Assessing the vulnerability of coastal vegetation to climate change using a dynamic vegetation model

Coastal vegetation plays a vital role in the regulation of coastline evolutions, and they are experiencing multiple stresses ranging from long-term presses (e.g., sea-level rise (SLR), warming) to extreme pulses (e.g., hurricane). Vegetation sub-models in most coastal geomorphological models are based on statistical responses to flooding, without representing eco-physiological processes that lead to changes in plant traits (e.g., density, biomass and height) affecting geomorphological processes. Although these models well-fit the measurements from one specific site, they may fail to capture the across-site vegetation responses and predict large-scale vegetation vulnerability to the changing climate. Therefore, we developed a process-based component for coastal marshes within DOE's dynamic vegetation model - the functional assembled terrestrial simulator (FATES). Specifically, we added the salinity stress for photosynthesis and updated the phenology and allometry for salt marsh. We parameterized the model for Spartina alterniflora, and calibrated it using a 16-year vegetation survey data from South Carolina, USA. Our results showed that the model explained 59% variance of the field measurement of aboveground plant biomass. Moreover, we predicted plant biomass at different salinity levels, and the model captured the growth patterns with salinity based on the field measurements. With our model development and application, the improved representation vegetation in geomorphological models could contribute to a more accurate vulnerability prediction of coastal systems facing complex interactions that occur between many different human and natural systems.