Enhanced oxygen consumption results in summertime hypoxia in Mikawa Bay, Japan.

In Mikawa Bay, where hypoxia occurs in the bottom layer during summer, six shipboard observations were conducted from the mouth to the head of the bay from May to August 2014 to investigate the spatiotemporal variation in the bottom layer oxygen consumption rate (OCR). The OCR was determined from the dark incubation of sample waters using an optical oxygen sensor, which showed a range of 5.7-38.3 mmol m-3 days-1. A high OCR was observed at the inner-most station, where higher concentrations of nutrients and chlorophyll a (Chl a) than at the other stations were found, and bottom hypoxic water appeared during the observation period after late June. These OCRs can deplete the oxygen dissolved in water within a week. The OCR showed a highly significant positive correlation with particulate organic carbon concentrations in the bottom water. Considering the relatively low carbon-to-nitrogen mole ratio (~ 6.4-7.6) and high carbon isotope ratio (between approximately - 20.2 and - 18.8‰) of particulate organic matter at the stations, the supply of fresh organic matter produced in the bay as opposed to the land may have affected the OCR by acting as a substrate for microbial aerobic respiration. High temporal resolution data from two automated observation buoys near the bay mouth and the inner area captured increases in Chl a at both sites in response to typhoon events, along with the subsequent appearance of bottom hypoxic water at the inner site and its expansion at the mouth. This supports our hypothesis that enhanced organic matter production due to nutrient input to the surface layer through vertical mixing would increase the bottom OCR, resulting in hypoxia. The apparent oxygen decline in the bottom layer from the buoy data was consistent with incubation-based OCRs during the observation period. Therefore, it is essential to model the OCR in numerical simulations of hypoxia, to which the variability characteristics that we revealed made significant contributions.

Globally consistent assessment of coastal eutrophication.

Eutrophication is an emerging global issue associated with increasing anthropogenic nutrient loading. The impacts and extent of eutrophication are often limited to regions with dedicated monitoring programmes. Here we introduce the first global and Google Earth Engine-based interactive assessment tool of coastal eutrophication potential (CEP). The tool evaluates trends in satellite-derived chlorophyll-a (CHL) to devise a global map of CEP. Our analyses suggest that, globally, coastal waters (depth ≤200 m) covering ∼1.15 million km2 are eutrophic potential. Also, waters associated with CHL increasing trends-eutrophication potential-are twofold higher than those showing signs of recovery. The tool effectively identified areas of known eutrophication with severe symptoms, like dead zones, as well as those with limited to no information of the eutrophication. Our tool introduces the prospect for a consistent global assessment of eutrophication trends with major implications for monitoring Sustainable Development Goals (SDGs) and the application of Earth Observations in support of SDGs.

Changes in community structure and photosynthetic activities of total phytoplankton species during the growth, maintenance, and dissipation phases of a Prorocentrum donghaiense bloom.

The potential interactions between the bloom-forming dinoflagellates and other phytoplankton during the algal bloom cycle are interesting, while the causes for the phytoplankton community changes were not fully understood. We hypothesized that phytoplankton community structure and photosynthetic activities of total phytoplankton have their special characteristics in different phases of the algal blooms. To test this hypothesis, a survey covering the process of a Prorocentrum donghaiense bloom in coastal waters between Dongtou and Nanji Islands was carried out from 9 to 20 May 2016, and the changes in the phytoplankton community and photosynthetic activities of total phytoplankton were determined. Surface seawater was sampled for microscopic analysis of phytoplankton composition and pulse amplitude modulated (PAM) chlorophyll fluorescence analysis of photosynthetic activities of the total phytoplankton species. A total of 25, 31, and 19 phytoplankton species were identified in its growth (9-12 May), maintenance (13-18 May) and dissipation phases (19-20 May), respectively. Diatoms were dominant in terms of species number while dinoflagellates were predominant at cell abundance. Dinoflagellates were the major dominant species during three phases of the bloom based on the dominance (Y) value, whereas the dominant species extended to dinoflagellates and diatoms including P. donghaiense, Coscinodiscus argus, Gonyaulax spinifera, Cyclotella sp. and Scrippsiella trochoidea in the dissipation phase. In the maintenance phase, the average cell abundances of the total phytoplankton and P. donghaiense were consistent with the chlorophyll a (Chla) concentration in the seawater; for the diversity indices of total phytoplankton species, Simpson index (C) was the highest while Shannon index (H') and Pielou evenness index (J') were the lowest. Furthermore, photosynthetic activities of the total phytoplankton species represented by the effective quantum yield (Fq'/Fm') and the maximum relative electron transport rate (rETRmax) in the maintenance phase were higher than those in the growth and dissipation phases. The results indicated that the characteristics of phytoplankton community structure and photosynthetic activities could be regarded as criteria in predicting the phases of algal blooms.

Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements.

Knowledge of phytoplankton community structures is important to the understanding of various marine biogeochemical processes and ecosystem. Fluorescence excitation spectra (F(λ)) provide great potential for studying phytoplankton communities because their spectral variability depends on changes in the pigment compositions related to distinct phytoplankton groups. Commercial spectrofluorometers have been developed to analyze phytoplankton communities by measuring the field F(λ), but estimations using the default methods are not always accurate because of their strong dependence on norm spectra, which are obtained by culturing pure algae of a given group and are assumed to be constant. In this study, we proposed a novel approach for estimating the chlorophyll a (Chl a) fractions of brown algae, cyanobacteria, green algae and cryptophytes based on a data set collected in the East China Sea (ECS) and the Tsushima Strait (TS), with concurrent measurements of in vivo F(λ) and phytoplankton communities derived from pigments analysis. The new approach blends various statistical features by computing the band ratios and continuum-removed spectra of F(λ) without requiring a priori knowledge of the norm spectra. The model evaluations indicate that our approach yields good estimations of the Chl a fractions, with root-mean-square errors of 0.117, 0.078, 0.072 and 0.060 for brown algae, cyanobacteria, green algae and cryptophytes, respectively. The statistical analysis shows that the models are generally robust to uncertainty in F(λ). We recommend using a site-specific model for more accurate estimations. To develop a site-specific model in the ECS and TS, approximately 26 samples are sufficient for using our approach, but this conclusion needs to be validated in additional regions. Overall, our approach provides a useful technical basis for estimating phytoplankton communities from measurements of F(λ).

A Neural Network Model for K(λ) Retrieval and Application to Global Kpar Monitoring.

Accurate estimation of diffuse attenuation coefficients in the visible wavelengths Kd(λ) from remotely sensed data is particularly challenging in global oceanic and coastal waters. The objectives of the present study are to evaluate the applicability of a semi-analytical Kd(λ) retrieval model (SAKM) and Jamet's neural network model (JNNM), and then develop a new neural network Kd(λ) retrieval model (NNKM). Based on the comparison of Kd(λ) predicted by these models with in situ measurements taken from the global oceanic and coastal waters, all of the NNKM, SAKM, and JNNM models work well in Kd(λ) retrievals, but the NNKM model works more stable and accurate than both SAKM and JNNM models. The near-infrared band-based and shortwave infrared band-based combined model is used to remove the atmospheric effects on MODIS data. The Kd(λ) data was determined from the atmospheric corrected MODIS data using the NNKM, JNNM, and SAKM models. The results show that the NNKM model produces <30% uncertainty in deriving Kd(λ) from global oceanic and coastal waters, which is 4.88-17.18% more accurate than SAKM and JNNM models. Furthermore, we employ an empirical approach to calculate Kpar from the NNKM model-derived diffuse attenuation coefficient at visible bands (443, 488, 555, and 667 nm). The results show that our model presents a satisfactory performance in deriving Kpar from the global oceanic and coastal waters with 20.2% uncertainty. The Kpar are quantified from MODIS data atmospheric correction using our model. Comparing with field measurements, our model produces ~31.0% uncertainty in deriving Kpar from Bohai Sea. Finally, the applicability of our model for general oceanographic studies is briefly illuminated by applying it to climatological monthly mean remote sensing reflectance for time ranging from July, 2002- July 2014 at the global scale. The results indicate that the high Kd(λ) and Kpar values are usually found around the coastal zones in the high latitude regions, while low Kd(λ) and Kpar values are usually found in the open oceans around the low-latitude regions. These results could improve our knowledge about the light field under waters at either the global or basin scales, and be potentially used into general circulation models to estimate the heat flux between atmosphere and ocean.

Remote estimation of phytoplankton size fractions using the spectral shape of light absorption.

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of phytoplankton size structure using a(ph)(λ) derived from satellite observations or rapid field instrument measurements in the future.

Alternative measuring method for water-leaving radiance using a radiance sensor with a domed cover.

A method for directly measuring water-leaving radiance using an above-water sensor with a domed cover painted flat black has been proposed. In order to confirm the validity of the result of this method, field experiments have been conducted in Katagami Bay. Radiances measured by this method are slightly less than those obtained from the vertical profile of the upwelling radiance in the sea. This can be attributed to self-shading due to the shape of the instrument used in this study. The advantages of the proposed method are that it does not require empirical processes and minimizes manual procedures after measurements.

Extended probit mortality model for zooplankton against transient change of PCO(2).

The direct injection of CO(2) in the deep ocean is a promising way to mitigate global warming. One of the uncertainties in this method, however, is its impact on marine organisms in the near field. Since the concentration of CO(2), which organisms experience in the ocean, changes with time, it is required to develop a biological impact model for the organisms against the unsteady change of CO(2) concentration. In general, the LC(50) concept is widely applied for testing a toxic agent for the acute mortality. Here, we regard the probit-transformed mortality as a linear function not only of the concentration of CO(2) but also of exposure time. A simple mathematical transform of the function gives a damage-accumulation mortality model for zooplankton. In this article, this model was validated by the mortality test of Metamphiascopsis hirsutus against the transient change of CO(2) concentration.