The estuary-bay system is a common and complex coastal environment. However, quantifying submarine groundwater discharge (SGD) and associated nutrient fluxes in the complex coastal environment is challenging due to more dynamic and complicated riverine discharge, ocean processes and human activities. In this study, SGD and SFGD (submarine fresh groundwater discharge) fluxes were evaluated by combining stable and radium isotopes in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), a typical estuary-bay system. We first built a spatially distributed radium mass balance model to quantify SGD fluxes in coastal areas of GBA integrating the Pearl River Estuary (PRE), bays and shelf. We then used the stable water isotope (δ2H and δ18O) end-member mixing model to distinguish submarine fresh groundwater discharge (SFGD) from SGD. Based on the 228Ra mass balance, the estimated SGD fluxes in the PRE, adjacent bay, and shelf areas were (6.14 ± 2.74) × 108 m3 d-1, (3.00 ± 1.11) × 107 m3 d-1, and (5.00 ± 5.64) × 108 m3 d-1, respectively. Results showed that the largest area-averaged SGD was in the PRE, followed by that in the adjacent shelf and the bay. These differences may be mainly influenced by ocean forces, urbanization and benthic topographies controlling the variability of groundwater pathways. Further, the three end-member mixing model of 228Ra and salinity was developed to confirm the validity of the estimated SGD using the Ra mass balance model. In the two models, groundwater end-member and water apparent age estimation were the main sources of uncertainty in SGD. The estimated SFGD flux was (1.39 ± 0.76) × 108 m3 d-1, which accounted for approximately 12% of the total SGD. Combining stable and radium isotopes was a useful method to estimate groundwater discharge. Moreover, the estimated SGD associated dissolved inorganic nitrogen (DIN) flux was one order of magnitude higher than other DIN sources. SGD was considered to be a significant contributor to the DIN loading to the GBA. The findings of this study are expected to provide valuable information on coastal groundwater management and environmental protection of the GBA and similar coastal areas elsewhere.

Submarine groundwater discharge and associated nutrient fluxes in the Greater Bay Area, China revealed by radium and stable isotopes

Machine Learning Application in Water Quality Using Satellite Data

Machine Learning Based Long‐Term Water Quality in the Turbid Pearl River Estuary, China

The abundance of phytoplankton is generally estimated by measuring the chlorophyll-a concentration (Cchla), which is an important factor in photosynthesis and can be used to analyze the density and biomass of phytoplankton in the ecosystem. The band-ratio-based empirical or semi-analytical algorithms are operationally applied to retrieve Cchla in global oceans, which generally experience difficulties from the diversity of optical properties and the complexity of the radiative transfer equations in analytical analyses, respectively. With an attempt to develop an accurate Cchla retrieval model for the optically complex coastal and estuarine waters, this study aimed to explore the deep learning (DL) methods in satellite retrieval of Cchla. A two-stage convolutional neural network (CNN), named Cchla-Net, was proposed, which utilized the spectral information of remote sensing reflectances at MODIS/Aqua’s visible bands. In the first-stage phase, the Cchla-Net was pretrained by a set of remote sensing patches, in which the Cchla was generated from an existing model (OC3M). The pretrained results were than used as the initial values to refine the network with the synthetic oversampled in-situ dataset in the second-stage training phase. Using in-situ samples for training with the new initial values has a higher probability to reach the global optimum. The quantitative analyses showed that the two-stage training was more likely to achieve a global optimum in the optimization than the one-stage training. Matchups of the in-situ Cchla measurements were used to evaluate the retrieval models. Results showed that the proposed Cchla-Net produced obvious better performance than the empirical and semi-analytical algorithms, implying the DL method was more effective for optically complex waters with extremely high Cchla. This study provided an applicable method for remote sensing retrieval of Cchla, which should be helpful for studying the spatial distribution and temporal variability in the productive Pearl River estuary (PRE) waters.

/pdf/deep-learning-for-chlorophyll-a-concentration-retrieval-a-430i7x5kix.pdf

Deep Learning for Chlorophyll-a Concentration Retrieval: A Case Study for the Pearl River Estuary

Phytoplankton is an important primary producer of the estuarine ecosystem, which is essential for the biogeochemical cycle of water elements and nutrient transfer. The Pearl River estuary (PRE) is a dynamically complex estuary, and the environment of PRE is significantly impacted by anthropogenic activities, variation of phytoplankton community structure in the PRE are complex. This review aims to compare phytoplankton species, species diversity, and abundance variation characteristics from the 1980s and 2020s, evaluate the overall trend of phytoplankton in the PRE, and discuss the main environmental factors affecting phytoplankton growth in the PRE. The data from the past 40 years in PRE showed that the number of phytoplankton species significantly decreased (p < 0.05). There was no significant difference in the abundance of phytoplankton at the 10-year scale, however, the fluctuation range of the abundance has increased. Under the conditions of a decreasing species number and no significant difference in abundance, the species diversity of phytoplankton showed a downward trend. In addition, the dominant phytoplankton species in the nearshore waters were relatively homogenous, and the abundance of phytoplankton in the nearshore waters was higher than that in the open waters, which suggested that human activities have a great influence. This review can form the basis for facilitating health management in the PRE ecosystem. Further, relevant guidelines can be developed and implemented for promoting the ecological health of the Guangdong-Hong Kong-Macao Greater Bay Area and ensuring its sustainable development.

/pdf/advances-in-phytoplankton-population-ecology-in-the-pearl-1hcndu4h.pdf

Advances in phytoplankton population ecology in the Pearl river estuary

The phytoplankton variability in the Pearl River estuary based on VIIRS imagery

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The phytoplankton variability in the Pearl River estuary based on VIIRS imagery