Flood risk assessment is primarily performed by a single flood driver at a specific location. A significant flaw in this approach is the oversight of the nonlinear interactions between various flood drivers (e.g., river flooding, tides, storm surges, and fluvial regimes), potentially resulting in compound flooding. This oversight can lead to underestimating the socioeconomic consequences of compound floods, which often surpass the risks posed by individual drivers acting alone. This study employs a deep learning model, mainly Long Short-Term Memory to predict water levels in tidal rivers under the influence of various flood drivers. The model is used to predict water levels at specific locations, using upstream river discharge, downstream water levels, and initial water levels as input variables. To account for coincidence/concurrence of drivers, we use Copula functions as a probabilistic approach to model the correlation between peak river discharge and coastal water levels as input features for the DL Model. The application of the proposed method is illustrated by applying it to a case study in the Buffalo Bayou area near Houston, TX. The results show that, for a 50-year flood, considering prior water level conditions represented by the 10th and 90th percentile baseflow scenarios, the projected flood inundation area can vary significantly, ranging from 30% to 70% for the same return period. The proposed methodology advances flood hazard assessment in coastal regions by capturing the complex interplay of different flood drivers and offering a robust yet practical flood inundation mapping approach.

Tides influence the allocation of river discharge to distributary channels interconnected by tidal junctions. Geometrical properties of distributary channels and nearshore coastal morphology might also play a significant role in tidal propagation, and hence control river discharge division and tidal energy transport at tidal junctions. Model simulations reveal how under different river discharge regimes, the discharge division is controlled by the tidal behavior at the junctions. Furthermore, analysis of tidal energy at the junctions indicates that the side distributaries drain or add tidal energy to the main river channel depending on the upstream discharge condition.

Sediment trapping behind dams is currently a major source of bias in large-scale hydro-geomorphic models, hindering robust analyses of anthropogenic influences on sediment fluxes in freshwater and coastal systems. This study focuses on developing a new reservoir trapping efficiency (Te) parameter to account for the impacts of dams in hydrological models. This goal was achieved by harnessing a novel remote sensing data product which offers high-resolution and spatially continuous maps of suspended sediment concentration across the Contiguous United States (CONUS). Validation of remote sensing-derived surface sediment fluxes against USGS depth-averaged sediment fluxes showed that this remote sensing dataset can be used to calculate Te with high accuracy (R2 = 0.98). Te calculated for 116 dams across the CONUS, using upstream and downstream sediment fluxes from their reservoirs, range from 0.3% to 98% with a mean of 43%. Contrary to the previous understanding that large reservoirs have larger Te and vice versa, these data reveal that large reservoirs can have a wide range of Te values. A suite of 21 explanatory variables were used to develop an empirical Te model using multiple regression. The strongest model predicts Te using five variables: dam height, incoming sediment flux, outgoing water discharge, reservoir length, and Aridity Index. A global model was also developed using explanatory variables obtained from a global dam database to conduct a global-scale analysis of Te. These CONUS- and global-scale Te models can be integrated into hydro-geomorphic models to more accurately predict river sediment transport by representing sediment trapping in reservoirs.