Author : Mirtha Karinna Nunez
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (119 download)
Book Synopsis Cross-scale Simulations by : Mirtha Karinna Nunez
Download or read book Cross-scale Simulations written by Mirtha Karinna Nunez and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: There is a universal consensus that global sea levels will rise at an increased rate from those in the recent past. Rising seas will dramatically increase the vulnerability of coastal communities and ecosystems. Tidal marshes are considered to be among the most valuable and vulnerable ecosystems in the world. The effects of sea-level rise (SLR) on tidal marshes are diverse, comprising changes in tidal amplitude and flow patterns, changes in sediment transport, shoreline erosion, changes in salinity gradients, landward migration of tidal habitats, variations in species composition, and habitat loss. There is an increasing concern over how accelerated rates of SLR will impact tidal marsh ecosystems. Many marshes will likely cross thresholds and experience significant and irreversible changes, such as marsh fragmentation and total disintegration due to erosion or drowning. The response of marshes to SLR is expected to vary based on different geomorphic settings, hydrodynamics, sediment sources, and anthropogenic stressors. Due to the increased need to assess tidal marsh vulnerability in the light of changing environments, different models have been developed to predict marsh spatial extent and future distribution. Current models are constrained by the limitations of the two modeling approaches: landscape-scale models and site-specific models. Despite the progress in evaluating marsh response under the effect of SLR, significant challenges still remain in simulating cross-scale processes related to marsh establishment and persistence. This dissertation presents a new approach to modeling marsh evolution. The Tidal Marsh Model (TMM) has been developed as a module within the SCHISM framework (Semi-implicit Cross-scale Hydroscience Integrated System Model). The TMM has unique features (e.g. dynamic rates, cross-scale simulations, and incorporation of anthropogenic stressors) that allow it to overcome many limitations that current marsh models possess. The study areas considered in this study (Carter Creek and Taskinas Creek, Virginia, USA) are representatives of other marsh systems found throughout the Chesapeake Bay and its tributaries. Marshes in these areas are associated with different geomorphic settings, hydrodynamics, and anthropogenic stressors. These study sites were the focus for model development and calibration, model upgrade, and applications. The TMM simulates marsh migration under the joint influence of tides, wind waves, sediment transport, shoreline structures, land use, and precipitation. The evaluation of model performance was conducted via hindacat (past 40 years). Marsh change was captured with an accuracy of 81% in Carter Creek, and an accuracy of 78% in Taskinas Creek. To refine the initial version of the model, a vegetation algorithm was developed within the TMM code, which accounts for the effects of vegetation on the nearshore hydrodynamics. This new functionality contributes to an improved understanding of how marsh plants affect the mean flow velocity and turbulence, and consequently, the sedimentation processes. The TMM was applied in the two study areas to forecast the potential impacts of SLR on marsh sustainability. Using two SLR scenarios, changes in marsh extent and distribution were projected over the next 50 years. Model outputs offer detailed information about potential areas of marsh loss, as well as identify lands where marshes might have the opportunity to transgress and persist under the effect of SLR. This innovative approach provides coastal managers and decision-makers with valuable and necessary information for monitoring activities, restoration, and strategic planning to support marsh sustainability in a changing system.
