Knowledge-guided mixture density network for chlorophyll-a retrieval and associated pixel-by-pixel uncertainty assessment in optically variable inland waters

Machine learning has been increasingly used to retrieve chlorophyll-a (Chl-a) in optically variable waters. However, without the guidance of physical principles or expert knowledge, machine learning may produce biased mapping relationships, or waste considerable time searching for physically infeasible hyperparameter domains. In addition, most Chl-a retrieval models cannot evaluate retrieval uncertainty when ground observations are not available, and the retrieval uncertainty is crucial for understanding the model limitations and evaluating the reliability of retrieval results. In this study, we developed a novel knowledge-guided mixture density network to retrieve Chl-a in optically variable inland waters based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The proposed method embedded prior knowledge derived from spectral shape classification into the mixture density network. Compared to another deterministic model, the knowledge-guided mixture density network outputted the conditional distribution of Chl-a given an input spectrum, enabling us to estimate the optimal retrieval and the associated uncertainty. The proposed method showed favorable correspondence with the field Chl-a, with root mean square error (RMSE) of 6.56 μg/L, and mean absolute percentage error (MAPE) of 43.64 %. Calibrated against Sentinel-3 OLCI spectrum, the proposed method also performed well when applied to field spectrum (RMSE = 4.58 μg/L, MAPE = 72.70 %), suggesting its effectiveness and good generalization. The proposed method provided the standard deviation of each estimated Chl-a, which enabled us to inspect the reliability of the estimated results and understand the model limitations. Overall, the proposed method improved the Chl-a retrieval in terms of model accuracy and uncertainty evaluation, providing a more comprehensive Chl-a observation of inland waters.

Assessing sandy beach macrofaunal assemblages across geographically diverse morphodynamic environments

While the physical characteristics of sandy beaches play a significant role in shaping the macrofaunal community features through morphodynamics, regional environmental factors may also account for deviations from the expected patterns. Here, we assess the concurrent effects of local morphodynamic factors and regional variables, such as sea surface temperature (SST), salinity, and chlorophyll-a (chl-a), on species richness and abundance of intertidal macrofaunal assemblages in four sandy beaches located along the estuarine gradient generated by the Río de la Plata (RdlP) in the southwestern Atlantic Ocean. Species richness was higher in dissipative beaches compared to intermediate ones, consistent with the predictions of the Swash Exclusion Hypothesis. However, this trend was not observed for total abundance, which significantly increased with chl-a. Both local and regional-scale environmental factors, such as salinity and chl-a, proved to be significant predictors in the arrangement of these communities. These results further support previous findings that highlight the critical role of the estuarine gradient of the RdlP in shaping life-history traits, population structure, and abundance of the resident intertidal macrofauna at both local and regional scales. The study underscores the importance of integrating environmental factors operating at different spatial scales to decipher community patterns in these physically-controlled environments.

Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

Long-term (2003-2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The degradation of water quality induced by the construction of large-scale hydraulic projects is one of the primary public concerns; however, it is rarely addressed with long-term field observation data. Here, we reported the long-term (2003-2021) trends, seasonal patterns, and overall condition of water quality of the Three Gorges Reservoir (TGR) with an enhanced water quality index (WQI). Specifically, to emphasize the importance of the biological role in water quality assessment, chlorophyll-a (Chla) was incorporated into WQI, and then a novel workflow using machine learning approach based on Random Forest (RF) model was constructed to develop a minimal water quality index (WQImin). The enhanced WQI indicated an overall "good" water quality condition, exhibiting a gradually improving trend subsequent to the reservoir impoundment in 2003. Meanwhile, the assessment revealed that the water quality has discernible seasonal patterns, characterized by poorer conditions in the spring and summer seasons. Furthermore, the RF model identified Chla, dissolved oxygen (DO), ammonium nitrogen (NH4-N), water temperature (WT), pH, and total nitrogen (TN) as key parameters for the WQImin, with Chla emerging as the most important factor in determining WQImin in our study. Moreover, weighted WQImin models exhibited improved performance in estimating WQI. Our study emphasizes the importance of biological parameters in water quality assessment, and introduces a systematic workflow to facilitate the development of WQImin for accurate and cost-efficient water quality assessment. Furthermore, our study makes a substantial contribution to the advancement of knowledge regarding long-term trends and seasonal patterns in water quality of large reservoirs, which provides a foundational basis for guiding water quality management practices for reservoirs worldwide.

Phytoplankton spring bloom in the NW Mediterranean Sea under climate change

The spring phytoplankton bloom is the main event influencing ecosystem richness in the pelagic realm of the Northwestern Mediterranean Sea (NW Med Sea). The Marine Strategy Framework Directive requires the achievement of a good ecological status for the pelagic habitat, and phytoplankton bloom phenology has been used as an indicator of the status of offshore waters. In this work we investigate interannual changes in the timing and magnitude of the phytoplankton bloom in the NW Med Sea, using phenological metrics. Daily maps of Chl-a concentration from 1998 to 2022 obtained by CMEMS were used to analyse bloom phenological metrics in 5 representative sites in the area. Chlorophyll-a data from 1998 to 2007 were used for determining the climatological behaviour, while 2008-2022 was identified as the study period. For this latter period, yearly spring bloom were identified and interannual variability and overall trends were analysed for each of the phenological metrics considered. Winter oceanographic and meteorological data were analysed to investigate possible correlations with the subsequent spring bloom. The frequency of anomalous years is increasing, both for bloom intensity and sea temperature. Bloom analysis revealed a negative trend only in some areas, but a steep decrease in the last 7 years was noticeable for all sites considered. Correlations of the Chl-a concentration during bloom with oceanographic variables revealed the importance of temperature, both marine and atmospheric, while Mixed Layer Depth played a lesser role. This work contributes to a better understanding of the dynamics of an area already under severe threat from human activities.

A low-cost and portable fluorometer based on an optical pick-up unit for chlorophyll-a detection

Chlorophyll-a (Chl-a) fluorescence detection is an important technique for monitoring water quality. In this work, we proposed an approach that employs the mass-produced low-cost optical pick-up unit (OPU) extracted from the high-definition digital versatile disc (HD-DVD) drive as the key optical component for our chlorophyll-a fluorometer. The built-in blue-violet 405 nm laser diode of the OPU acts as the excitation light to perform laser-induced fluorescence (LIF). The laser driver and a series of intrinsic lenses within the OPU, such as an objective lens with a numerical aperture (NA) of 0.65 and a collimating lens, help reduce the size, cost, and system complexity of the fluorometer. By integrating off-the-shelf electronic components, miniaturized optical setups, and 3D-printed assemblies, we have developed a low-cost, easy-to-make, standalone, and portable fluorometer. Finally, we validated the performance of the device for chlorophyll-a fluorescence detection under laboratory and on-site conditions, which demonstrated its great potential in water monitoring applications. The limit of detection (LOD) for chlorophyll-a is 0.35 μg/L, the size of the device is 151 × 100 × 80 mm3, and the total cost of the proposed fluorometer is as low as 137.5 USD. © 2023 Elsevier Science. All rights reserved.

Past environmental changes: using sedimentary photosynthetic pigments to enhance subtropical reservoir management

The historical impacts of eutrophication processes were investigated in six subtropical reservoirs (São Paulo, Brazil) using a paleolimnological approach. We questioned whether the levels of pigment indicators of algal biomass could provide information about trophic increase and whether carotenoid pigments could offer additional insights. The following proxies were employed: organic matter, total phosphorus, total nitrogen, photosynthetic pigments (by high-performance liquid chromatography), sedimentation rates, and geochronology (by 210 Pb technique). Principal component analysis indicated a gradient of eutrophication. In eutrophic reservoirs (e.g., Rio Grande and Salto Grande), levels of lutein and zeaxanthin increased over time, suggesting growth of Chlorophyta and Cyanobacteria. These pigments were significantly associated with algal biomass, reflecting their participation in phytoplankton composition. In mesotrophic reservoirs, Broa and Itupararanga, increases and significative linear correlations (r > 0.70) between pigments and nutrients are mainly linked to agricultural and urban activities. In the oligotrophic reservoir Igaratá, lower pigment and nutrient levels reflected lesser human impact and good water quality. This study underscores eutrophication's complexity across subtropical reservoirs. Photosynthetic pigments associated with specific algal groups were informative, especially when correlated with nutrient data. The trophic increase, notably in the 1990s, may have been influenced by neoliberal policies. Integrated pigment and geochemical analysis offers a more precise understanding of eutrophication changes and their ties to human factors. Such research can aid environmental monitoring and sustainable policy development.

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m3 across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.

Dynamic monitoring and analysis of chlorophyll-a concentrations in global lakes using Sentinel-2 images in Google Earth Engine

Remote estimation of Chlorophyll-a (Chl-a) has long been used to investigate the responses of aquatic ecosystems to global climate change. High-spatiotemporal-resolution Sentinel-2 satellite images make it possible to routinely monitor and trace the spatial distributions of lake Chl-a if reliable retrieval algorithms are available. In this study, Sentinel-2 images and in-situ measured data were used to develop a Chl-a retrieval algorithm based on 13 optical water types (OWTs) with a satisfying performance (R2 = 0.74, RMSE = 0.42 mg/m3, MAE = 0.33 mg/m3, and MAPE = 55.56 %). After removing the disturbance of algal blooms and other factors, the distribution of Chl-a in 3067 of the largest global lakes (≥50 km2) was mapped using the Google Earth Engine (GEE). From 2019 to 2021, the average Chl-a concentration was 16.95 ± 5.95 mg/m3 for the largest global lakes. During the COVID-19 pandemic, global lake-averaged Chl-a concentration reached its lowest value in 2020. From the perspective of spatial distribution, lakes with low Chl-a concentrations were mainly distributed in high-latitude, high-elevation, or economically underdeveloped areas. Among all the potential influencing factors, lake surface temperature had the largest contribution to Chl-a and showed a positive correlation with Chl-a in approximately 92.39 % of the lakes. Conversely, factors such as precipitation and tree cover area around the lake were negatively correlated with Chl-a concentration in nearly 61.44 % of the lakes.

The influence mechanism of water level operation on algal blooms in canyon reservoirs and bloom prevention

The water level operation of reservoirs affects the spatiotemporal patterns of water quality, light-heat, hydrodynamics and phytoplankton, which have implications for algal bloom prevention. However, the theoretical analysis and practical applications of related research are limited. Based on prototype observations and numerical modeling, data on algae, water level operation and environmental factors in the Zipingpu Reservoir from April and September in 2015 to 2017 and 2020 to 2022 were collected. An in-depth analysis of the causal mechanisms between algal blooms and water level operation was performed, and prevention strategies with practical application assessments were developed. Water level operation control in the reservoir from April to September can be divided into five stages (falling-rising-oscillating-falling-rising), with algal blooms occurring only in the second stage. The rising water level with inflow into the middle layers shapes a closed-loop circulation in the surface waters. This distributes the nutrients that were trapped in the surface layer during the first stage, helping algae avoid to phosphorus limitation and thrive in the closed loop circulation, leading to algal blooms (chlorophyll-a exceeding 10 mg/m3). There is a significant positive correlation (p < 0.05) between algal blooms and the rapid rise in water levels in the second stage, occurring within a span of three days. To contain the algal bloom, a water level operation limit of rising waters on the third day after a two-day consecutive rise in water level was examined. This was found to be effective after its practical application to the case reservoir in 2022, with chlorophyll-a concentrations consistently below 10 mg/m3. This study unveils the mechanisms through which water level operation affects algal blooms and presents a successful case of bloom prevention. Furthermore, it serves as a valuable reference for the management of canyon reservoirs.

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Identifying the spatial pattern and the drivers of the decline in the eastern English Channel chlorophyll-a surface concentration over the last two decades

It has been established from previous studies that chlorophyll-a surface concentration has been declining in the eastern English Channel. This decline has been attributed to a decrease in nutrient concentrations in the rivers. However, the decrease in river discharge could also be a cause. In our study, rivers outflows and in-situ data have been compared to time series of satellite-derived chlorophyll-a concentrations. Dynamic Linear Model has been used to extract the dynamic and seasonally adjusted trends of several environmental variables. The results showed that, for the 1998-2019 period, chlorophyll-a levels stayed significantly lower than average and satellite images revealed a coast to offshore gradient. Chlorophyll-a concentration of coastal stations appeared to be related to the declining fluxes of phosphate while offshore stations were more related to nitrate-nitrite. Therefore, we can exclude that the climate variability, through river flows alone, has a dominant effect on the decline of chlorophyll-a concentration.

Young-of-the-year fish as bioindicators of eutrophication and temperature regime of water bodies

Young-of-the-year fish communities are widely used as bioindicators of various environmental disturbances. This study was conducted from 1997 to 2015 and aims to develop fish trait-based indices of changes in the temperature regime and eutrophication of water bodies in the Dnipro River basin. We identified fish traits that significantly correlate with both temperature and chlorophyll-a concentration optimum: reproduction habitat, oxygen tolerance, and toxicity tolerance. Compared to other ecological groups, lithophilic species exhibited the lowest degree of thermal and eutrophication optimum, indicating this species' greater vulnerability to environmental alteration. Fish species that are intolerant to water quality and low oxygen concentration were the most sensitive to changes in temperature regime and eutrophication level. Salinity preferences and water quality tolerance emerged as reliable predictors of temperature optimum. Freshwater fish had an average temperature optimum that was 4.5% higher than that of freshwater-brackish and freshwater-brackish-marine fish. Species tolerance to the temperature factors and nutrient loads correlated only with rheophily, with rheophilic species having an average 13.8% higher temperature tolerance than other fish species and a 10.4% higher chlorophyll-a concentration tolerance. The fish temperature index increased over time during the study period in all the studied water bodies, consistent with ongoing warming affecting all sites. In contrast, the Fish Eutrophication Index showed greater temporal heterogeneity in studied water bodies, indicating various adaptative potentials of fish communities to eutrophication. These indices can be relevant for assessing disturbed situations caused by changes in climatic and anthropogenic impacts on water bodies.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

A novel hybrid model based on two-stage data processing and machine learning for forecasting chlorophyll-a concentration in reservoirs

The accurate and efficient prediction of chlorophyll-a (Chl-a) concentration is crucial for the early detection of algal blooms in reservoirs. Nevertheless, predicting Chl-a concentration in multivariate time series poses a significant challenge due to the complex interrelationships within the aquatic environment and the discrete and non-stationary nature of online monitoring of water quality data. To address the aforementioned issue, this paper proposes a novel prediction model named SGMD-KPCA-BiLSTM (SKB) for predicting Chl-a concentration. The model combines two-stage data processing and machine learning (ML). To capture nonlinear relationships in multivariate time series data, the optimal data subset is determined by combining symplectic geometry mode decomposition (SGMD) and kernel principal component analysis (KPCA). This subset is then input into a bidirectional long short-term memory (BiLSTM) model, and the model's hyperparameters are optimized using the sparrow search algorithm (SSA) to improve the accuracy of predictions. The performance of the model was evaluated at Qiaodian Reservoir in Shandong, China. To assess its superiority, the evaluation criteria included the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of determination (R2), frequency histograms of the prediction error, and the Taylor diagram. The prediction performance of five single models, namely the back-propagation (BP) neural network, support vector regression (SVR), long short-term memory (LSTM), convolutional neural network with long short-term memory (CNN-LSTM), and BiLSTM, as well as three hybrid models, namely SGMD-LSTM, SGMD-KPCA-LSTM, and SGMD-BiLSTM, were compared against the SKB model. The results demonstrated that the SKB model performs best in predicting Chl-a concentration (R2 = 96.19%, RMSE = 1.05, MAE = 0.65, MAPE = 0.08). It significantly reduced the prediction error compared to other models for comparison. Furthermore, the multi-step predictive capabilities of the SKB model are also discussed. The analysis shows a decline in predictive performance with larger prediction time steps, and the SKB model exhibits slightly superior performance compared to the other model at corresponding prediction intervals. The model has significant advantages in terms of its ability to accurately predict the non-smooth and nonlinear Chl-a sequences observed by the online monitoring system. This study presents a potential solution for controlling and preventing reservoir eutrophication, as well as an innovative approach for predicting water quality.

The impact of extreme precipitation on physical and biogeochemical processes regarding with nutrient dynamics in a semi-closed bay

An extreme precipitation event in August 2012 changed the ecosystem of Jiaozhou Bay (JZB), China. Biochemical variables in the sea, river mouths, and rainwater were monitored simultaneously during the event. The impact of the following excessive riverine input and wet atmospheric deposition on nutrient dynamics were studied before. However, regulatory processes of nutrient dynamics were not quantified and analyzed. Therefore, a coupled physical-biological model (FVCOM-ERSEM) was used to study the physical and biochemical mechanisms of the variation of the dissolved inorganic nitrogen (DIN), phosphorus (DIP), and silicon (DISi), as well as chlorophyll-a (Chl-a). The results indicate that physical processes increase nutrients, while biological processes reduce them. The exchange with the Yellow Sea, as an important physical process, exports DIN to the Yellow Sea, but imports DIP and DISi to the JZB. Only 20 % of the excessive DIN due to extreme precipitation event was reduced by water exchange with the Yellow Sea. The rest (80 %) was reduced and changed into organic nitrogen through biological processes. This paper also examines the variation of the pelagic and benthic cycles of biochemical processes. In these cycles, phytoplankton take up and use nutrients in the bay, while zooplankton excretion in the pelagic cycle and benthic releases resupply them. Precipitation enriched the surface nutrients, which boosted primary production and organic matter transport to the bottom water.

A semi-analytical model to estimate Chlorophyll-a spatial-temporal patterns from Orbita Hyperspectral image in inland eutrophic waters

It can be challenging to accurately estimate the Chlorophyll-a (Chl-a) concentration in inland eutrophic lakes due to lakes' extremely complex optical properties. The Orbita Hyperspectral (OHS) satellite, with its high spatial resolution (10 m), high spectral resolution (2.5 nm), and high temporal resolution (2.5 d), has great potential for estimating the Chl-a concentration in inland eutrophic waters. However, the estimation capability and radiometric performance of OHS have received limited examination. In this study, we developed a new quasi-analytical algorithm (QAA716) for estimating Chl-a using OHS images. Based on the optical properties in Dianchi Lake, the ability of OHS to remotely estimate Chl-a was evaluated by comparing the signal-to-noise ratio (SNR) and the noise equivalent of Chl-a (NEChl-a). The main findings are as follows: (1) QAA716 achieved significantly better results than those of the other three QAA models, and the Chl-a estimation model, using QAA716, produced robust results with a mean absolute percentage difference (MAPD) of 11.54 %, which was better than existing Chl-a estimation models; (2) The FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model (MAPD = 22.22 %) was more suitable for OHS image compared to the other three atmospheric correction models we tested; (3) OHS had relatively moderate SNR and NEChl-a, improving its ability to accurately detect Chl-a concentration and resulting in an average SNR of 59.47 and average NEChl-a of 72.86 μg/L; (4) The increased Chl-a concentration in Dianchi Lake was primarily related to the nutrients input, and this had a significant positive correlation with total nitrogen. These findings expand existing knowledge of the capabilities and limitations of OHS in remotely estimating Chl-a, thereby facilitating effective water quality management in eutrophic lake environments.

Marine heatwaves during the pre-monsoon season and their impact on Chlorophyll-a in the north Indian Ocean in 1982-2021

Indian Ocean has been undergoing rapid warming in recent years, which increases the likelihood of Marine heatwave (MHW). MHWs are extreme warm ocean surface conditions in which temperature exceeds the 95th percentile for three or more consecutive days. We investigate MHW events occurred in Arabian Sea (AS) and Bay of Bengal (BoB) during pre-monsoon for 1982-2021 period, their impact on Chlorophyll-a (Chl-a) and net primary productivity (NPP). There were 42 (68) MHW events with a significant trend of 8.1 (6.3) MHW days dec-1 in AS (BoB). There is a distinct decrease in Chl-a concentration associated with MHW, especially in medium and long duration events. In general, AS and BoB have witnessed more frequent and long-lasting MHWs in the 2002-2021 period, which reduce NPP of north Indian Ocean. A decrease in Chl-a and NPP, 10 % in AS and 2 % in BoB, is estimated, but only severe MHWs inflict a notable reduction.

Estimation of sea surface nitrate from space: Current status and future potential

Sea surface nitrate (SSN) plays an important role in assessing phytoplankton growth and new production in the ocean. Field sampling of SSN data is important, but limited by data quantity both spatially and temporally. Satellite remote sensing can contribute through providing spatial and temporal data to such assessments. During the past 30 years many studies have been published focusing on SSN retrievals from satellites to a greater or less extent. In this study, we reviewed the progresses of SSN estimation from satellites in both open ocean and coastal waters. Because of the lack of electromagnetic properties of SSN, satellite retrievals of SSN were most realized by developing relationships between SSN and related environmental variables (e.g., sea surface temperature, chlorophyll-a concentration, sea surface salinity), using traditional empirical regressions and novel machine learning techniques. We synthesized most of the peer-reviewed studies for both open and coastal oceans, in terms of study areas, model inputs, regression formulas, and model uncertainties. In general, regional SSN algorithms were most developed in coastal oceans with upwelling or river discharges. The published SSN algorithms had varying uncertainties with a wide range of 0.83-6.87 μmol/L, and the uncertainties were significantly reduced in recent studies, with more field measurements available and better understanding of the physical and biogeochemical processes in driving nitrate dynamics.

Exploratory study of the Sentinel-3 level 2 product for monitoring chlorophyll-a and assessing ecological status in Danish seas

In situ Chl-a data were used to perform empirical calibration and validation of Sentinel-3 level 2 product in Danish marine waters. Comparing in situ data with both same-day and ±5 days moving averaged Sentiel-3 Chl-a values yielded two similar positive correlations (p > 0.05) with rpearson values of 0.56 and 0.53, respectively. However, as the moving averaged values resulted in significantly more available data than daily matchups (N = 392 vs. N = 1292) at a similar quality of correlation with similar model parameters (slope (1.53 and 1.7) and intercept (-0.28 and -0.33) respectively), which were not significantly different (p > 0.05), the further analyses were focused on ±5 days moving averaged values. A thorough comparison of seasonal and growing season averages (GSA) also showed a very good agreement, except for a few stations characterized by very shallow depth. Overestimation by the Sentinel-3 occurred in shallow coastal areas and was attributed to the interferences from benthic vegetation and high levels of Colored Dissolved Organic matter (CDOM) interfering with the Chl-a signals. Underestimation observed in the inner estuaries with shallow Chl-a rich waters, however, seen as a result of self-shading at high Chl-a concentrations, reducing effective absorption by phytoplankton. Besides the observed minor disagreements, there was no significant difference when the GSA values from in situ and Sentinel-3 were compared for all three water types (p > 0.05, N = 110). Analyzing Chl-a estimates along a depth gradient showed significant (p < 0.001) non-linear trends of declining concentrations from shallow to deeper waters for both in situ (explaining 15.2 % of the variance (N = 109)) and Sentinel-3 data (explaining 36.3 % of the variance (N = 110)), with higher variability in shallow waters. Furthermore, Sentinel-3 enabled full spatial coverage of all 102 monitored water bodies providing GSA data at much higher spatial and temporal resolutions for good ecological status (GES) assessment compared to only 61 through in situ sampling. This underlines the potential of Sentinel-3 for substantially extending the geographical coverage of monitoring and assessment. However, the systematic over- and underestimation of Chl-a in shallow nutrient rich inner estuaries through Sentinel-3 requires further attention to enable routine application of the Sentinel-3 level 2 standard product in the operational Chl-a monitoring in Danish coastal waters. We provide methodological recommendations on how to improve the Sentinel-3 products' representation of in situ Chl-a conditions. Continued frequent in situ sampling remains important for monitoring as these measurements provide essential data for empirical calibration and validation of satellite based estimates to reduce possible systematic bias.

First report on the occurrence of Gonyaulax polygramma bloom during the onset of Noctiluca scintillans bloom along the Tuticorin coast, southeast coast of India

Dense and green-coloured patches were encountered on the sea surface waters of the Tuticorin coast on 22nd October 2022. Microscopic investigation revealed that the discoloration is caused by plankton, green Noctiluca scintillans. In order to find out the causes that trigger the bloom of N. scintillans, plankton samples were collected for 5 days in fourteen days duration from 22nd October to 4th November. During the peak bloom period, the abundance and biovolume of N. scintillans reached 1.56 × 104 cells/L and 21.8 × 1010μm3/L, respectively. The highest concentration (73.65 mg/m3) of chlorophyll-a was recorded during blooming period that was caused by Gonyaulax polygramma and endosymbiont, Pedinomonas noctilucae in N. scintillans. Formation of G. polygramma bloom is being reported for the first time in Tuticorin, southeast coast of India, with a species abundance of 36.9 × 104 cells/L. Present study concluded that besides the optimum hydrological conditions and eutrophic nature of the system, abundant prey (G. polygramma) facilitated the N. scintillans bloom.

The effect of catchment load reductions on water quality in the crown-of-thorn starfish outbreak initiation zone

Crown-of-Thorns Starfish (CoTS) population outbreaks contribute to coral cover decline on Indo-Pacific reefs. On the Great Barrier Reef (GBR), enhanced catchment nutrient loads are hypothesised to increase phytoplankton food for CoTS larvae in the outbreak initiation zone. This study examines whether catchment load reductions will improve water quality in this zone during the larval period. We defined the i) initiation zone's spatial extent; ii) larval stage's temporal extent; and iii) water quality thresholds related to larval food, from published information. We applied these to model simulations, developed to quantify the effect of catchment load reductions on GBR water quality (Baird et al., 2021), and found a consistently weak response of chlorophyll-a, total organic nitrogen and large zooplankton concentrations in the initiation zone. Model results indicate marine and atmospheric forcing are more likely to control the planktonic biomass in this zone, even during major flooding events purported to precede CoTS outbreaks.

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and 'SMOTE and cluster-based under-sampling technique' (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.

Macroecology of rocky intertidal benthic communities along the southwestern Atlantic: Patterns of spatial variation and associations with natural and anthropogenic variables

Assessing spatial variability in biodiversity and its relationships with potential drivers is necessary for understanding and predicting changes in ecosystems. Here, we evaluated spatial patterns in sessile macrobenthic communities in rocky intertidal habitats along the southwestern Atlantic (SE Brazil), spanning over 500 km of coastline. We applied a rapid-survey approach focusing on the main space occupiers and habitat-forming taxa. We partitioned community variance into spatial scales ranging from metres to hundreds of kilometres and assessed whether community patterns were associated with variation in shore topography, nearshore ocean, and human influence. The communities from the mid-midlittoral level exhibited equivalent variation (31-35%) at the scales of quadrats (metres), sites (kilometres), and sub-regions (tens of kilometres). For the communities from the low-midlittoral and infralittoral fringe levels, most variability occurred at the scales of quadrats and sites (30-42%), followed by sub-regions (22%). Wave fetch, sea surface temperature (SST), and shore inclination were the variables that best explained community structure at the mid-midlittoral. At the low-midlittoral and infralittoral fringe, the most influential variables were related to oceanic forcing (SST, total suspended solids, particulate organic carbon, chlorophyll-a concentration) and human influence. Univariate analyses also revealed strong associations between the abundance of the main components of the communities and the predictor variables evaluated. Our results suggest that urbanised estuarine bays and coastal upwelling regimes have a strong influence on adjacent benthic communities, driving macroecological patterns in the study area. This study advances the knowledge in macroecology and biogeography of rocky shores in an understudied coastline and globally and provides valuable insights for future assessments of ecological changes resulting from unfolding human impacts.

Spatiotemporal variation in the water quality of Vembanad Lake, Kerala, India: a remote sensing approach

Water quality is one of the essential parameters of environmental monitoring; even a slight variation in its characteristics may significantly influence the ecosystem. The water quality of Vembanad Lake is affected by anthropogenic effects such as industrial effluents and tourism. The optical parameters representing water quality, such as diffuse attenuation (Kd), turbidity, suspended particulate matter (SPM), and chlorophyll-a (Chl-a), are considered in this study to evaluate the water quality of Vembanad Lake, Kerala, India. As this lake is regarded as of ecological importance by the Ramsar Convention and has faced severe concerns over recent years, there was a substantial change in the water quality during the lockdowns of the COVID-19 pandemic. This research is aimed at examining the change in water quality using optical data from Sentinel-2 satellites in the ACOLITE processing software from 2016 to 2021. The analyses showed a 2.5% decrease in the values of Kd, whereas SPM and turbidity show a reduction of about 4.3% from the year 2016 to 2021. The flood and the COVID lockdown had an impact on the improvement in the quality of water from 2018 to 2021. The findings indicated that the reduction in industrial activities and tourism had a more significant effect on the improvement in the water quality of the lake. There was no substantial change in the Chl-a until 2020, whereas an average decrease of 12% in Chl-a values was observed throughout 2021. This decrease can be attributed to the reduction in the lake's hydrological residence time (HRT). Thus, these findings will be a valuable reference to help the government and non-government organizations (NGO) during strategic planning.

Ground-based remote sensing provides alternative to satellites for monitoring cyanobacteria in small lakes

Cyanobacteria are the most prevalent bloom-forming harmful algae in freshwater systems around the world. Adequate sampling of affected systems is limited spatially, temporally, and fiscally. Remote sensing using space- or ground-based systems in large water bodies at spatial and temporal scales that are cost-prohibitive to standard water quality monitoring has proven to be useful in detecting and quantifying cyanobacterial harmful algal blooms. This study aimed to identify a regional 'universal' multispectral reflectance model that could be used for rapid, remote detection and quantification of cyanoHABs in small- to medium-sized productive reservoirs, such as those typical of Oklahoma, USA. We aimed to include these small waterbodies in our study as they are typically overlooked in larger, continental wide studies, yet are widely distributed and used for recreation and drinking water supply. We used Landsat satellite reflectance and in-situ pigment data spanning 16 years from 38 reservoirs in Oklahoma to construct empirical linear models for predicting concentrations of chlorophyll-a and phycocyanin, two key algal pigments commonly used for assessing total and cyanobacterial algal abundances, respectively. We also used ground-based hyperspectral reflectance and in-situ pigment data from seven reservoirs across five years in Oklahoma to build multispectral models predicting algal pigments from newly defined reflectance bands. Our Oklahoma-derived Landsat- and ground-based models outperformed established reflectance-pigment models on Oklahoma reservoirs. Importantly, our results demonstrate that ground-based multispectral models were far superior to Landsat-based models and the Cyanobacteria Index (CI) for detecting cyanoHABs in highly productive, small- to mid-sized reservoirs in Oklahoma, providing a valuable tool for water management and public health. While satellite-based remote sensing approaches have proven effective for relatively large systems, our novel results indicate that ground-based remote sensing may offer better cyanoHAB monitoring for small or highly dendritic turbid lakes, such as those throughout the southern Great Plains, and thus prove beneficial to efforts aimed at minimizing public health risks associated with cyanoHABs in supply and recreational waters.

Trophic state and potential productivity assessment for Qaroun Lake using spatial techniques

Qaroun Lake is one of the most important Egyptian lakes which, recently, have been exposed to severe degradation in water quality and fish productivity. In this manuscript, Carlson's trophic state index (CTSI) was used to evaluate the trophic state, while the trophometric index (TMI) was used to assess the potential productivity of Qaroun Lake. The present study is one of the initial attempts to investigate these indices in Qaroun Lake. To achieve this work, an integrated multidisciplinary approach was adopted integrating field investigation, geographic information system, and data analysis. CTSI combines three variables of water quality: chlorophyll-a (CHL-a), total phosphorus (TP), and transparency measured by Secchi disk depth (SDD). The result of overall CTSI showed the hypereutrophic state is represented by 62% and eutrophic state is represented by 38% of the total lake's area. Moreover, the calculated TMI indicated the average potential productivity value (PP) is 619 t. It can be concluded that the hypereutrophic is the dominant state in Qaroun Lake. The present study recommends the application of TMI model to evaluate and monitor the changes in Qaroun Lake's potential productivity in response to the changing environmental conditions and other biological pressures (e.g., Isopoda paraside).

Mapping of tide-dominated Hooghly estuary water quality parameters using Sentinel-3 OLCI time-series data

The study explores the spatio-temporal variation of water quality parameters in the Hooghly estuary, which is considered an ecologically-stressed shallow estuary and a major distributary for the Ganges River. The estimated parameters are chlorophyll-a, total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM). The Sentinel-3 OLCI remote sensing imageries were analyzed for the duration of October 2018 to February 2019. We observed that the water quality of the Hooghly estuaries is comparatively low-oxygenated, mesotrophic, and phosphate-limited. Ongoing channel dredging for maintaining shipping channel depth keeps the TSM in the estuary at an elevated level, with the highest amount of TSM observed during March of 2019 (41.59g m-3) at station A, upstream point. Since the pre-monsoon season, TSM data shows a decreasing trend towards the mouth of the estuary. Chl-a concentration is higher during pre-monsoon than monsoon and post-monsoon periods, with the highest value observed in April at 1.09 mg m-3 in station D during the pre-monsoon period. The CDOM concentration was high in the middle section (January-February) and gradually decreased towards the estuary's head and mouth. The highest CDOM was found in February at locations C and D during the pre-monsoon period. Every station shows a significant correlation among CDOM, TSM, and Chl-a measured parameters. Based on our satellite data analysis, it is recommended that SNAP C2RCC be regionally used for TSM, Chl-a, and CDOM for water quality product retrieval and in various algorithms for the Hooghly estuary monitoring.

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

Recent changes in atmospheric input and primary productivity in the north Indian Ocean

Global oceanic regions are rapidly changing in terms of their temperature, oxygen, heat content, salinity and biogeochemistry. Since the biogeochemistry of the oceans is important and pivotal for global food production, and a major part of the world population relies on marine resources for their daily life and livelihood, it is imperative to monitor and find the spatio-temporal changes in the primary productivity of oceans. Here, we estimate the changes in Chlorophyll-a (Chl-a) and Net Primary Productivity (NPP) in the north Indian Ocean (NIO) basins of Bay of Bengal and Arabian Sea for the period 1998-2019. We find a substantial reduction of NPP in NIO since 1998 (-0.048 mg m^-3 day^-1 yr^-1) and the increase in sea surface temperature (SST) (+0.02 °C yr^-1) is the primary driver of this change. Furthermore, there is a significant (10-20%) change in the air mass or dust transport to NIO from the period Decade 1 (1998-2008) to Decade 2 (2009-2019). This change in air mass trajectories has also altered NPP in both basins through the changes in nutrient input and associated biogeochemistry. Henceforth, this study cautions the changes in primary productivity of NIO, and suggests regular assessments and continuous monitoring of the physical and biological processes from a perspective of food security and ecosystem dynamics.

The air temperature change effect on water quality in the Kvarken Archipelago area

The Kvarken Archipelago is Finland's World Heritage site designated by UNESCO. How climate change has affected the Kvaken Archipelago remains unclear. This study was conducted to investigate this issue by analyzing air temperature and water quality in this area. Here we use long-term historical data sets of 61 years from several monitoring stations. Water quality parameters included chlorophyll-a; total phosphorus; total nitrogen; coliform bacteria thermos tolerant; temperature; nitrate as nitrogen; nitrite-nitrate as nitrogen, and Secchi depth and correlations analysis was conducted to identify the most relevant parameters. Based on the correlation analysis of weather data and water quality parameters, air temperature showed a significant correlation with water temperature (Pearson's correlations = 0.89691, P < 0.0001). The air temperature increased in April (R² (goodness-of-fit) = 0.2109 &P = 0.0009) and July (R² = 0.1207 &P = 0.0155) which has indirectly increased the chlorophyll-a level (e.g. in June increasing slope = 0.39101, R² = 0.4685, P < 0.0001) an indicator of phytoplankton growth and abundance in the water systems. The study concludes that there might be indirect effects of the likely increase in air temperature on water quality in the Kvarken Archipelago, in particular causing water temperature and chlorophyll-a concentration to increase at least in some months.

Increase in chlorophyll-a concentration in Lake Taihu from 1984 to 2021 based on Landsat observations

Lake Taihu, located in a densely populated and highly industrialized area in eastern China, has experienced dramatic changes in water quality since the reform and opening-up in the 1980s. Landsat data can be used to trace water quality changes over approximately 40 years. However, chlorophyll-a (Chla) estimation, which characterizes the trophic status, has not been thoroughly explored (especially in turbid water using wide bandwidth Landsat) due to the interference of suspended particulate matter (SPM) to Chla. In this study, we used Landsat TM/OLI for turbid water Chla inversion and to analyze the spatiotemporal variation of Chla in Lake Taihu for 38 years and its influencing factors. An optical classification algorithm based on R_rs(green)/R_rs(red) was used to exclude highly turbid waters dominated by SPM; Chla was estimated only in waters with low SPM. We constructed an exponential estimation model based on R_rs(NIR)/R_rs(red), and verified the accuracy of the model using the measured Chla synchronized with satellite data. The model was applied to Landsat images to calculate the Chla concentration in Lake Taihu during 1984-2021, and its spatiotemporal distribution patterns were further analyzed. Spatially, the Chla concentrations in the western and northern regions of Lake Taihu were higher than those in other regions, probably because these areas are estuaries with large exogenous pollutant discharge and more nutrients are imported from exogenous sources. Chla showed an overall significant upward trend from 1984 to 2021 probably because of temperature rise, wind speed reduction, and nutrient increase. The results of the spatial and temporal variation of Chla and the influencing factors in this study provide supporting data for eutrophication monitoring and management in Lake Taihu. The proposed Chla estimation method can be extended to assess the spatial and temporal distribution of eutrophication in other inland waters with similar optical properties.

Sample based water quality monitoring of coastal seas: How significant is the information loss in patchy time series compared to continuous ones?

The high temporal and spatial variability of tidal dominated coastal areas poses a challenge for characterising water quality. Water quality monitoring relies often on information collected by water sampling from a vessel or by satellites, and covers limited time periods and therefore limited tidal and meteorological conditions. To assess the loss of information from discrete sampling, continuous time series of one year (suspended particulate matter (SPM) concentration, SPM flux and Chlorophyll a (Chl) concentration) were used. Eight different schemes of sampling into these time series were applied that are typical for many monitoring programs. They differ in the time between sampling events (synodic or half-synodic) and the duration of the sampling (tidal cycle, half a tidal cycle, one or more samples). The information loss was quantified by applying a bootstrap method to calculate the mean and standard deviation over the considered period. These were then compared with the true mean calculated from the continuous series. The probability to match the true mean within a certain margin depends on the sampling period and the season, but it is always low, especially if the allowed uncertainty is stringent (e.g., ±2.5 % about the true mean). For the SPM concentration this probability is lower than 10 % and for Chl concentration lower than 20 %. Similarly, conclusions arise for the detection of trends in a 20 year time series of SPM concentration with an artificial yearly increase of 0.5 %. None of the sampling schemes was able to assess statistical significant interannual trends with probabilities above 60 %. Further, the significant trends overestimated the increase by a factor 2 to 8. Here, present modus operandi is thus inadequate for basic trend detection, but may be acceptable for the more marine, lower turbid areas where higher probabilities were obtained in this study.

Role of salinity on zooplankton assemblages in the tropical Indian estuaries during post monsoon

The estuary is the transition zone between the riverine and marine environments, in which the zooplankton act as a trophic connector in the energy transfers from primary producers to secondary consumers. Zooplankton biovolume and species assemblages with reference to physical, chemical and biological properties in the Indian estuaries are rarely studied. To examine the zooplankton variability in abundance and diversity, we therefore, investigated seventeen Indian estuaries during the post monsoon of the year 2012. Based on salinity conditions, estuaries were classified into oligohaline, mesohaline and polyhaline. A marked spatial gradient in salinity was observed between the upstream and downstream estuaries. Relatively, salinity was high in downstream areas, resulting in high zooplankton biovolume and diversity perceived in downstream areas. In contrast, nutrient concentrations were higher in the upstream than the downstream estuaries, resulting in high phytoplankton biomass (in terms of chlorophyll-a) perceived in the upstream estuaries. Zooplankton abundance was numerically dominated by Copepoda, constituting approximately 76 % of the total zooplankton count. Zooplankton population was highly similar between upstream and downstream in the oligohaline estuaries. In contrast, heterogeneous assemblages were noticed between upstream and downstream in the mesohaline and polyhaline estuaries. Under oligohaline conditions, surface waters dominated by Acartia clausi, A. dane, A. plumosa, Cyclopina longicornis, Oithona rigida and Tigriopus sp. In contrast, under mesohaline and polyhaline conditions, Acartia tonsa, Acartia southwelli, Acartia spinicauda, Paracalanus spp. Centropages typicus, Temora turbinate, Oithona spinirostris and O. brevicornis become the key dominant species. Eucalanus spp., and Corycaeus spp. were indicator species in the downstream estuaries. Our findings suggest that zooplankton diversity and numerical abundance were chiefly governed by salinity rather than phytoplankton biomass (chlorophyll-a) in the Indian estuaries during the post monsoon.

Evaluating the impacts of biochemical processes on nitrogen dynamics in a tide gate-controlled river flowing into the South China Sea

This study aimed to evaluate the nitrogen (N) dynamics in Lijiang River, a tide gate-controlled river flowing into South China Sea, and to quantify the biochemical processes affecting nitrate fate and transport during the closed-tide gate period. The continuous on-line water monitoring indicates a chemostatic NH₄⁺-N pattern with respect to variable discharges in the upstream section. The survey via daily grab water sampling from July to December 2020 at four equidistant locations in the lower stretch showed that a gradual increase in NO₃^--N and decrease in NH₄⁺-N concentrations occurred along the river from upstream to downstream sections and with the time from September to December (the closed-tide gate period). The mean difference between nitrification and denitrification rate peaked at 0.43 mg L^-1 d^-1 in October in the upper section and gradually reduced to -0.26 mg L^-1 d^-1 in December in the middle section, indicating the increased advantage of denitrification over nitrification with time. A gradual increase in the mean NO₃^--N assimilatory uptake rate with time and a decrease from upstream to downstream were also observed. These results show that the closed-tide gate promoted N biotransformation in Laingian River and significant N removal was achieved through coupled nitrification-denitrification.

Spatiotemporal analysis of lake chlorophyll-a with combined in situ and satellite data

We estimated chlorophyll-a (Chl-a) concentration using various combinations of routine sampling, automatic station measurements, and MERIS satellite images. Our study site was the northern part of the large, shallow, mesotrophic Lake Pyhäjärvi located in southwestern Finland. Various combinations of measurements were interpolated spatiotemporally using a data fusion system (DFS) based on an ensemble Kalman filter and smoother algorithms. The estimated concentrations together with corresponding 68% confidence intervals are presented as time series at routine sampling and automated stations, as maps and as mean values over the EU Water Framework Directive monitoring period, to evaluate the efficiency of various monitoring methods. The mean Chl-a calculated with DFS in June-September was 6.5-7.5 µg/l, depending on the observations used as input. At the routine monitoring station where grab samples were used, the average uncertainty (standard deviation, SD) decreased from 2.7 to 1.6 µg/l when EO data were also included in the estimation. At the automatic station, located 0.9 km from the routine monitoring site, the SD was 0.7 µg/l. The SD of spatial mean concentration decreased from 6.7 to 2.9 µg/l when satellite observations were included in June-September, in addition to in situ monitoring data. This demonstrates the high value of the information derived from satellite observations. The conclusion is that the confidence of Chl-a monitoring could be increased by deploying spatially extensive measurements in the form of satellite imaging or transects conducted with flow-through sensors installed on a boat and spatiotemporal interpolation of the multisource data.

Monitoring trophic status using in situ data and Sentinel-2 MSI algorithm: lesson from Lake Malombe, Malawi

With excessive nutrient enrichment exacerbated by anthropogenic drivers, many standing water bodies are changing from oligotrophic to mesotrophic, eutrophic, and finally hypertrophic-negatively affecting ecosystem functioning, biodiversity, and human populations. Efforts have been devoted to developing novel algorithms for estimating chlorophyll-a (chl-a), cyno-blooms, and floating vegetation. However, to this date, little research has focused on freshwater lakes in the data-scarce Sub-Saharan African countries such as Malawi. We, therefore, estimated the trophic status of Lake Malombe in Malawi-a lake likely to be affected by eutrophication and algal bloom-emerging threats to freshwater ecosystem functioning globally-especially with the onset of climatic and anthropogenic drivers. We integrated in situ data with high-resolution Sentinel-2 Multispectral Imagery Analysis (MSI). We independently assessed the remote sensing technique using in situ data and tested the model at multiple stages. The scatter plot showed that most points were in the 95% confidence interval. The validation results between the measured in situ chl-a concentrations and the Sentinel-2 MSI-based chl-a retrieval had a root mean square error (RMSE) of 2.88 µg/L. The chl-a concentrations retrieved from MSI images were consistent with in situ data, indicating that the normalized difference chlorophyll index (NDCI) algorithm estimated chl-a concentrations in Lake Malombe with acceptable accuracy. Dissolved oxygen (DO), sulfate (SO₄^2-), nitrite [Formula: see text], soluble reactive phosphorous [Formula: see text]), total dissolved solids (TDS), and chl-a, except for temperatures from the hot-dry-season, cold-dry-windy-season, and rainy-season, were significantly different (P < 0.05). The Sentinel-2 MSI imagery analysis also depicted similar results, with high chl-a concentration reported in March (rainy season) and October (hot-dry season) and the lowest from May to August (cold-dry-windy season). On the contrary, the ANOVA results for water quality parameters from all five points had P > 0.05. The correlation matrix showed coefficients of (0.798 < r < 0.930, n = 30, P < 0.005), suggesting that Lake Malombe is homogenous. Our results demonstrate that integrating remote sensing based on MSI imagery and in situ data to estimate chl-a can provide an effective tool for monitoring eutrophication in small, medium, and large standing waterbodies-crucial information required to respond to global ecological and climatic dynamics.

Spatiotemporal patterns and threshold of chlorophyll-a in Lake Taihu based on microcystins

Chlorophyll-a (Chl-a) is considered as an indicator of phytoplankton biomass dynamically reflecting the growth of algae. Therefore, determination of Chl-a threshold is of vital importance to the health of aquatic ecosystems and drinking water security. This research is aimed to investigate the spatial and temporal distributions of Chl-a and microcystin (MC) concentrations using Geographic Information System (GIS) and identify the Chl-a threshold in Lake Taihu based on available guideline values of MCs. Nearly, the same characteristics of spatiotemporal variation of Chl-a and MCs were observed in Lake Taihu. Overall, the lakewide distributions of Chl-a and MCs were highly variable over time and space. The Chl-a concentration in the winter and spring was relatively low, and gradually increasing in summer and autumn, with the maximum concentration observed in August. But the maximum MCs concentration appeared in October, 2 months lagging behind the Chl-a. The highest annual average Chl-a and MCs concentrations were observed in Zhushan Bay, Meiliang Bay, and Gonghu Bay in northwest of Lake Taihu, following by West Zone and Center Zone. Dongtaihu Bay, East Zone, and South Zone always present good water quality. Referring to the guideline value of MCs, the Chl-a threshold was determined as 10-15 mg·m^-3 based on the linear regression correlation between Chl-a and MCs. The establishment of Chl-a threshold is useful for eutrophication control, water quality management, and drinking water utilities in developing water safety plans.

A 75-year history of microplastic fragment accumulation rates in a semi-enclosed hypoxic basin

Plastic budgets in the marine environment and their long-term trends are yet to be fully understood. Measuring the accumulation rates in bottom sediments is crucial to solving the riddle of missing ocean plastics. Previous studies based on coastal sediment cores have found that accumulation rates have increased with increases in plastic production and/or regional populations. However, the correlations between the rates and bioactivities or ocean dynamics, which are crucial for modeling the microplastic sinking process, have not been examined. We revealed a 75-year microplastic fragment (0.3-5.0 mm) accumulation rate history in a hypoxic basin, Beppu Bay, Japan, based on multi-core analysis and 210Pb dating of the sediment which was cross-checked by time control with 137Cs radioactivity peaks. We found that a long-term linear increasing trend with an approximately 20-year variation overlapped with significant peaks around 1990 and 2014 with the first polypropylene microplastic fragment detected from a 1958.8-1961.0 CE sediment layer. The maximum rate was 203 pieces m-2 y-1 with an abundance of 86 pieces kg-1-dry in 2014. Smaller fragments in the size range of 0.3-2.0 mm have been consistently dominant in terms of the accumulation rate throughout the 1955-2015 period, accounting for 85.3 % of the total accumulation rate. The three major polymers (polyethylene, polypropylene, and polystyrene) accounted for 96.6 % of the total rate. The rate was highly and positively correlated with the chlorophyll-a accumulation rate and concentration in the sediment. Based on the microplastic accumulation rates and concentration in the seawater, the mean sinking velocity of microplastics was estimated to be in the order of 101 m d-1. Our results will contribute to significant progress in modeling the microplastic sinking process by offering the first field measurement-based mean sinking velocity and significant correlations between the rate and bioactivity-related signals.

Outwelling of nutrients into the Pasur River estuary from the Sundarbans mangrove creeks

The Pasur River estuary (PRE), the largest estuary in the Sundarbans mangrove area, provides vital fishery resources and supports millions of livelihoods in the southwestern coastal region of Bangladesh. This study focused on the tidal and run-off effects on the outwelling of nutrients from the Sundarbans mangrove creeks to the PRE. Spatial and temporal variations of nutrient and chlorophyll-a concentrations were assessed by water sampling at 11 stations in the study area from January to December 2019. Dissolved inorganic nutrients and chlorophyll-a were analyzed by standard methods using a spectrophotometer. In the tidal mangrove creeks, dissolved inorganic nitrogen, phosphate, and silica concentrations were significantly higher (p < 0.05) during the spring tide than those during the neap tide, suggesting that these nutrients were flushed from the mangrove area by the inundation and tidal mixing of the spring tide. In general, chlorophyll-a (mean ± SD) concentrations in the PRE and the tidal mangrove creeks were 5.62 ± 1.30 μg/L and 9.03 ± 0.59 μg/L in the wet season, respectively. During the dry season, the chlorophyll-a decreased to 4.37 μg/L ± 0.68 and 4.94 ± 1.52 μg/L in the PRE and the tidal mangrove creek, respectively. The amount of nutrients outwelled from the mangrove creeks to the estuary was 1.53 ± 0.67 mg/L DIP, 0.001 ± 0.0004 mg/L DIN, and 1.38 ± 0.48 mg/L dissolved silica. DIP, silica, and chlorophyll-a concentrations were significantly higher (p < 0.05) during the spring tide compared to the neap tide, but salinity was not significantly (p > 0.05) different between the two tidal levels. This study showed that the mangrove creeks formed an important link in transporting nutrients from the mangrove forest to the estuary.

A Study on Algae Bloom Pigment in the Eutrophic Lake Using Bio-Optical Modelling: Hyperspectral Remote Sensing Approach

Inland lake is one of the important sources of freshwater ecosystem and serves as a sentinel to the changing aquatic biodiversity. Chlorophyll-a (Chl-a) is a major biological indicator and essential measure of the eutrophic status of lake water because it is strongly related to algae biomass. In the present research, bio-optical algorithms were developed based on the semi-empirical approach using the spectral wavelengths of 400 to 800 nm from hyperspectral remote sensing measurement and compared with Sentinel-2MSI image for estimation of Chl-a concentration in the lake water. The results show that the bio-optical algorithm can estimate and predict the algae pigment (Chl-a) concentration in the eutrophic lake with good accuracy of R² of 0.8958, root mean squared error of 13.028, and mean absolute percentage error of 8.44%. The developed algorithm will be suitable and potential for monitoring algae spatial dynamics and assessment in an inland lake.

Effect of salinity on valves morphology in freshwater diatoms

Increased salt concentration is one of the most widespread problems affecting freshwater worldwide. Aquatic communities, and in particular periphytic diatoms, react to this alteration in water quality by modifying their structural parameters and physiology at the individual level, which is commonly manifested by the appearance of teratological forms. The present work presents the results of an experimental laboratory study in which a biofilm grown on artificial substrates was subjected to a gradient of water conductivities for 4 weeks. The results show an increase in the number of deformed valves over time proportionally to the increase in conductivity for each experimental treatment. These effects are also verified by analyzing the concentration of chlorophyll-a in the experimental biofilms, which demonstrate a metabolic response to the induced osmotic stress. No changes were recorded; however, in species richness or diversity of taxa present in the treatments. Our results, therefore, confirm at the experimental level numerous previous field observations about the harmful effect of salinity on periphytic diatoms, and also their ability to reintegrate with the new stress conditions.

A novel algicidal bacteria isolated from native snail lived in Taihu Lake against algal blooms: identification, degradation kinetic, and algicidal mechanism

Harmful algal blooms (HABs) impacted negatively the water ecosystem, and produced toxic microcystins that poses toxic effect on liver, nervous, and genital system. The introduction of useful and adaptive algae-degrading microbes or bio-augmentation can be regarded as an efficient way to inhibit the outbreak of HABs. The purpose of this study is to evaluate the application potential of algicidal bacteria named XMC, which is isolated from native snails. Response surface methodology (RSM) experiments showed that self-characteristic and various external conditions affected the actual algae inhibition ability of XMC. In particular, actual algicidal efficiency was strongly depend on the temperature and growth stage of XMC, and the maximum algicidal rate could reach 93.95% within 7 days. The degradation curve of Microcystis aeruginosa was compliant with the first-order kinetic model, which could be used to predict the degradation effect of Microcystis aeruginosa in engineering applications. The analysis results of algae dissolution products showed that algicidal bacteria XMC had both direct and indirect algicidal capacity. In addition, XMC had strong algicidal ability and greater environmental adaptability, and its algae dissolution products were environmentally friendly. All results indicated that XMC had the potential to be used in the bio-degradation of cyanobacteria bloom.

Modeling ocean surface chlorophyll-a concentration from ocean color remote sensing reflectance in global waters using machine learning

The spatial and temporal variations of Chlorophyll-a (Chl-a) in clear and coastal waters are critical for assessing the health of the marine environment. Machine learning models have been proven to model complex relationships and provide better accuracy estimates of the derived parameters compared to traditional empirical models. The present study proposes a novel approach to derive Chl-a by using multi-layer perceptron Neural Network (MLPNN) with Resilient backpropagation method based on the four ocean color bands existent in most of the ocean color sensors. The NNs are trained on NASA's bio-optical Marine Algorithm Dataset (NOMAD) and tested on three different datasets (i) SeaWiFS and, (ii) MODIS Aqua matchup dataset, and (iii) simulated dataset for the Red Sea. These three datasets cover significant variations range in Chl-a levels under both oligotrophic and eutrophic conditions. The influence of different variations in inputs used in NN training is assessed and hyperparameter tuning of the NN is performed to obtain best NN configuration to derive Chl-a. Accuracy assessment of the present study with other global algorithms are performed by comparing the modeled and observed values of the Chl-a. The performance matrices computed from the developed model were promising. Therefore, this study provides a potential approach for the retrieval of improved Chl-a estimates in the global clear and coastal waters as compared to the traditional blue-green band ratio algorithms. Furthermore, the developed algorithm and existing algorithms are applied to SeaWiFS, MODIS, VIIRS, and Hawkeye satellite ocean color data to demonstrate how it may be utilized to accurately depict the spatial distribution of ocean color features in global waters, phytoplankton blooms and some of the physical processes in the Arabian Sea and the Red Sea. The findings of this work have potential to advance the ocean color remote sensing and biogeochemical cycles and processes in coastal and open ocean waters.

Data reconstruction of daily MODIS chlorophyll-a concentration and spatio-temporal variations in the Northwestern Pacific

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.

Analysis of the eutrophication in a wetland using a data-driven model

Eutrophication is a major problem in the international Anzali wetland (northern Iran). The present research initially aimed to determine the trophic state index (TSI) in ten sampling sites in the main parts of the Anzali wetland (western, eastern, central, and Siahkeshim parts). After determining the TSI in the wetland, a data-driven method (classification tree model with a J48 algorithm) was implemented to predict the trophic condition in the wetland based on a set of water quality and physical-structural variables. One hundred twenty samples related to chlorophyll-a (the model's output) and environmental variables (the model's inputs) were measured monthly during 1-year study period (2017-2018). Based on the TSI calculation, the western, Siahkeshim, eastern, and central parts of the wetland are classified as eutrophic, super-eutrophic, hyper-eutrophic, and hyper-eutrophic, respectively. When all environmental variables were introduced to the model (with five-time randomization effort, pruning confidence factor = 0.01, and seven-fold cross-validation), eight variables (bicarbonate, pH, water temperature, electric conductivity, dissolved oxygen, total phosphorus, water depth, and water turbidity) were predicted by the model. The model predicted that an increase in total phosphate, water turbidity, and electric conductivity concentration may contribute to the hyper-eutrophic state of the wetland. In contrast, the hyper-eutrophic of the wetland is associated with a decrease in water depth, dissolved oxygen, and pH concentration. According to ANOVA test, the trophic condition in the wetland can be affected by spatial and temporal patterns. Anthropogenic pressures such as the influx of chemicals particularly the nutrients (phosphorus and nitrogen) are the main cause of water enrichment (eutrophication problem) in main parts of the Anzali wetland ecosystem.

Evidence of long term biogeochemical interactions in carbonate weathering: The role of planktonic microorganisms and riverine bivalves in a large fluviokarst system

The infiltration of organic-rich surface waters towards groundwaters, is known to play a significant role in carbonate weathering and in contributing to the atmospheric continental carbon sink. This paper investigated biogeochemical interactions in karst critical zones, with strong surface water /groundwater interactions, and in particular the role of planktonic microorganisms and riverine bivalves through the analysis of particulate organic matter (OM) oxidation on carbonate weathering. In the large Val d'Orléans fluviokarst aquifer (France), a 20-year monthly dataset of Nitrates, Dissolved Oxygen (DO), dissolved inorganic and organic Carbon (DIC and DOC) fluxes was gathered. The surface water-groundwater comparison of geochemical trends showed that planktonic microorganisms had drastically decreased in surface waters, related to the proliferation of Corbicula bivalves spreading and a decrease in nutrients. This decrease in planktonic microorganisms was followed by a DO increase and an DIC decrease at the karst resurgence. The degradation of planktonic microorganisms consumes DO and produces NO₃, dissolved inorganic carbon (DIC) and a proton that in turn, dissolves calcite and produces DIC. Without the input from planktonic microorganisms, the fluviokarst has lost 29 % of this nitrification and 12 % of the carbonate dissolution capacities. Thus, the oxidation of particulate organic matter of planktonic microorganisms, which is part of heterotrophic respiration, appears to be a significant source of the inorganic carbon flux in riverine ecosystems. This shows how weathering can remain active under waters saturated versus calcite and suggests that the oxidation of organic matter can be a more appropriate mechanism than autotrophic respiration to explain the relationship between global warming and DIC flux change in rivers. Through the consumption of plankton, the animal life in rivers thus influences the inorganic carbon in groundwaters, creating a negative feedback in the carbon cycle.

Uncovering the influence of hydrological and climate variables in chlorophyll-A concentration in tropical reservoirs with machine learning

Climate variability and change, associated with increasing water demands, can have significant implications for water availability. In the Brazilian semi-arid, eutrophication in reservoirs raises the risk of water scarcity. The reservoirs have also a high seasonal and annual variability of water level and volume, which can have important effects on chlorophyll-a concentration (Chla). Assessing the influence of climate and hydrological variability on phytoplankton growth can be important to find strategies to achieve water security in tropical regions with similar problems. This study explores the potential of machine learning models to predict Chla in reservoirs and to understand their relationship with hydrological and climate variables. The model is based mainly on satellite data, which makes the methodology useful for data-scarce regions. Tree-based ensemble methods had the best performances among six machine learning methods and one parametric model. This performance can be considered satisfactory as classical empirical relationships between Chla and phosphorus may not hold for tropical reservoirs. Water volume and the mix-layer depth are inversely related to Chla, while mean surface temperature, water level, and surface solar radiation have direct relationships with Chla. These findings provide insights on how seasonal climate prediction and reservoir operation might influence water quality in regions supplied by superficial reservoirs.

Machine learning predictions of chlorophyll-a in the Han river basin, Korea

This study developed a model to predict concentrations of chlorophyll-a ([Chl-a]) as a proxy for algal population with data from multiple monitoring stations in the Han river basin, by using machine-learning predictive models, then analyzed the relationship between [Chl-a] and the input variables of the optimized model. Daily water quality and meteorological data from 2012 to 2020 were collected from the real-time water quality information system and the meteorological administration of Korea. To quantify model accuracy, the coefficient of determination, root mean square error, and mean absolute error were applied. Among random forest (RF), support vector machine, and artificial neural network, the RF with random dataset showed the highest accuracy. The RF was optimized when 78 trees were applied to the model. Input variables for the best RF model were total organic carbon (feature importance: 27%), total nitrogen (19%), pH (13%), water temperature (8%), total phosphorus (8%), electrical conductivity (7%), dissolved oxygen (6%), minimum air temperature (AT) (4%), mean AT (3%), and maximum AT (3%). The feature-importance analysis showed that total organic carbon was the most important variable to predict [Chl-a] in the Han river basin. Total nitrogen was a more important variable than total phosphorus.

Effects of wildfire ash from native and alien plants on phytoplankton biomass

Wildfires are natural or anthropogenic phenomena increasing at alarming rates globally due to land-use alterations, droughts, climatic warming, hunting and biological invasions. Whereas wildfire effects on terrestrial ecosystems are marked and relatively well-studied, ash depositions into aquatic ecosystems have often remained overlooked, but have the potential to significantly impact bottom-up processes. This study assessed ash-water-phytoplankton biomass dynamics using six plant species [i.e., three natives (apple leaf Philenoptera violacea, Transvaal milk plum Englerophytum magalismontanum, quinine tree Rauvolfia caffra) and three aliens (lantana Lantana camara, gum Eucalyptus camaldulensis, guava Psidium guajava)] based on a six-week mesocosm experiment with different ash concentrations (1 and 2 g L^-1). We assessed concentrations of chemical elements, i.e., N, P, K, Ca, Mg, Na, Mn, Fe, Cu, Zn and B from ash collected, and examined potential differences among the species. High concentrations of P, K, Mn, Fe, Cu, Zn and B were recorded from Transvaal milk plum ash and low concentrations of P, K, Ca, Mg, Cu and Zn were recorded from apple leaf. An increase in phytoplankton biomass (using chlorophyll-a concentration as a proxy) for all treatments i.e., 1 and 2 g L^-1 and plant species was observed one week after, followed by decreases in the following weeks, with the exception of 2 g L^-1 for lantana, gum and control groups. Silicate concentrations (i.e., used as a proxy for diatom abundance) showed increasing patterns among all ash treatments, with the exception of controls. However, no clear patterns were observed between native and alien plant ash for both chl-a and silicate concentrations. We found that ash has notable effects on water chemistry, particularly nitrate, which increased throughout the weeks, whereas, pH and conductivity were high at low ash concentrations. The impacts of ash on water chemistry, chl-a and silicate concentrations vary with individual species and the amount of ash deposited into the system.