Stochastic description of salt-marsh inundation from mixed astronomical-wind driven tides, with implications for macrophyte growth

Details Stochastic description of salt-marsh inundation from mixed astronomical-wind driven tides, with implications for macrophyte growth Stochastic description of salt-marsh inundation from mixed astronomical-wind driven tides, with implications for macrophyte growth

Dec 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009agufmep32b..08h&link_type=abstract

American Geophysical Union, Fall Meeting 2009, abstract #EP32B-08

Astronomy and Astrophysics

Astronomy

[0497] Biogeosciences / Wetlands, [1813] Hydrology / Eco-Hydrology, [1869] Hydrology / Stochastic Hydrology, [1872] Hydrology / Time Series Analysis

Scientific paper

Sea-level rise and sedimentation interact to control productivity on coastal salt marshes since the mean sea level influences flood frequency. Irregularly flooded marshes are inundated during spring and storm tides and during extended periods of north-easterly winds. The weak and irregular inundation in marshes may effect rates of decomposition, organic matter accumulation, and the vertical distribution of marsh vegetation. Whereas astronomical tides are predictable, wind driven tides depend on the strength and direction of the wind. Because these systems are stochastic, a non-hydrodynamic approach is used to describe the tides and determine the distribution of water depths. Here we present a description of salt-marsh inundation from mixed astronomical-wind driven tides that removes the astronomical forcing from water level records to determine the role of wind, storms, and forecasting of stochastic platform wetting. Using a 3 year record of water level and wind from sites in Carteret County, North Carolina, we calculate the mean high water (MHW) level and the ratio of inundation for a given elevation and corresponding macrophyte. The frequency of inundation or marsh platform wetting will vary from the frequency of MHW level, yet it is this stochastic wetting process that determines productivity and plant distribution since infrequent flooding may cause stress or hypersaline conditions. An ARIMA model is used to describe this higher order structure of the inundation signal. Wind can be described as an AR1 and a transfer function model is used to determine the dynamic response of the effect of noise and sustained winds on water levels. Harmonic analysis is also performed for comparison of predicted water levels using various tidal constituents to determine the phases and amplitudes and to explore model simplification.

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