Modeling the potential effects of sea-level rise on the coast of New York: Integrating mechanistic accretion and stochastic uncertainty. (October 2016)

• Record Type: Journal Article
• Authors: Clough, Jonathan; Polaczyk, Amy; Propato, Marco
• Is Part Of: Environmental modelling & software. Volume 84(2016:Oct.)
• Page Start: 349
• Page End: 362
• Publication Date: 2016-10
• Subjects: Sea-level rise -- Accretion -- Uncertainty -- SLAMM -- New York; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.06.023
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The Sea-Level Affecting Marshes Model was applied to coastal New York State at a 5 m horizontal resolution to investigate marsh conservation and potential migration under multiple sea-level rise scenarios. Feedbacks between sea-level rise and marsh accretion rates based on mechanistic modeling were included. Simulation results predict extensive marsh losses in microtidal regimes behind the barrier islands of Long Island, vulnerable dry lands on barrier islands, and opportunities for upland migration of coastal marshes. Results also indicate changes in the composition of marsh types. Confidence of predictions due to model parameter variabilities and spatial data error were estimated with the uncertainty estimation module. Likelihood maps of land cover changes were produced. Uncertainty results suggest that variability in land cover projections is mostly due to the wide range in potential sea-level rise signals by 2100 while impact from uncertainties in model parameters, spatial data errors and linked models is less significant. Highlights: Marsh fate modeling was applied to coastal NY State at a 5 m horizontal resolution. Simulation results predict extensive marsh losses in microtidal regimes. Results also indicate changes in the composition of marsh types. An uncertainty estimation of model results was also reported on. Uncertainty results suggest output variability is primarily due to SLR signal.