Trophic State Index Research Articles

Remote Sensing Evaluation of Trophic Status in the Daihai Lake Based on Fuzzy Classification

Trophic state index (TSI) is a critical ecological and environmental issue in water resource management that has garnered significant attention. Given the complexity of optical characteristics in aquatic environments, this study employs fuzzy classification methods (FCM) and composite nutrient status indices to meticulously classify in-situ remote sensing reflectance data, aiming to develop evaluation models for different nutrient status categories to facilitate the assessment of the Daihai River in Inner Mongolia, China. Subsequently, we applied this model to MSI data to analyze the nutrient status of Daihai Lake from 2016 to 2021. Furthermore, a structural equation model (SEM) was utilized to explore the primary driving factors influencing nutrient status. The results indicated that the water bodies in Daihai Lake can be broadly classified into three categories, with the nutrient status models demonstrating robust performance for each category (R2 = 0.80, R2 = 0.83, and R2 = 0.74). Comparisons were made between nutrient status accuracies obtained through the NCM and FCM based on measured data, yielding R2 values of 0.74 and 0.85, respectively. Furthermore, the TSI results derived from MSI inversion were validated, with NCM achieving an R2 of 0.49, RMSE of 6.88, and MAPE of 10.36%, while FCM exhibited an R2 of 0.55, RMSE of 8.89, and MAPE of 13.18%. An SEM–based analysis revealed that over the long term, human activities exerted a more substantial impact on eutrophication in Daihai Lake, while climatic factors played an accelerating and reinforcing role. These results are consistent with prior research in the Daihai area, indicating a state of mild eutrophication and the potential of the fuzzy classification method and comprehensive trophic status index method in eutrophication assessment.

Inter-annual variability in the phytoplankton abundance and community structure of a tropical monsoonal estuary in response to variable monsoonal patterns

The study elucidates the inter-annual and seasonal variability in the phytoplankton abundance and community structure concurrent to variable rainfall patterns in Chettuva, a tropical monsoonal estuary along the southwest coast of India. The phytoplankton community exhibited statistically significant inter-annual variation with a 1.2-fold increase in abundance (av. 19.44 ×104cellsL-1) in 2021 in comparison with 2019 (av. 15.30 ×104cellsL-1). A total of 87 phytoplankton species were observed during the study, among which Bacillariophyceae dominated with 52 species, followed by Dinophyceae (20 species), and to a lesser extent by Chlorophyceae (11 species) and Cyanophyceae (2 species). Phytoplankton community structure also showed variation, with 71 species contributing to the diversity in 2021 and 62 species in 2019. Though Bacillariophycea dominated during both years, the community was more diverse in 2021comparatively a more saline year Biddulphia sp. Coscinodiscus sp., Cyclotella striata, Melosira sulcata, Pleurosigma sp., and Skeletonema costatum were the diatoms common to both the years, though with varied abundance. Dinophyceae did not show a conspicuous change in community structure in both years, with mostly the same species contributing to the total phytoplankton population but showed slight variation in abundance. Cyanophycea was observed exclusively during the year 2021. The abundance and community structure of the species varied from year to year, but they also changed seasonally, with lower numbers observed during the monsoon compared to the post-monsoon period in both years. CCA analysis identified two groups of phytoplankton assemblages in relation to the prevailing environmental conditions. The Group I species preferred lower temperature and salinity and elevated nutrient concentrations and they dominated during the monsoon season. Group II species preferred high saline well oxygenated, nutrient-poor waters and they dominated during the post-monsoon. Furthermore, the trophic state index analysis indicated mesotrophic conditions during the monsoon, while eutrophic conditions were observed in the post-monsoon period.

Exploring the effects of climate change and urban policies on lake water quality using remote sensing and explainable artificial intelligence

Climate change and urban policies significantly impact lake water quality, especially in rapidly developing regions. Existing impact assessments are limited by the duration and resolution of water quality data, while long-term impacts and contributions of climate and socioeconomic drivers have rarely been quantified. In this study, we utilized remote sensing imagery to obtain high spatiotemporal resolution data and Explainable Artificial Intelligence (XAI) to reveal how the comprehensive impacts vary over time. We obtained Trophic State Index (TSI) data between 2000 and 2022 from remote sensing imagery. XAI was employed to elucidate the intricate connections between TSI and the multifaceted factors that encompass climate and socioeconomic dynamics. Six typical lakes in East China were selected for this study. Results showed that the TSI of these lakes had generally decreased over the past two decades, being typically higher in the littoral zones compared to the center. Moreover, higher temperatures increased lake TSI. Meanwhile, higher rainfall increased TSI through increased lake nutrient inputs from surrounding watersheds where agricultural and industrial activities are intensive. It was also found that economic development impacts on lake TSI shifted around 2013. Economic development caused severe negative effects on lake water quality before 2013, while this phenomenon decreased thereafter. This is attributed to the implementation of a series of green transformation policies in the region. This study demonstrates the long-term contribution of climate change and urban policies in driving lake water quality variations, and provides implications for policy makers to establish positive economic, social, and environmental linkages.

Long-term monitoring of water quality and phytoplankton community structure of Lake Manzala, Mediterranean Coast of Egypt

Coastal wetlands play a crucial role in supporting biodiversity, providing valuable ecosystem services, and contributing to the resilience of coastal ecosystems, making the preservation and restoration of these wetlands essential for sustainable development in coastal regions. This study focuses on Lake Manzala, a coastal wetland located on the Mediterranean Coast of Egypt, highlighting the significance of conserving and managing this unique environment. The objective of this research was to evaluate the water quality and phytoplankton structure of Lake Manzala and establish an updated long-term ecological database for the region, specifically evaluating the effectiveness of development plans that were carried out in 2017. Surface water samples were collected seasonally at eleven sites between 2010 to 2022. The findings revealed that prior to the development plans, the phytoplankton abundance in Lake Manzala exhibited high levels of eutrophication, characterized by increased abundance and species richness. The dominant phytoplankton classes in Lake Manzala were Bacillariophyceae, Chlorophyceae, and Cyanophyceae. Prior to development plans, they accounted for 46.5% and 45.0% respectively. Post-development, Bacillariophyceae increased to 62.8%, while Chlorophyceae decreased to 25.1%. Dinophyceae increased from 1.3% to 9.04%, while Cyanophyceae decreased from 6.1% to 1.6%. Based on the Trophic State Index for chlorophyll a, Lake Manzala underwent a shift from predominantly hypertrophic to eutrophic conditions. The study explored the relationship between biological factors and environmental conditions using principal component analysis, cluster analysis, and the modified water quality index (WQI). The results indicated positive signs of improvement in Lake Manzala during the post-development phase, as it transitioned from a poor to a moderate state. This research emphasizes the need for integrated land and water management approaches. By informing policy direction and development, this research underscores the importance of preserving and restoring ecosystems for the long-term well-being of both local communities and the global environment.

The future of algal blooms in lakes globally is in our hands

Lakes are fundamental to society and nature, yet they are currently exposed to excessive nutrients and climate change, resulting in algal blooms. In the future, this may change, but how and where still needs more scientific attention. Here, we explore future trends in algal blooms in lakes globally for >3500 ‘representative lakes’ for the year 2050, considering the attribution of both nutrient and climate factors. We soft-coupled a process-based lake ecosystem model (PCLake+) with a watershed nutrient model (MARINA-Multi) to assess trends in algal blooms in terms of the Trophic State Index for chlorophyll-a (TSI-Chla). Globally between 2010 and 2050, we show a rising trend in algal blooms under fossil-fuelled development (TSI-Chla increase in 91 % of lakes) and a declining trend under sustainable development (TSI-Chla decrease in 63 % of lakes). These changes are significantly attributed to nutrients. While not always significant, climate change attributions point to being unfavourable for lakes in 2050, exacerbating lake water quality. Our study stresses prioritising responsible nutrient and climate management on policy agendas. This implies that the future of algal blooms in lakes is in our hands.

Eutrophication of Lakes: From Global Process to Regional Implication in the Kola Arctic Region

ABSTRACTEutrophication of water bodies is analysed as a global process. The volumes of globally increasing use of nitrogen and phosphorus are demonstrated, with the dispersion of these elements leading to increased nutrient contents in lakes and rivers. Results of original studies on remote lakes in the Arctic zone indicate that the content of nutrients in these lakes has increased over the past decades. Concentrations of nitrogen, phosphorus and organic matter in lake waters tend to increase in the absence of anthropogenic effects. Simultaneously, the silicon concentrations were found to decrease because of the consumption by diatoms. Low concentrations of bioavailable nutrients confirm that these nutrients are rapidly spent in the production processes of ecosystems. The calculated trophic state index (according to R. Carlson) indicates that the number of oligotrophic lakes in the forest tundra zone decreased by 50% by 2010–2018, and these lakes are absent from the northern taiga zone. Temperature increase and climate warming in the Arctic zone first caused the increase in the contents of nutrients in the lakes and their trophic states.

Revisiting zooplankton as indicators in the Great Lakes: Which indicators detect temporal changes in the zooplankton community composition?

Zooplankton are a vital component of the food web as the major conduit of energy from primary producers to planktivorous fishes. Therefore, changes in the structure of the zooplankton community affect how energy moves through aquatic ecosystems. Changes in the zooplankton community structure are typically documented through detailed community analysis, which can be difficult to interpret and communicate to non-experts. Alternatively, a few key summary indicators of community structure may better disseminate this information to a broader audience. In this study we analyzed zooplankton data from 1997 to 2019 from the US Environmental Protection Agency’s Great Lakes National Program Office to select indicators that summarized changes in the zooplankton community. Two indicators (percent of calanoids by biomass and areal density of herbivorous cladocerans) detected known changes in lakes Huron, Michigan, and Ontario, correlated with the crustacean zooplankton areal biomass above (herbivorous cladocerans) or in the hypolimnion (percent calanoids), and were related to the lake or basin-wide Carlson’s Trophic State Index based on chlorophyll (TSIchl) (percent calanoids by biomass). Our findings suggest that employing one or both selected indicators in addition to TSIchl and total crustacean zooplankton areal biomass, an already reported indicator in the Great Lakes that captures overall food availability for fish, would provide additional key information about zooplankton community structure in simple metrics that could be effectively communicated to stakeholders.

Source identification of organic C and N in suspended particulate matter and sediments in plateau lakes as influenced by trophic status using stable isotopic signatures

Understanding the relationship between suspended particulate matter (SPM), sediment organic C, N stable isotopes, and lake trophic state index (TSI) is essential for managing lake pollution and eutrophication. According to the δ13C, δ15N, and C/N we found that the organic C in SPM and sediment of Caohai Lake primarily originated from macrophytes, while N was sourced from chemical fertilizers, phytoplankton, and aquatic plants. Total nitrogen, total phosphorus, NO3−-N, oxidation reduction potential, and Chl.a were identified as key factors influencing the sources and variations of SPM and sediment organic C and N stable isotopes in Caohai Lake, with a significant linear correlation observed between C, N stable isotopes, and TSI in sediments. To mitigate eutrophication in Caohai Lake, it is recommended that farmers apply fertilizers judiciously to minimize nutrient loss and that aquatic plants be regularly harvested to reduce N release from plant residues.

Eutrophication Levels of Hydropower Plant Water Reservoirs Via Trophic State Index With Evaluation of the Fate of Pollutants Affected By the Land Use Model

Eutrophication Levels of Hydropower Plant Water Reservoirs Via Trophic State Index With Evaluation of the Fate of Pollutants Affected By the Land Use Model

Identifying factors influencing reservoir eutrophication using interpretable machine learning combined with shoreline morphology and landscape hydrological features: A case study of Danjiangkou Reservoir, China

Reservoir nearshore areas are influenced by both terrestrial and aquatic ecosystems, making them sensitive regions to water quality changes. The analysis of basin landscape hydrological features provides limited insight into the spatial heterogeneity of eutrophication in these areas. The complex characteristics of shoreline morphology and their impact on eutrophication are often overlooked. To comprehensively analyze the complex relationships between shoreline morphology and landscape hydrological features, with eutrophication, this study uses Danjiangkou Reservoir as a case study. Utilizing Landsat 8 OLI remote sensing data from 2013 to 2022, combined with a semi-analytical approach, the spatial distribution of the Trophic State Index (TSI) during flood discharge periods (FDPs) and water storage periods (WSPs) was obtained. Using Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), explained the relationships between landscape composition, landscape configuration, hydrological topography, shoreline morphology, and TSI, identified key factors at different spatial scales and validated their reliability. The results showed that: (1) There is significant spatial heterogeneity in the TSI distribution of Danjiangkou Reservoir. The eutrophication levels are significant in the shoreline and bay areas, with a tendency to extend inward only during the WSPs. (2) The importance of landscape composition, landscape configuration, hydrological topography, and shoreline morphology to TSI variations during the FDPs are 25.12 %, 29.6 %, 23.09 %, and 22.19 % respectively. Besides shoreline distance, the Landscape Shape Index (LSI) and Hypsometric Integral (HI) are the two most significant environmental variables overall during the FDPs. Forest and grassland areas become the most influential factors during the WSPs. The influence of landscape patterns and hydrological topography on TSI varies at different spatial scales. At the 200 m riparian buffer zone, the increase in cropland and impervious areas significantly elevates eutrophication levels. (3) Morphology complexity, shows a noticeable threshold effect on TSI, with complex shoreline morphology increasing the risk of eutrophication.

“Efficiency of Carlson Modeling of Eutrophication of the Inner Bay - Puno Lake Titicaca”

Objectives: Determine the degree of water contamination, expressed as a percentage of pure water; Quantify and evaluate water quality; recognize the sources of pollution and know the most relevant pollutants of the channels and mechanisms of eutrophication. Theoretical Framework: Pollution, water quality, pollution sources, tributaries-channels and eutrophication mechanisms. Method: The trophic state index of Carlson (1977) or TSI (Trophic State Index) was used. It is a longitudinal study from 2008 to 2016. With 15 sampling points, 12 times a year, evaluating the physical, chemical and microbiological parameters at each sampling point at 20% and 80% of the water column. Results and Discussion: Use modeling to predict the concentration of contaminants for the different treatment alternatives. Likewise, give a description of a model to estimate water quality. In conclusion, we indicate that the Carlson trophic state index has determined that the most relevant pollutants in the eutrophication process of the Interior Bay are nutrients and phosphates, these coming from the discharge of domestic wastewater from the city of Puno. Research implications: The study will serve to evaluate the behavior of water quality and implement mitigation policies. Originality/Value: This study will contribute to monitoring and treating wastewater.

Case study: Nutrients distribution and assessment of the current trophic status of Plastiras lake in Thessaly (central Greece) after tropical storm Daniel

Based on the interactions of three water quality variables, chlorophyll-a (Chl-a), secchi depth (SD – water transparency) and total phosphorus (TP), this study was aimed to assess the trophic state of Plastiras Lake through the modified method of Carlson's trophic state index (CTSI). Eutrophication is one of the most serious problems for water quality and CTSI is an important index for monitoring water quality status and eutrophication assessment. The classification scale for Trophic State Index (TSI) fluctuates from 0 to 100 and the results of this study showed that the mean TSI for Chl-a was 42.95, for SD was 45.50 and for TP was 75.15. The overall CTSI finally was 54.53. These show that Plastiras Lake is presently classified as mesotrophic to eutrophic which reflects the current levels of nutrients in the lake.

Linking sediment geochemistry with catchment processes, internal phosphorus loading and lake water quality

Research in the field of sediment geochemistry suggests potential linkages between catchment processes (land use), internal phosphorus (P) loading and lake water quality, but evidence is still poorly quantified due to a limited amount of data. Here we address the issues based on a comprehensive data set from 27 lakes in southern Finland. Specifically, we aimed at: 1) elucidating factors behind spatial variations in sediment geochemistry; 2) assessing the impact of diagenetic transformation on sediment P regeneration across lakes based on the changes in the vertical distribution of sediment components; 3) exploring the role of the sediment P forms in internal P loading (IL), and 4) determining the impact of IL on lake water quality. The relationship between sediment P concentration and field area percentage (FA%) was statistically significant in (mainly eutrophic) lakes with catchments that included more than 10 % of fields. We found that sediment iron-bound P (Fe-P) increased with increasing FA%, which agrees with the high expected losses from the cultivated areas. Additionally, populated areas increased the pool of sediment Fe-P. Internal P loading was significantly positively related to both sediment Fe-P and sediment organic P (Org-P). However, Org-P was not significant (as the third predictor) in models that had a trophic state variable as the first predictor and Fe-P as the second predictor. Further, the vertical profiles of sediment components indicated a role of diagenetic transformations in the long-term sediment P release, especially in lakes with deeper maximum depth and longer water residence time. Finally, IL was significantly positively correlated to water quality variables including phytoplankton biomass, its proportion of cyanobacteria, chlorophyll a concentration and trophic state index. Our findings suggest that reduction of P losses from the field and populated areas will decrease internal P loads and increase water quality through a reduced pool of Fe-P.

Evaluating impacts of climate and management on reservoir water quality using environmental fluid dynamics code

Climate change and human interference, notably nutrient input, affect the water quality. Nitrogen (N) and phosphorus (P) are pivotal in managing eutrophication. This study investigated the effects of water dynamics and chemical constituents on water quality in Hongfeng Lake, a typical weakly stratified reservoir suffering from algae blooms in Southwest China, using the Environmental Fluid Dynamics Code. Leveraging climate, hydrological, and water quality data, we constructed, calibrated, and validated the temperature-hydrodynamics-water quality-sediment model. Various scenarios were analyzed, including wind speed, air temperature, solar radiation, rainfall, water discharge, N and P external input, and internal release. The findings revealed that no rain and warming increased trophic state index (TSI) and chlorophyll-a (Chl-a) concentration, and no solar radiation initially elevated nitrate concentration, followed by an increase in ammonium concentration. Besides, no solar radiation and changes in rainfall significantly increased total phosphate concentration. The management scenarios of N and P reduction, halving tributary, and mainstream flow scenarios improved water quality and reduced eutrophication. The wind speed under the N and P reduced scenarios showed that a doubling in wind led to increased concentrations of the particulate organic matter, Chl-a, and dissolved oxygen, alongside decreased ammonium and nitrate, while TSI exhibited minimal change. However, 5- and 10-times wind speed scenarios amplified TSI in shallow water, potentially due to a substantial rise in internal nutrient release. The degradation trend observed in drinking water quality amid climate change (warming and flooding) raises concerns regarding health-related risks. These simulations provided the quantified influence of climate change and environmental management strategies on water quality in the weakly stratified reservoir, notably highlighting the looming threat of exacerbated eutrophication due to warming, necessitating more stringent N and P reduction measures compared to current practices.

Ecological assessment of water quality in freshwater wetlands based on the effect of environmental heterogeneity on phytoplankton communities in Northeast China.

Phytoplankton community characterized by strong vitality response to environmental change in freshwater ecosystems. This study aims to evaluate the effectiveness of using phytoplankton diversity as a water quality indicator in wetlands, and find out the main environmental variables affecting the distribution of phytoplankton. From 2020 to 2021, we examined phytoplankton assemblages and water environmental variables in spring, summer, and autumn at eight sampling sites from Hulanhe Wetland, Northeast (NE) China. The results showed that Bacillariophyta was the dominant species. Phytoplankton composition and abundance differed among sampling sites in each season; the abundance in summer (613.71 × 104 ind. L-1) was higher than that in autumn and spring. The water quality assessment of the trophic state index (TSI) based on the four physicochemical indicators was compared with phytoplankton diversity indices, which indicated that the phytoplankton community was stable, and these two indices were significantly lower in summer than in spring and autumn. According to redundancy analysis (RDA), total phosphorus (TP) and nitrogen (TN) were the main environmental variables affecting the distribution of phytoplankton. Temperature and dissolved oxygen (DO) changes also played a role, and their impact on the community was discussed. This work can provide relevant scientific references on the usefulness of phytoplankton diversity structure in assessing water quality in cold regions, in which the succession can be significantly affected by nutrients and temperatures.

Spectral characteristics of dissolved organic matter in Plateau Lakes: Identifying eutrophication indicators in Southwest China

Dissolved organic matter (DOM) acts as a chemical intermediary between terrestrial and lacustrine ecosystems and significantly affects the structure and function of lakes. The trophic state of lakes, driven by terrestrial input and phytoplankton biomass, alters the optical properties of DOM. From November 2018 to July 2019, we collected 119 water samples from the Erhai watershed and analyzed them using UV–Vis and EEM-PARAFAC to study the optical properties of DOM in relation to the trophic conditions. Our result indicated that the tyrosine-like protein (C1), tryptophan-like protein (C2), and humic-like compounds (C3) were among the mostly autochthonous components of the DOM. The percentage of the C3 was higher in eutrophic lakes than in mesotrophic and light-eutrophic lakes. The ultraviolet absorption coefficients at 254 nm (aCDOM(254)) and fluorescence intensity at 355 nm (Fn(355)) increased significantly (p < 0.01) with an increased trophic state. Our findings indicate that the influence of nutrients and environmental factors (such as pH and water temperature) on DOM varies with the trophic state. The development of novel predictive models for trophic state assessment was largely based on the significant correlations between TSI and aCDOM(254) (R2 = 0.762, p < 0.01) and Fn(355) (R2 = 0.705, p < 0.01). This neural network model facilitates the creation of a novel fast assessment tool by highlighting the connection between DOM features and the trophic state index. By enabling swift experimental measurements, this model offers a high-resolution monitoring solution for tracking the eutrophication of plateau lakes and rivers.

A dataset of trophic state index for nation-scale lakes in China from 40-year Landsat observations

Trophic state index (TSI) serves as a key indicator for quantifying and understanding the lake eutrophication, which has not been fully explored for long-term water quality monitoring, especially for small and medium inland waters. Landsat satellites offer an effective complement to facilitate the temporal and spatial monitoring of multi-scale lakes. Landsat surface reflectance products were utilized to retrieve the annual average TSI for 2693 lakes over 1 km2 in China from 1984 to 2023. Our method first distinguishes lake types by pixels with a decision tree and then derives relationships between trophic state and algal biomass index. Validation with public reports and existing datasets confirmed the good consistency and reliability. The dataset provides reliable annual TSI results and credible trends for lakes under different area scales, which can serve as a reference for further research and provide convenience for lake sustainable management.

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 &gt;30, while TIN:TRP was &lt;10. The TSIs for chlorophyll a, SD, and TP ranged from 42 to 59, and for TN it was &gt;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.

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.