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Coastal marsh survival may be compromised by sea‐level rise, limited sediment supply, and subsidence. Storms represent a fundamental forcing for sediment accumulation in starving marshes because they resuspend bottom material in channels and tidal flats and transport it to the marsh surface. However, it is unrealistic to simulate at high resolution all storms that occurred in the past decades to obtain reliable sediment accumulation rates. Similarly, it is difficult to cover all possible combinations of water levels and wind conditions in fictional scenarios. Thus, we developed a new method that derives long‐term deposition rates from short‐term deposition generated by a finite number of storms. Twelve storms with different intensity and frequency were selected in Terrebonne Bay, Louisiana, USA and simulated with the 2D Delft3D‐FLOW model coupled with the Simulating Waves Nearshore (SWAN) module. Storm impact was analyzed in terms of geomorphic work, namely the product of deposition and frequency. To derive the long‐term inorganic mass accumulation rates, the new method generates every possible combination of the 12 chosen storms and uses a linear model to fit modeled inorganic deposition with measured inorganic mass accumulation rates. The linear model with the best fit (highestR²) was used to derive a map of inorganic mass accumulation rates. Results show that a storm with 1.7 ± 1.6 years return period provides the largest geomorphic work, suggesting that the most impactful storms are those that balance intensity with frequency. Model results show higher accumulation rates in marshes facing open areas where waves can develop and resuspend sediments. This method has the advantage of considering only a few real scenarios and can be applied in any marsh‐bay system.

 

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically. 

Coastal wetlands are nourished by rivers and periodical tidal flows through complex, interconnected channels. However, in hydrodynamic models, channel dimensions with respect to model grid size and uncertainties in topography preclude the correct propagation of tidal and riverine signals. It is therefore crucial to enhance channel geomorphic connectivity and simplify sub‐channel features based on remotely sensed networks for practical computational applications. Here, we utilize channel networks derived from diverse remote sensing imagery as a baseline to build a ∼10 m resolution hydrodynamic model that covers the Wax Lake Delta and adjacent wetlands (∼360 km²) in coastal Louisiana, USA. In this richly gauged system, intensive calibrations are conducted with 18 synchronous field‐observations of water levels taken in 2016, and discharge data taken in 2021. We modify channel geometry, targeting realism in channel connectivity. The results show that a minimum channel depth of 2 m and a width of four grid elements (approximatively 40 m) are required to enable a realistic tidal propagation in wetland channels. The optimal depth for tidal propagation can be determined by a simplified cost function method that evaluates the competition between flow travel time and alteration of the volume of the channels. The integration of high spatial‐resolution models and remote sensing imagery provides a general framework to improve models performance in salt marshes, mangroves, deltaic wetlands, and tidal flats.