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The dynamics of physico-chemical, nutrient, and chlorophyll-a variables were studied in the bloom and non-bloom locations along the off-Gujarat coastal waters to understand the variability in biogeochemistry using multivariate analytical tests. The dissolved oxygen was significantly lower in the bloom stations (3.89 ± 0.44 mgL−1) than in the non-bloom stations (5.50 ± 0.70 mg L−1), due to the biological degradation of organic matter in addition to anaerobic microbial respiration. Nutrients (PO4 and NO3) and Chl-a concentrations were recorded higher in the bloom locations at 0.83 ± 0.21 µmol L−1, 4.47 ± 0.69 µmol L−1, 4.14 ± 1.49 mg m−3, respectively. PO4 and NO3 have shown a significantly higher positive correlation of r = 0.73 and r = 0.69 with Chl-a for bloom data than the non-bloom data. The percentage variance contributed by PC1 and PC2 for both bloom and non-bloom locations were estimated at 52.33%. The variable PO4 explains the highest 24.19% variability in PC1, followed by Chl-a (19.89%). The PO4 triggers the bloom formation and also correlates to the higher concentrations of Chl-a in the bloom locations. The bloom concentration ranges from 9553 to 12,235 trichomes L−1. The bloom intensity has shown a significant positive correlation with Chl-a (r = 0.77), NO3 (r = 0.56), and PO4 (r = 0.30), but a negative correlation was noticed with DO (r = − 0.63) and pH (r = − 0.49). The study also initiates a way forward research investigation on ocean-color technologies to identify and monitor blooms and climate change–driven factors for bloom formation. The occurrence of bloom and its influence on fishery resources and other marine biotas will open many research windows in marine fisheries, oceanography, remote sensing, marine biology, and trophodynamics. [ABSTRACT FROM AUTHOR]

The primary productivity of an aquatic system like the Arabian Sea is majorly supported by the concentration of Chlorophyll-a (Chl-a/Ca) pigment. The present study is narrated to validate the Chl-a data set retrieved from the prominent ocean color sensors (OC3M-MODIS, OC-OCM2, and OC3V-VIIRS) with sea truth data, collected from 204 stations for three years (2015–2017). The in situ concentrations of Chl-a depict the geographic region under the mesotrophic and eutrophic spans with a mean of 1.36 mg m−3 (0.1 > Ca > 1.0 mg m−3). The ratio of CaOCM2/CaIn-situ was 0.97 ± 0.27 mg m−3 (n = 199), but the ratios were higher with CaVIIRS/CaIn-situ is 1.75 ± 0.79 mg m−3 (n = 170) and CaMODIS/CaIn-situ is 2.53 ± 1.42 mg m−3 (n = 158). The coefficient of determination proclaims a significant relationship for MODIS (R2 = 0.36; p < 0.001), followed by OCM2 (R2 = 0.32; p < 0.001) and VIIRS (R2 = 0.19; p < 0.001). The OCM2 showed the lowest RMSE at 0.13, which is relatively lower than the reference error limit by global ocean color missions at 0.35. In overall performance among the three algorithms evaluated for the region, the OCM2 will provide a better estimation of Chl-a with a prediction of 32% accuracy and 34.37% of bias. The log bias values for MODIS (0.35) and VIIRS (0.20) algorithms indicate the overestimation of Chl-a with in situ Chl-a, but the OCM2 algorithm is suitable in the region with a negligible bias (−0.03). The biogeochemical processes and ecosystem characteristics are dynamic from region to region, as yet in its urgent need to validate global sensors to fine-tune the regional algorithms periodically. [ABSTRACT FROM AUTHOR]

We examined the spatial and temporal variations in monthly primary productivity (PP; mg C m−2 day−1) in the northern Arabian Sea from 2015 to 2017 using the PP model (Vertically Generalized Production Model, VGPM) to identify and tag productivity zones and seasons. The major objective was to validate the existing satellite algorithms and compare them with the regional parameterization and further define the most accurate model for the region. The models use both in-situ chlorophyll-a (Chl-a), SST, and euphotic depth (Zeu) (derived from Chl-a), and satellite-retrieved (photosynthetically available radiance, PAR and day length, DL) variables as input parameters. The measured PP values showed significant intra-annual variations, and the maximum was during December (3689.2 ± 505.3 mg C m−2 day−1) and the minimum during August (2207.7 ± 202.2 mg C m−2 day−1). The linear regression depicted that the input variables (Chl-a, PAR, Zeu, DL) together explained 48.68% variation in the PP. The chlorophyll-a showed significant variability in PP, followed by DL, PAR, and Zeu. We tested three modified models with minor modifications in input variables of basic VGPM. Among the models validated in the region, the VGPM-KI could explain 38.3% of the variance with the in-situ PP data, followed by other models (range of variance explained from 18.7 to 38.3%). The average model precision, as determined by RMSD and the bias, was lowest for the modified model (VGPM-KI, VGPM-BF, and VGPM-E), but highest in case of models with satellite as a sole source of input variables (VGPM and EVGPM). In the northern Arabian Sea, VGPM-KI and VGPM-E performed better than the other models. However, VGPM-KI overestimated the PP values when compared to in-situ estimates. The biogeochemical cycles and ocean processes like coastal upwelling and winter convection by winds, which are the key determiners for perennial productivity in the region, will also affect PP in the region. [ABSTRACT FROM AUTHOR]

The present study attempts to understand the seasonal and spatial variations in the physico-chemical (temperature, pH, salinity, dissolved oxygen, and nutrients) and productivity characteristics of the northern Arabian Sea off the Indian coast. Samples were collected from four different sites off the Veraval coast. The values of the physical and chemical variables were higher during the summer season, whereas nutrient concentrations were high during the winter season due to the maturity of intake nutrients during post-monsoon and winter convective mixing during the northeast monsoon. The dissolved oxygen (DO) concentration was strongly and positively correlated with the net primary productivity (NPP) and chlorophyll a (Chl-a) content to support productivity along the region. Dissimilarity in study variables was observed between the inshore and offshore locations. Principal component analysis revealed a strong relationship between nutrients and productivity variables (Chl-a and NPP). Nutrient levels were high at inshore sites, which can be attributed to the heavy nutrient load from land-based anthropogenic activities and impact due to natural processes like water mixing, sedimentation, and wave activities. Nutrients were strongly and positively correlated with the productivity variables, i.e., Chl-a and NPP. Chl-a positively correlated with NPP (r = 0.90), which indicates that it is a principle productivity pigment in the marine ecosystem. [ABSTRACT FROM AUTHOR]