Wetlands are endangered ecosystems that provide valuable services to society and contribute to maintaining biodiversity in low-lying areas. Hurricanes, among other stressors such as sea level rise (SLR) and anthropogenic activities, alter wetland dynamics and shape coastal morphology by redistributing sediments in estuaries and bays. Hurricane forcing plays a key role in sediment deposition and erosion within coastal wetlands and their surroundings; hence maintaining marsh elevation relative to SLR as well as eroding the edge of marsh platforms. In this study, we reconciled observed spatiotemporal patterns of wetland coverage change from multi-source remote sensing imagery with hydrodynamic simulations of both average and extreme(hurricane-like) scenarios in Mobile Bay, AL, USA. To account for sediment deposition and erosion in coastal wetlands, we constructed ‘generic’ LiDAR-derived digital elevation models (DEMs) corrected for wetland elevation errors (vertical bias) and used them as a proxy of historical DEMs. We then associated changes in wetland elevation and coverage to inundation duration and estimated the likelihood of wetlands to be either fully exposed or inundated in both scenarios. Results indicated that the likelihood of sediment deposition peaks between 4-h and 7-h of inundation for both average ande xtreme conditions. The likelihood of erosion for average conditions peaks between 11-h and 16-h, whereas that of extreme conditions is highly dependent on hurricane forcing characteristics and peaks around 6-h in the case of Hurricane Ivan (Sep/2004) and 21-hfor Hurricane Katrina (Aug/2005). Results revealed that Hurricane Ivan and Katrina had a two-sided effect on Mobile Bay’s coastal wetlands: (i) erosion along shorelines and marsh edges due to extreme coastal water levels and strong winds, and(ii) sediment deposition in landward direction due to both hurricane-induced sediment deposition and fluvial sediment input. We acknowledge that, next to hurricane forcing, an increase in sea levels could also affect sediment dynamics and so alter coastal morphology and compromise wetland survivability.

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.