications is drawn from solvers included in the publicdomain International River Interface Cooperative (iRIC) software package. For mesoscale river features such as bars, predictions of alternate and higher mode river bars are shown for flows with equilibrium sediment supply and for a single case of oversupplied sediment. For microscale bed features such as bedforms, computational results are shown for the development and evolution of two-dimensional bedforms using a simple closure-based two-dimensional model, for two-and three-dimensional ripples and dunes using a three-dimensional large-eddy simulation flow model coupled to a physics-based particle transport model, and for the development of bed streaks using a three-dimensional unsteady Reynolds-averaged Navier-Stokes solver with a simple sedimenttransport treatment. Finally, macroscale or channel evolution treatments are used to examine the temporal development of meandering channels, a failure model for cantilevered banks, the effect of bank vegetation on channel width, the development of channel networks in tidal systems, and the evolution of bedrock channels. In all examples, computational morphodynamics results from iRIC solvers compare well to observations of natural bed morphology. For each of the three scales investigated here, brief suggestions for future work and potential research directions are offered.

Abstract. Several studies have demonstrated the importance of alluvial cover; furthermore, several mathematical models have also been introduced to predict the alluvial cover on bedrock channels. Here, we provide an extensive review of research exploring the relationship between alluvial cover, sediment supply and bed topography of bedrock channels, describing various mathematical models used to analyse the deposition of alluvium. To test one-dimensional theoretical models, we performed a series of laboratory-scale experiments with varying bed roughness under simple conditions without bar formation. Our experiments show that alluvial cover is not merely governed by increasing sediment supply and that bed roughness is an important controlling factor of alluvial cover. A comparison between the experimental results and the five theoretical models shows that (1) two simple models that calculate alluvial cover as a linear or exponential function of the ratio of the sediment supplied to the capacity of the channel produce good results for rough bedrock beds but not for smoother bedrock beds; (2) two roughness models which include changes in roughness with alluviation and a model including the probability of sediment accumulation can accurately predict alluvial cover in both rough and smooth beds; and (3), however, except for a model using the observed hydraulic roughness, it is necessary to adjust model parameters even in a straight channel without bars.

Mountain rivers with fully exposed bedrock are rare (Stark et al., 2009;Tinkler & Wohl, 1998). In most bedrock rivers, the bedrock is partially covered by gravel (Turowski & Cook, 2017). In the model for vertical erosion of Dietrich (2001, 2004), a larger sediment supply increases alluvial coverage and reduces the vertical bedrock erosion rate (i.e., cover effect). Conversely, a smaller sediment supply decreases the frequency of collision of bedload particles with the bed, also reducing the vertical bedrock erosion rate (i.e., tools effect). In other words, the bedrock erosion rate is small when the sediment supply is either very large or very small, but is maximized at an intermediate supply rate (Sklar & Dietrich, 2001, 2004. The importance of sediment supply in defining the morphology of bedrock rivers has been underlined by field observations (

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.