Coastal wetlands play an important role in the global water and biogeochemical cycles. Climate change is making them more difficult to adapt to the fluctuation of sea levels and other environment changes. Given the importance of eco-geomorphological processes for coastal wetland resilience, many eco-geomorphology models differing in complexity and numerical schemes have been developed in recent decades. But their divergent estimates on the response of coastal wetlands to climate change indicate that substantial structural uncertainties exist in these models. To investigate the structural uncertainty of coastal wetland eco-geomorphology models, we developed a multi-algorithm model framework of eco-geomorphological processes, such as mineral accretion and organic matter accretion, within a single hydrodynamics model. The framework is designed to explore possible ways to represent coastal wetland eco-geomorphology in Earth system models and reduce the related uncertainties in global applications. We tested this model framework at three representative coastal wetland sites: two saltmarsh wetland (Venice Lagoon and Plum Island Estuary) and a mangrove wetland (Hunter Estuary). Through the model-data comparison, we showed the importance to use a multi-algorithm ensemble approach for more robust predictions of the evolution of coastal wetlands. We also find that more observations of mineral and organic matter accretion at different elevations of coastal wetlands and evaluation of the coastal wetland models at different sites of diverse environments can help reduce the model uncertainty.
Flow direction modeling consists of (1) an accurate representation of the river network and (2) digital elevation model (DEM) processing to preserve characteristics with hydrological significance. In part 1 of our study, we presented a mesh-independent approach to representing river networks on different types of meshes. This follow-up part 2 study presents a novel DEM processing approach for flow direction modeling. This approach consists of (1) a topological relationship-based hybrid breaching-filling method to conduct stream burning for the river network and (2) a modified depression removal method for rivers and hillslopes. Our methods minimize modifications to surface elevations and provide a robust two-step procedure to remove local depressions in DEM. They are mesh-independent and can be applied to both structured and unstructured meshes. We applied our new methods to the Susquehanna River Basin with different model configurations. The results show that topological relationship-based stream burning and depression-filling methods can reproduce the correct river networks, providing high-quality flow direction and other characteristics for hydrologic and Earth system models.
River networks are important features in surface hydrology. However, accurately representing river networks in spatially distributed hydrologic and Earth system models is often sensitive to the model’s spatial resolution. Specifically, river networks are often misrepresented because of the mismatch between the model’s spatial resolution and river network details, resulting in significant uncertainty in the projected flow direction. In this study, we developed a topological relationships-based river network representation method for spatially distributed hydrologic models. This novel method uses (1) graph theory algorithms to simplify real-world vector-based river networks and assist in mesh generation; and (2) a topological relationship-based method to reconstruct conceptual river networks. The main advantages of our method are that (1) it combines the strengths of vector-based and DEM raster-based river network extraction methods; and (2) it is mesh-independent and can be applied to both structured and unstructured meshes. This method paves a path for advanced terrain analysis and hydrologic modeling across different scales.