Chl-a Concentration Research Articles

Seasonal sediment phosphorus release across sediment-water interface and its potential role in supporting algal blooms in a large shallow eutrophic Lake (Lake Taihu, China)

Seasonal sediment internal phosphorus (P) release is known to affect annual algal blooms in eutrophic lakes. In this study, a year-long field investigation and laboratory sediment core incubation were conducted to study the relationship between sediment internal P cycling and algal growth in Lake Taihu. The results indicated that the concentrations of water total phosphorus (TP) and chlorophyll-a (Chla) correlated with seasonal temperature and were assumed to be caused by internal P release. From cold winter to warm seasons, sediment internal P (porewater P concentration and P flux) exhibits dynamic changes. Sediment porewater soluble reactive phosphorus (SRP) and its flux in the summer were approximately five times and eight times those during winter, respectively. The release of sediment mobile P in the summer decreases its concentration and can supply SRP for algal blooms. Laboratory core incubation indicated that Chla and phycocyanin concentrations in the overlying water showed similar changes to sediment porewater P and P flux when cores were incubated from low to high temperature. The results of this study indicated that warmer conditions could increase the sediment porewater P concentration and sediment P flux into the bottom waters and consequently enhance sediment P availability to algae. This study provides new insights into the relationship between internal sediment P cycling and algal blooms in Lake Taihu.

Analyzing factors driving of eutrophication of river-type urban landscape lakes.

The eutrophication of river-type urban landscape (RTUL) lakes is different from that of natural lakes. In this study, Xiaofu Lake, a typical RTUL lake with high anthropogenic interference, was used as the study area. Monitoring data from 2018 to 2020 were used to analyze the temporal and spatial distribution characteristics of chlorophyll a (Chl-a) concentrations with meteorological, hydrodynamic, and nutrient factors. Correlation and regression analyses were used to identify the relationship between the factors influencing eutrophication and the Chl-a. Simulation of the variation pattern of the main water quality indicators using the MIKE21 model. The study determined the relationship between river water quality and environmental factors and explored the causes of eutrophication in the water bodies of Xiaofu Lake. The results showed that from 2018 to 2020, the water quality showed seasonal variation and differences in spatial distribution. Except for total nitrogen, which remained at a high level (average 8.23 mg/l), other water quality remained between classes II and IV. The proportions of mild, moderate, and severe eutrophication in the study area were 25%, 69%, and 6%, respectively. Indicators that were highly correlated with water eutrophication were turbidity, water temperature, total phosphorus, and permanganate index. The contribution of water temperature, ammonia nitrogen, and permanganate index to eutrophication was 30.5%, 22.6%, and 20.9%, respectively. The high proportion of sewage in the source of recharge water is one of the reasons for the deterioration of water quality. In addition, the change in water eutrophication was closely related to the gate operation in the region.

Study of the Response of Environmental Factors of the Coastal Area in Zhoushan Fishery to Typhoon In-fa Based on Remote Sensing

The response of typical environmental factors in Zhoushan Fishery, including sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll a (Chl-a), before and after Typhoon In-fa was analyzed using satellite data and reanalysis data in this study. Additionally, this study simultaneously elucidated the mechanism by which the typhoon affected these factors. The results showed that: (1) the strong vertical mixing caused by In-fa provoked a decrease in SST, while the asymmetric typhoon wind stress and vertical difference in temperature structure before the typhoon caused a more robust cooling of SST on the right side of the In-fa track; (2) despite the strong mixing and inflow of hypersaline seawater increasing SSS, the combined effect of intense rainfall and diluted water inflow caused an overall decrease in SSS after In-fa’s landing; (3) In-fa caused the Chl-a concentration to decrease first and then increase. The high cloudiness and low Chl-a seawater inflow inhibited the phytoplankton growth during the typhoon, while the abundant light, rich surface nutrients under the upwelling effect, and transport of rich land-based substances induced rapid phytoplankton reproduction after the typhoon; and (4) the change in Chl-a concentration, current, temperature, and salinity induced by a typhoon are essential factors that affect fish behavior and community composition in fisheries. This study provides a point of reference to reveal the response of environmental factors to typhoons and their effects on fishery resources in fisheries located on nearshore estuarine shallow waters with intensive islands.

An Assessment of the Effects of Light Intensities and Temperature Changes on Cyanobacteria’s Oxidative Stress via the Use of Hydrogen Peroxide as an Indicator

Humans and other organisms are adversely affected by cyanobacterial blooming. This study aims to investigate the long-term effects of light intensities and different temperatures on Phormidium ambiguum and Pseudanabaena foetida. Enough P. ambiguum and P. foetida cells were acclimated for 24 days at 30 °C, 20 °C, and 10 °C in separate incubators. The starting day sample was collected after 24 days of acclimatization, and a second sample was collected seven days later at light intensities of 10, 30, 50, 200, and 600 µmol m−2 s−1 for each temperature. The optical density (OD730), hydrogen peroxide (H2O2) concentration, protein content, chlorophyll-a (Chl-a) concentration, and catalase (CAT) activity were measured. Light intensity changes soon after collection resulted in nearly identical starting day samples at each individual temperature. The H2O2 concentration and algal biomass increased until a light intensity of 200 µmol m−2 s−1 was reached and decreased afterward in each temperature for both species after seven days. In association with an increasing H2O2 concentration, the Chl-a concentration decreased after 50 µmol m−2s−1 of light intensity, affecting the protein content. The algal biomass was significantly lower at 10 °C compared to 30 °C. The CAT activity increased proportionately with the H2O2 concentration and algal biomass. Therefore, water bodies in the field can be illuminated with long-term high light intensities in different temperatures to reduce algal biomass.

Characterizing spatial patterns of satellite-derived chlorophyll-a in the Bohai and Yellow Seas of China using self-organizing maps (SOM) approach

Dynamic of chlorophyll-a (Chl-a) concentration is essential information to understand the status and trends of marine ecosystems. In this study, a Self-Organizing Map (SOM) was applied to delineate space-in-time patterns of Chl-a from satellite dataset during 2002–2022 over the Bohai and Yellow Seas of China (BYS). Six typical Chl-a spatial patterns were discerned through a 2 × 3 nodes SOM, while temporal evolutions of dominant spatial patterns were analyzed. The Chl-a spatial patterns were characterized by different concentrations and gradients, and obviously changed over time. The Chl-a spatial patterns and their temporal evolutions were mainly shaped by joint effects of nutrient level, light availability, water column stability, and other factors. Our findings provide first glimpse of space-in-time Chl-a dynamics in the BYS, and complement to the traditional time-in-space Chl-a pattern studies. The accurate identification and classification of the Chl-a spatial patterns are of great significance to marine regionalization and management.

Drastic hydrodynamic changes in the western Bay of Bengal caused by tropical cyclone Nada

To understand the interaction between tropical cyclones (TCs) and the upper ocean in the Bay of Bengal (BoB), TC Nada in 2016 is studied by analyzing ocean reanalysis data, ocean satellite remote sensing data and Argo buoy data. The results show that Nada influenced the anticyclonic eddy (ACE) present at 83.8°E,9.8°N, which propagated westward and formed a new ACE east of Sri Lanka, leading to the EICC bifurcation. An Eastern Sri Lankan Jet (ESLJ) was produced. After the ACE disappeared, the boundary current velocity increased from 1.4 m·s−1 to 1.6 m·s−1. Meanwhile, the main axis of the boundary current is shifted westward. The barrier layer thickness (BLT) was thickened by 33 m at the ACE and thinned by 10 m at the cyclonic eddy (approximately 11°N, 82°E). The mixed layer depth (MLD) became shallower within both the CE and the ACE, which can explain why there were no large-scale phytoplankton blooms in the eddies. Additionally, a part of the sea boundary current intruded into the Palk Strait affected by Nada. The chlorophyll-a (Chl-a) concentration in the Palk Strait increases from 0.08 to 0.67 mg·m−3, causing a phytoplankton bloom. This study helps to estimate the ecological impact of TCs in the western BoB.

Evaluating the Performance of Sentinel-3A OLCI Products in the Subarctic Northeast Pacific

The subarctic northeast Pacific (SNEP) is a high-nitrate, low-chlorophyll (HNLC) region in the ocean, where phytoplankton growth and productivity are limited by iron. Moreover, there is a limited application of high spatial (300 m) and temporal resolution (daily) ocean color (OC) satellite imagery in studying the phytoplankton dynamics in this region. To address this issue, we aim to validate the remote sensing reflectance (Rrs; sr−1(λ)) and chlorophyll-a (Chla) concentration derived from the Polymer atmospheric correction algorithm against in situ data for the SNEP obtained during 2019 and 2020. Additionally, we performed qualitative analysis using weekly binned surface Chla maps to determine whether the product reflects the general pattern over a latitudinal and longitudinal domain. We processed the daily Level-1 image using Polymer and binned them weekly using Graphic Processing Tool (GPT). The validation results indicate that Polymer exhibits higher radiometric performance in the blue and green bands and fails to represent in situ Rrs in the red band. Furthermore, the Polymer slightly over- and underestimates reflectance between 0.0012 and 0.0018 sr−1 in the green band. On the other hand, excellent agreement was found between satellite-derived versus in situ Chla, followed by a slight overestimation of in situ Chla in the range from 0.17 to 0.28 mg/m3. The weekly binned Chla spatial map revealed a spatially homogeneous distribution of surface Chla in Central Alaska, but a substantial increase in Chla (≥0.7 mg/m3) was recorded toward Southeast Alaska (SEA) and the British Columbia (BC) shelf. Furthermore, Chla derived from latitudinal and longitudinal transects indicates high Chla toward 57°N and −135°W, respectively. Overall, the results of this study emphasize the need to obtain high-quality matchups from under-sampled oligotrophic waters, which are crucial for satellite validation, in addition to highlighting the importance of using high spatial and temporal resolution satellite imagery to study phytoplankton dynamics in the SNEP.

Improving lake chlorophyll-a interpreting accuracy by combining spectral and texture features of remote sensing.

Cyanobacterial blooms in lakes fueled by increasing eutrophication have garnered global attention, and high-precision remote sensing retrieval of chlorophyll-a (Chla) is essential for monitoring eutrophication. Previous studies have focused on the spectral features extracted from remote sensing images and their relationship with chlorophyll-a concentrations in water bodies, ignoring the texture features in remote sensing images which is beneficial to improve interpreting accuracy. This study explores the texture features in remote-sensing images. It proposes a retrieval method for estimating lake Chla concentration by combining spectral and texture features of remote sensing images. Remote sensing images from Landsat 5 TM and 8 OLI were used to extract spectral bands combination. The gray-level co-occurrence matrix (GLCM) of remote sensing images was used to obtain a total of 8 texture features; then, three texture indices were calculated using texture features. Finally, a random forest regression was used to establish a retrieval model of in situ Chla concentration from texture and spectral index. Results showed that texture features are significantly correlated with lake Chla concentration, and they can reflect the temporal and spatial distribution change of Chla. The retrieval model combining spectral and texture indices performs better (MAE = 15.22μg·L-1, bias = 9.69%, MAPE = 47.09%) than the model without texture features (MAE = 15.76μg·L-1, bias = 13.58%, MAPE = 49.44%). The proposed model performance varies in different Chla concentration ranges and is excellent in predicting higher concentrations. This study evaluates the potential of incorporating texture features of remote sensing images in lake water quality estimation and provides a novel remote sensing method to better estimate lake Chla concentration.

Comparing Unmanned Aerial Multispectral and Hyperspectral Imagery for Harmful Algal Bloom Monitoring in Artificial Ponds Used for Fish Farming

This work aimed to assess the potential of unmanned aerial vehicle (UAV) multi- and hyper-spectral platforms to estimate chlorophyll-a (Chl-a) and cyanobacteria in experimental fishponds in Brazil. In addition to spectral resolutions, the tested platforms differ in the price, payload, imaging system, and processing. Hyperspectral airborne surveys were conducted using a push-broom system 276-band Headwall Nano-Hyperspec camera onboard a DJI Matrice 600 UAV. Multispectral airborne surveys were conducted using a global shutter-frame 4-band Parrot Sequoia camera onboard a DJI Phantom 4 UAV. Water quality field measurements were acquired using a portable fluorometer and laboratory analysis. The concentration ranged from 14.3 to 290.7 µg/L and from 0 to 112.5 µg/L for Chl-a and cyanobacteria, respectively. Forty-one Chl-a and cyanobacteria bio-optical retrieval models were tested. The UAV hyperspectral image achieved robust Chl-a and cyanobacteria assessments, with RMSE values of 32.8 and 12.1 µg/L, respectively. Multispectral images achieved Chl-a and cyanobacteria retrieval with RMSE values of 47.6 and 35.1 µg/L, respectively, efficiently mapping the broad Chl-a concentration classes. Hyperspectral platforms are ideal for the robust monitoring of Chl-a and CyanoHABs; however, the integrated platform has a high cost. More accessible multispectral platforms may represent a trade-off between the mapping efficiency and the deployment costs, provided that the multispectral cameras offer narrow spectral bands in the 660–690 nm and 700–730 nm ranges for Chl-a and in the 600–625 nm and 700–730 nm spectral ranges for cyanobacteria.

Incorporating carbon sequestration into lake management: A potential perspective on climate change

Exploring the carbon sequestration capacity of water ecosystems would contribute to coping with climate change. This study conducted an integrated method framework to achieve an improved understanding of the relationship between carbon sequestration and lake ecosystem components, as well as provide a new perspective on climate change for policymakers. The vertically generalized production model revealed the carbon sequestration capacity of lakes. The hierarchical linear model identified the cross-scale factors affecting phytoplankton. Then a developed multi-agents-based model with scenario analysis provided adaptive management strategies for carbon sequestration. Furthermore, we applied the integrated framework in the 63 polluted lakes of Wuhan. The results showed that the average carbon sequestration per unit area was at 0.87 kgC·m−2·a−1, which was greater than that of the ocean and forest ecosystems, indicating that the lakes had a potential capacity for carbon sequestration. Total phosphorus had the strongest effect on the Chl-a (chlorophyll a) concentration (fixed effect (γ) =6.82, P < 0.1), followed by total nitrogen (γ = 6.38, P < 0.05), Rotifer biomass (γ = 1.95, P < 0.01) and water temperature (γ = 1.27, P < 0.05). These results indicated that the bottom-up effect of chemical factors on phytoplankton was greater than the top-down effect of zooplankton. The proportion of grassland at the whole-lakes level would have a negative synergistic impact on the Chl-a with changing the micro water temperature at the part-lakes level (γ = −46.64, P < 0.05). There was no significant interaction effect between land cover change and total nitrogen (phosphorus) on the Chl-a. Therefore, we could indirectly confirm that point source pollution emissions would synergistically affect the Chl-a and carbon sequestration along with the effects of physical-chemical conditions. The coordinated proportional control of nitrogen and phosphorus and the artificial controlling biomass of zooplankton-feeding fish were proposed to improve carbon sequestration and water quality for lake management.

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Data of chlorophyll (Chl-a) concentration obtained from polar-orbiting ocean color satellite sensors are subject to major spatial and temporal gaps in data coverage owing to the intrinsically low sampling frequency that coincides with adverse conditions such as cloud obstruction. Such gaps can be minimized with geostationary satellites owing to their higher scanning frequency over a given region, allowing more sampling opportunities as clouds move. However, geostationary ocean color missions have not been available for most regions of the globe. The Advanced Baseline Imager (ABI) aboard the Geostationary Operational Environmental Satellite R series (GOES-R) is a potential alternative for the U.S. owing to improved signal-to-noise ratio compared with its predecessors. Nonetheless, retrieving [Chl-a] from ABI measurements is challenging; the number, placement, and width of its spectral bands are suboptimal and an “ocean-grade” atmospheric correction scheme is lacking. Here we describe the construction of a deep learning-based algorithm to derive [Chl-a] from GOES-16 ABI L1B radiance and ancillary data without an explicit atmospheric correction algorithm. A large (∼22 million examples) training dataset of paired ABI observations and [Chl-a] estimates derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) were used to train, test and validate a 10-layer deep neural network model to retrieve [Chl-a] from ABI data. Preliminary results show that without atmospheric correction but with the aid of deep learning, it is possible to detect open ocean features with strong contrast such as across eddies and fronts, but there are still significant issues in coastal waters and occasionally in the open oceans. In general, the ABI-derived [Chl-a] agrees reasonably well with VIIRS-derived values in the open ocean during 3–5 h of the day. The location of oceanographic features such as fronts and eddies in the ABI-derived [Chl-a] maps are also coincident with those obtained from traditional ocean color and sea surface temperature retrievals. The findings of this study demonstrate that deep learning is a powerful tool for capturing intricate patterns that are too subtle for traditional algorithms developed by humans, Furthermore, the utilization of GOES-R ABI has the potential to complement polar-orbiting satellite data and mitigate data gaps.

Microalgal consortium tolerance to bisphenol A and triclosan in wastewater and their effects on growth, biomolecule content and nutrient removal

Amongst the many treatments available for the removal of emerging contaminants in wastewater, microalgal cultures have been shown to be effective. However, the effectiveness of exposure of a native microalgal consortium to emerging contaminants such as bisphenol-A (BPA) and triclosan (TCS) to determine the half-maximum effective concentrations (EC50) has not yet been determined. The effect on growth and nutrient removal of such a treatment as well as on the production of biomolecules such as carbohydrates, lipids, and proteins are, at present, unknown. In this study, the EC50 of BPA and TCS (96-hour experiments) was determined using a consortium of native microalgae (Scenedesmus obliquus and Desmodesmus sp.) to define the maximum tolerance to these contaminants. The effect of BPA and TCS in synthetic wastewater (SWW) was investigated in terms of microalgal growth, chlorophyll a (Chl-a), carbohydrate, lipid, and protein content, as well as nutrient removal. Assays were performed in heterotrophic conditions (12/12 light/dark cycles). EC50-96 h values of 17 mg/L and 325 µg/L for BPA and TCS, respectively, were found at 72 h. For an initial microalgal inoculum of ≈ 300 mg TSS/L (total suspended solids per litre), growth increased by 16.1% when exposed to BPA and 17.78% for TCS. At ≈ 500 mg TSS/L, growth increased by 8.25% with BPA and 9.92% with TCS, respectively. At the EC50-96 h concentrations determined in the study, BPA and TCS did not limit the growth of microalgae in wastewater. Moreover, they were found to stimulate the content of Chl-a, carbohydrates, lipids, proteins, and enhance nutrient removal. Availability of data and materialData sharing not applicable to this article as no datasets were generated or analysed during the present study.

A novel framework to predict chlorophyll-a concentrations in water bodies through multi-source big data and machine learning algorithms.

Eutrophication happens when water bodies are enriched by minerals and nutrients. Dense blooms of noxious are the most obvious effect of eutrophication that harms water quality, and by increasing toxic substances damage the water ecosystem. Therefore, it is critical to monitor and investigate the development process of eutrophication. The concentration of chlorophyll-a (chl-a) in water bodies is an essential indicator of eutrophication in them. Previous studies in predicting chlorophyll-a concentrations suffered from low spatial resolution and discrepancies between predicted and observed values. In this paper, we used various remote sensing and ground observation data and proposed a novel machine learning-based framework, a random forest inversion model, to provide the spatial distribution of chl-a in 2m spatial resolution. The results showed our model outperformed other base models, and the goodness of fit improved by over 36.6% while MSE and MAE decreased by over 15.17% and over 21.26% respectively. Moreover, we compared the feasibility of GF-1 and Sentinel-2 remote sensing data in chl-a concentration prediction. We found that better prediction results can be obtained by using GF-1 data, with the goodness of fit reaching 93.1% and MSE only 3.589. The proposed method and findings of this study can be used in future water management studies and as an aid for decision-makers in this field.

Remote sensing water quality inversion using sparse representation: Chlorophyll-a retrieval from Sentinel-2 MSI data

Remote sensing has been widely applied to estimate the water quality parameters using empirical approaches. Numerous algorithms have been developed for water quality retrieval. However, the identification of key features for inversion and the reduction of uncertainty with limited field measurements are crucial aspects. In this study, satellite-based sparse representation is developed as an optimization model to simultaneously determine the major spectral features, and estimate water quality maps under noisy environment. Result shows that the blue–green ratio is the important feature for estimation of chlorophyll-a (Chl-a) concentration, whereas the NIR-red algorithm is the better one with in retrieving Chl-a in a high concentration case. The Chl-a map is estimated by using main spectral features of Sentinel 2 MSI data constrained with observations (correlations between observations and estimations: over 0.9). The rapid mapping of Chl-a in inland water allows us to assess spatial distribution of the water quality. This study provides reliable and interpretable information for policymakers to implement effective water quality management practices.

Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a

Accurate predictions of coastal ocean chlorophyll-a (Chl-a) concentrations are necessary for dynamic water quality monitoring, with eutrophication as a critical factor. Prior studies that used the driven-data method have typically overlooked the relationship between Chl-a and marine particulate carbon. To address this gap, marine particulate carbon was incorporated into machine learning (ML) and deep learning (DL) models to estimate Chl-a concentrations in the Yang Jiang coastal ocean of China. Incorporating particulate organic carbon (POC) and particulate inorganic carbon (PIC) as predictors can lead to successful Chl-a estimation. The Gaussian process regression (GPR) model significantly outperforming the DL model in terms of stability and robustness. A lower POC/Chl-a ratio was observed in coastal areas, in contrast to the higher ratios detected in the southern regions of the study area. This study highlights the efficacy of the GPR model for estimating Chl-a and the importance of considering POC in modeling Chl-a concentrations.

Transcriptomic and physiological responses of a model diatom (Phaeodactylum tricornutum) to heat shock and heat selection

Diatoms are ecologically important phytoplankton as they contribute 40% of the marine primary production and play an essential role in the biogeochemical cycles of many elements. Ocean warming, as a pressing environmental stressor, can profoundly affect the population structure and ecological services of diatoms. However, the molecular mechanisms underpinning cellular and physiological changes of diatoms under heat wave and heat selection conditions remain underexplored. This study revealed the transcriptomic and physiological responses of a model diatom species P. tricornutum to ocean warming at an elevated temperature (25 °C in comparison with 20 °C) under scenarios of heat shock (∼7 days; ∼7 generations) and heat selection (∼175 days; ∼165 generations). While unchanged cell growth and reduced Fv/Fm were recorded in the heat shock treatment, significant reductions in the cell growth rate and Fv/Fm were evident in the heat selection treatment. Contents of Chl-a, carotenoid, total soluble protein, carbon (C), and nitrogen (N) increased significantly in both treatments. Transcriptomics analysis showed that elevated temperature suppressed signaling pathways related to photosynthesis but activated signaling pathways involving in fatty acid metabolism and nitrogen metabolism following the heat selection, associated with elevated substance storage and enlarged cell size. As a result, the increase of both C and N suggest that more energy was likely allocated to growth than division under ocean warming. Notably, P. tricornutum showed a higher resistance to nitrogen limitation condition after acclimating to ocean warming possibly by activation of nitrogen metabolism and uptake. P. tricornutum responded to the projected ocean warming through readjusting energy metabolism.

Spatiotemporal variability of micronekton at two central North Pacific Fronts

The North Pacific Subtropical Frontal Zone (STFZ) seasonally aggregates economically important fish and protected species. The aggregation of top predators is hypothesized to be a response to convergent flow at the prominent thermohaline Subtropical Front (STF) in the STFZ and a sharp northward increase in primary productivity, the Transition Zone Chlorophyll Front (TZCF), which is thought to link primary productivity to top predators via secondary and tertiary consumers. Given existing data gaps in our knowledge on forage biomass, distribution, and composition in the area, characteristics of micronekton, forage for top predators, were investigated using in situ multi-frequency active acoustics from three springtime shipboard surveys conducted along the 158°W meridional. The effects of STF and TZCF on micronekton was accessed using in situ CTD profiles. Results of this study showed a significant positive effect of the STF on micronekton biomass. The acoustic data implied that the STF also acted as a boundary for the distribution of micronekton with differing taxonomic composition from south to its north. The TZCF, as well as Chl-a concentrations, did not show a significant effect on micronekton relative biomass or composition that might be due to effects of larger-scale variability masked in the in situ data. Contrary to expectation, significantly higher relative micronekton biomass was associated with higher temperatures, the mechanisms of which still need to be determined.

Rainstorm events trigger algal blooms in a large oligotrophic reservoir

Algal blooms occasionally occur in oligotrophic lakes or reservoirs; however, the mechanisms and conditions that trigger bloom formation remain unclear. Therefore, high-frequency (every 3 days) monitoring and high-resolution (100 sites) surveying of the algal biomass were performed during the summer of 2018–2020 to explore rainfall effects on algal growth and its associated mechanism in Xin'anjiang Reservoir, a large, deep, and oligotrophic reservoir in the subtropical region of China. The results showed that, in the short-term, rainfall inhibited algal growth in the river zone, while promoted algal growth in the lake zone. Path analysis (PLS-PM) and correlation analysis revealed that rainfall inhibited algal growth in the river zone by increasing discharge and decreasing light availability (turbidity) and water temperature, while promoted algal growth in the lake zone by increasing the nutrient concentrations, especially for phosphorus. In addition, we found that the stronger the rainfall is, the wider regions of the reservoir it impacted. Monthly observations at 100 sites showed substantial increases in both algal biomass (mean Chla concentration doubled) and nutrients concentrations in almost the whole reservoir after rainstorm events in Xin'anjiang Reservoir, indicating that rainstorm-induced nutrient pulsing loading could trigger algal blooms in oligotrophic reservoirs in the long-term. Extreme weather events are predicted to occur more frequently as the global climate changes in the future, and oligotrophic reservoirs are facing a greater risk of algal blooms.

The distribution and emission of CO2, CH4 and light hydrocarbons in an anticyclonic eddy of the Kuroshio extension

Mesoscale eddies are energetic and swirling circulations that frequently occur in the open ocean. The effects of mesoscale eddies on the biogeochemical cycling of climate-relevant gases remain poorly constrained. We investigated the distribution and air-sea fluxes of CO2, methane, and five non–methane hydrocarbons (NMHCs) in an anticyclone eddy of Kuroshio Extension during September 2019. Within eddy core, intense stratification hindered the replenishment of nutrients and favored the growth of small-size phytoplankton, such as Prochlorococcus. Seawater pCO2 decreased from 406.1 μatm at the eddy outside to 377.5 μatm at the eddy core, accompanied by a decrease in surface seawater temperature from 26.7 °C to 25.2 °C. The vertical distribution of methane (0.3-9.9 nmol L-1) was influenced by the eddy process, with a maximum at 80 m in the eddy core, which might be attributed to the degradation of phosphonates sustained by Prochlorococcus. The concentrations of five NMHCs (ethane, ethylene, propane, propylene, and isoprene) ranged from 17.2-126.2, 36.7-168.1, 7.5-29.2, 22.6-64.1, 54.5-172.1, 3.5-27.9 pmol L-1, respectively. Isoprene correlated well with Chl-a concentrations at the eddy core, while no significant correlation was observed at the eddy outside. Air-sea fluxes of CO2 and isoprene associated with the eddy core were higher than those of the eddy outside, while the maximum ventilation of methane and other NMHCs (ethane, ethylene, propane, and propylene) was found at the eddy outside. Collectively, physical processes such as eddies impact the production and distribution of light hydrocarbons in seawater and further influence their regional emissions to the atmosphere.

Retrieval of Chlorophyll a Concentration Using GOCI Data in Sediment-Laden Turbid Waters of Hangzhou Bay and Adjacent Coastal Waters

The Geostationary Ocean Color Imager (GOCI) provided images at hourly intervals up to 8 times per day with a spatial resolution of 500 m from 2011 to 2021. However, in the typical sediment-laden turbid water of Hangzhou Bay, valid ocean color parameters in operational data products have been extensively missing due to failures in atmospheric correction (AC) and bio-optical retrieval procedures. In this study, the seasonal variations in chlorophyll a (Chl-a) concentrations in Hangzhou Bay derived using GOCI data in 2020 were presented. First, valid remote sensing reflectance data were obtained by transferring neighboring aerosol properties of less to more turbid water pixels. Then, we improved a regionally empirical Chl-a retrieval algorithm in extremely turbid waters using GOCI-derived surface reflectance and field Chl-a measurements and proposed a combined Chl-a retrieval scheme for both moderately and extremely turbid water in Hangzhou Bay. Finally, the seasonal variation in Chl-a was obtained by the GOCI, which was better than operational products and in good agreement with the buoy data. The method in this study can be effectively applied to the inversion of Chl-a concentration in Hangzhou Bay and adjacent sea areas. We also presented its seasonal variations, offering insight into the spatial and seasonal variation of Chl-a in Hangzhou Bay using the GOCI.