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Geomorphological knowledge is critical in understanding watershed scale surface processes, including steep mountainous areas and flat lowlands, particularly if the mid- and downstream areas are densely populated and hazard assessments are highly required. However, our knowledge about such surface processes has relatively been limited in some areas in South Asia due likely to the lack of comprehensive studies of geomorphology and related fields. This article undertakes an overview of the geomorphological processes of the disaster-prone deltaic landscape of the Ganges–Brahmaputra–Meghna (GBM), particularly focusing on fluvial processes. The area locates in the downstream of the watershed system including Himalayan Mountains and highly connected with the upper basin morphodynamics, hydrology, and sediment flux. The previous studies are summarized at different geomorphic settings concerning hillslopes, fluvial plains, and coastal areas to provide clarity about the geomorphic processes linking erosion-prone upstream source areas to deposition-dominated downstream areas. The review found that most of the geomorphic researches in Bangladesh are exploring landslide inventory and susceptibility mapping in hilly areas; river channel or riverbank shifting, riverbank erosion and accretion in fluvial environments; watershed morphometric analysis and geomorphic unit identification in plain land; and coastline shifting or coastal erosion and accretion in coastal environments at a small scale. Then, we discuss the fluvial dynamics and sediment transport of the GBM river system to address the knowledge gap in the context of deltaic plain land in Bangladesh, where upstream fluvial sedimentation processes impact the geomorphic connectivity from Himalayan to the Bay of Bengal. Although some studies on the fluvial dynamics and sediment dispersal in the upstream GBM river basin are present, the fluvial processes in the downstream domain of Bangladesh are not fully understood with a limited number of research with field-based approaches. Some future perspectives of geomorphic research in Bangladesh are then mentioned to understand better the complex geomorphological settings in the entire GBM watershed and to strengthen the existing research capacity. This review will also develop a holistic understanding of fluvial geomorphic processes of the GBM River to the policymakers and may be helpful to improve the transboundary river basin management policies or strategies. [ABSTRACT FROM AUTHOR]

Globally, and in China, landslides constitute one of the most important and frequently encountered natural hazard events. In the present study, landslide susceptibility evaluation was undertaken using novel ensembles of bivariate statistical-methods-based (evidential belief function (EBF), statistical index (SI), and weights of evidence (WoE)) kernel logistic regression machine learning classifiers. A landslide inventory comprising 222 landslides and 15 conditioning factors (slope angle, slope aspect, altitude, plan curvature, profile curvature, stream power index, sediment transport index, topographic wetness index, distance to rivers, distance to roads, distance to faults, NDVI, land use, lithology, and rainfall) was prepared as the spatial database. Correlation analysis and selection of conditioning factors were conducted using multicollinearity analysis and classifier attribute evaluation methods, respectively. The receiver operating characteristic curve method was used to validate the models. The areas under the success rate (AUC_T) and prediction rate (AUC_P) curves and landslide density analysis were also used to assess the prediction capability of the landslide susceptibility maps. Results showed that the EBF-KLR hybrid model had the highest predictive capability in landslide susceptibility assessment (AUC values of 0.814 and 0.753 for the training and validation datasets, respectively; AUC_T value of 0.8511 and AUC_P value of 0.7615), followed in descending order by the SI-KLR and WoE-KLR hybrid models. These findings indicate that hybrid models could improve the predictive capability of bivariate models, and that the EBF-KLR is a promising hybrid model for the spatial prediction of landslides in susceptible areas. [ABSTRACT FROM AUTHOR]

Submarine landslides can generate sediment-laden flows whose scale is impressive. Individual flow deposits have been mapped that extend for 1,500 km offshore from northwest Africa. These are the longest run-out sediment density flow deposits yet documented on Earth. This contribution analyses one of these deposits, which contains ten times the mass of sediment transported annually by all of the world’s rivers. Understanding how this type of submarine flow evolves is a significant problem, because they are extremely difficult to monitor directly. Previous work has shown how progressive disintegration of landslide blocks can generate debris flow, the deposit of which extends downslope from the original landslide. We provide evidence that submarine flows can produce giant debris flow deposits that start several hundred kilometres from the original landslide, encased within deposits of a more dilute flow type called turbidity current. Very little sediment was deposited across the intervening large expanse of sea floor, where the flow was locally very erosive. Sediment deposition was finally triggered by a remarkably small but abrupt decrease in sea-floor gradient from 0.05° to 0.01°. This debris flow was probably generated by flow transformation from the decelerating turbidity current. The alternative is that non-channelized debris flow left almost no trace of its passage across one hundred kilometres of flat (0.2° to 0.05°) sea floor. Our work shows that initially well-mixed and highly erosive submarine flows can produce extensive debris flow deposits beyond subtle slope breaks located far out in the deep ocean. [ABSTRACT FROM AUTHOR]