Abstract
Abstract. Phytoplankton taxonomy, pigment composition and photo-physiological state were studied in Galveston Bay (GB), Texas (USA), following the extreme flooding associated with Hurricane Harvey (25–29 August 2017) using field and satellite ocean color observations. The percentage of chlorophyll a (Chl a) in different phytoplankton groups was determined from a semi-analytical IOP (inherent optical property) inversion algorithm. The IOP inversion algorithm revealed the dominance of freshwater species (diatom, cyanobacteria and green algae) in the bay following the hurricane passage (29 September 2017) under low salinity conditions associated with the discharge of floodwaters into GB. Two months after the hurricane (29–30 October 2017), under more seasonal salinity conditions, the phytoplankton community transitioned to an increase in small-sized groups such as haptophytes and prochlorophytes. Sentinel-3A Ocean and Land Colour Instrument (OLCI)-derived Chl a obtained using a red ∕ NIR (near-infrared) band ratio algorithm for the turbid estuarine waters was highly correlated (R2>0.90) to the (high-performance liquid chromatography) HPLC-derived Chl a. Long-term observations of OLCI-derived Chl a (August 2016–December 2017) in GB revealed that hurricane-induced Chl a declined to background mean state in late October 2017. A non-negative least squares (NNLS) inversion model was then applied to OLCI-derived Chl a maps of GB to investigate spatiotemporal variations of phytoplankton diagnostic pigments pre- and post-hurricane; results appeared consistent with extracted phytoplankton taxonomic composition derived from the IOP inversion algorithm and microplankton pictures obtained from an Imaging FlowCytobot (IFCB). OLCI-derived diagnostic pigment distributions also exhibited good agreement with HPLC measurements during both surveys, with R2 ranging from 0.40 for diatoxanthin to 0.96 for Chl a. Environmental factors (e.g., floodwaters) combined with phytoplankton taxonomy also strongly modulated phytoplankton physiology in the bay as indicated by measurements of photosynthetic parameters with a fluorescence induction and relaxation (FIRe) system. Phytoplankton in well-mixed waters (mid-bay area) exhibited maximum PSII photochemical efficiency (Fv∕Fm) and a low effective absorption cross section (σPSII), while the areas adjacent to the shelf (likely nutrient-limited) showed low Fv∕Fm and elevated σPSII values. Overall, the approach using field and ocean color data combined with inversion models allowed, for the first time, an assessment of phytoplankton response to a large hurricane-related floodwater perturbation in a turbid estuarine environment based on its taxonomy, pigment composition and physiological state.
Highlights
Phytoplankton, which form the basis of the aquatic food web, are crucial to marine ecosystems and play a strong role in marine biogeochemical cycling and climate change
Aphy (λ) = Chl ai × ap∗hi; ap∗hi is the spectral shape of each phytoplankton group
Note: ap∗hi (λ) for 10 different groups of phytoplankton used in this study were extracted from Dierssen et al (2006) and Dutkiewicz et al (2015)
Summary
Phytoplankton, which form the basis of the aquatic food web, are crucial to marine ecosystems and play a strong role in marine biogeochemical cycling and climate change. Chlorophyll a (Chl a), an essential phytoplankton photosynthetic pigment, has been considered a reliable indicator of phytoplankton biomass and primary productivity in aquatic systems (Bracher et al, 2015). Improvements in semianalytical inversion algorithms to derive IOPs from in situ and remotely sensed reflectance spectra have been reported (Roesler and Perry, 1995; Hoge and Lyon, 1996; Lee et al, 1996; Garver and Siegel, 1997; Carder et al, 1999; Maritorena et al, 2002; Roesler and Boss, 2003; Chase et al, 2017). Roesler et al (2003) further modified an earlier IOP inversion algorithm used in Roesler and Perry (1995) by adding a set of five species-specific phytoplankton absorption spectra and derived the phytoplankton taxonomic composition from the field-measured remote-sensing reflectance
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