Next Article in Journal
Visualising, Illustrating and Communicating Future Water Visions to Support Learning and Sustainability Transitions
Previous Article in Journal
Effects of Sodium Selenite on the Growth and Photosystem II Activity of Arthrospira platensis Gom.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 16
Issue 1
10.3390/w16010018
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

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

by
Mikhail S. Golubkov
* and
Sergey M. Golubkov
Zoological Institute of Russian Academy of Sciences, Universitetskaya Emb. 1, 199034 Saint-Petersburg, Russia
*
Author to whom correspondence should be addressed.
Water 2024, 16(1), 18; https://doi.org/10.3390/w16010018
Submission received: 26 November 2023 / Revised: 17 December 2023 / Accepted: 19 December 2023 / Published: 20 December 2023
Browse Figures
Versions Notes

Abstract

:
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.

1. Introduction

The measurement of water transparency is a widely used method for assessing the quality of the aquatic environment [1,2,3,4] and is included in the list of environmental indicators in the monitoring systems of many countries [5,6,7]. Water transparency plays a key role in the life of aquatic organisms because light is necessary for the creation of primary production by plankton [8,9,10], and changes in water transparency impact trophic interactions in aquatic ecosystems [11,12,13]. Moreover, water transparency serves as a simple and understandable indicator for assessing water quality in aquatic environments, even for non-specialists, as low transparency makes the water less attractive for swimming and boating and diminishes the aesthetic appeal of the reservoir [14].
Water loses its transparency due to the presence of various impurities, such as inorganic and organic suspended matter, dissolved colored organic compounds, algae, and other microscopic organisms [15]. Water transparency can decrease as a result of natural processes [16] as well as due to human activities [17,18].
The Secchi disk is used in the classical method of determining water transparency [19]. Measurement is performed by immersing a matte white disk with a diameter of 20 or 30 cm into the water until it becomes invisible to the observer. This depth is referred to as the Secchi disk depth [20]. This method, initially proposed by Secchi in the mid-19th century (1864), is simple to use, cost-effective, and based on the fact that the spectrum of visible light and the spectrum of photosynthetically active radiation (PAR) (400–700 nm) significantly overlap [8]. It is included in various standards for assessing the ecological status of aquatic systems [21].
Another more modern method for assessing water transparency is the measurement of water turbidity in nephelometric turbidity units (NTU) using turbidity meters. The term “turbidity” is used to describe the degree of light scattering, which leads to the loss of transparency in liquid for human perception. Turbidity in natural water is measured relative to the turbidity of an artificial standard with known light-scattering properties. The standard can be a suspension of particles, such as latex or formazin, prepared according to a specified method [8].
The Secchi disk provides the most accurate results in deep lake or oceanic waters where the presence of impurities is minimal [8]. Studies of lakes in Alaska have shown that the Secchi disk can reliably determine the depth of light penetration in large, transparent lakes with low concentrations of suspended matter [22].
The greatest challenges arise when using the Secchi disk for measurements in shallow waters with high levels of dissolved and suspended substances [23,24,25]. At the same time, shallow water bodies are among the most common types of continental water bodies, and they are frequently characterized by high concentrations of various dissolved and suspended matter often due to human activities [14,26]. Therefore, the development of a methodological framework for assessing water transparency in such water bodies is essential, enabling more cost-effective measures to maintain these reservoirs in a condition suitable for human use [6].
This study aimed to determine which of the two indicators of water transparency, Secchi disk depth or water turbidity, is better suited for assessing water quality in shallow eutrophic-hypereutrophic water bodies. A comparison of the reliability of using each of these two methods for differentiating water quality was carried out on the Sestroretsky Razliv reservoir. This is a shallow freshwater reservoir located in the second most populous megacity of Russia, St. Petersburg. It was artificially created more than 300 years ago for the needs of a factory located on its shore. Currently, the reservoir is used for recreation and water sports by city residents. However, since the 1980s, the reservoir has experienced intense summer algae blooms. According to the level of primary plankton production in summer, it was classified as eutrophic and hypereutrophic in 2015–2018 [27].

2. Materials and Methods

2.1. Study Area

The Sestroretsky Razliv reservoir is located in the northwest of Russia, on the northern shore of the Neva River estuary in the northeastern part of the Baltic Sea region (Figure 1). It belongs to protected nature conservation zones, and the use of civilian motorized water transport is prohibited in its waters. The area of the reservoir is 10.3 km2. The bottom is relatively flat, and depths do not exceed 1.5–2.5 m in the main part of the reservoir. The watershed of the reservoir covers an area of 566 km2 and is drained by the Sestra and Chernaya rivers. The water level has been maintained at approximately the same level since 1991, with excess water discharged through a drainage canal into the Neva Estuary.
The Sestroretsky Razliv reservoir warms up quickly in spring. By mid-May, the water temperature fluctuates between 16 and 20 °C, and it remains nearly constant until mid-August. Similarly, the reservoir cools rapidly, with water temperatures decreasing to just 5–6 °C by October. From late December to mid-April, the surface of the reservoir is covered with ice.
In the spring and summer of 2015–2018, diatoms Aulacoseira muzzanensis (F. Meister) Krammer dominated the phytoplankton biomass; cyanobacteria Microcystis wesenbergii (Komárek) Komárek ex Komárek had the second place in the summer biomass. In autumn, the phytoplankton was dominated by the cyanobacteria Planktothrix agardhii (Gomont) Anagnostidis and Komárek, and cryptophytes. In December, during the ice-free period, cryptophytes Cryptomonas obovata Skuja dominated, constituting approximately 40% of the phytoplankton biomass [27].

2.2. Data and Methods

Samples in the Sestroretsk Razliv reservoir were collected at eight stations in May 2016 and 2018, August 2015, 2016, and 2018, October 2015 and 2016, and December 2015 (Figure 1). Temperature, chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) concentrations were measured in the water column from the surface to the bottom using a C-6 multisensor platform with CYCLOP-7 sensors from Turner Designs (San Jose, CA, USA). Water turbidity (Turb) was measured using a bulkhead version of the SEAPoint turbidity meter (Exeter, NH, USA), which was connected to a CTD-90M probe and controlled by software from Sea & Sun Tech rev. 2.06F (Trappenkamp, Germany). Data from the probes were recorded at a 1 s interval, which roughly corresponded to a 10 cm depth increment. The obtained results of vertical profiling for each parameter were averaged over the water column and used in further statistical analysis.
The turbidity meter was calibrated using formazin (CSOVV LLC, St. Petersburg, Russia) according to the manufacturer’s recommendations. The CYCLOP-7 sensor was additionally calibrated for CDOM following the corrections proposed by Downing et al. [29]. Test material of humic substances (HS) from typical watershed soils was obtained from the Department of Soil Science and Soil Ecology of St. Petersburg State University (St. Petersburg, Russia). Humic substances solutions were prepared by dissolving 1 g of the test material in 1 L of deionized water devoid of organics, followed by dilution to several solutions with HS concentrations of 0.0005, 0.001, 0.002, 0.003, 0.004, 0.005, 0.007, and 0.01 mass %. Fluorimeter measurements were carried out following the method of Downing et al. [28]. Carbon concentration in these solutions was determined using the high-temperature catalytic oxidation method with a Shimadzu TOC-LCPN/CSN instrument (Shimadzu Scientific Instrument, Kyoto, Japan) based on the Bird et al. [30] method. The CYCLOP-7 sensor for chlorophyll-a was calibrated using the spectrophotometric method for Chl-a determination after a preliminary extraction with 90% acetone (ECOS-1 JSC, Moscow, Russia) following the Grasshoff et al. [31].
Secchi disk depth (Dsd) was determined using a matte white disk with a diameter of 30 cm. The concentration of total suspended solids (TSS) in the water was measured using the standard gravimetric method [31]. Water samples for TSS determination were collected from the surface and at 4 water horizons every 40 cm each using a 2 L bathometer. This allowed the collection of composite samples of 10 L, from which 0.5 L of water was taken for laboratory TSS analysis.

2.3. Statistical Analysis

The Dsd and TSS data were averaged for each station and each sampling month for 2015, 2016, and 2018, and then visualized using SURFER version 8.0 (Golden Software LLC, Golden, CO, USA). Non-transformed data were used for statistical analysis (Table S1). Regression analysis was performed using Microsoft Excel. The significance of the Pearson correlation coefficient was calculated with the Bonferroni correction. To investigate the relationship between optical water characteristics and environmental variables, a Principal Component Analysis (PCA) was applied using the R software (version 4.3.2) [32]. The PCA analysis was conducted with the “prcomp” command from the “Stat” package [32], and the results were visualized using “fviz_pca_biplot” from the “factoextra” package [33].

3. Results

Secchi disk depth did not vary significantly and did not exceed 0.8 m in the shallow Sestroretsky Razliv reservoir in May, October, and December; however, in August it was significantly lower (Figure 2a). Water turbidity in May, October, and December ranged from 10 to 20 NTU, while in August it increased to almost 70 NTU (Figure 2b).
Secchi disk depth demonstrated a power-law dependence on the water turbidity (Figure 3). These two parameters exhibited a statistically significant negative correlation. However, the most significant changes in Dsd were observed at Turb up to 20 NTU. With further increases in turbidity, Dsd remained relatively constant (Figure 3).
Environmental factors that could influence the optical characteristics of the water are presented in Table 1. The concentration of TSS was highest in August and remained approximately at the same level during the other observation periods. Chlorophyll-a concentration was also highest in August, with an average value of 141 mg m−3 across all stations for this month over three years. In May, this parameter also showed relatively high values, averaging 59 mg m−3. In October and December, the concentration of this photosynthetic algal pigment was noticeably lower, probably due to the end of the active growing season, since the average water temperature in December was nearly 0 °C (Table 1), and subsequently, the water surface froze. The highest proportion of Chl-a in TSS was observed in spring, and the highest concentration of CDOM was recorded in December (Table 1).
The principal component analysis (PCA) revealed that Dsd was inversely proportional to Turb, TSS, and Chl-a concentrations (Figure 4). The relationship between Turb, TSS, and Chl-a concentrations was most pronounced in August (Figure 4), when the Chl-a concentration in the water reached its maximum values, and Turb was also at its highest (Table 1, Figure 2). The concentration of CDOM did not have a relation with Dsd, but the proportion of Chl-a in TSS was positively related to Dsd (Figure 4).
The spatial distribution of Dsd indicates that in May the lowest values were observed in the northern part of the Sestroretsky Razliv reservoir, where rivers flow into the reservoir, as well as near the discharge channel on its western shore. The highest Dsd was observed in the central part of the reservoir (Figure 5a). In August and October, the waters of the Sestroretsky Razliv reservoir, according to Dsd, were almost uniform (Figure 5b,c). In August, over most of the reservoir, Dsd did not exceed 0.35 m, except for the northwestern part near the mouth of the Sestra River, where the water was more transparent. A similar pattern of almost uniform, but higher Dsd values as compared to August, was observed in October with minimum values in the northeastern part of the reservoir, near the mouth of the Chernaya River (Figure 5c).
In general, the spatial distribution of Turb and Dsd in the reservoir was very similar (Figure 5). However, unlike Dsd, Turb differentiated reservoir waters in more detail in August and October (Figure 5b–e) when high Chl-a concentrations were observed in the water (Table 1). During these months, Turb values were above 20 NTU in most of the Sestroretsky Razliv reservoir, gradually increasing (by three times in August) in the direction from north to south (Figure 5d,e). These details of water transparency variation were not clearly visible in the Dsd data but were evident in the Turb data. Additionally, the distribution of turbidity shows that in May, the waters flowing from the Sestra and Chernaya rivers were more turbid compared to the waters in Sestroretsky Razliv. In August and October, on the contrary, the river waters were more transparent.
Contrary to Turb, data on the Secchi disk depth did not make it possible to differentiate waters by water transparency in most of the reservoir in August, when maximum concentrations of chlorophyll-a were observed in its waters (Figure 5b and Figure 6b). At concentrations above 120 mg m−3, Dsd was 0.35 m and a further increase in Chl-a concentration did not reduce Dsd. At the same time, the turbidity of the water changed as the Chl-a concentration increased towards the southern part of the reservoir (Figure 5e and Figure 6b).
The results of the regression analysis indicated a statistically significant negative relationship between Dsd and Chl-a concentration (Figure 7a). However, the correlation coefficient and statistical significance of the relationship between chlorophyll-a concentration and turbidity were found to be significantly higher compared to those statistics for the relationship between ChL-a and Dsd (Figure 7a,b). The reason for this is that the Secchi disk depth did not change when the chlorophyll concentration increased above 100 mg m−3 (Figure 7a). In contrast, water turbidity increased proportionally with an increase in Chl-a concentration up to 300 mg m−3 (Figure 7b). From the analysis of Figure 7, it follows that a concentration of 100 mg m−3 approximately corresponded to a turbidity of 40 NTU (Figure 7), which was the threshold value above which Dsd ceased to decrease (Figure 3).

4. Discussion

The social and economic importance of shallow water bodies located in densely populated areas around the world makes it critical to develop recommendations for better management of their ecosystems and combating the process of eutrophication [6,26]. This is very true for the Sestroretsky Razliv reservoir, which is intensively used for recreational purposes. There are several sandy beaches along its shores, making it popular for beach holidays, water sports, and fishing. Because of this, regulatory authorities constantly monitor the water quality of the reservoir, including measuring water transparency using a Secchi disk [27].
A comparison of the Sestroretsky Razliv reservoir with natural lakes located in the same geographical region on the shores of the Gulf of Finland shows that the Dsd values in the reservoir are in the range of values of this indicator in Estonian lakes, where Dsd changed from 0.1 to 3.0 m. Moreover, the maximum summer concentrations of Chl-a (up to 389 mg m−3) and concentrations of TSS (up to 65 g m−3) in these lakes [34,35] were even slightly higher than in the Sestroretsky Razliv reservoir (Table 1).
In Polish lakes located along the southern coast of the Baltic Sea, Dsd was at least 1.8 m, and the Chl-a concentration reached no more than 32 mg m−3 [36], which is correspondently two times higher and five times lower than the values of these environmental variables in the Sestroretsky Razliv reservoir (Table 1). The reason is probably that this reservoir is located within a metropolis and its ecosystem is subject to significant anthropogenic impact. Despite all efforts to protect it, abundant nutrient runoff from adjacent areas flows into it, which leads to eutrophication [27]. The average concentration of total phosphorus in its waters in summer reached 340 mg m−3, while in Estonian lakes the maximum concentrations were 100–200 mg m−3 [27,37].
Small lakes are more susceptible to eutrophication, and the occurrence of a great number of eutrophic shallow water bodies around the world is no coincidence, so public expectations, political objectives, and management plans should take this pattern into account [38]. Nutrient runoff into the Sestroretsk Razliv reservoir can occur not only due to the direct discharge of wastewater into the reservoir but also due to more active flushing and overflow of storm drains from adjacent urban areas. This is because there has been an increased amount of atmospheric precipitation in this region in recent years, stimulating the development of phytoplankton [39,40]. This trend is characteristic of the entire northern Baltic Sea region [41,42]. For instance, in Estonian lakes, similar to the Sestroretsky Razliv reservoir, eutrophication of shallow lakes has been observed since the 1980s due to increased precipitation [27,37].
Our analysis shows that a decrease in water transparency, for example, due to algal blooms, leads to the impossibility of differentiating waters by the depth at which the Secchi disk becomes invisible to the observer, since with very high turbidity the observer’s eye ceases to detect changes in water transparency. In the case of the Sestroretsky Razliv reservoir, with turbidity above 20 NTU, Dsd changed very slowly, and above 40 NTU, the Dsd values remained approximately at a constant level (Figure 3 and Figure 5).
Values above 20–40 NTU in natural waters are common. For instance, in many shallow lakes in China, the annual average Turb exceeded 40 NTU [43]. In a sample of 576 lakes located in flat areas in the USA, the average Turb was 21.4 NTU [14]. Overall, mean annual Secchi disk depths of less than 1.1 m were found in 25% of the 14,421 lakes in the United States [44]. Therefore, the Sestroretsky Razliv reservoir, according to its water characteristics (Table 1), belongs to a fairly common type of reservoir found all over the world.
In addition to high concentrations of phytoplankton, water transparency is often reduced by suspended matter [2]. For example, in the upper part of the Neva Estuary, with an average depth of 5 m, the concentration of suspended mineral matter explained 91% of the turbidity variance [10]. In the Sestroretsky Razliv reservoir, the TSS concentration also reveals a negative relationship with Dsd (Figure 4). Moreover, the positive relationship between the Secchi disk depth and the portion of Chl-a in TSS (Figure 4) may indirectly indicate that Dsd is determined not only by the concentration of algae but also by other components of the suspension. Unlike Dsd, Turb did not show a relationship with the proportion of chlorophyll in suspended matter but showed a positive relationship with the concentration of chlorophyll-a in water. Thus, the Secchi disk depth was more dependent on the nonliving components of suspended matter than the turbidity of the water determined using a turbidity meter. It has previously been shown that even minor impurities in the water not associated with algae can lead to inaccurate assessments of water eutrophication and Chl-a concentrations calculated from Secchi depth measurements [24,45]. In the Sestroretskiy Razliv reservoir, under high Chl-a concentrations and high turbidity, this effect was particularly significant (Table 1, Figure 5). If one calculated the Chl-a concentration based on Secchi disk depth values during the summer, the concentration would appear the same throughout the water body, although the spatial variation in turbidity indicated a significant difference between the southern and northern parts of the reservoir (Figure 5).
For assessing the degree of eutrophication, a commonly used metric is the trophic state index (TSI), which can be calculated based on both the concentration of chlorophyll-a and the Secchi disk depth [46]. A recent study analyzing data from 120 lakes and reservoirs in the Chinese Eastern Plains ecoregion revealed that deviations in TSI calculations based on Chl-a concentration and Dsd readings could reach up to 10 points in shallow lakes [47]. The authors note that such discrepancies can sometimes lead to an incorrect indication of eutrophication levels, resulting in not enough or excessive lake protection measures [47]. In the case of Sestroretskiy Razliv reservoir and other hypereutrophic waterbodies, this issue may be even more pronounced. The extensive proliferation of algae bloom has led to an increase in water turbidity up to 70 NTU and chlorophyll concentrations up to 300 mg m−3, while the Secchi disk depth only changed up to 40 NTU and 100 mg m−3, respectively (Figure 7). Consequently, the use of the Secchi disk becomes ineffective in assessing the degree of eutrophication in situations where the chlorophyll-a concentration exceeds 100 mg m−3. In other words, if you rely only on the Secchi disk depth, It is impossible to fully assess the degree of eutrophication of this urban reservoir. Similarly, Zhang and colleagues [47] showed that calculating TSI from Secchi disk depth in lakes less than 2 m deep is not reliable. Our results complement this conclusion, showing that the same calculation is not appropriate for water turbidity above 40 NTU and chlorophyll-a concentrations above 100 mg m−3 (Figure 3 and Figure 7).
It is worth agreeing with Angradi and colleagues [14] that the units of Secchi disk depth (meters), unlike the units of turbidity, nephelometric turbidity units, are easier to understand for non-scientists and managers who typically conduct routine monitoring and make decisions about the aesthetic significance of a lake, its importance for public health, or the economic benefits to the surrounding region related to tourism development. Furthermore, there is a great amount of historical Secchi disk depth data available for water bodies worldwide, making it easy to conduct a comparative analysis of historical trends in lakes based on this parameter [43]. However, there is evidence that under high turbidity conditions, the Secchi disk can provide incorrect data, such as about the depth of the euphotic zone [25]. This can be especially pronounced at high concentrations of colored dissolved organic matter in the water, as shown, for instance, in the case of the Neva River estuary [10]. Although CDOM did not have a direct pronounced effect on Secchi disk depth in the Sestroretskiy Razliv reservoir (Figure 4), high CDOM concentrations, as an additional factor, can disrupt the contrast of the white disk and yield incorrect water transparency values [10,25] because photons reflected from the Secchi disk can be absorbed strongly by the CDOM on their way back to the water surface [25].
Field studies of water transparency are labor-intensive and costly. This is one of the reasons why remote sensing methods for determining this parameter have been actively developed in recent decades. These methods allow for the assessment of water transparency not only over large areas and the investigation of large-scale patterns but also the exploration of daily mechanisms influencing changes in water body transparency [48]. Many researchers link satellite data to field measurements of Secchi disk depth (e.g., [49,50,51]). This approach makes sense because both Secchi disk and remote sensing methods work best in clear waters with fewer impurities that scatter light [8,22,52,53]. Reduced water transparency, especially due to increased algal biomass and the resulting increase in the concentration of suspended particles, can lead to cases when models obtained in clear water will not perform well in turbid water [54]. Therefore, for waters with turbidity exceeding 40 NTU, such as eutrophic and hyper-eutrophic waters, it makes sense to develop algorithms for interpreting remote sensing data based on water turbidity rather than Secchi disk transparency. Such studies have been conducted, but so far, they are few in number (see, for example, [54,55,56]).

5. Conclusions

The data obtained at the Sestroretsky Razliv reservoir demonstrate that when water turbidity levels exceed 40 NTU, measuring water transparency using the white Secchi disk no longer allows for water differentiation. Consequently, Dsd ceases to be a useful indicator of water conditions in cases of intensive eutrophication or high concentrations of suspended substances. This becomes even more relevant due to the accelerated development of eutrophication in water bodies within the Baltic Sea catchment area, taking into account the current trends in weather conditions and the increased inflow of nutrients into water bodies of this region. In such situations (Turb > 40 NTU), it is preferable to use the water turbidity parameter, which remains an effective indicator even in highly turbid waters. However, in water bodies with turbidity levels below 20 NTU, the Secchi disk can still be used as a more familiar and comprehensible water quality characteristic for a wide range of specialists. These findings can be essential for the more precise management of water resources in turbid eutrophic lakes, reservoirs, ponds, and shallow coastal zones of seas and oceans. It would also be reasonable to develop a trophic state index for waters based on water turbidity, which would be useful for assessing trophic status and differentiating waters in hypereutrophic water bodies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w16010018/s1. Table S1: Data for linear regression and principal component analysis. Each number is the average of three measurements.

Author Contributions

Conceptualization, M.S.G. and S.M.G.; field sampling, M.S.G. and S.M.G.; laboratory analyses, M.S.G.; data analysis, M.S.G.; visualization, M.S.G.; writing—original draft preparation, M.S.G. and S.M.G.; project administration, S.M.G.; funding acquisition, S.M.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Zoological Institute RAS, grant number 122031100274-7.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

The authors thank the three reviewers for their constructive comments that significantly improved the early version of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Padial, A.A.; Thomaz, S.M. Prediction of the light attenuation coefficient through the Secchi disk depth: Empirical modeling in two large Neotropical ecosystems. Limnology 2008, 9, 143–151. [Google Scholar] [CrossRef]
  2. Bachmann, R.W.; Hoyer, M.V.; Croteau, A.C.; Canfield, D.E., Jr. Factors related to Secchi depths and their stability over time as determined from a probability sample of US lakes. Environ. Monit. Assess. 2017, 189, 206. [Google Scholar] [CrossRef] [PubMed]
  3. Robertson, D.M.; Hubbard, L.E.; Lorenz, D.L.; Sullivan, D.J. A surrogate regression approach for computing continuous loads for the tributary nutrient and sediment monitoring program on the Great Lakes. J. Great Lakes Res. 2018, 44, 26–42. [Google Scholar] [CrossRef]
  4. Guo, J.; Nie, Y.; Sun, B.; Lv, X. Remote sensing of transparency in the China seas from the ESA-OC-CCI data. Estuar. Coast. Shelf Sci. 2022, 264, 107693. [Google Scholar] [CrossRef]
  5. Fleming-Lehtinen, V.; Laamanen, M. Long-term changes in Secchi depth and the role of phytoplankton in explaining light attenuation in the Baltic Sea. Estuar. Coast. Shelf Sci. 2012, 102–103, 1–10. [Google Scholar] [CrossRef]
  6. Zou, W.; Zhu, G.; Cai, Y.; Vilmi, A.; Xu, H.; Zhu, M.; Gong, Z.; Zhang, Y.; Qin, B. Relationships between nutrient, chlorophyll a and Secchi depth in lakes of the Chinese Eastern Plains. J. Environ. Manag. 2020, 260, 109923. [Google Scholar] [CrossRef]
  7. Wu, J.; He, Y.; Jiang, X.; Zhao, Y.; Cui, Y.; Wang, H. Assessing ecosystem health of floodplain lakes using an Integrated Bioassessment Index. Ecol. Indic. 2023, 154, 110644. [Google Scholar] [CrossRef]
  8. Kirk, J.T. Light and Photosynthesis in Aquatic Ecosystems, 3rd ed.; Cambridge University Press: Cambridge, UK, 2011; 665p. [Google Scholar]
  9. Hall, R.O., Jr.; Yackulic, C.B.; Kennedy, T.A.; Yard, M.D.; Rosi-Marshall, E.J.; Voichick, N.; Behn, K.E. Turbidity, light, temperature, and hydropeaking control primary productivity in the Colorado River, Grand Canyon. Limnol. Oceanogr. 2015, 60, 512–526. [Google Scholar] [CrossRef]
  10. Golubkov, M.; Golubkov, S. Photosynthetically Active Radiation, Attenuation Coefficient, Depth of the Euphotic Zone, and Water Turbidity in the Neva Estuary: Relationship with Environmental Factors. Estuar. Coast. 2023, 46, 630–644. [Google Scholar] [CrossRef]
  11. Nurminen, L.; Horppila, J. Efficiency of fish feeding on plant-attached prey: Effects of inorganic turbidity and plant-mediated changes in the light environment. Limnol. Oceanogr. 2006, 51, 1550–1555. [Google Scholar] [CrossRef]
  12. Lunt, J.; Smee, D.L. Turbidity influences trophic interactions in estuaries. Limnol. Oceanogr. 2014, 59, 2002–2012. [Google Scholar] [CrossRef]
  13. Reustle, J.W.; Smee, D.L. Cloudy with a chance of mesopredator release: Turbidity alleviates top-down control on intermediate predators through sensory disruption. Limnol. Oceanogr. 2020, 65, 2278–2290. [Google Scholar] [CrossRef]
  14. Angradi, T.R.; Ringold, P.L.; Hall, K. Water clarity measures as indicators of recreational benefits provided by U.S. lakes: Swimming and aesthetics. Ecol. Indic. 2018, 93, 1005–1019. [Google Scholar] [CrossRef] [PubMed]
  15. Grobbelaar, J.U. Turbidity. In Encyclopedia of Inland Waters; Likens, G.E., Ed.; Academic Press: Cambridge, MA, USA, 2009; pp. 699–704. [Google Scholar] [CrossRef]
  16. Valipour, R.; Boegman, L.; Bouffard, D.; Rao, Y.R. Sediment resuspension mechanisms and their contributions to high-turbidity events in a large lake. Limnol. Oceanogr. 2017, 62, 1045–1065. [Google Scholar] [CrossRef]
  17. Golubkov, M.; Golubkov, S. Impact of the Construction of New Port Facilities on Primary Production of Plankton in the Neva Estuary (Baltic Sea). Front. Mar. Sci. 2022, 9, 851043. [Google Scholar] [CrossRef]
  18. Golubkov, M.S.; Nikulina, V.N.; Golubkov, S.M. Impact of the Construction of New Port Facilities on the Biomass and Species Composition of Phytoplankton in the Neva Estuary (Baltic Sea). J. Mar. Sci. Eng. 2023, 11, 32. [Google Scholar] [CrossRef]
  19. Wetzel, R.G. Limnology, 3rd ed.; Academic Press: New York, NY, USA, 2001; pp. 49–69. [Google Scholar] [CrossRef]
  20. Secchi, P.A. Relazione delle esperienze fatte a bordo della pontificia pirocorvetta Imacolata Concezione per determinare la trasparenza del mare; Memoria del P. A. Secchi. Il Nuovo Cimento (1855–1868) 1864, 20, 205–238. [Google Scholar] [CrossRef]
  21. Aas, E.; Høkedal, J.; Sørensen, K. Secchi depth in the Oslofjord–Skagerrak area: Theory, experiments and relationships to other quantities. Ocean Sci. 2014, 10, 177–199. [Google Scholar] [CrossRef]
  22. LaPerriere, J.D.; Edmundson, J.A. Limnology of two lake systems of Katmai National Park and Preserve, Alaska: Part II. Light penetration and Secchi depth. Hydrobiologia 2000, 418, 209–216. [Google Scholar] [CrossRef]
  23. Lee, Z.; Shang, S.; Du, K.; Wei, J. Resolving the long-standing puzzles about the observed Secchi depth relationships. Limnol. Oceanogr. 2018, 63, 2321–2336. [Google Scholar] [CrossRef]
  24. Harvey, E.T.; Walve, J.; Andersson, A.; Karlson, B.; Kratzer, S. The effect of optical properties on Secchi depth and implications for eutrophication management. Front. Mar. Sci. 2019, 5, 496. [Google Scholar] [CrossRef]
  25. Bowers, D.G.; Roberts, E.M.; Hoguane, A.M.; Fall, K.A.; Massey, G.M.; Friedrichs, C.T. Secchi disk measurements in turbid water. J. Geophys. Res.-Oceans 2020, 125, e2020JC016172. [Google Scholar] [CrossRef]
  26. Meerhoff, M.; Jeppesen, E. Shallow Lakes and Ponds. In Encyclopedia of Inland Waters; Likens, G.E., Ed.; Academic Press: Cambridge, MA, USA, 2009; pp. 649–655. [Google Scholar] [CrossRef]
  27. Golubkov, S.M.; Belyakov, V.P.; Golubkov, M.S.; Litvinchuk, L.F.; Petukhov, V.A.; Gubelit, Y.I. Energy Flows and Phosphorus Turnover in the System of Shallow Reservoir under Anthropogenic Stress. Russ. J. Ecol. 2019, 50, 560–566. [Google Scholar] [CrossRef]
  28. International Organization for Standardization (ISO). Country Codes—ISO 3166. Available online: https://www.iso.org/iso-3166-country-codes.html (accessed on 18 December 2023).
  29. Downing, B.D.; Pellerin, B.A.; Bergamaschi, B.A.; Saraceno, J.F.; Kraus, T.E.C. Seeing the light: The effects of particles, dissolved materials, and temperature on in situ measurements of DOM fluorescence in rivers and streams. Limnol. Oceanogr. Meth. 2012, 10, 767–775. [Google Scholar] [CrossRef]
  30. Bird, S.M.; Fram, M.S.; Crepeau, K.L. Method of analysis by the U.S. Geological Survey California District Sacramento Laboratory—Determination of dissolved organic carbon in water by high temperature catalytic oxidation, method validation, and quality-control practices. In Open–File Report 03-366; U.S. Geological Survey: Sacramento, CA, USA, 2003. Available online: http://pubs.usgs.gov/of/2003/ofr03366/text.html (accessed on 18 December 2023).
  31. Grasshoff, K.; Ehrhardt, M.; Kremling, K. Methods of Seawater Analysis, 3rd ed.; Wiley-VCH: New York, NY, USA, 1999. [Google Scholar]
  32. The R Project for Statistical Computing (Version 4.3.2). Available online: https://www.r-project.org (accessed on 21 November 2023).
  33. Factoextra: Extract and Visualize the Results of Multivariate Data Analyses (Version 1.0.7). [Software]. Available online: https://CRAN.R-project.org/package=factoextra (accessed on 21 November 2023).
  34. Arst, H. Optical Properties and Remote Sensing of Multicomponental Water Bodies; Springer: Berlin, Germany, 2003. [Google Scholar]
  35. Paavel, B.; Arst, H.; Reinart, A. Variability of bio-optical parameters in two North-European large lakes. Hydrobiologia 2008, 599, 201–211. [Google Scholar] [CrossRef]
  36. Ficek, D.; Zapadka, T. Variability of bio-optical parameters in Lake Jasień Północny and Lake Jasień Południowy. Limnol. Rev. 2010, 10, 67–76. [Google Scholar] [CrossRef]
  37. Tammeorg, O.; Möls, T.; Kangur, K. Weather conditions influencing phosphorus concentration in the growing period in the large shallow Lake Peipsi (Estonia/Russia). J. Limnol. 2014, 73, 27–35. [Google Scholar] [CrossRef]
  38. Zhou, J.; Leavitt, P.R.; Zhang, Y.; Qin, B. Anthropogenic eutrophication of shallow lakes: Is it occasional? Water Res. 2022, 221, 118728. [Google Scholar] [CrossRef]
  39. Golubkov, M.; Golubkov, S. Eutrophication in the Neva Estuary (Baltic Sea): Response to temperature and precipitation patterns. Mar. Freshwater Res. 2020, 71, 583–595. [Google Scholar] [CrossRef]
  40. Golubkov, M.; Golubkov, S. Relationships Between Northern Hemisphere Teleconnection Patterns and Phytoplankton Productivity in the Neva Estuary (Northeastern Baltic Sea). Front. Mar. Sci. 2021, 8, 735790. [Google Scholar] [CrossRef]
  41. Sharov, A.N.; Berezina, N.A.; Nazarova, L.E.; Poliakova, T.N.; Chekryzheva, T.A. Links between biota and climate-related variables in the Baltic region using Lake Onega as an example. Oceanologi 2014, 56, 291–306. [Google Scholar] [CrossRef]
  42. Rutgersson, A.; Jaagus, J.; Schenk, F.; Stendel, M.; Bärring, L.; Briede, A.; Claremar, B.; Hanssen-Bauer, I.; Holopainen, J.; Moberg, A.; et al. Recent change—Atmosphere. In Second Assessment of Climate Change for the Baltic Sea Basin; Bolle, H.-J., Menenti, M., Rasool, S.I., Eds.; Springer International Publishing: Cham, Switzerland, 2015; pp. 69–97. [Google Scholar] [CrossRef]
  43. Zhang, Y.; Qin, B.; Shi, K.; Zhang, Y.; Deng, J.; Wild, M.; Li, L.; Zhou, Y.; Yao, X.; Liu, M.; et al. Radiation dimming and decreasing water clarity fuel underwater darkening in lakes. Sci. Bull. 2020, 65, 1675–1684. [Google Scholar] [CrossRef] [PubMed]
  44. Bigham Stephens, D.L.; Carlson, R.E.; Horsburgh, C.A.; Hoyer, M.V.; Bachmann, R.W.; Canfield, D.E., Jr. Regional distribution of Secchi disk transparency in waters of the United States. Lake Reserv. Manag. 2015, 31, 55–63. [Google Scholar] [CrossRef]
  45. Lind, O.T. The effect of non-algal turbidity on the relationship of Secchi depth to chlorophyll a. Hydrobiologia 1986, 140, 27–35. [Google Scholar] [CrossRef]
  46. Carlson, R.E. A Trophic State Index for Lakes. Limnol. Oceanogr. 1977, 22, 361–369. [Google Scholar] [CrossRef]
  47. Zhang, F.; Xue, B.; Cai, Y.; Xu, H.; Zou, W. Utility of Trophic State Index in lakes and reservoirs in the Chinese Eastern Plains ecoregion: The key role of water depth. Ecol. Indic. 2023, 148, 110029. [Google Scholar] [CrossRef]
  48. Binding, C.E.; Jerome, J.H.; Bukata, R.P.; Booty, W.G. Trends in water clarity of the lower Great Lakes from remotely sensed aquatic color. J. Great Lakes Res. 2007, 33, 828–841. [Google Scholar] [CrossRef]
  49. Binding, C.E.; Greenberg, T.A.; Watson, S.B.; Rastin, S.; Gould, J. Long term water clarity changes in North America’s Great Lakes from multi-sensor satellite observations. Limnol. Oceanogr. 2015, 60, 1976–1995. [Google Scholar] [CrossRef]
  50. Alikas, K.; Kratzer, S. Improved retrieval of Secchi depth for optically-complex waters using remote sensing data. Ecol. Indic. 2017, 77, 218–227. [Google Scholar] [CrossRef]
  51. Gomes, A.C.; Alcântara, E.; Rodrigues, T.; Bernardo, N. Satellite estimates of euphotic zone and Secchi disk depths in a colored dissolved organic matter-dominated inland water. Ecol. Indic. 2020, 110, 105848. [Google Scholar] [CrossRef]
  52. Brezonik, P.L. Effect of organic color and turbidity of Secchi disk transparency. J. Fish. Res. Board Can. 1978, 35, 1410–1416. [Google Scholar] [CrossRef]
  53. Lee, Z.P.; Du, K.P.; Arnone, R. A model for the diffuse attenuation coefficient of downwelling irradiance. J. Geophys. Res. 2005, 110, C02016. [Google Scholar] [CrossRef]
  54. Moore, G.K. Satellite remote sensing of water turbidity/Sonde de télémesure par satellite de la turbidité de l’eau. Hydrol. Sci. B. 1980, 25, 407–421. [Google Scholar] [CrossRef]
  55. Garg, V.; Aggarwal, S.P.; Chauhan, P. Changes in turbidity along Ganga River using Sentinel-2 satellite data during lockdown associated with COVID-19. Geomat. Nat. Hazards Risk 2020, 11, 1175–1195. [Google Scholar] [CrossRef]
  56. Miglino, D.; Jomaa, S.; Rode, M.; Isgro, F.; Manfreda, S. Monitoring Water Turbidity Using Remote Sensing Techniques. Environ. Sci. Proc. 2022, 21, 63. [Google Scholar] [CrossRef]
Water 16 00018 g001
Figure 1. Map of study site with indication of sample stations. Red frames in the top block of the map—the location of the Sestroretsky Razliv reservoir. Two-letter country codes are given according to ISO 3166-1 alpha-2 [28].
Figure 1. Map of study site with indication of sample stations. Red frames in the top block of the map—the location of the Sestroretsky Razliv reservoir. Two-letter country codes are given according to ISO 3166-1 alpha-2 [28].
Water 16 00018 g001
Water 16 00018 g002aWater 16 00018 g002b
Figure 2. Interseasonal changes in water transparency according to the Secchi disk depth (Dsd) (a) and water turbidity (Turb) (b), averaged over all stations for 2015, 2016, and 2018.
Figure 2. Interseasonal changes in water transparency according to the Secchi disk depth (Dsd) (a) and water turbidity (Turb) (b), averaged over all stations for 2015, 2016, and 2018.
Water 16 00018 g002aWater 16 00018 g002b
Water 16 00018 g003
Figure 3. Relationship between Secchi disk depth (Dsd) and water turbidity (Turb). r is the Pearson correlation coefficient, and p is its significance taking into account the Bonferroni correction.
Figure 3. Relationship between Secchi disk depth (Dsd) and water turbidity (Turb). r is the Pearson correlation coefficient, and p is its significance taking into account the Bonferroni correction.
Water 16 00018 g003
Water 16 00018 g004
Figure 4. Results of principal component analysis of relationships between water transparency indices and environmental variables. All observation sites are ranked according to average data for May, August, and October. Yellow dots are observation sites in May. Blue dots are observation sites in August. Grey dots are observation sites in October. The contours show the distribution of data points for each data set in May, August, and October, respectively. The large yellow, blue, and grey dots are the centers of the dispersion ellipses for each data set. The arrows show the vectors of changes in the indicators of the optical characteristics of water and environmental variables. The designations of the indicators are the same as in Table 1.
Figure 4. Results of principal component analysis of relationships between water transparency indices and environmental variables. All observation sites are ranked according to average data for May, August, and October. Yellow dots are observation sites in May. Blue dots are observation sites in August. Grey dots are observation sites in October. The contours show the distribution of data points for each data set in May, August, and October, respectively. The large yellow, blue, and grey dots are the centers of the dispersion ellipses for each data set. The arrows show the vectors of changes in the indicators of the optical characteristics of water and environmental variables. The designations of the indicators are the same as in Table 1.
Water 16 00018 g004
Water 16 00018 g005
Figure 5. Spatial distribution of Secchi disk depth (Dsd) in May (a), August (b), October (c) and water turbidity in May (d), August (e), and October (f) 2015, 2016 and 2018; data are averaged over all stations. The scale values in (a–c) are the same.
Figure 5. Spatial distribution of Secchi disk depth (Dsd) in May (a), August (b), October (c) and water turbidity in May (d), August (e), and October (f) 2015, 2016 and 2018; data are averaged over all stations. The scale values in (a–c) are the same.
Water 16 00018 g005
Water 16 00018 g006
Figure 6. Spatial distribution of chlorophyll-a (Chl-a) concentration in May (a), August (b), and October (c); data averaged over all sampling stations for 2015, 2016, and 2018.
Figure 6. Spatial distribution of chlorophyll-a (Chl-a) concentration in May (a), August (b), and October (c); data averaged over all sampling stations for 2015, 2016, and 2018.
Water 16 00018 g006
Water 16 00018 g007
Figure 7. Relationship between chlorophyll-a (Chl-a) concentration and Secchi disk depth (Dsd) (a), and water turbidity (Turb) (b). r is the Pearson correlation coefficient, and p is its significance taking into account the Bonferroni correction.
Figure 7. Relationship between chlorophyll-a (Chl-a) concentration and Secchi disk depth (Dsd) (a), and water turbidity (Turb) (b). r is the Pearson correlation coefficient, and p is its significance taking into account the Bonferroni correction.
Water 16 00018 g007
Table 1. Values of environmental variables in different months for the Sestroretsk Razliv reservoir averaged for 2015, 2016, and 2018. D—depth, T—water temperature, Chl-a—concentration of chlorophyll-a, TSS—concentration of total suspended solids, Chl-a/TSS—portion of chlorophyll-a in total suspended solids, CDOM—concentration of colored dissolved organic matter, Std—standard deviation.
Table 1. Values of environmental variables in different months for the Sestroretsk Razliv reservoir averaged for 2015, 2016, and 2018. D—depth, T—water temperature, Chl-a—concentration of chlorophyll-a, TSS—concentration of total suspended solids, Chl-a/TSS—portion of chlorophyll-a in total suspended solids, CDOM—concentration of colored dissolved organic matter, Std—standard deviation.
Month D,
(m)		T,
(°C)		Chl-a,
(mg m–3)		TSS,
(g m–3)		Chl-a/TSS,
(%)		CDOM,
(g m–3)
MayAverage1.7219.6459.178.360.1129.74
Std0.701.6013.236.470.083.68
AugustAverage1.8520.46141.1732.030.0522.68
Std0.700.5781.1219.270.038.39
OctoberAverage1.775.7517.219.790.0330.92
Std0.490.088.593.540.025.89
DecemberAverage1.950.8010.3010.280.0744.96
Std0.630.002.241.700.025.35
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Golubkov, M.S.; Golubkov, S.M. Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir. Water 2024, 16, 18. https://doi.org/10.3390/w16010018

AMA Style

Golubkov MS, Golubkov SM. Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir. Water. 2024; 16(1):18. https://doi.org/10.3390/w16010018

Chicago/Turabian Style

Golubkov, Mikhail S., and Sergey M. Golubkov. 2024. "Secchi Disk Depth or Turbidity, Which Is Better for Assessing Environmental Quality in Eutrophic Waters? A Case Study in a Shallow Hypereutrophic Reservoir" Water 16, no. 1: 18. https://doi.org/10.3390/w16010018

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop