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.
Lakes account for about 10% of the boreal landscape and are responsible for approximately 30% of biogenic methane emissions. However, its quantification is still of large uncertainty under changing climate conditions. Finland has the densest lake system in the world with most lakes situated in the boreal zone. This study uses a large observational dataset of lake methane concentrations to constrain its methane emissions with an extant process-based lake biogeochemical model. We found that the total current diffusive emission from Finnish lakes is 0.12±0.03 Tg CH yr and will increase by 26-59% by the end of this century. We discovered that while warming lake water and sediment temperature played an important role, the climate impact on ice-on periods was a key indicator to the degree of emission increase in the future. We concluded that these boreal lakes remain as a significant methane source under warming climate in this century.