Trophic Status Research Articles

Effects of water replenishment on lake water quality and trophic status: An 11-year study in cold and arid regions

Water replenishment is an important measure for maintaining and improving the aquatic environmental quality of lakes. The problems of water quality deterioration and water shortage can be alleviated by introducing water of higher quality. However, the mechanism of water replenishment in the improvement of the water quality and trophic status of lakes remains unclear. This study investigated water replenishment in Wuliangsuhai Lake (WLSHL) from 2011 to 2021 by collecting seasonal water samples and conducting laboratory analyses. Water replenishment was found to be capable of significantly improving lake water quality and alleviating eutrophication. It is worth noting that single long-term water replenishment measures have limitations in improving the water quality and trophic status. The whole process was divided into three stages according to the water quality and trophic status, namely the buffer period, decline period, and stable period. During the buffer period, the water quality and trophic status showed only slight improvement because of the small amount of water replenishment and the low proportion of higher-quality water from the Yellow River. In the decline period, with increasing water replenishment, the proportion of higher-quality water from the Yellow River gradually increased, leading to the most significant and stable degree of improvement. In the stable period, increases in the amount of water replenishment had little effect on improving the water quality and trophic status, which is attributable to the balance between internal pollutants (lake water–sediment), and the balance between internal-external pollutants (lake water–irrigation return flow + Yellow River water). On the premise of stable water quality, with eutrophication control as the management goal, the optimal water replenishment would be approximately 10.58 ×108 m3. Further necessary measures for solving aquatic environmental problems include the combination of sediment dredging, optimization of the water replenishment route, and implementation of quality management in water replenishment.

Trophic Status of Lake Niesłysz (Poland) and Related Factors

In order to ensure the protection of lakes against eutrophication, an ongoing global problem, its causes should be determined on an individual basis. In this study, we investigated Lake Niesłysz in northwestern Poland in terms of (i) the impact of nitrogen and phosphorus on primary production, (ii) the Trophic State Index (TSI), and (iii) the hydromorphological characteristics and watershed features. We determined the thermal conditions, dissolved oxygen, organic matter, and selected forms of nitrogen and phosphorus. TSI was determined using Secchi depth (SD), chlorophyll a, total phosphorus (TP), and total nitrogen (TN). Hypolimnetic anoxia was observed in summer. Surface concentrations of chlorophyll a and organic carbon, total inorganic nitrogen (TIN), and total reactive phosphorus (TRP) were 5 μg L−1, 11.7 mg C L−1, 0.049 mg N L−1, and 0.018 mg P L−1, respectively. The TN:TP ratio was >30, while TIN:TRP was <10. The TSIs for chlorophyll a, SD, and TP ranged from 42 to 59, and for TN it was >145. The total trophic state index (T-TSI) exceeded 72. In conclusion, Lake Niesłysz has an average resistance to degradation and the catchment has little influence on the release and transport of biogenic matter into the lake. The limiting nutrient for primary production was phosphorus, but the influence of nitrogen or covariates of nitrogen cannot be excluded. Based on the oxygen conditions in the hypolimnion, the lake should be classified as eutrophic. Most of the TSIs were in the mesotrophic range, while the TSIs for TN and T-TSI classified the lake as hypertonic. The results show that Lake Niesłysz is currently at a critical stage of progressive degradation, and it is advisable to develop and implement protective measures immediately.

A four-season exploration of surface water quality and trophic status in the highly dynamic waters of Karun-4 Dam Lake, SW Iran

This study explores seasonal fluctuations in water quality and phytoplankton communities within Karun-4 Dam Lake, the largest double-arch dam in the Middle East situated in Iran. Employing a systematic approach, 26 sampling stations were strategically selected to collect surface water samples at a depth of 50 cm during the midpoints of each season throughout 2019. Significant seasonal variations in water quality parameters and phytoplankton composition were observed. Predominant species included Chrysophyceae (38%) and Bacillariophyceae (32%), indicative of nutrient-rich conditions, particularly during spring and summer, as evidenced by the eutrophic state (Carlson Trophic State Index: 59.43 and 53.96, respectively). Summer exhibited the highest diversity (Shannon–Wiener Index = 2.27) and lowest evenness (Pielou’s Evenness Index = 0.21). PCA and CCA analyses revealed season-specific preferences for nutrients and ions among phytoplankton species. Water temperature emerged as a crucial factor in spring and summer, while environments with elevated bicarbonate and alkalinity levels were less favorable during winter and fall. This study provides essential insights into Karun-4 Lake's dynamic ecological conditions, underscoring the necessity for ongoing monitoring to discern long-term trends and anthropogenic impacts for effective ecosystem management.

A Broad-Scale Look at Nutrient Limitation and a Shift toward Co-limitation in United States Lakes.

There is a longstanding debate about the role of nitrogen (N) vs phosphorus (P) in limiting primary production in lakes and whether co-nutrient limitation should be considered for managing eutrophication. We evaluated nutrient limitation and eutrophication at a subcontinental scale. Using U.S. Environmental Protection Agency National Lakes Assessment data, we assessed broad-scale patterns in nutrient limitation and compared samples of all surveyed lakes and lakes resurveyed in multiple surveys. We found that N correlated more strongly with productivity in the western U.S., while P correlated more strongly in the eastern U.S. The aggregated subcontinental effect suggests the importance of factors like N-deposition, terrestrial vegetation, underlying geology, and land use for understanding drivers of nutrient dynamics in lakes. Our study showed how patterns can aggregate across subcontinental scales yet still demonstrate considerable variation when more deeply examined on an individual lake level. Overall, we found that nutrient limitation is dynamic over space and time, with most lakes being co-limited. The prevalence of co-limitation also increased from 2007 to 2017. Trophic states within each limitation category varied substantially. Our findings underscore the need for combined N and P reductions to mitigate accelerated eutrophication.

Investigating the potential of remote sensing-based machine-learning algorithms to model Secchi-disk depth, total phosphorus, and chlorophyll-a in Lake Urmia

Many terminal lakes in agricultural basins are prone to eutrophication due to restricted inflows and receiving excess nutrients from their basin.The synergy of using satellite data andmachine learning models is a low-cost way to monitor the root-cause water quality variables (WQVs) of eutrophication. This study investigates the potential ofremote sensing-based machine learning algorithms to modelchlorophyll-a(Chl-a), total phosphorus (TP), Secchi disk depth (SD),and Carlson trophic state index (CTSI)in the north part of Lake Urmia (LU).The multiple linear regression (MLR) and artificial neural network (ANN) models were developed using Landsat-8 (L8) and Sentinel-2 (S2) data with nearly concurrent in-situ WQVs of the north part of LU from February 2016 to January 2017. Results showed that models based on L8 were superior to those with S2. Moreover, the ANN models based on L8 for Chl-a, SD, and TP having NSE = 0.75, 0.98, and 0.96, respectively, outperformed MLRs (with NSE = 0.74, 0.81, 0.58). Applying atmospheric correction (i.e., ACOLITE, C2RCC, and C2RCCX) enhances the models.The resultant Chl-aand SD maps indicated an inverse spatiotemporal pattern that agrees with the variation of the abiotic condition in the lake (e.g., surface temperature and total suspended sediments).According to the CTSI maps,the north part of LU was mesotrophic in February and March and eutrophic between June and October 2016. Our study indicates the promising application ofremote sensing-based machine learning algorithms to model the spatiotemporal variation of eutrophication in LU, which provides valuable insights into cost-effective lake monitoring.

Spatio-temporal variation in the zooplankton community of the Zahara-El Gastor Reservoir (Cádiz, Spain)

Reservoirs play a crucial role in providing essential social and economic services at different scales. However, in recent years, anthropogenic impacts such as climate change and pollution have increased, affecting this type of ecosystem. Zooplankton communities are fundamental for regulating the biological balance in these environments, so it is essential to understand the processes that affect their dynamics. This study investigated the seasonal and spatial variation in species abundance of the zooplankton community of the Zahara- El Gastor Reservoir, Cádiz, Spain. This reservoir it is located in the Guadalete-Barbate River basin and is included in the Sierra de Grazalema Natural Park, the area with the highest rainfall in the Iberian Peninsula. Water and zooplankton samples were collected monthly over a year at four sampling sites located along the reservoir’s long-axis to determine its physicochemical characteristics and zooplankton species composition. The reservoir presented a mesotrophic state and a monomictic thermal cycle, with the mixing period occurring in winter. Temperature, water depth and volume, phosphates, carbonates, pH, and conductivity were the main environmental variables that affected the seasonal and spatial patterns of the zooplankton community. Rotifers presented the highest number of species and were predominant at the tail of the reservoir, especially in autumn, where Polyarthra spp. reached high densities. Relatively low densities of copepods were found throughout the year. Small branchiopods and cyclopoids were predominant in summer, while Daphnia longispina was linked to the colder seasons. The zooplankton community of the Zahara-El Gastor Reservoir reflected changes in the trophic state of the reservoir both in space and time.

Spatiotemporal Assessment of Water Pollution for Beira Lake, Sri Lanka

Beira Lake, located in Colombo, Sri Lanka, has suffered severe anthropogenic impacts, with previous restoration attempts failing due to a limited understanding of pollutant dynamics. Aiming to fill this gap, a comprehensive study was conducted during dry and wet seasons to assess the spatiotemporal water pollution of Beira Lake, employing key physicochemical parameters, numerical indices, and remote sensing analysis. The water pollution index (WPI) results categorize Beira Lake as highly polluted, with WPI values ranging from 2.38 ± 0.92 in the wet season to 2.53 ± 1.32 in the dry season. Comparatively higher COD levels recorded in the Beira Lake network, especially for Gangarama Lake show significant pollution levels during both the dry and wet seasons, e.g., the highest COD levels, at 306.40 mg/L, were observed during the wet season. The Trophic State Index (TSI) results indicate eutrophic and hypereutrophic conditions in Beira Lake, which are particularly pronounced during the wet season. The heavy metal pollution index (HPI) results suggest elevated heavy metal concentrations in Beira Lake, especially in the wet season. Combined with field investigation results, a remote sensing data analysis between 2016 and 2023 reveals significant improvements in water transparency, suggesting positive effects of recent management interventions. Parameters demanding attention include COD, nitrate, and total phosphate levels due to their consistent exceedance of permissible limits. The PCA results of indices correlations between wet and dry seasons offer valuable insights into the complex dynamics of Beira Lake’s water quality. The study makes recommendations for restoring Beira Lake, including stringent pollution controls, regular dredging, green infrastructure implementation, implementing new rules and regulations, and community engagement.

Analysing Spatiotemporal Variability of Chlorophyll-a Concentration and Water Surface Temperature in Coastal Lagoons of the Ebro Delta (NW Mediterranean Sea, Spain)

Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ data to (1) develop preliminary algorithms for retrieving the Chl-a concentration and water surface temperature of six lagoons located in the Ebro Delta (NE Mediterranean Sea, Spain), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms’ accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons’ trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms’ robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters.

Long‐Term Trends in Productivity Across Intermountain West Lakes Provide No Evidence of Widespread Eutrophication

AbstractEutrophication represents a major threat to freshwater systems and climate change is expected to drive further increases in freshwater primary productivity. However, long‐term in situ data is available for very few lakes and makes identifying trends and drivers of eutrophication challenging. Using remote sensing data, we conducted a retrospective analysis of long‐term trends in trophic status among lakes greater than 10 ha across the Intermountain West, a region with understudied water quality trends and limited long‐term data sets. We found that most lakes (55%) were not exhibiting shifts in trophic status from 1984 to 2019. Our results also show that increases in eutrophication were rare (3% of lakes) during this period, and that lakes becoming increasingly oligotrophic were more common (17% of lakes). Lakes that were not trending occupied a wide range of lake and landscape characteristics, whereas lakes that were becoming more oligotrophic tended to have larger residence times and were located in catchments with greater clay content and more development. Our results highlight that while there are well‐established narratives that climate change can lead to more eutrophication of lakes, this is not broadly observed in our data set, where we found more lakes in the Intermountain West becoming more oligotrophic than lakes becoming eutrophic.

Primary Productivity Variation in Banten Bay and Java Sea and Its Relationship with Fish Catches

Abstract Ecosystems carry out internal activities, one of which is by carrying out production which is the process of inputting and storing energy from sunlight as a primary energy source called primary productivity. The development of wider coastal areas is a major factor in the importance of assessing the quality of water fertility as a measure of ecosystem change. The aim of this research is to predict the value of primary productivity in Banten Bay and the Java Sea seasonally from 2005-2020 based on MODIS Fluorescence satellite image data and processed using the Vertically Generalized Production Model (VGPM). Net Primary Productivity results obtained in Banten Bay in the West Season 148.23 mgC.m−3.d−1; Transitional Season I 148.06 mgC.m−3.d−1. East Season with 154.05 mgC.m−3.d−1; Transitional Season II 170.10 mgC.m−3.d−1 while in the Java Sea in the West Season 100.05 mgC.m−3.d−1; Transitional Season I 82.20 mgC.m−3.d−1; East Season 110.84 mgC.m−3.d−1; Transitional Season II 95.97 mgC.m−3.d−1 which decreased in value from 2005 to 2020 in both regions. Water fertility in Banten Bay and the Java Sea is higher in the nearshore area than the open sea which is influenced by chlorophyll-a, PAR availability, day length, temperature, maximum carbon fixation, euphotic zone depth, and area. Trophic status in each season in Banten Bay is included in the Oligotrophic class while in the Java Sea it is included in the Oligotrophic and Mesotrophic classes. NPP has a positive correlation with the overall capture of small pelagic fish species of 0.654 (average).

Assessment of water quality and plankton production in managed and unmanaged fresh water ponds in district Hisar, Haryana, India

Managed and unmanaged extensive fisheries culture systems in small village ponds in district Hisar of Haryana, India to correlate the water quality and biological cycles in ponds with fish production were undertaken. The studies revealed that the ponds are in good trophic status. Chlorides, total hardness, calcium, magnesium, biochemical oxygen demand (BOD), phosphates (o-PO4) and ammonia were high whereas dissolved oxygen and fish growth/yield was low in unmanaged ponds in comparison to managed ponds. This may be due to high organic loads.The deterioration of water quality, as indicated by very high ammonia and BOD in unmanaged ponds might have decreased the fish growth and a variety of factors like organic load, ammonia production, nature/quantity of fertilizers may have influenced such correlations. Management of water quality by managing the optimization of waste input in village ponds may help in enhancing the fish production through such small scale aquaculture.. KEYWORDS :Water quality, Physico-chemical characteristics, Fish yield, BOD, Ammonia

Environmental indicators of lake ecosystem health in Aotearoa New Zealand: current state and trends

Assessments of lake ecosystem health, based on indicators of physical–chemical water quality, ecological conditions, and human health risks, are needed to apprise the public and facilitate lake management. While unrefereed lake water quality reports exist, this is the first peer-reviewed study to report data processing and analysis methods and results for current state and recent trends in multiple environmental indicators in monitored lakes across Aotearoa New Zealand. Lake grading based on national criteria indicated that many lakes are failing to achieve statutory bottom lines for phytoplankton chlorophyll a, total nitrogen, and total phosphorus concentrations. In contrast, most monitored lakes had high grades for two human health risk indicators, E. coli and cyanobacteria concentrations, and for potential toxicity of ammonia for biological communities. We included 166 lakes and considered indicator state and trends for two lake classifications based on geomorphology and land cover. Elevated scores for an indicator of trophic state (the trophic level index; TLI) in most shoreline, riverine, peat, landslide, dam, and aeolian lakes indicated eutrophic conditions or worse. Lower TLI scores were indicative of oligotrophic conditions in most glacial lakes. Nutrient, E. coli and suspended sediment concentrations and TLI scores were positively correlated, and Secchi depths negatively correlated, with high-intensity agricultural land cover. Ten-year trend analyses indicated that lakes with increasing Secchi disk depth, chlorophyll a, dissolved reactive phosphorus, E. coli, ammoniacal nitrogen, TLI, total nitrogen, and total suspended solids trends outnumbered those with decreasing trends. Twenty-year trend analyses indicated that the numbers of lakes with increasing and decreasing trends in most of the indicators we assessed were comparable, but lakes with increasing 20-year trends in total nitrogen outnumbered those with decreasing trends, and lakes with decreasing 20-year trends in ammoniacal nitrogen, dissolved reactive phosphorus, total phosphorus, and nitrate nitrogen outnumbered those with increasing trends. This study presents clear data processing steps and trend analysis methods as applied to a nationwide dataset for Aotearoa New Zealand, while acknowledging data limitations. For example, the choice of trend periods is challenging and can greatly affect conclusions. The results of this empirical study and the collective results of preceding studies of lake ecosystem health indicate that eutrophication poses a threat to the ecological health of many if not most lakes in Aotearoa New Zealand. Recommendations include monitoring network expansion especially for the indicators bottom dissolved oxygen and cyanobacteria (toxic and total cyanobacteria biovolumes) and judicious selection of trend periods in lake-specific trend analyses to determine causes for increasing or decreasing trends in environmental indicators.

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes.

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Структура и динамика гидробиоценозов (фитоперифитон, зоопланктон, зообентос) рек Карельского побережья Белого моря.

This paper summarizes the results of the studies of biota structural organization and dynamics of small rivers of the Karelian White Sea coast. The chemical features and the structure of biological communities in the watercourses were investigated. Analysis of the species composition, abundance and biomass of phytoperiphyton, zooplankton, and zoobenthos was carried out. Variations in the structure of aquatic communities primarily are due to specific hydrographic and hydrological characteristics of river stretches. The paper discusses the main principles of the changes in a structure and functioning of hydrobiont communities in rivers. The trophic status of the rivers, their saprobiological state, and the significance of individual communities and biotic indices for bioindication of the ecological state of the rivers,are estimated. We concluded that the consideration of three groups of organisms enables a more comprehensive and reliable monitoring than the assessment based on a single group.

Сравнительная характеристика зоопланктона озер Сисяярви и Лещевое в 2020–2021 гг. (Валаамский архипелаг, Ладожское озеро).

Taxonomic composition, some structural and functional zooplankton characteristics of Lake Sisyarvi and Lake Leschovoye were analysed for the period of 2020–2021. In spite of close location of the lakes and their connection through the channels system differences in dominant species were discovered. In contrast to Lake Sisyarvi great variability in the zooplankton abundance and biomass in Lake Leschovoye was recorded as well as low values of the zooplankter individual mass. Comparative evaluation of the lakes trophic status was given.

Long-term prediction of algal chlorophyll based on empirical models and the machine learning approach in relation to trophic variation in Juam Reservoir, Korea

This study analyzed spatiotemporal variation and long-term trends in water quality indicators and trophic state conditions in an Asian temperate reservoir, Juam Reservoir (JR), and developed models that forecast algal chlorophyll (CHL-a) over a period of 30 years, 1993–2022. The analysis revealed that there were longitudinal gradients in water quality indicators along the reservoir, with notable influences from tributaries and seasonal variations in nutrient regimes and suspended solids. The empirical model showed phosphorus was found to be the key determinant of algal biomass, while suspended solids played a significant role in regulating water transparency. The trophic state indices indicated varying levels of trophic status, ranging from mesotrophic to eutrophic. Eutrophic states were particularly observed in zones after the summer monsoons, indicating a heightened risk of algal blooms, which were more prevalent in flood years. The analysis of trophic state index deviation suggested that phosphorus availability strongly influences the reservoir trophic status, with several episodes of non-algal turbidity at each site during Mon. Increases in non-algal turbidity were more prevalent during the monsoon in flood years. This study also highlighted overall long-term trends in certain water quality parameters, albeit with indications of shifting pollution sources towards non-biodegradable organic matter. According to the machine learning tests, a random forest (RF) model strongly predicted CHL-a (R2 = 0.72, p < 0.01), except for algal biomass peaks (>60 μg/L), compared to all other models. Overall, our research suggests that CHL-a and trophic variation are primarily regulated by the monsoon intensity and predicted well by the machine learning RF model.

Seasonal shift in trophic status induced by bacterial metabolic activity in tropical mangrove-dominated estuaries of Southwest India

Seasonal shift in trophic status induced by bacterial metabolic activity in tropical mangrove-dominated estuaries of Southwest India

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

Machine learning modeling of lake chlorophyll content in a data scarce region (Northern Patagonia, Chile): insights for environmental monitoring

Among South America, Chile is highly susceptible to climate change impacts on water resources and ecosystems. Chilean lakes and rivers have been impacted by anthropogenic activities leading to chemical pollution and eutrophication. Concerns for conservation and management of water resources has led to the current development of secondary norms for environmental quality of Northern Patagonian lakes. In this context, we analyze historical limnological databases (1979-2022) for these lakes utilizing Random Forest (RF) models. After filtering, we retained data for 11 lakes including key variables of: dissolved oxygen, electric conductivity, transparency, temperature, pH, total nitrogen, total phosphorus and chlorophyll-a. This dataset yielded robust results, accurately predicting chlorophyll-a content. Furthermore, we added lake geomorphological parameters, enhancing the performance of the model. Our study demonstrates the need to improve long-term monitoring programs, optimizing environmental data recording and decreasing costs. We conclude that the studied lakes generally maintain their oligotrophic characteristics, however further analysis suggests that these lakes are more sensitive to nitrogen loading than phosphorus. Our results highlight the need to implement adaptative management plans at the watershed level to regulate anthropogenic nitrogen contamination (from agriculture, pisciculture and urbanization). The features selected by RF, coupled with the assessment of historical trophic state variation, allow the establishment of permissible concentration thresholds for major nutrients and other sentinel parameters, informing the development of regulations such as the secondary norms for environmental quality. Lastly, the enhanced performance of RF modeling when including geographical parameters unveils the need to standardize and integrate geographical data in monitoring practices.

Investigating the spatial-temporal pattern of the trophic state index in Hanoi’s largest urban lake (Vietnam) using Sentinel-2 images

ABSTRACT Urban lakes provide aesthetic, recreational and ecological benefits, making understanding their trophic status vital for effective management. Utilizing Sentinel-2 imagery, we investigated the spatial-temporal patterns of the Trophic State Index (TSI) in Hanoi’s West Lake over a 2-year period. Our study proposed a robust TSI estimation model, incorporating three band ratios: R rs 655 / R rs 490 , R rs 705 / R rs 655 , R rs 655 / R rs 443 , along with 124 in situ point data. The model exhibited high accuracy (the determination coefficient, R 2 = 0.63) and minimal errors (the root mean square errors, RMSE = 1.1 and 0.9). Consequently, West Lake consistently registered as hypereutrophic (TSI > 70), with seasonal variations linked to climate factors, leading to algal blooms and reduced water clarity. Spatial analysis highlighted the influence of sewage drains on TSI, indicating an urgent need for wastewater control. Our study demonstrates the cost-effective utility of Sentinel-2 imagery for monitoring urban lake eutrophication and emphasizes the urgency of managing eutrophication in these vital ecosystems.