Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 beten sea-level rise and marsh accretion rates based on mechanistic modeling re 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 re estimated with the uncertainty estimation module. Likelihood maps of land cover changes re 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.
['Jonathan Clough', 'Amy Polaczyk', 'Marco Propato']
I am writing a research paper on a idea Modeling the potential effects of sea-level rise on the coast of New York: Integrating mechanistic accretion and stochastic uncertainty
[{"from": "human", "value": "I am writing a research paper on a idea Modeling the potential effects of sea-level rise on the coast of New York: Integrating mechanistic accretion and stochastic uncertainty"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The Sea-Level Affecting Marshes Model was applied to coastal New York State at a 5\u00a0m horizontal resolution to investigate marsh conservation and potential migration under multiple sea-level rise scenarios. Feedbacks beten sea-level rise and marsh accretion rates based on mechanistic modeling re 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 re estimated with the uncertainty estimation module. Likelihood maps of land cover changes re 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."}]