Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model

Regional models of tidal marsh elevation response to sea-level rise are needed to support coastal climate change adaptation decisions, including those related to land use planning, habitat management and infrastructure design. The Marsh Equilibrium Model (MEM) is a one-dimensional mechanistic elevation model that incorporates feedbacks of organic and inorganic inputs within the tidal frame to project elevations under sea-level rise scenario. We tested the feasibility of deriving two key MEM inputs average annual suspended sediment concentration (SSC) and aboveground peak biomass from remote sensing data in order to apply MEM across a broader geographic region. We compared biomass models derived from Landsat 8, Digital Globe World View-2 (WV2) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery, and SSC models derived from Landsat 8, WV2, and Portable Remote Imaging Spectrometer (PRISM) imagery in a brackish tidal marsh and surrounding channels in San Francisco Bay. Landsat 8 derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable (%RMSE 15 17%) though peak biomass from Landsat 8 best matched field-measured values. The SSC PRISM model was most accurate (%RMSE 16% vs. 23%), though a Landsat 8 time series enabled the estimate of annual average SSC. Trend response surface analysis identified significant diversion ( P < 0.05) between field and remote sensing-based model runs at 60 years due to model sensitivity at the marsh edge (80 140 cm NAVD88), though at 100 years, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150 200 cm NAVD88). Both model runs produced the same distributions for mudflat, low marsh and high marsh at 100 years. Results demonstrate the utility of Landsat 8 for landscape scale tidal marsh elevation projections due to its comparable performance with the other sensors, its temporal frequency and data access. The integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially while accounting for the organic and inorganic feedbacks in marsh accretion. Improving information for coastal modeling will help support planning for coastal ecosystem services, including habitat, carbon and flood protection.