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Tropical cyclone impacts can have devastating effects on the population, infrastructure, and on natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed/intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world, as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecast the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results three days before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management. [ABSTRACT FROM AUTHOR]

The sediment carrying capacity is one of the fundamental issues in sediment simulation. It is of great importance both in theory and practice to develop process-based approaches for the sediment carrying capacity for a wider range of silt-sand sediment. The current study focuses on the approach for depth-averaged concentration of silt-sand sediment under non-breaking wave conditions. By integrating process-based suspended sediment concentration (SSC) profiles, new synthetic expressions for depth-averaged SSC for vortex rippled beds and sheet flow conditions were obtained. The proposed expressions involve several basic physical processes, including the effects of bed forms, stratification, hindered settling, mobile bed, etc. A number of experimental datasets were collected for verification and reasonable results were obtained. Discussions were made on the changes of sediment concentration under increasing wave dynamics conditions, which show that the proposed formulas can describe the phenomenon which has been observed in experimental tests that SSC does not always increase when wave dynamics increase due to the effects of bed forms. In short, the current research provides a process-based approach for wave-induced sediment carrying capacity, which is expected to be applicable for numerical modeling and engineering practice. [ABSTRACT FROM AUTHOR]