Secchi Depth Research Articles

Contribution of combined stressors on density and gene expression dynamics of the copepod Temora longicornis in the North Sea.

The impact of multiple environmental and anthropogenic stressors on the marine environment remains poorly understood. Therefore, we studied the contribution of environmental variables to the densities and gene expression of the dominant zooplankton species in the Belgian part of the North Sea, the calanoid copepod Temora longicornis. We observed a reduced density of copepods, which were also smaller in size, in samples taken from nearshore locations when compared to those obtained from offshore stations. To assess the factors influencing the population dynamics of this species, we applied generalised additive models. These models allowed us to quantify the relative contribution of temperature, nutrient levels, salinity, turbidity, concentrations of photosynthetic pigments, as well as chemical pollutants such as polychlorinated biphenyls and polycyclic aromatic hydrocarbons (PAHs), on copepod density. Temperature and Secchi depth, a proxy for turbidity, were the most important environmental variables predicting the densities of T. longicornis, followed by summed PAH and chlorophyll concentrations. Analysing gene expression in field-collected adults, we observed significant variation in metabolic and stress-response genes. Temperature correlated significantly with genes involved in proteolytic activities, and encoding heat shock proteins. Yet, concentrations of anthropogenic chemicals did not induce significant differences in the gene expression of genes involved in the copepod's fatty acid metabolism or well-known stress-related genes, such as glutathione transferases or cytochrome P450. Our study highlights the potential of gene expression biomonitoring and underscores the significance of a changing environment in future studies.

Satellite imagery as a management tool for monitoring water clarity across freshwater ponds on Cape Cod, Massachusetts

Water clarity serves as both an indicator and a regulator of biological function in aquatic systems. Large-scale, consistent water clarity monitoring is needed for informed decision-making. Inland freshwater ponds and lakes across Cape Cod, a 100-km peninsula in Massachusetts, are of particular interest for water clarity monitoring. Secchi disk depth (SDD), a common measure of water clarity, has been measured intermittently for over 200 Cape Cod ponds since 2001. Field-measured SDD data were used to estimate SDD from satellite data, leveraging the NASA/USGS Landsat Program and Copernicus Sentinel-2 mission, spanning 1984 to 2022. Random forest machine learning models were generated to estimate SDD from satellite reflectance data and maximum pond depth. Spearman rank correlations (rs) were “strong” for Landsat 5 and 7 (rs = 0.78 and 0.79), and “very strong” for Landsat 8, 9, and Sentinel-2 (rs = 0.83, 0.86, and 0.80). Mean absolute error also indicated strong predictive capacity, ranging from 0.65 to 1.05 m, while average bias ranged from −0.20 to 0.06 m. Long- and recent short-term changes in satellite-estimated SDD were assessed for 193 ponds, selected based on surface area and the availability of maximum pond depth data. Long-term changes between 1984 and 2022 established a retrospective baseline using the Mann-Kendall test for trend and Theil-Sen slope. Generally, long-term water clarity improved across the Cape; 149 ponds indicated increasing water clarity, and 8 indicated deteriorating water clarity. Recent short-term changes between 2021 and 2022 identified ponds that may benefit from targeted management efforts using the Mann-Whitney U test. Between 2021 and 2022, 96 ponds indicated deteriorations in water clarity, and no ponds improved in water clarity. While the 193 ponds analyzed here constitute only one quarter of Cape Cod ponds, they represent 85% of its freshwater surface area, providing the most spatially and temporally comprehensive assessment of Cape Cod ponds to date. Efforts are focused on Cape Cod, but can be applied to other areas given the availability of local field data. This study defines a framework for monitoring and assessing change in satellite-estimated SDD, which is important for both local and regional management and resource prioritization.

Remote monitoring of water clarity in coastal oceans of the Guangdong-Hong Kong-Macao Greater Bay Area, China based on machine learning

The development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), one of the most developed and densely populated regions in China, has posed an increasing threat to the health of the water environment in adjacent coastal oceans. However, the spatiotemporal variations of water clarity in the coastal oceans of the GBA (COGBA) have not been well-documented. Therefore, this study aims to develop a remote sensing retrieval algorithm for assessing water clarity, represented by Secchi disk depth (ZSD), based on Landsat-8 OLI imagery for the COGBA using a machine learning method. Cross-validation demonstrated that the algorithm performed exceptionally well, with an R2 value greater than 0.8. By applying the algorithm to 263 Landsat-8 OLI images, we obtained a time series of ZSD for the period of 2013–2021 over the COGBA. Results indicate that the Pearl River Estuary (PRE) exhibited the highest turbidity, followed by the Daya Bay and the Mirs Bay. Generally, ZSD increased from the northwest to the southeast of the COGBA. Seasonal, interannual, and long-term variations in ZSD were also observed with long-term increases in the PRE and the Daya Bay. The interannual variations in ZSD during fall were primarily regulated by different factors in each region. In the PRE, the negative effects of sediment discharge and wind speed played a significant role. In Mirs Bay, wind speed and sediment discharge had a negative impact on ZSD. In Daya Bay, precipitation and wind speed were the key factors influencing ZSD. The development and findings of our algorithm contribute to the protection and management of the water environment in the COGBA, facilitating effective governance measures.

Remote Sensing-Based Water Quality Parameters Retrieval Methods: A Review

Water quality is a sensitive global environmental issue, as it is important for long-term economic development and environmental sustainability. It is a general descriptor of water properties in terms of physical, chemical, thermal, and/or biological characteristics. Laboratory analysis is used to measure and analyze water quality parameter, however, it is a conventional, time-consuming, and expensive approach often providing discrete data at a single point in space and time and making it difficult to characterize a larger waterbody, while remote sensing methods is a cost effective and accurate methods of water quality monitoring with a high spatial and temporal resolution for large area of waterbodies. To this end, this review focused on novel findings in the field of water quality estimation using remote sensing techniques, and the result revealed that remote sensing method has used to retrieve water quality parameters which are optically active (Chlorophyll-a, Secchi Disk depth, Water temperature, Turbidity, Total Suspended Matter, Electrical conductivity, Sea Surface Salinity and Colored Dissolved Organic Matter), and optically non active (Dissolved Oxygen, Chemical Oxygen Demand, Biochemical Oxygen Demand, Total Nitrogen, Ammonia Nitrogen and Total Phosphorus). Various properties (spectral, spatial and temporal, etc.) of the more commonly employed multi spectral and hyper spectral sensors of both satellite and non-satellite-born data sources are tabulated to be used as a sensor selection guide. Furthermore, this paper summarizes the commonly used different retrieval algorisms (analytical, empirical, and artificial intelligence/machine learning (AI/ML)) employed in evaluating and quantifying the water quality parameters. As a whole, remote sensing technology is a permissible method for water quality monitoring across the world in its spatio-temporal coverage, accuracy, and its cost effectiveness

Productividad primaria en un estanque de cría de peces (31°12’S; 57°55’W)

Gross and net primary productivity, respiration and related parameters were estimated in an experimental fish pond where silverside (Basilichthys bonariensis) were bred, using the oxygen light-dark bottle method. The results obtained showed a positive correlation (r = 0.83, p < 0.005) with temperature. The chlorophyll a concentration, the number of phytoplankton cells and the Secchi disk showed no correlation with primary productivity.The mean gross primary productivity on sunny days was 2022 g C m-2 day-1 in the deepest zone, with a 47% variation. For the whole pond the results was 358.7 KgC ha-1 year-1 considering a correction for statistical average of sunny days. The respiration/ gross primary productivity relation was 0.50 for the whole pond.With comparative purposes the primary productivity was estimated with the simplified method of daily curves and by the method of oxygen changes in open waters.

What's good for fish is good for fishes: Interspecific consistency of growth, condition, and abundance among glacial lakes

AbstractObjectiveFish abundance and growth are regulated by a combination of bottom‐up and top‐down forces, but many management techniques depend more heavily on the latter. Here, we evaluated whether intersystem variation in nutrient loading and bottom‐up forces play more dominant roles in the control of abundance and growth of species across similar lakes than intra‐ and interspecific compensatory effects. We aimed to assess whether patterns of abundance and growth are consistent among fish species across lakes.MethodsWe tested this by evaluating pairwise comparisons of catch‐per‐unit‐effort, condition, and length‐at‐age data for 11 common warmwater fish species from 184 mesotrophic and eutrophic glacial lakes in Indiana, United States. We characterized the environmental conditions of each lake using limnological measurements (e.g., chlorophyll‐a concentration and surface temperature), lake morphology descriptions (e.g., depth and size), catchment characteristics (e.g., percent agricultural land cover), and nutrient load modeling using the Long‐Term Hydrologic Impact Analysis model. Taking a meta‐analysis approach, we used effect size calculation from pairwise correlations among lakes to identify environmental and community impacts on species abundance, condition, and length at age.ResultWe demonstrated that there were positive associations among most species comparisons (i.e., multiple species experiencing relatively fast growth in the same lake). Evaluations of environmental conditions among systems suggested that differences in estimated phosphorus input and the limnological measurements of total phosphorus, Secchi depth, and chlorophyll‐a concentration were good predictors of length at age and catch per unit effort for fish.ConclusionOur results indicated that there is a strong and consistent influence of environmental conditions and bottom‐up processes in determining species abundance and growth. This suggests that bottom‐up forces and environmental conditions linked to nutrient loading likely determine the upper boundary of fish abundance and growth in these lakes.

Strategies for regulating the intensity of different cyanobacterial blooms: Insights from the dynamics and stability of bacterioplankton communities

The occurrence of cyanobacterial blooms is increasing in frequency and magnitude due to climate change and human activities, which poses a direct threat to drinking water security. The impacts of abiotic and biotic factors on the development of blooms have been well studied; however, control strategies for different bloom intensities have rarely been explored from the perspective of the dynamics and stability of bacterioplankton communities. Here, a network analysis was used to investigate the interactions and stability of microbial communities during different periods of R. raciborskii bloom in an inland freshwater lake. The abundance and diversity of rare taxa were significantly higher than that of abundant taxa throughout the bloom cycle. At the pre-bloom (PB) stage, microbial interactions among the different bacterial groups were weak but strongly negatively correlated, indicating low robustness and weak disturbance resistance within the community. However, community stability was better, and microbial interactions became more complicated at the high-bloom (HB) and low-bloom (LB) stages. Interestingly, rare taxa were significantly responsible for community stability and connectivity despite their low relative abundance. The Mantel test revealed that Secchi depth (SD), orthophosphate (PO43−-P), and dissolved oxygen (DO) were significantly positively correlated with abundant taxa, rare taxa and PB. DO was significantly positively correlated with HB, intermediate taxa, and rare taxa, while water temperature (WT), N/P and total nitrogen (TN) were significantly positively correlated with LB, abundant taxa, intermediate taxa, and rare taxa. These findings suggest that reducing the PO43−-P concentration at the PB stage may be an effective approach to preventing the development of R. raciborskii blooms, while regulating rare taxa at the HB and LB stages may be a key factor in controlling R. raciborskii blooms.

Ocean-colour anomalies quantified by the human eye.

Phytoplankton turn seawater green when their concentration increases. This allows us to monitor them using ocean colour. However, as the spectral properties of phytoplankton and their relationship with other coloured substances in seawater vary, subtle differences (anomalies) in ocean colour occur that can cause large errors in estimates of phytoplankton abundance. Identifying and understanding these anomalies is required to interpret ocean-colour data properly, but not all scientists have access to, or can afford, the in-situ instrumentation needed to do this. We show that practical, low-cost tools developed in the 19th century (a Secchi disk and Forel-Ule colour scale) can be used to quantify a colour anomaly in the Weddell Sea. Our findings imply that ocean-colour anomalies can be identified using affordable methods. Furthermore, records collected over the last century may contain clues on how ocean ecosystems have changed with climate.

Predicting Imminent Cyanobacterial Blooms in Lakes Using Incomplete Timely Data

AbstractToxic cyanobacterial blooms (CBs) are becoming more frequent globally, posing a threat to freshwater ecosystems. While making long‐range forecasts is overly challenging, predicting imminent CBs is possible from precise monitoring data of the underlying covariates. It is, however, infeasibly costly to conduct precise monitoring on a large scale, leaving most lakes unmonitored or only partially monitored. The challenge is hence to build a predictive model that can use the incomplete, partially‐monitored data to make near‐future CB predictions. By using 30 years of monitoring data for 78 water bodies in Alberta, Canada, combined with data of watershed characteristics (including natural land cover and anthropogenic land use) and meteorological conditions, we train a Bayesian network that predicts future 2‐week CB with an area under the curve (AUC) of 0.83. The only monitoring data that the model needs to reach this level of accuracy are whether the cell count and Secchi depth are low, medium, or high, which can be estimated by advanced high‐resolution imaging technology or trained local citizens. The model is robust against missing values as in the absence of any single covariate, it performs with an AUC of at least 0.78. While taking a major step toward reduced‐cost, less data‐intensive CB forecasting, our results identify those key covariates that are worth the monitoring investment for highly accurate predictions.

Wide ecological niches ensure frequent harmful dinoflagellate blooms

Harmful algal blooms (HABs) and their consequences cause multiple devastating effects in various freshwater, brackish and marine ecosystems. However, HAB species at moderate population densities have positive ecological roles as primary producers of organic matter and food for zooplankton and fish. They also enhance benthic-pelagic coupling and participate in the biogeochemical cycles. The consequences of HABs are transported across the conventional environmental boundaries by numerous cascade effects in the food webs and beyond. Meanwhile, forecasts of bloom events are still limited, largely because of scarcity of reliable information on ecological niches of the bloom-forming algae. To fill up this knowledge gap, this study focused on dinoflagellates, a diverse group of mostly photosynthesizing protists (unicellular eukaryotes) capable of mixotrophy, since they play a key role in primary production and formation of blooms in marine and brackish waters worldwide. In this study, ecological niches of 17 abundant bloom-forming dinoflagellate species from coastal regions of the southern Baltic Sea were identified for the first time. It was hypothesized that wider ecological niches ensure more frequent dinoflagellate blooms compared to the species with narrower niches. This hypothesis was verified using the long-term (44 years) database on phytoplankton abundance and physical-chemical characteristics of the environment. It were analyzed 4534 datasets collected from 1972 to 2016. Fourteen abiotic parameters (water temperature, salinity, Secchi depth, pH, Chl a, and concentration of basic nutrients) were considered as ecological niche dimensions. The Principal Component Analysis presented the dissolved inorganic nitrogen, total nitrogen, Chl a, and temperature as principal niche dimensions of dinoflagellates. The algal bloom criteria were refined. It was for the first time proved statistically that HAB frequency of dinoflagellate species robustly correlated with the width of their ecological niches.

Comparison of the zooplankton-based RCC to Carlson’s trophic state indices and water quality parameters

Zooplankton is not included as a biological quality element, even though it is a key component of pelagic food webs. The data on zooplankton community structure can be used to evaluate the trophic state of lakes through an RCC Index, based on quantitative proportions between the main zooplankton groups. As previous investigations have related the index only to the Secchi disc transparency, the aim of this study was to compare the Index to a set of water quality parameters and trophic state indices. Two sandpit lakes with contrasting characteristics were selected as model lakes for the study. A total of 57 samples were collected simultaneously with the physicochemical water quality parameters between 2016 and 2020. Carlson’s trophic state indices were used to assess the trophic state of the lakes. Pearson correlation coefficient and Principal Component Analysis were used to assess the observed correlations between the measured parameters and to distinguish what factors regulate zooplankton community structure. Overall, 86 zooplankton taxa were identified. RCC Index ranged from 2 in the small sandpit lake to 87 in the big sandpit lake. A highly significant negative correlation was found between the RCC Index and the values of TP and COD. The results complement the arguments for the inclusion of zooplankton as a BQE for the assessment of the status of lakes.

Variaciones fisicoquímicas del agua de una gran presa Hidroeléctrica Mexicana, Región Centro semi-árida

The Zimapan hydropower plant built in 1995 was the first project to take advantage of Mexico's City wastewater for electric power generation. This tropical reservoir receives pollutants that affect water quality from municipal, agricultural, and industrial wastewater through the Tula and San Juan rivers. The study considered the changes in the physicochemical parameters (surface to 20 m depth) for May (wet warm), November (wet cold-after rains), and February (dry cold) in seven dam sites: two lotic, two transitional, and three lentic sites. Mean values ranges were temperature 20.71-24.38 °C, 1.59-7.56 mg/L dissolved oxygen, Secchi disk transparency 2.26-3.98 m, pH 7.12-8.40, electrical conductivity 48-1334 µS/cm, total nitrogen 14.20-30.74 mg/L, total phosphorus 10.45-15.83 mg/L, and dissolved organic carbon 9.11-20.63 mg/L. In November, all the reservoir surface water was hypoxic due to increased dissolved organic carbon by river input. Even though dissolved oxygen has a seasonal variation, this reservoir has a sustainable fishery of tilapia and black bass (~400 tons/y). The dam is meromictic and hypereutrophic, with seasonal water quality variations with no significant spatial variations. Water quality variation observed in this study can be useful to Mexican decision-makers on water and fisheries management, prevent massive fish kills, and be a support guide for commercial and sport fishers.

Evaluating methods for measuring in‐river bathymetry: Remote sensing green LIDAR provides high‐resolution channel bed topography limited by water penetration capability

AbstractThe objective was to evaluate the feasibility of measuring bathymetry using airborne green LiDAR in long and variable river reaches (4 km or more), across three rivers with varying gradients, water depths and light penetration (3.5–10 m), using four alternative LiDAR sensors. The accuracy of green LiDAR data was compared to in situ measurements collected by stratified transect point sampling and Multibeam bathymetry. Factors potentially limiting the feasibility of green LIDAR in rivers were explored. If remote sensing signals were reflected by the riverbed, the sensors generally provided elevation data consistent with in situ elevation measurements, indicating high accuracy (±10 cm) across different hydraulic conditions. The loss of green LiDAR data was mainly a consequence of limited signal water penetration capability, that is, water clarity. Secchi depth was a proxy variable strongly associated with green LiDAR penetration capability across rivers. Data loss was low up to the Secchi depth but increased rapidly thereafter. Surface water turbulence (‘white water’) and dark riverbed vegetation also increased green LiDAR signal loss. Sensors with lower point density and therefore less spatial resolution had more signal strength and therefore penetrated deeper water. However, with increasing coverage of surface turbulence (‘white water’), the importance of high point density also increased. Signal power should be balanced with signal density (spatial resolution), depending on river characteristics and project objectives. We conclude that remote sensing green LiDAR bathymetry is a robust method that efficiently provides accurate elevation data across rivers with different hydraulic conditions and water depths.

Analysis of water quality and trophic status of reservoirs in Chuzhou City, China

AbstractReservoir is a vital tool for human utilization of water resources, and the deterioration of its water quality can seriously threaten the water cycle and sustainable urban development. However, there are relatively few studies in academia that analyse and evaluate the water quality of multiple reservoirs at the same time. To address this knowledge deficit, we collected 108 water samples from three different reservoirs in Chuzhou City for 36 months (from 2019 to 2021), explored the drivers of changes in water quality parameters over time and the extent of eutrophication. Our results indicated that the water quality of the reservoirs was deteriorating during the study period, among which Huanglishu Reservoir and Shahe Reservoir reached mild eutrophic status, and both had higher eutrophication levels than Chengxi Reservoir. Secchi depth, total nitrogen and total phosphorus were the principal factors inducing eutrophication. The biassed utilization of reservoir functions was the major contributor to the discrepancy in the degree of eutrophication. Furthermore, Pearson correlation analysis revealed that there are significant correlations between many water quality parameters. Cluster analysis grouped the 12 months of each year into three clusters (stable water level period, rainy season high flow period and winter low flow period). Based on this, analysis of variance showed that most water quality parameters varied considerably between the clusters. Collectively, this study identified the actual water quality conditions of three reservoirs in Chuzhou City and provided guidance for local water quality management and environmental protection.

Temporal phytoplankton dynamics and environmental variables in four Ethiopian soda lakes

We investigated the spatio-temporal dynamics of phytoplankton composition, chlorophyll-a as a proxy for algal biomass, and abundance in relation to environmental parameters in four Ethiopian soda lakes: Arenguade, Beseka, Chittu, and Shala. Triplicate water samples were collected from each lake from January to December 2020, four times in different seasons. Lake Chittu had the highest chlorophyll-a concentration, followed by Lake Arenguade, Beseka and Shala. Chlorophyll-a concentrations generally increased during the post rainy and dry season. The results of LR models are high for lakes Arengude, Beseka and Chittu. Lakes Shala and Beseka had the highest number of phytoplankton taxa, with both taxa composition and abundance dominated by Bacillariophyceae. Cyanoprokaryota, particularly Limnospira fusiformis, predominated in the abundance of Lakes Arenguade and Chittu. Water temperature, Secchi depth, turbidity, electrical conductivity, soluble reactive phosphorus, nitrate and silica significantly influenced the phytoplankton community structure. Long-term trend analysis revealed changes in phytoplankton biomass and lake taxonomic composition. The alteration in phytoplankton biomass and species composition of the lakes could be attributed to three factors: (1) frequent high-velocity explosions conducted for seismological studies in the past. This impact caused a dramatic increase in lake level in the case of Lake Beseka leading to a drop in nutrient concentration; (2) climate change and (3) salt content. Overall, our findings suggest that phytoplankton composition, biomass, and abundance varied according to seasonal fluctuations, emphasizing the possible effects of anthropogenic and natural causes on their community structure.

Influence of hydrological features on CO2 and CH4 concentrations in the surface water of lakes, Southwest China: A seasonal and mixing regime analysis

Due to the large spatiotemporal variability in the processes controlling carbon emissions from lakes, estimates of global lake carbon emission remain uncertain. Identifying the most reliable predictors of CO2 and CH4 concentrations across different hydrological features can enhance the accuracy of carbon emission estimates locally and globally. Here, we used data from 71 lakes in Southwest China varying in surface area (0.01‒702.4 km2), mean depth (< 1‒89.6 m), and climate to analyze differences in CO2 and CH4 concentrations and their driving mechanisms between the dry and rainy seasons and between different mixing regimes. The results showed that the average concentrations of CO2 and CH4 in the rainy season were 23.9 ± 18.8 μmol L−1 and 2.5 ± 4.9 μmol L−1, respectively, which were significantly higher than in the dry season (10.5 ± 10.3 μmol L−1 and 1.8 ± 4.2 μmol L−1, respectively). The average concentrations of CO2 and CH4 at the vertically mixed sites were 24.1 ± 21.8 μmol L−1 and 2.6 ± 5.4 μmol L−1, being higher than those at the stratified sites (14.8 ± 13.4 μmol L−1 and 1.7 ± 3.5 μmol L−1, respectively). Moreover, the environmental factors were divided into four categories, i.e., system productivity (represented by the contents of total nitrogen, total phosphorus, chlorophyll a and dissolved organic matter), physicochemical factors (water temperature, Secchi disk depth, dissolved oxygen and pH value), lake morphology (lake area, water depth and drainage ratio), and geoclimatic factors (altitude, wind speed, precipitation and land-use intensity). In addition to the regression and variance partitioning analyses between the concentrations of CO2 and CH4 and environmental factors, the cascading effects of environmental factors on CO2 and CH4 concentrations were further elucidated under four distinct hydrological scenarios, indicating the different driving mechanisms between the scenarios. Lake morphology and geoclimatic factors were the main direct drivers of the carbon concentrations during the rainy season, while they indirectly affected the carbon concentrations via influencing physicochemical factors and further system productivity during the dry season; although lake morphology and geoclimatic factors directly contributed to the carbon concentrations at the vertically mixed and stratified sites, the direct effect of system productivity was only observed at the stratified sites. Our results emphasize that, when estimating carbon emissions from lakes at broad spatial scales, it is essential to consider the influence of precipitation-related seasons and lake mixing regimes.

Examining the effectiveness of artificially replicated lake systems in predicting eutrophication indicators: a comparative data-driven analysis

Abstract Data-driven models for the prediction of lake eutrophication essentially rely on water quality datasets for a longer duration. If such data are not readily available, lake management through data-driven modeling becomes impractical. So, a novel approach is presented here for the prediction of eutrophication indicators, such as dissolved oxygen, Secchi depth, total nitrogen, and total phosphorus, in the waterbodies of Assam, India. These models were developed using water quality datasets collected through laboratory investigation in artificially simulated lake systems. Two artificial prototype lakes were eutrophied in a controlled environment with the gradual application of wastewater. A periodic assessment of water quality was done for model development. Data-driven modeling in the form of multilayer perceptron (MLP), time-delay neural network (TDNN), support vector regression (SVR), and Gaussian process regression (GPR) were utilized. The trained model's accuracy was evaluated based on statistical parameters and a reasonable correlation was observed between targeted and model predicted values. Finally, the trained models were tested against some natural waterbodies in Assam and a satisfactory prediction accuracy was obtained. TDNN and GPR models were found superior compared to other methods. Results of the study indicate feasibility of the adopted modeling approach in predicting lake eutrophication when periodic water quality data are limited for the waterbody under consideration.

Remote sensing estimation of water transparency for Saguling Dam in the past decade (2013-2022) based on Landsat 8

Water transparency (i.e., Secchi disk depth (SDD) reflects light transmission capacity of a water body and influences growth of aquatic plants, aquatic organisms, and primary productivity. In this study, using remote sensing reflectance (Landsat 8) and SDD datasets, we predicted the water transparency in Saguling Dam for the first time. The results indicated that the models in the visible (Band 1, Band 2, and Band 3) combined with near infrared (Band 4 and Band 5) are the most robust and reliable to estimate SDD for Saguling Dam. Subsequently, multiple linear regression model was built using 125 pairs of in situ SDD results and concurrent with Landsat images during the last decade (i.e., 2013-2022). The models were validated with an independent dataset of 33 SDD measurement. The in situ remote sensing model compared well with the in situ SDD measurement where determinant coefficient (adjusted R2) and root mean square error (RMSE) ranging from 0.35 - 0.94 and 0.3 - 1.4 m, respectively. Finally, the model was applied to Landsat 8 images acquired between 2013-2022 to elucidate the spatial distribution of SDD in Saguling Dam for each year (i.e. temporal variations) with water area around 33.7 km2. The estimation results indicated that water transparency values in the inlet and outlet zone have relatively low SDD, with SDD value ranging from 0.019 – 1.01 m and 0.24 – 1.04 m, respectively. Furthermore, based on the National Water Quality Standards (i.e., Indonesia Government Regulations No. 22 year 2021/ Peraturan Pemerintah No. 22 Tahun 2021), the estimation of spatial SDD results indicated that &gt; 99% of Saguling Dam water bodies were classified as Class IV (i.e., could be only used for irrigation). This study provides the first comprehensive remote sensing model for Saguling Dam and can provide essential information for local water quality conservation.

The nearshore aquatic environment of eastern Lake Erie: A thirty-year perspective from a monitoring station

Abstract Dreissenid mussels arrived at the Waverly Shoal located above the inflow of the Niagara River in 1989, initiating marked changes in the water and sediment chemistry, and benthic invertebrate community composition at an Ontario Ministry of the Environment, Conservation and Parks monitoring station. Here we examine change in the nearshore of eastern Lake Erie until 2019, inferred from monitoring at this station. Dreissenid numbers peaked in 1991, exceeding 200,000 individual m-2, and remained above 20,000 individual m-2 until 2004 after which numbers progressively declined. In 1993, the population transitioned from mixed Dreissena polymorpha and D. bugensis, to D. bugensis in subsequent years. The decrease in particle size and increase in organic content of surficial sediment which began the year after mussel arrival has persisted until present time. The low concentrations of trace metals and PAHs at the station increased slightly after the physical alteration in bed sediments and has either not changed or declined. After an initial increase in Secchi depth, water clarity changed little over post invasion years, with prevailing moderate water clarity interspersed with periods of bed resuspension and high turbidity. Calcium concentration in the water column, which fell dramatically after the arrival of dreissenids, has gradually increased in recent years. Chlorophyll a and total phosphorus levels indicating oligo-mesotrophic conditions have not varied systematically over the years, other than a modest decline in chlorophyll a after dreissenid arrival. Benthic invertebrate assemblages have gone through multiple alterations, with shifting abundance of amphipods, oligochaetes, chironomids, gastropods and sphaeriids attributed to invasive species rather than the physical environment. Collectively, the data suggests the nearshore ecosystem has shifted in benthic productivity and trophic transfers mediated by the benthos and invasive species, with water and sediment quality appearing to not vary beyond the range driven by inherently fluctuating physical conditions.

Long-term dynamics of spectral water transparency in the surface layer of Lake Teletskoye in summer

The article presents the long-term experimental data on spectral water transparency in the surface layer of Lake Teletskoye (21 sampling stations) obtained in summer of 2017‒2022. During the study period, values of the light attenuation index calculated at the natural logarithmic base at four wavelengths (430, 450, 550 and 670 nm) ranged within 0.2‒4.0 m‒1. The index of light absorption by yellow substance varied from 0.1 to 3.2 m‒1. Relative transparency measured with the use of a white Secchi disk made up 0.8‒11.7 m at its average of 6.3 m. Over a 6-year study period, the content of chlorophyll-a in the surface layer was 0.1‒4.1 mg/m3 that corresponded to the oligotrophic type of lakes. The concentration of yellow substance in the lake, optically determined through measuring light absorption by yellow substance at a wavelength of λ=450 nm, ranged from 0.9 to 15.0 g/m3. Calculations of the spectral contribution of the main optically active components of lake water to light attenuation in the surface layer of Lake Teletskoye in various sampling sites indicated that yellow substance and suspension had the greatest optical effect on the total attenuation. It is shown that the optic structure of the study reservoir (dynamics of major primary hydro-optical properties) depends on spatial-temporal variability of concentrations of different optically active components influenced by the in-water processes closely related with those occurred in the lake catchment.