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.