The role of sediment-induced light attenuation on primary production during Hurricane Gustav (2008)

Abstract

Abstract. We introduced a sediment-induced light attenuation algorithm into a biogeochemical model of the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system. A fully coupled ocean–atmospheric–sediment–biogeochemical simulation was carried out to assess the impact of sediment-induced light attenuation on primary production in the northern Gulf of Mexico during the passage of Hurricane Gustav in 2008. When compared with model results without sediment-induced light attenuation, our new model showed a better agreement with satellite data on both the magnitude of nearshore chlorophyll concentration and the spatial distribution of offshore bloom. When Hurricane Gustav approached, resuspended sediment shifted the inner shelf ecosystem from a nutrient-limited one to a light-limited one. Only 1 week after Hurricane Gustav's landfall, accumulated nutrients and a favorable optical environment induced a posthurricane algal bloom in the top 20 m of the water column, while the productivity in the lower water column was still light-limited due to slow-settling sediment. Corresponding with the elevated offshore NO3 flux (38.71 mmol N m−1 s−1) and decreased chlorophyll flux (43.10 mg m−1 s−1), the outer shelf posthurricane bloom should have resulted from the cross-shelf nutrient supply instead of the lateral dispersed chlorophyll. Sensitivity tests indicated that sediment light attenuation efficiency affected primary production when sediment concentration was moderately high. Model uncertainties due to colored dissolved organic matter and parameterization of sediment-induced light attenuation are also discussed.

A Numerical reassessment of the Gulf of Mexico carbon system in connection with the Mississippi River and global ocean

“…COAWST is an opensource community model which includes the Model Coupling Toolkit to allow data exchange among three state-ofthe-art numerical models: Regional Ocean Modelling System (ROMS, svn 820, Haidvogel et al, 2008;Shchepetkin and McWilliams, 2005), the Weather Research and Forecast- ing model (WRF, Skamarock, et al, 2005), and the Simulating Waves Nearshore model (SWAN, Booij et al, 1999). The carbon model presented in this study is based on a wellvalidated coupled physical-biogeochemical model by Zang et al (2019Zang et al ( , 2020, which covers the entire GoM waters (Gulf-COAWST, Fig. 1).…”

“…We performed a 20-year model hindcast covering the period of 1 January 2000 to 31 December 2019. The physical model setup was similar to that of Zang et al (2020), with ocean physical initial and boundary conditions interpolated from the 1/12 • data-assimilated Hybrid Coordinate Ocean Model (HYCOM/NCODA, GLBu0.08/expt_19.1, expt_90.9, expt_91.0, expt_91.1, expt_91.2, and GLBv0.08/expt_93.0, https://www.hycom.org, last access: 27 September 2021; Chassignet et al, 2003Chassignet et al, , 2013. Physical boundary conditions are of daily frequency and include u, v, ubar, vbar, zeta, temperature, and salt.…”

“…Missing river alkalinity values are interpolated from climatological values, and missing river DIC values are calculated from pH and alkalinity using the MATLAB program CO2SYS (Lewis and Wallace, 1998). Validations of the model's performance in physics, nutrient cycle, and primary production can be found in Zang et al (2019Zang et al ( , 2020. In this study, we focus on the model's performance in the carbon cycle, which is presented in the next section.…”

Offshore Windfarm Footprint of Sediment Organic Matter Mineralization Processes

“…Furthermore, this approach is not limited to the chosen model domain. Both COAWST and OMEXDIA are flexible models that have been applied to a variety of marine settings [OMEXDIA: (e.g., Soetaert et al, 1996a;Laurent et al, 2016;Khalil et al, 2018;De Borger et al, 2021), COAWST: Warner et al, 2010;Fennel et al, 2013;Bolaños et al, 2014;Zang et al, 2020], including other regions where OWF development may take place. So for now, we have provided a simplification of a complex ecological chain of events, that may be used as a basis in future model developments.…”

The Spatiotemporal Characteristics and Dynamic Changes of Tidal Flats in Florida from 1984 to 2020

“…Particularly, the coastal environment of Florida is advantageous to tidal flat accretion, which makes this state an ideal place to implement the case study. First, Florida is the most hurricane-prone state in the US [36,37], and numerous studies [38][39][40][41][42][43][44] agree that hurricanes could bring in tremendous sediments along the coast. Second, over a half of the coastal wetlands in the conterminous US are distributed along the Gulf Coast [45].…”