Chl-a Concentration Research Articles

Linking land use and precipitation changes to water quality changes in Lake Victoria using earth observation data

Due to the continued increase in land use changes and changing climatic patterns in the Lake Victoria basin, understanding the impacts of these changes on the water quality of Lake Victoria is imperative for safeguarding the integrity of the freshwater ecosystem. Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin between 2000 and 2022 using global satellite products. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake, i.e., Winam Gulf and Inner Murchison Bay (IMB). Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with an increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, changes in land use and land cover (LULC) at 5-year intervals showed no substantial correlation with water quality changes at selected hotspots even though a broader LULC change analysis over the past two decades indicated a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas.

Photoautotrophic picoplankton of the Kara Sea in the middle of summer: Effect of first-year ice retreat on carbon and chlorophyll biomass and primary production

The Arctic warming leads to a decline in sea-ice extent and thickness, rapid warming and freshening of the sea surface which impact the distribution of phytoplankton size composition. Picophytoplankton is an ecologically important component of Arctic pelagic marine ecosystems, and its role may be altered by global warming. In this study, the abundance and biomass, the chlorophyll a (Chl-a) and primary production (PP) of picophytoplankton, and its spatial and temporal distribution were investigated in the Kara Sea during the ice-melt season in July 2019. Picophytoplankton played a major role in the surface PP in the southern and western areas of the Kara Sea. In the surface layer, the contribution of picophytoplankton to total Chl-a increased insignificantly, and the contribution of picophytoplankton to total PP decreased significantly with the time of sea ice retreat. In the euphotic zone, the Chl-a concentration of picophytoplankton and its contribution to total Chl-a decreased with the time of sea ice retreat. The average picophytoplankton biomass determined in the present study (2.72 ± 5.10 mg C m−3) corresponded to the biomass estimates in the Arctic. The picophytoplankton community was strongly dominated by eukaryotes, cyanobacteria were only detected at 3 out of 11 stations, with maximum abundances (0.07 × 109 cells m−3) observed at depths below 15 m. The obtained results contribute significantly to the study of the picophytoplankton dynamics during the ice-melting season in the hard-to-reach Kara Sea.

Determining Seasonal Spatial and Temporal Distribution of Chlorophyll-a, and its Concentration in Kuwait and the Arabian Gulf using Satellite and In-situ Data

As an indicator of phytoplankton density in an area, the concentration of chlorophyll-a (Chl-a) is an important reflection of marine water quality. Remote sensing techniques have been developed to measure the near-surface concentration of Chl-a in water by combining spectral bands and in situ data. This algorithm can be applied to sensors of varying spatial, temporal and spectral resolutions. However, in this study, Chl-a level 2 and 3 products of SNPP – VIIRS spectrometer (Equation OC3) from NASA’s OceanColor suite were relied upon to establish the spatial and temporal distribution of Chl-a concentration in the Arabian Gulf (also known as the Persian Gulf) and the territorial waters of the State of Kuwait (located in the north-eastern part of the Arabian Gulf) in 2012-2019. Ground truthing points (n = 192) matched with level 2 products have been used to build and cross-validate an empirical model. The correlation was positive, with r^2 = 0.79 and validation RMSE = ± 0.64 mg/m-3. The derived algorithm was then applied to Chl-a level 3 seasonal products. Additionally, the Chl-a concentration values in Kuwaiti waters were enhanced using the IDW algorithm to increase spatial resolution, given its small size compared to the spatial resolution of level 3 Chl-a products. The model derived from IDW was tested using the Mann Whitney test (Sig = 0.948 p > 0.01). However, the result showed that Chl-a concentration was higher in the Kuwait Bay (average = 2.8 mg/m-3) than in Kuwaiti waters (average = 2.3 mg/m-3), and higher in Kuwaiti waters (average = 2.1 mg/m-3) than in the Arabian Gulf (average = 0.7 mg/m-3). Coasts have higher concentrations than open water. Generally, the Chl-a increases in winter and had a semi-regular cycle during the years of study; this cycle is more regular in the Gulf than in Kuwait.

Large-scale oyster farming accelerates the removal of dissolved inorganic carbon from seawater in Sanggou Bay

While the direct impact of oyster calcification and respiration on the seawater inorganic carbon system is well-acknowledged, their indirect effect through filter feeding activities remains unclear. Here we studied the impact of large-scale oyster farming on the removal of dissolved inorganic carbon (DIC) from seawater. Field investigations showed that the DIC level in the oyster farming area in Sanggou Bay, China were significantly lower than that in the non-farming area. In-lab incubation showed that regardless of whether incubated in high or low-transparent environments, the DIC removal rate of seawater from the oyster farming area was significantly higher than that of the non-farming area. These results indicate that cultivated oyster facilitates the removal of seawater DIC in the farming area. To reveal the indirect effect of filter feeding activities on DIC removal, we used 6-m3 ponds to simulate the oyster-farming environment. Results showed that the average DIC level of the oyster-cultivating groups was 105.83 μmol/kg lower than that of the control groups (without of oyster) after a six-day cultivation. Surprisingly, the average concentration of Chl-a in oyster-cultivating groups was significantly higher than that of the control group at the end of the experiment. Similarly, DIC level declined faster while Chl-a concentration increased faster in seawater that previously experienced 12～20 hours of oyster cultivation than that in the control seawater. It was noticed that the transparence of seawater within 6-m3 ponds increased significantly just after hours of oyster cultivation. This enhanced transparency created a favorable light environment that supported phytoplankton photosynthesis and simultaneously accelerated the DIC removal rate. Overall, oysters not only remove the inorganic carbon in the seawater through calcification but also create a suitable environment for phytoplankton photosynthesis through their filtering activity, and subsequently accelerating the removal of inorganic carbon in the seawater of the oyster farming area.

Composited sea surface responses to tropical cyclones in the Bay of Bengal during 2003–2020

Tropical cyclones (TCs) in the Bay of Bengal (BoB) caused significant changes in the sea surface as they passed through the sea. To quantitatively study the differences in oceanic responses caused by TCs during pre-monsoon and post-monsoon seasons in the BoB from 2003 to 2020 and the varying importance of TC intensity and translation speed in modulating oceanic responses, the oceanic environmental parameters affected by TCs were composited and analyzed. The spatial distributions show that the responses of sea surface temperature (SST) rather than chlorophyll-a (Chl-a) concentrations and sea surface salinity (SSS) coincided with the maximum response centers of the wind power index (WPi). All the temporal changes in SST, Chl-a, and SSS indicated that the changes induced by TCs began approximately 2–3 days before TC passage, continued to increase during cyclone passage, and then slowly returned to the initial state after at least 8 days. Compared with TC intensity, TC translation speed generally played a less important role in influencing the ocean responses of SST, Chl-a and SSS. This result is somewhat different from previous results in which the response of Chl-a was correlated with TC translation speed rather than TC intensity in the northern Indian Ocean. In addition, the SSS response was positively proportional (R = 0.886) to the TC translation speed in the BoB, which was different from that in the Northwest Pacific. The differences in the responses during pre-monsoon and post-monsoon seasons were mainly in terms of quantity, as all the composited results were similar. In addition, they were more pronounced in their responses but less significant in their correlations during pre-monsoon.

Fingerprints of El Niño Southern Oscillation on global and regional oceanic chlorophyll-a timeseries (1997–2022)

With the advancement of present-day ocean color satellites, the spatiotemporal variations of oceanic Chlorophyll-a (Chl-a) concentration, a proxy for phytoplankton population, can be monitored at regional and global scales. Estimating long-term changes in Chl-a concentration, however, is mainly constrained by the limited availability of ocean color data and the significant influence of climate oscillations like the El Niño Southern Oscillation (ENSO). In this study, we investigate the influence of ENSO on regional and global Chl-a timeseries using two ocean color datasets spanning from September 1997 to December 2022. Our analysis found that global Chl-a concentration exhibits a significant increasing trend over the study period. Most of the increasing trends are detected in the Southern Hemisphere (SH) and high-latitudinal regions, including the Southern Ocean. In light-limited regions like the Southern Ocean, enhanced Chl-a concentration shows a strong positive correlation with warming sea surface temperature (SST). Conversely, most oceans in the Northern Hemisphere (NH) exhibit decreasing trends, which are highly correlated with warming SST and ENSO phases. Additionally, we identify distinctive fingerprints of four extreme ENSO events: two extreme El Niño events (1997–1998 and 2015–2016) and two triple La Niña events (1998–2001 and 2020–2022) in these timeseries. Furthermore, empirical orthogonal function (EOF) analysis reveals that the dominant mode of interannual Chl-a variability is associated with an ENSO-like pattern, accounting for about 13 % of the total variability. Intriguingly, this principal mode is highly correlated with the phases of ENSO (R = -0.87) and Pacific Decadal Oscillation (PDO) (R = −0.90) on an interannual scale. This study provides insights into assessing the impacts of ENSO on long-term Chl-a trend estimation.

Spatial and seasonal variability of chlorophyll-a, total suspended matter, and colored dissolved organic matter in the Sundarban mangrove forest using earth observation and field data

The Sundarbans, the world's largest mangrove forest, confronts potential threats from various anthropogenic activities leading to degradation of its aquatic ecosystem. To examine the current status of the aquatic ecosystem, this study aimed to evaluate the spatial and seasonal fluctuation of three principal water quality attributes namely Chlorophyll-a (Chl-a), Total Suspended Matter (TSM), and Colored Dissolved Organic Matter (CDOM) in the complex tidal river systems of the Sundarban mangroves forest using earth observation and in-situ data. A set of two bio-optical algorithms, Ocean color-2 (OC-2) and Ocean color-3 (OC-3), were applied to measure Chl-a concentration, Green/NIR and the Red/NIR band ratio algorithms were used for TSM and the Case-2 Regional Coast Color (C2RCC) processor in the SNAP software was applied to obtain CDOM concentration in study area. A total of 50 in-situ samples were collected during post-monsoon and pre-monsoon to validate the results. Our results clearly demonstrated seasonal variability with higher Chl-a concentrations in post-monsoon than pre-monsoon. This was due to the OC-2 algorithm which produced better results with R2 = 0.73, RMSE = 0.27 for post-monsoon and R2 = 0.55, RMSE = 0.32 for pre-monsoon. Whilst, TSM concentration performed the best with R2 = 0.77; RMSE = 15.82 and R2 = 0.65; RMSE = 33.96 in post-monsoon and pre-monsoon according to the Green/NIR band ratio method. The nearshore and narrow waterway regions had the highest concentrations of TSM and Chl-a, whereas the offshore regions had the lowest. Strong association were observed between the in-situ and satellite derive absorption coefficient, aCDOM (m−1). The R2 for a CDOM during pre-monsoon was 0.65 and throughout the post-monsoon, it was 0.74. Pre-monsoon concentrations were found to be higher due to marine sources and higher wind speeds, possibly due to sediment resuspension. This kind of baseline evaluation will help to detect threats, direct preventive measures for the protection of biodiversity, and deepen our knowledge of these distinct ecosystems. The results will help develop flexible management and preservation plans that can adjust to both natural and man-made changes.

Enclosing gases by gas circulation to establish photosynthetic O2-supported algal-bacterial granular system in pseudo-closed sequencing batch reactors for greenhouse gas emission mitigation

Photosynthetic O2-supported algal-bacterial aerobic granular sludge (AGS) presents great potential in carbon emission mitigation and operation cost saving in wastewater treatment processes. This study used gas circulation to enclose the photosynthetic O2 and the emitted CO2 for the development of photosynthetic O2-supported algal-bacterial AGS in a pseudo-closed sequencing batch reactors (SBR), targeting greenhouse gas emission mitigation (including CO2, N2O and CH4). Results showed that photosynthetic O2-supported AGS with abundant Cyanobacteria were achieved in this pseudo-closed SBR under strong light conditions. Although gas circulation enclosed acidic CO2, excessive Chlorophyll-a (Chl-a) in biomass still led to the increase of pH, followed by the occurrence of CO2 limitation and the loss of functional strains and extracellular polymeric substances, eventually collapsing granular structure. Interestingly, gas circulation can reduce GHG emissions due to prolonged contact time between gases and microorganisms. Chl-a contents showed an inversely proportional relationship with CO2 emission factors (0.04–0.61 kg-CO2/kg-COD), but over-high algae in biomass may induce more N2O emissions (0.45 % to 5.3 %) by affecting nitrification and denitrification processes. Compared to CO2 and N2O, the emission of CH4 was negligible. It was estimated that zero non-CO2 GHG emissions could be achieved when stable granules containing Chl-a content of > 5.8 mg/g-MLVSS. This study proposed that achieving low-carbon photosynthetic O2-supported algal-bacterial AGS requires minimizing the adverse impact of existing algae on denitrification and nitrification.

Explainable artificial intelligence for the interpretation of ensemble learning performance in algal bloom estimation.

Chlorophyll-a (Chl-a) concentrations, a key indicator of algal blooms, were estimated using the XGBoost machine learning model with 23 variables, including water quality and meteorological factors. The model performance was evaluated using three indices: root mean square error (RMSE), RMSE-observation standard deviation ratio (RSR), and Nash-Sutcliffe efficiency. Nine datasets were created by averaging 1hour data to cover time frequencies ranging from 1hour to 1month. The dataset with relatively high observation frequencies (1-24 h) maintained stability, with an RSR ranging between 0.61 and 0.65. However, the model's performance declined significantly for datasets with weekly and monthly intervals. The Shapley value (SHAP) analysis, an explainable artificial intelligence method, was further applied to provide a quantitative understanding of how environmental factors in the watershed impact the model's performance and is also utilized to enhance the practical applicability of the model in the field. The number of input variables for model construction increased sequentially from 1 to 23, starting from the variable with the highest SHAP value to that with the lowest. The model's performance plateaued after considering five or more variables, demonstrating that stable performance could be achieved using only a small number of variables, including relatively easily measured data collected by real-time sensors, such as pH, dissolved oxygen, and turbidity. This result highlights the practicality of employing machine learning models and real-time sensor-based measurements for effective on-site water quality management. PRACTITIONER POINTS: XAI quantifies the effects of environmental factors on algal bloom prediction models The effects of input variable frequency and seasonality were analyzed using XAI XAI analysis on key variables ensures cost-effective model development.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Latitudinal variation in zooplankton over the Emperor Seamounts (34°–44° N, 170°–171° E) during the summer of 2019

During a research expedition to the Northwest Pacific between July 13 and August 11, 2019, we collected oceanic zooplankton samples over the Emperor Seamounts (Koko, Jingu, Nintoku, and Suiko guyots). We analyzed the horizontal and vertical distributions of abundance and biomass and also the structure of the zooplankton in the highly productive but poorly studied ocean waters between 34° and 44° N. The zooplankton was represented by four genera of amphipods, seven genera of pteropods, 39 genera of copepods, and larvae of benthic invertebrates and fish. The abundance and biomass of zooplankton increased towards higher latitudes along the series Koko < Jingu < Nintoku < Suiko guyots. The highest index of diversity was recorded over the Koko and Jingu guyots; the highest species richness occurred over the Jingu and Nintoku guyots. Small-sized copepods, appendicularians, chaetognaths, euphausiids, and fish eggs and larvae concentrated in the epipelagic; large copepods and ostracods concentrated in the mesopelagic. We identified three types of zooplankton assemblages: a Subtropical assemblage between 34°–35° N characterized by tropical/subtropical forms; a Transition assemblage characterized by subtropical, subarctic, and widespread species between 38°–41° N; and a Subarctic assemblage characterized by subarctic and widespread species between 43°–44° N. The spatial distribution of zooplankton was a function of environmental variables such as surface salinity, temperature at 200-m, and, to a certain extent, by surface Chl-a concentrations.

Chlorophyll-a interannual and seasonal variability in Panay Gulf: Identification of potential productive sites in the municipal waters and offshore blooms aided by water currents

Chlorophyll-a (Chl-a) is often used as a substitute for marine primary productivity measurements in fishing grounds. Panay Gulf stands as one of the Philippines’ vital fishing grounds, however, there are limited studies on its Chl-a interannual and seasonal variability. As such, this study used remotely sensed Chl-a data from 2017-2023 to assess areas with high Chl-a concentrations, and high primary productivity. The relationship of Chl-a to location, month, year, and water current variables was evaluated using a generalized additive model (GAM). Chl-a value of >10mg.m-3 was observed approximately <6.5, <2km, and <1km from the coast of Negros, Guimaras, and Panay Island respectively. During the Northeast Monsoon, short-term offshore pockets with elevated Chl-a levels were noted 13-24km from the coast of Binalbagan, Negros Occidental that function as brief feeding grounds for marine organisms. These Chl-a pockets originated from the coast of Negros Occidental and were advected by the water currents to the center of the gulf. Furthermore, 2017 and 2022 have the highest Chl-a value in Panay Gulf, coinciding with the region's seasonal high fisheries production. Major peaks were regularly observed in January and December when offshore Chl-a pockets were present. A minor peak was observed in July corresponding to the Southwest Monsoon season and attributed to summer blooms. The lowest Chl-a values were observed in September during the transition months. Furthermore, high Chl-a values were found in low current speed near the coast and water moving west and southwest during the Northeast Monsoon. This study is the first to evaluate the Chl-a dynamics in Panay Gulf, which is valuable information to understand the influence of the environment on marine productivity. This study may prompt the local government to intensify monitoring efforts and coastal protection in these valuable areas within the municipal waters.

Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images

Monitoring water quality is crucial for water exchange, precise feeding, and quality control of water products in freshwater aquaculture. In light of the issue of spatial heterogeneity in freshwater aquaculture pond waters and the constraints of conventional sensor detection techniques and traditional machine learning models. In this study, UAV multispectral images were combined with four machine learning algorithms (Ridge, XGBoost, CatBoost, RF) and the Stacking model to model the estimation of Chlorophyll a (Chl-a) and Turbidity and map their spatial distribution. The findings indicate that, in contrast to machine learning models, the Stacking model of water quality parameter performs better with higher accuracy. Meanwhile,for Chl-a and Turbidity the optimal sub-model combination in the Stacking model varies, with the most effective estimation model for Chl-a concentration identified as RF-XGB-Ridge (R2 = 0.84, RMSE=1.882 µg/L, MAE=3.433 µg/L and Slope = 0.791). As to Turbidity, the RF-CAB-Ridge model demonstrates superior performance, with macro-averaged precision (macro-p) of 93.3 %, macro-averaged recall (macro-R) of 88.8 %, macro-averaged F1-score (macro-F1) of 0.895, and Kappa coefficient of 0.813. Furthermore, the results of the joint analyses, which included measured samples and management measures at the test site, demonstrated that the spatial distribution maps of Chl-a and Turbidity were in alignment with the current status of water quality at the test site. This consistency was observed across both temporal and spatial scales. The results demonstrate that the integration of UAV multispectral images with the Stacking model can enhance the precision of water quality parameter models, facilitates the examination of the spatial and temporal distribution of water quality parameters and the underlying influencing factors, and advances the capability for dynamic monitoring of water quality parameters in freshwater aquaculture regions. Concurrently, it offers fundamental theoretical and methodological assistance for the precise regulation of water quality in freshwater aquaculture ponds and the formulation of optimal production management strategies.

The interaction between water quality and meteorological factors on chlorophyll-a concentration in Honghu Lake: based on PiecewiseSEM-generalized additive model coupling model.

In order to explore the interactive effects of environmental factors on the chlorophyll-a (Chl-a) concentration variation in Honghu Lake, this study was based on the monitoring data of Chl-a mass concentration and water quality factors (water temperature, pH, dissolved oxygen, permanganate index, total nitrogen, total phosphorus) and meteorological factors (evaporation, precipitation, sunshine hours, average wind speed) at three research sites (Dakou, Chatan Island, Lantian) in Honghu Lake from January 2010 to December 2019. Time series analysis, piecewise structural equation model (PiecewiseSEM), and generalized additive model (GAM) were used to quantitatively study the spatial and temporal changes of different environmental factors and their interaction with chlorophyll-a concentration in Honghu Lake. The results showed that the effects of TN and DO on Chl-a at Dakou and Chatan Island were more significant than other environmental meteorological factors, while the effects of DO and CODMn on Chl-a at Lantian were more obvious. At the same time, it was found that Chl-a had a non-linear relationship with TN and DO at Dakou and Chatan Island, a non-linear relationship with DO at Lantian, and a linear relationship with CODMn. The interaction effect of dominant environmental meteorological factors on Chl-a was significantly higher than that of a single factor, and the explanation rates were 80.6%, 72.8%, and 64.6%, respectively. In conclusion, based on the Piecewise SEM and GAM model, it not only can reveal the influence of the interaction of influencing factors on the change of Chl-a concentration, but also has important significance for the early warning and control of lake eutrophication.

Multiple remotely sensed datasets and machine learning models to predict chlorophyll-a concentration in the Nakdong River, South Korea.

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 - 0.86 greater in R2 and 0.36 - 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring.

Microbial nitrogen cycling in Microcystis colonies and its contribution to nitrogen removal in eutrophic Lake Taihu, China

Cyanobacterial blooms induced by excessive loadings of nitrogen (N) and other nutrients are a severe ecological problem in aquatic ecosystems. Previous studies of N removal have primarily focused on sediment-water interface, yet the role of cyanobacterial colonies has recently been attracting more research attention. In this study, N cycling processes were quantified for cyanobacterial colonies (primarily Microcystis colonies) and their contribution to N removal was estimated for a large, shallow eutrophic lake in China, Lake Taihu. Various N cycling processes were determined via stable 15N isotope, together with 16S rRNA gene sequencing and quantitative microbial element cycling (QMEC) chip. Denitrification was found to be the most prominent process, estimated to be 36.63, 9.85, 3.35, and 3.15 times higher than dissimilatory nitrate reduction to ammonium (DNRA), nitrification, ammonium (NH4+) uptake and nitrate (NO3−) uptake rates, respectively. Denitrifiers accounted for a large part of the bacterial taxa (35.50 ± 24.65%), and the nirS gene was the most abundant among N cycling-related genes, with (2.54 ± 0.51) × 109 copies g−1Microcystis colonies. A field investigation revealed a positive correlation between the potential denitrification rate and the Chl-a concentration (mostly derived from Microcystis colonies). Based on a multiple stepwise regression model and historical data from 2007 to 2015 for Lake Taihu, the total amount of N removed via denitrification by Microcystis colonies was estimated at 171.72 ± 49.74 t yr−1; this suggests that Microcystis colonies have played an important role in N removal in Lake Taihu since the drinking water crisis in 2007. Overall, this study revealed the importance of denitrification within Microcystis colonies for N removal in eutrophic lakes, like Lake Taihu.

Identifying environmental impacts on planktonic algal proliferation and associated risks: a five-year observation study in Danjiangkou Reservoir, China

Understanding the risks of planktonic algal proliferation and its environmental causes is crucial for protecting water quality and controlling ecological risks. Reservoirs, due to the characteristics of slow flow rates and long hydraulic retention times, are more prone to eutrophication and algal proliferation. Chlorophyll-a (Chl-a) serves as an indicator of planktonic algal biomass. Exploring the intricate interactions and driving mechanisms between Chl-a and the water environment, and the potential risks of algal blooms, is crucial for ensuring the ecological safety of reservoirs and the health of water users. This study focused on the Danjiangkou Reservoir (DJKR), the core water source of the Middle Route of the South-to-North Water Diversion Project of China (MRSNWDPC). The multivariate statistical methods and structural equation modeling were used to explore the relationships between chlorophyll-a (Chl-a) contents and water quality factors and understand the driving mechanisms affecting Chl-a variations. The Copula function and Bayesian theory were combined to analyze the risk of changes in Chl-a concentrations at Taocha (TC) station, which is the core water source intake point of the MRSNWDPC. The results showed that the factors driving planktonic algal proliferation were spatially heterogeneous. The main factors affecting Chl-a concentrations in Dan Reservoir (DR) were water physicochemical factors (water temperature, dissolved oxygen, pH value, and turbidity) with a total contribution rate of 60.18%, whereas those in Han Reservoir (HR) were nutrient factors (total nitrogen, total phosphorus, and ammonia nitrogen) with a total contribution rate of 73.58%. In TC, the main factors were water physicochemical factors (turbidity, pH, and water temperature) and nutrient factors (total phosphorus) with total contribution rates of 39.76% and 45.78%, respectively. When Chl-a concentrations in other areas of the DJKR ranged from the minimum to the uppermost quartile, the probabilities that Chl-a concentrations at the TC station exceeded 3.4 μg/L (the benchmark value of Chl-a for lakes in the central-eastern lake area of China) owing to the influence of these areas were all less than 10%. Thus, the risk of planktonic algal proliferation at the MRSNWDPC intake point is low. This study developed an integrated framework to investigate spatiotemporal changes in algal proliferation and their driving factors in reservoirs, which can be used to support water quality management in mega hydro projects.

Tiber River-Driven Chlorophyll-a and Total Suspended Matter Dynamics and Their Impacts along the Central Tyrrhenian Sea Coast: A Sentinel-2 Approach

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating the coastal Chl-a concentration levels. The present research primarily focuses on understanding the significance of Tiber River −driven spatial dynamics of Chl-a and TSM along the central Tyrrhenian Sea coasts. The research also focuses on evaluating the applicability of Sentinel-2 and identifying a suitable method for estimating Chl-a and TSM from Sentinel-2. Neural networks and dark spectrum fitting techniques were applied using multiple algorithms to estimate the dynamic distribution of Chl-a and TSM driven by the Tiber River in the study area. Multiple statistical analyses were performed, and statistically significant relationships were observed. The Case-2 Regional Coast Colour Neural Network (C2RCC-Net) outperformed all other algorithms, with an R2 value of 0.903 for Chl-a and an R2 value of 0.966 for TSM. Furthermore, the present research also identified a positive pixel to pixel spatial correlation between Chl-a and TSM in all four seasons, highlighting the positive impact of Tiber River on maintaining Chl-a levels along the coasts of Tyrrhenian Sea. This stands in contrast with the negative trend seen in the Mediterranean scale.

Metabolic processes drive spatio-temporal variations of carbon sink/source in a karst river

Riverine carbon dioxide (CO2) exchange is a crucial component of the global carbon cycle. However, the changes in the CO2 sink/source in karst rivers caused by differences in lithological features and climate, hindered the resolution of the spatio-temporal heterogeneity of global inland water carbon emissions. Here, we use hydrochemical data and CO2 gas isotopic data to reveal the spatio-temporal variations of CO2 sink/source in karst rivers and their controlling mechanisms. Fifty-two monitoring transects were set up in the subtropical Lijiang River in southwest China in June and December 2019. Our results indicated that the CO2 flux across the water-air interface (FCO2) in the Lijiang River basin ranged from −43.77 to 519.67 mmol/(m2·d). In June, the Lijiang River acted as an atmospheric carbon source due to higher water temperatures (Twater). However, driven by hydrodynamic conditions and the metabolism of aquatic photosynthesis, the river shifts from being an atmospheric carbon source in June to an atmospheric carbon sink in December. The stable isotopes of CO2 (δ13C-CO2) show significant differences in the spatio-temporal variations of CO2 sink/source. In December, the transects of the Lijiang River basin with a negative CO2 flux are significantly negatively correlated with dissolved oxygen (DO) and chlorophyll-a (Chl-a) concentration (p < 0.05). This confirms that the enhancement of aquatic photosynthesis efficiency increased water DO concentrations, which resulted in the positive movement of water δ13C-CO2 and a decrease in the partial pressure of CO2 (pCO2) and FCO2. Comparative analysis with global river FCO2 indicates that under the combined driving forces of metabolic processes of aquatic photosynthetic organisms and hydrodynamic conditions, rivers tend to act more frequently as CO2 sinks, particularly in subtropical and temperate rivers. In conclusion, this study represents a new example focusing on CO2 dynamics to address the spatio-temporal heterogeneity of carbon emissions in inland waters on a global scale.

Impact and Mechanism of Medium and Small Flood Regulation in the Three Gorges Reservoir on the Phytoplankton in Tributary Bays

The regulation of small- and medium-sized floods （RSMF） has become the main mode of regulation in the flood season of the Three Gorges Reservoir （TGR）. To study the response of phytoplankton in the tributary bays of the TGR to the RSMF, a typical eutrophic tributary of the TGR, Xiangxi River, was investigated for the spatiotemporal distribution characteristics of phytoplankton and nutrients in the main and tributary streams from 2020 to 2021. The response characteristics of phytoplankton in the tributary bays to the RSMF were analyzed. The results indicated that during the RSMF, the chlorophyll a （Chl-a） in the water body of the Xiangxi River decreased with the increase in the water level in front of the dam, whereas during the reservoir impounding at the end of flood season, the concentration of Chl-a increased again. During the RSMF, the Chlorophyta and Diatoma were the main communities of planktonic algae in the Xiangxi River. The phytoplankton community changed with the RSMF. When the water level fluctuation increased, diatoms were the main species, whereas when the water level fluctuation was small, blue and green algae were the main species. The concentration of Chl-a was more sensitive to changes in TN concentration. When the flow velocity was &gt;0.25 m·s-1 or the suspended sediment content was &gt;10 mg·L-1, the concentration of Chl-a in the water was inhibited. After 2010, the typical outbreak time of algal blooms in the Xiangxi River Reservoir Bay shifted to the flood season, with only two non-flood season algal blooms. Further attention needs to be paid to the response of algal blooms in the reservoir to small- and medium-sized flood control during the flood season.