Secchi Depth Research Articles

Weakened casual feedback loops following intensive restoration efforts and climate changes in a large shallow freshwater lake

Understanding how phytoplankton interacts with local and regional drivers as well as their feedbacks is a great challenge, and quantitative analyses of the regulating role of human activities and climate changes on these feedback loops are also limited. By using monthly monitoring dataset (2000–2017) from Lake Taihu and empirical dynamic modelling to construct causal networks, we quantified the strengths of causal feedbacks among phytoplankton, local environments, zooplankton, meteorology as well as global climate oscillation. Prevalent bidirectional causal linkages between phytoplankton biomass (chlorophyll a) and the tested drivers were found, providing holistic and quantitative evidence of the ubiquitous feedback loops. Phytoplankton biomass exhibited the highest feedbacks with total inorganic nitrogen and ammonia and the lowest with nitrate. The feedbacks between phytoplankton biomass and environmental factors from 2000 to 2017 could be classified into two groups: the local environments (e.g., nutrients, pH, transparency, zooplankton biomass)-driven enhancement loops promoting the response of the phytoplankton biomass, and the climate (e.g., wind speed)-driven regulatory loops suppressing it. The two counterbalanced groups modified the emergent macroecological patterns. Our findings revealed that the causal feedback networks loosened significantly after 2007 following nutrient loading reduction and unsuccessful biomanipulation restoration attempts by stocking carp. The strength of enhancement loops underwent marked decreases leading to reduced phytoplankton responses to the tested drivers, while the climate (decreasing wind speed, warming winter)-driven regulatory loops increased– like a tug-of-war. To counteract the self-amplifying feedback loops, the present eutrophication mitigation efforts, especially nutrient reduction, should be continued, and introduction of alternative measures to indirectly regulate the critical components (e.g., pH, Secchi depth, zooplankton biomass) of the loops would be beneficial.

Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir

Water transparency is widely used in environmental monitoring programs and in assessing water quality in aquatic environments. The purpose of this study was to determine which of two water transparency-measuring tools, a Secchi disk or a water turbidity meter, is better to assess environments in shallow eutrophic waters. Measurements of the Secchi disk depth (Dsd) and water turbidity (Turb) were carried out simultaneously at eight stations of the hypereutrophic Sestroretsky Razliv reservoir in 2015–2018. In May, October, and December, Dsd varied around 0.6 m but was significantly lower in August during algal blooms. Turbidity ranged from 10 to 20 nephelometric turbidity units (NTU) in different seasons but increased to almost 70 NTU in August. Principal component analysis revealed that Dsd was inversely proportional to Turb, total suspended solids, and chlorophyll concentrations. The data showed that at turbidities below 20 NTU, the Secchi disk clearly distinguishes differences in water transparency, but when Turb exceeds 40 NTU, measuring transparency using the Secchi disk no longer allows for water differentiation. In this case, it is preferable to use water turbidity measurements, which remain an effective indicator even in highly turbid waters. This should be taken into account when assessing the environment in eutrophic waters.

Toxicological Assessment of Clarias gariepinus Exposed to Leachates from Awotan Landfill, Ibadan, Oyo State, Nigeria.

Background: This study aims to determine the physical and chemical characteristics of leachate samples from the Awotan dumpsite in Ibadan, Oyo State, Nigeria, and provide a comprehensive assessment of the effects of these leachates on the haematology of Clarias gariepinus. Methods: Two Leachate samples were obtained from a leachate section at the Awotan dumpsite, and the physical and chemical parameters of the samples were estimated using standard methods. Physical and chemical parameters such as temperature, dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), and pH were measured in-situ using a calibrated multi 3630 digital meter and probe. Turbidity was measured using a calibrated Secchi Disc. Other chemical parameters such as Cl–, NO3–, SO42-, and PO42- were taken to the laboratory and measured using the titrimetric method. Samples of juveniles were obtained from the Department of Aquaculture and Fisheries Management at the University of Ibadan and transported to the genetics laboratory at Lead City University, where they were acclimatized in chlorine-free borehole water for 21 days. After the acclimation period, the toxicity assessment setup was initiated. Five treatment bowls, including control (0%), 5%, 10%, 25%, and 50% leachate concentrations were set up in triplicates and 15 samples of Clarias gariepinus were introduced into each of the treatment bowls, where they were bred and observed for 21 days before taking them to the laboratory for haematology analysis. Results: The physicochemical properties of the leachate samples showed that EC (4200 ± 13.0), TDS (2450 ±14.4), NO3– (87.00 ± 5.7), and PO42-(3.134 ± 0.11) all exceeded the SON and WHO recommended standards. Also, the concentration of heavy metals such as Cu (9.28 ± 3.2), Mn (45.64 ± 6.3), and Fe (604.48 ± 10.62) all exceeded the SON and WHO recommended limit. Conclusion: The haematological test showed that high concentrations of the leachate samples significantly reduced the number of Red blood cells (RBC), Haemoglobin (Hb), White Blood Cells (WBC) and Packed Cell Volume (PCV) in Clarias gariepinus..

Water transparency analysis in fish farming environment through unmanned aerial vehicles

Since water transparency in a fish farming environment is an important parameter in fish development, and it is important that the fish farmers be attentive to the control of the same. However, the process of measuring water transparency in fish ponds is usually done manually, which makes it expensive and inefficient. In order to solve this problem, a prototype was developed containing 2 optical sensors capable of capturing images in the NIR, R, G and B bands, connected to a microcomputer coupled to a Remotely Piloted Aircraft (RPAS), in order to capture aerial images of water and estimate water transparency through an image processing algorithm. The prototype was tested by an experiment conducted in a rural estate of the city of Toledo – PR, where 66 images were captured on a conical target, constructed to serve as a reflective reference for the images, comparing the results with the measurements collected by a Secchi disk, using Pearson's correlation analysis. The results showed a strong correlation (r = 0.98) in the estimates made by the NIR band processing, followed by the RGB, R, G and B bands. The developed system proved to be effective in data collection, processing and analysis, optimizing the fish farming management.

The influence of locks on zooplankton in canals (the Bydgoszcz Canal and the Noteć Canal, Poland)

Artificial waterways are characterized by dams and locks that slow water flow. Such conditions can influence the environment of freshwater organisms, including the zooplankton community. Our research aimed to assess the impact of environmental and hydrological conditions on the zooplankton of sites upstream and downstream of canal locks. The study was carried out in 2021 and 2022. Water samples were collected monthly from two canals during the growing season. We evaluated how water flow velocity and selected environmental parameters (i.e., water temperature, Secchi disk visibility, conductivity, oxygen concentration, saturation, pH and chl-a concentrations) influence the zooplankton diversity (T) density (N) and biomass (B). Our study showed that the zooplankton of the canals was highly diverse. Rotifers dominated over crustaceans in diversity and density at all sites of canals. The statistical analysis revealed differences in environmental and biological conditions between habitats located upstream and downstream of locks. Rotifers density and biomass changed approximately proportionally to changes in chlorophyll at sites upstream of locks. It supports the hypothesis that rotifer communities are defined by the bottom-up effect linked to food supply (phytoplankton). The growth of crustaceans (density and biomass), as well as total zooplankton biomass, was affected by water temperature at sites downstream of the lock.

Satellite Estimation of pCO2 and Quantification of CO2 Fluxes in China’s Chagan Lake in the Context of Climate Change

The massive increase in the amount of greenhouse gases in the atmosphere, especially carbon dioxide (CO2), has had a significant impact on the global climate. Research has revealed that lakes play an important role in the global carbon cycle and that they can shift between the roles of carbon sources and sinks. This study used Landsat satellite images to analyze the spatiotemporal characteristics and factors influencing the CO2 changes in Chagan Lake in China. We conducted six water sampling campaigns at Chagan Lake in 2020–2021 and determined the partial pressure of carbon dioxide (pCO2) from 110 water samples. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 75 matched pairs. A regression analysis indicated strong associations between pCO2 and both the band difference model of the near-infrared band and green band (Band 5-Band 3, R2 = 0.83, RMSE = 27.55 μatm) and the exponential model [((exp(b3) − exp(b5))2/(exp(b3) + exp(b5))2, R2 = 0.82, RMSE = 27.99 μatm]. A comparison between the performances of a linear regression model and a machine learning model found that the XGBoost model had the highest fitting accuracy (R2 = 0.94, RMSE = 16.86 μatm). We used Landsat/OLI images acquired mainly in 2021 to map pCO2 in Chagan Lake during the ice-free period. The pCO2 in the surface water of Chagan Lake showed considerable spatiotemporal variability within a range of 0–200 μatm. pCO2 also showed significant seasonal variations, with the lowest and highest mean values in autumn (30–50 μatm) and summer (120–150 μatm), respectively. Spatially, the pCO2 values in the southeast of Chagan Lake were higher than those in the northwest. The CO2 fluxes were calculated based on the pCO2 and ranged from −3.69 to −2.42 mmol/m2/d, indicating that Chagan Lake was absorbing CO2 (i.e., it was a weak carbon sink). Temperature, chlorophyll a, total suspended matter, and turbidity were found to have reinforcing effects on the overall trend of pCO2, while the Secchi disk depth was negatively correlated with pCO2. The results of this study provide valuable insights for assessing the role of lakes in the carbon cycle in the context of climate change.

A DECADE OF PROGRESS IN TOPO-BATHYMETRIC LASER SCANNING EXEMPLIFIED BY THE PIELACH RIVER DATASET

Abstract. Topo-bathymetric laser scanning featuring high spatial resolution and a seamless transition within the littoral zone from land to water evolved considerably in the last decade due to progress in both sensor technology and processing methods. Unlike early systems that focused solely on maximizing depth of penetration, topo-bathymetric scanners enable detailed description of coastal and inland waters at a level of detail that opens up applications in hydromorphology, hydraulic engineering, ecohydraulics, and hydrobiology. Since 2013, a near-natural river section of the pre-alpine Pielach River has been repeatedly surveyed with bathymetric LiDAR sensors from manned and unmanned aerial platforms. The captured time series not only constitutes a valuable data basis for analyzing morphometric change in response to recurring flood peaks, but also allows to trace the progress in sensor, platform and data processing technology. In this contribution we demonstrate that over the last ten years, the depth performance could be increased by approximately 60 %, starting from 1 Secchi depths to more than 2 Secchi depths with sub-m spatial resolution. We furthermore focus on current approaches for improving the geometric sensor calibration, which allow integrated processing of GNSS-, IMU- and LiDAR observations for concurrent calculation of both flight trajectories and 3D point clouds with an absolute accuracy better than 5 cm. This is specifically important for repeat surveys and monitoring of fluvial processes. While this contribution confirms substantial progress in the field, further topics like precise modeling of dynamic water surfaces, full waveform processing in complex target situations including littoral vegetation and submerged deadwood, and detection and modeling of underwater vegetation are identified as future research areas.

Biológiai vízminőségi paraméterek hatásainak vizsgálata a szarvasi Holt-Körösön

One of the essential natural resources is water resource in Hungary. It is threatened by many threats, both quantitative and qualitative. For these reasons, the protection of surface water and groundwater is a priority. The main objective of the present study is to determine which parameters have the greatest influence on the horizontal and vertical distribution patterns of phytoplankton in the water body. The study area is the Holt-Körös, which plays an important ecological, social, economic and recreational role in the region. Between the field and laboratory tests, the following physical, chemical and biological water quality parameters were investigated: water temperature, UV radiation index, Secchi depth, underwater light condition, dissolved oxygen content, suspended solids content, chlorophyll-a content, Fe, NO2-, NO3- NH4+, PO43-, Na+, K+, Mg2+, and pH value.

Plankton Community Composition and Water Quality in Gili Ketapang, Probolinggo Regency, East Java

Gili Ketapang is an area with quite high fisheries potential, reaching 40% of the resource production in East Java. The catch season is often associated with the presence of plankton as the main source of food availability in marine ecosystems, influenced by season, weather, temperature, and water quality. The aim of this research is to report water quality results and analysis of phytoplankton and zooplankton composition in the coastal waters of Gili Ketapang, East Java. The method used in this research is quantitative descriptive to describe plankton composition and water quality in Gili Ketapang, East Java. In this study, 26 phytoplankton genera and 35 zooplankton genera were found. The abundance of phytoplankton and zooplankton measured in the West Season was lower compared to the abundance in Transition Season I. The phytoplankton and zooplankton diversity index showed moderate diversity (1<H'<3). In general, the results of water quality measurements obtained in the West Season were temperature 32.8 , pH 8.54, dissolved oxygen 6.72 mg/L, salinity 32.66 ppt, Secchi depth 13.11 m, and nitrate 1.39 mg/L. Meanwhile, the water quality in Transition Season I was respectively temperature 30.26 , pH 7.8, dissolved oxygen 8.24 mg/L, salinity 31.47 ppt, Secchi depth 10.92 m, and nitrate 1.21 mg/L.

Contemporary community composition, spatial distribution patterns, and biodiversity characteristics of zooplankton in large alpine Lake Sevan, Armenia

We studied the quantitative composition, spatial distribution, and temporal dynamics of the zooplankton community of the alpine Lake Sevan, Armenia, the largest surface water in the Caucasus region. This article is providing a long-term information and fills the research gap of multiyear data on zooplankton, as the previous research on zooplankton provided only snapshots of the community, and a consistent assessment over multiple years was missing. However, an initial mini-review of historical studies indicated that zooplankton biomass and fish abundance were undergoing large fluctuations, indicating the importance of top-down control. We analysed 239 samples from the period 2016-2019 from 32 sampling sites in Lake Sevan and recorded 37 species of meso- and macrozooplankton (Rotifers, Copepods, Cladocera). Biomass fluctuations were high with peaking biomasses in 2016 and lowest biomasses in 2018, yearly averaged biomass varied about one order of magnitude. Variability over time was hence much higher than spatial variability. The pelagic habitat at the deepest part of the lake showed the highest diversity and biomasses but contrasts between sampling sites remained smaller than changes from year to year or seasonally. Many samples were dominated by a single species, and these key species explain observed biomass dynamics to a wide extent. We applied hierarchical clustering in order to identify phenological groups that appear to show similar patterns of occurrence. This clustering resulted in 6 groups where of 5 groups just consisting of one single species and these 5 key species were the Cladocerans Daphnia magna, Daphnia hyalina, Diaphanosoma sp. as well as the calanoids Arctodiaptomus bacillifer and Acanthodiaptomus denticornis. The most important species in Lake Sevan’s zooplankton during the observation period was D. magna, which reached high biomasses in 2016 and 2017 but then suddenly almost disappeared in 2018 and 2019. When there were more D. magna present, the water became clearer, which was measured using Secchi depth. This shows that these large water fleas effectively controlled the amount of phytoplankton in the water. Daphnia magna, in turn, managed to dominate zooplankton community only during times of extremely low fish biomass indicating strong top-down control of this large Cladoceran by fish. Both observations together imply a strong trophic linkage between fish, zooplankton, and phytoplankton and provide evidence for trophic cascades in Lake Sevan. Besides the novel insights into zooplankton community dynamics of this unique lake of high socio-economical, cultural, and ecological importance, our study also points to potential management opportunities for eutrophication control by biomanipulation, as well as our investigation allows us to conclude that probably biotic factors were more important than abiotic factors in explaining the observed changes and dynamics within the plankton community.

An application of 1D convolution and deep learning to remote sensing modelling of Secchi depth in the northern Adriatic Sea

ABSTRACT This paper presents a novel approach for predicting the water quality indicator – Secchi disk depth (ZSD). ZSD indirectly reflects water clarity and serves as a proxy for other quality parameters. This study utilizes Deep Neural Network (DNN) trained on satellite remote sensing and measured data from three sources: two datasets obtained from official agencies in Croatia and Slovenia, and one citizen science data source, all covering the northern coastal region of the Adriatic Sea. The proposed model uses 1D Convolutional Neural Network (CNN) in the spectral dimension to predict ZSD. The model’s performance indicates a strong fit to the observed data, proving capability of 1D-CNN to capture changes in water transparency. On the test dataset, the model achieved a high R-squared value of 0.890, a low root mean squared error (RMSE) of 0.023 and mean absolute error (MAE) of 0.014. These results demonstrate that employing a 1D-CNN in the spectral dimension of Sentinel-3 OLCI data is an effective approach for predicting water quality. These findings have significant implications for monitoring ZSD in coastal areas. By integrating diverse data sources and leveraging advanced machine learning algorithms, a more accurate and comprehensive assessment of water quality can be achieved.

Watershed Development and Eutrophying Potable Source-Water Reservoirs in a Warming Temperate/Subtropical Region

Reservoirs are increasingly valuable worldwide as potable source waters, yet in many geographic regions, their limnology and trophic status are poorly known. We characterized 14 drinking water reservoirs and their watersheds across the warming temperate/subtropical southeastern USA. Selected reservoirs had at least three years of accessible summer water quality data during 2010–2020, including Secchi depth, nutrients, and algal biomass as chlorophyll a, and depth profiles for temperature and dissolved oxygen. Most watersheds, including lands within a 10-km radius of the reservoirs, had sustained substantial urbanization and/or intensive industrialized animal production, in some cases including the discharge of partially treated human sewage or livestock slaughterhouse wastes near or into the reservoirs. Five reservoirs were assessed as mesotrophic; the others were eutrophic. Most were stratified, but ephemeral near-surface thermoclines were common, and many were too shallow (median depth 5.0 m) to maintain uniform temperatures in the relatively warm hypolimnia. Bottom-water hypoxia/anoxia occurred throughout the summers but, surprisingly, in 8 of 14 reservoirs hypoxia commonly extended to surface waters. In the Southeast as in many regions, drinking water reservoirs are poorly protected and degrading as livestock production and/or urban development increasingly characterize their watersheds. The eutrophication trajectory of these valuable resources should be used as an indicator of ecosystem health and water quality in developing more protective management and policy actions.

Regional to global assessments of ocean transparency dynamics from 1997 to 2019

Water transparency, often quantified using the Secchi disk depth (Zsd), is a key parameter for assessing the water quality and ecological health in marine environments. However, a limited understanding of global Zsd dynamics over the past two decades exists. To assess the variations of ocean transparency over multiple timescales, the Ocean Colour Climate Change Initiative (OC-CCI) datasets are used to retrieve monthly global Zsd products during 1997–2019. On regional to global scales, significant Zsd variations on seasonal, interannual, and long-term timescales are identified. Seasonal and interannual variations of Zsd in most open oceans are found to be closely linked to phytoplankton dynamics and climate change. In the coastal zones, the Zsd variability is attributed to various physical and environmental factors, including seasonal monsoons, river outflow, sediment resuspension, eutrophication, etc. Moreover, the climatological seasonal Zsd variations of 22 typical ocean areas and the associated influencing factors are investigated on regional scales. The long-term trends of Zsd suggest the widespread expansion of oligotrophic waters within the North Pacific, North Atlantic, and South Indian Ocean gyres, indicating ongoing ocean desertification. The OC-CCI Zsd datasets can be used to build a merged centennial time series of Zsd in marine optics by linking to historical measurements. By relying on satellite-derived products, global and time-varying images offer a more comprehensive understanding of regional to global Zsd dynamics.

Towards global long-term water transparency products from the Landsat archive

Secchi Disk Depth (Zsd) is one of the most fundamental and widely used water-quality indicators quantifiable via optical remote sensing. Despite decades of research, development, and demonstrations, currently, there is no operational model that enables the retrieval of Zsd from the rich archive of Landsat, the long-standing civilian Earth-observation program (1972 – present). Devising a robust Zsd model requires a comprehensive in situ dataset for testing and validation, enabling consistent mapping across optically varying global aquatic ecosystems. This study utilizes Mixture Density Networks (MDNs) trained with a large in situ dataset (N = 5689) from 300+ water bodies to formulate and implement a global Zsd algorithm for Landsat sensors, including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) aboard Landsat-5, -7, -8, and -9, respectively. Through an extensive Monte Carlo cross-validation with in situ data, we showed that MDNs improved Zsd retrieval when compared to other commonly used machine-learning (ML) models and recently developed semi-analytical algorithms, achieving a median symmetric accuracy (ε) of ∼29% and median bias (β) of ∼3%). A fully trained MDN model was then applied to atmospherically corrected Landsat data (i.e., remote sensing reflectance; Rrs) to both further validate our MDN-estimated Zsd products using an independent global satellite-to-in situ matchup dataset (N = 3534) and to demonstrate their utility in time-series analyses (1984 – present) via selected lakes and coastal estuaries. The quality of Rrs products rigorously assessed for the Landsat sensors indicated sensor-/band-dependent ε ranging from 8% to 37%. For our Zsd products, we found ε ∼ 39% and β ∼ 8% for the Landsat-8/OLI matchups. We observed higher errors and biases for TM and ETM+, which are explained by uncertainties in Rrs products induced by uncertainties in atmospheric correction and instrument calibration. Once these sources of uncertainty are, to the extent possible, characterized and accounted for, our developed model can then be employed to evaluate long-term trends in water transparency across unprecedented spatiotemporal scales, particularly in poorly studied regions of the world in a consistent manner.

Spatio-temporal variations in sediment phosphorus dynamics in a large shallow lake: Mechanisms and impacts of redox-related internal phosphorus loading

The role of redox-related sediment phosphorus (P) release in shallow polymictic lakes remains poorly understood. Our previous studies in large and shallow Lake Peipsi suggested the importance of the redox-related P release in internal P loading. In the current study, we explored the validity of this hypothesis by also considering organic sediment P (Org-P). We analysed spatio-temporal changes in diffusive P flux and sediment P forms determined by P fractionation and nuclear magnetic resonance (NMR) spectroscopy in summer 2021. Using 1997–2021 data, we computed internal P load (IL) by two methods and studied their relationships with several water quality variables. Anoxia of sediment surfaces and P release progressed with an increase in water temperature during summer. In the long-term, IL estimates by two methods were similar (mean values: 315 and 346 mg/m2/year) and correlated with the predicted anoxia of sediment surfaces (AApred). A contribution of sediment iron-bound P (Fe-P) to P release was indicated by the positive correlation of Fe-P with orthophosphate (NMR) in the short-term studies. No similar evidence was found for Org-P, which contradicts the common tendency to attribute internal P loads largely to Org-P in eutrophic lakes. Nevertheless, organic matter seemed to support reductive dissolution, because seasonal changes in sediment Org-P correlated with those in Fe-P, and organic matter content and diffusive P flux were negatively correlated over different sites. Complex bottom morphology and hydrology affected spatial distribution of the sediment P forms and masked the relationships between sediment P variables and P release. Finally, the importance of redox-related release was reflected in significant relationships between AApred and associated IL with Secchi depth transparency, chlorophyll a concentration, and the biomass of phytoplankton and cyanobacteria. To our knowledge, this is the first time when such direct evidence was provided for a large polymictic lake.

Biological characteristics of inland Lake Whitefish populations in Ontario

AbstractObjectiveTo assess the biological characteristics of Lake Whitefish Coregonus clupeaformis within inland lakes in Ontario at multiple scales and test whether there have been any changes in relative abundance, measured by catch per unit effort, spatially and temporally over 15 years.MethodsA Broad‐scale Monitoring Program, which uses a standardized random sample of the fish assemblage, has been conducted within inland lakes in Ontario since 2008 in roughly 5‐year cycles on about 750 lakes. Lake Whitefish attribute and catch per unit effort data were used to assess variation in characteristics across the landscape.ResultFrom 2008 to 2022, 54,941 Lake Whitefish were sampled among 524 different water bodies. Lake Whitefish relative abundance varied among fisheries management zones (FMZs), but not across cycles within or among FMZs. Relative abundance of Lake Whitefish was greater in lakes with lower large‐bodied fish species diversity, greater Secchi depth, mean depth, and higher levels of hypolimnetic dissolved oxygen. Their relative abundance was greatest in the 12–35‐m depth strata. Growth potential, age, and length at 50% maturity were similar between the sexes at the provincial and FMZ scales. Mean annual survival for populations with at least 50 Lake Whitefish sampled was 87%.ConclusionThe demographics and relative abundance of Lake Whitefish throughout inland lakes in Ontario was indicative of limited exploitation on these populations at a landscape scale.

Initial Limnology of Laguna Pozo Verde, Costa Rica: Bathymetry, Water, Sediments, and Diatoms

Introducción : Costa Rica tiene cientos de lagos, muchos de los cuales nunca han sido estudiados científicamente. Objetivo: Conocer limnológia de la Laguna Pozo Verde en el Parque Nacional Juan Castro Blanco, Costa Rica (~1935 m de elevación), para proporcionar datos de referencia para estudiar cambios futuros. Métodos: Medimos la profundidad y temperatura del agua y la profundidad de Secchi; sedimentos superficiales analizados; y examinó mapas e imágenes de satélite. Resultados: Aunque algunos la describen como formada por procesos volcánicos, la Laguna Pozo Verde probablemente se formó en un deslizamiento de tierra, que ocurre con frecuencia en esta zona lluviosa en la empinada ladera sur del inactivo volcán Porvenir. Nuestros sondeos mostraron una profundidad máxima de 9,25 m cerca del centro del lago. El agua era moderadamente transparente (profundidad de Secchi 2,6 m), con pH circunneutro y temperaturas de 15,9 a 18,1 °C, con estratificación débil a 0,5 m. Los sedimentos superficiales contenían 27% de materia orgánica y tenían relaciones C/N y valores de isótopos de carbono estables consistentes con algas lacustres y plantas C 3 ; Surirella angusta compuso más del 90% de las diatomeas. Conclusión: La laguna es significativamente más omera de lo informado y los sedimentos superficiales albergar una combinación de diatomeas única entre los 88 lagos examinados en Costa Rica.

Assessing the suitability of lakes and reservoirs for recreation using Landsat 8

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for esthetic and recreational purposes. Water clarity is often assessed using a Secchi disk attached to a measured line and lowered to a depth where it can be no longer seen. We have applied an approach which uses atmospherically corrected Landsat 8 data to estimate the water clarity in freshwater bodies by using the quasi-analytical algorithm (QAA) and Contrast Theory to predict Secchi depths for more than 270 lakes and reservoirs across the continental US. We found that incorporating Landsat 8 spectral data into methodologies created to retrieve the inherent optical properties (IOP) of coastal waters was effective at predicting in situ measures of the clarity of inland water bodies. The predicted Secchi depths were used to evaluate the recreational suitability for swimming and recreation using an assessment framework developed from public perception of water clarity. Results showed approximately 54% of the water bodies in our dataset were classified as “marginally suitable to suitable” with approximately 31% classed as “eminently suitable” and approximately 15% classed as “totally unsuitable–unsuitable”. The implications are that satellites engineered for terrestrial applications can be successfully used with traditional ocean color algorithms and methods to measure the water quality of freshwater environments. Furthermore, operational land-based satellite sensors have the temporal repeat cycles, spectral resolution, wavebands, and signal-to-noise ratios to be repurposed to monitor water quality for public use and trophic status of complex inland waters.

Estimating Water Transparency Using Sentinel-2 Images in a Shallow Hypertrophic Lagoon (The Albufera of Valencia, Spain)

Water transparency, a crucial environmental indicator, was assessed during fieldwork via Secchi disk depth (ZSD) measurements. Three optical models (R490/R560, R490/R705, and R560/R705) were explored to establish a robust algorithm for ZSD estimation. Through extensive field sampling and laboratory analyses, weekly data spanning 2018 to 2023 were collected, including water transparency, temperature, conductivity, and chlorophyll-a concentration. Remote sensing imagery from the Sentinel-2 mission was employed, and the images were processed using SNAP 9.0 software. The R560/R705 index, suitable for turbid lakes, proved to be the most optimal, with an R2 of 0.6149 in calibration and 0.916 during validation. In contrast, the R490/R705 and R490/R560 indices obtained R2 values of 0.2805 and 0.0043 respectively. The algorithm calibrated in the present study improved the pre-existing algorithm, with an NRMSE of 17.8% versus 20.7% of the previous one for estimating the Secchi disk depth in the Albufera de Valencia, highlighting the importance of developing specific algorithms for specific water body characteristics. The study contributes to improved water quality assessment and resource management, underscoring the value of remote sensing in environmental research.

A2DWQPE: Adaptive and automated data-driven water quality parameter estimation

Accurate remote sensing estimation of inland water quality parameters (WQPs) plays a crucial role in guiding water resource management. To achieve this, researchers have explored various data-driven approaches utilizing machine learning (ML) techniques. However, there are two major challenges in WQPs estimation for inland waters. Firstly, current data-driven approaches focus on building a unified estimation model for an entire study area, which underestimates the complex dynamics of water constituents and optical properties. Secondly, ML models, particularly neural networks, require extensive hyperparameter tuning and are not user-friendly for researchers lacking relevant background and experience. In this paper, we propose an innovative method called adaptive and automated data-driven water quality parameter estimation (A2DWQPE) to address both challenges. Our method operates under the assumption that water bodies with similar spectral characteristics should share the same WQP estimation model. A2DWQPE is composed of three phases. Firstly, water types are automatedly classified by unsupervised hierarchical clustering according to spectral similarity. Then, optimal Deep Neural Network (DNN) models for estimating WQPs from multi-spectral satellite images are customized for each water type utilizing Bayesian optimization (BO). Finally, the target WQP is estimated based on the type-specific estimates and degree of membership of each water type. To evaluate the effectiveness of A2DWQPE, we applied it to estimate Secchi disk depth (SDD) in Lake Erie with in situ measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) images. The results demonstrate that A2DWQPE outperforms the traditional approaches of developing a unified model for the entire study area. A2DWQPE achieved high accuracy with coefficient of determination (R2) over 0.72 and root mean square error (RMSE) below 1.4 m. Our method also outperforms the methods that applied Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) instead of BO, and several traditional ML algorithms. We firmly believe that A2DWQPE holds great potential for accurate inland water quality estimation and will contribute significantly to various applications in water quality monitoring and pollution prevention.