†These authors contributed equally.
Academic Editor: Graham Pawelec
Background: Two closely located reservoirs on the Southern Bug River and its tributary in the southern region of Ukraine were compared to study the impact of temperature on hydrobionts and pelagic communities, a major ecologic issue in the climate warming context, using in-situ and satellite remote sensing data. These reservoirs are parts of the South-Ukraine electric power-producing complex. The Tashlyk reservoir is a cooling reservoir for the nuclear power plant, and Oleksandrivske reservoir is used for production of hydroelectricity and irrigation. The cooling reservoir is replenished by pumping water from the upper part of the Oleksandrivske reservoir. Methods: The relationships of temperature, transparency, and distribution of phytoplankton and zooplankton communities were established based on satellite remote sensing data and in-situ during 2013–2021. The main variables of phytoplankton and zooplankton were compared, and for improved understanding features, spatial distribution maps were created. Results: It was found that the distribution of coenotic groups of phytoplankton and zoonplankton in the cooling reservoir (Tashlyk) corresponds to thermal conditions. Three communities of phytoplankton and two communities of zooplankton were identified in the Tashlyk reservoir. However, in the Oleksandrivske reservoir, separate communities of phytoplankton and zooplankton were reported along its length. Conclusions: It was shown that both on land and in the Oleksandrivske reservoir, there is an increase in temperature in summer, an increasing trend in the global warming context, but that was not observed in the cooling reservoir of the nuclear power plant (NPP). It let us assume that the factors such as temperature or nutrients impact can be assessed as external significant factors related to the catchment area for the reservoirs with different types of using.
The water quality in many natural water bodies and artificial reservoirs has been declining over the last few decades. The conventional method of water quality monitoring is point data collection, which is often employed [1]. Point-based sampling is more expensive, time-consuming, labor-intensive and with a lower areal extent. To overcome the limitations of in-situ water quality monitoring, there is a need for real-time water quality measurements, for many purposes [2].
In the 1970s there was an idea [3] that studies in the field of the effect of high temperatures, which usually do not occur in nature, are of a purely academic in nature, associated with the physiology of extreme conditions, or refer to particular problems of local impact of thermal power plants and nuclear power plants on water bodies. However, climate change has posed this problem on a completely different, global scale [4].
There are four nuclear power plants in Ukraine with a total capacity of 13.8 MW. Forecast data [5] show that by 2050, electricity generation in Ukraine may increase by 27%, while the share of nuclear energy will be 50%, and the share of thermal power plants will be 25% (those stations that consume water for cooling). If at present in Ukraine an amount of water is used for cooling that is comparable with the flow of such a river as the Southern Bug, then by 2050 this amount may increase by almost a third. Forecasts in the 1970s showed [6] that in the year 2000 up to 1/3 of all US river flow would pass through the cooling systems of power plants. Data on the development of the world energy sector [7] do not allow us to assume that the structure of the energy sector in the world has changed dramatically and will change in the near future.
With the advent of high resolution satellite remote sensing products, it is easy to monitor surface water quality using different satellite sensors and to assess the status of water bodies at synoptic scale in almost near real time [8, 9, 10, 11, 12, 13, 14, 15, 16]. Landsat series satellites have the largest archive of earth resource data sets. Landsat sensors have been widely used in the estimation of water quality parameters such as total suspended matter, chlorophyll-a, turbidity, water depth, total phosphorus, dissolved oxygen (DO), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) [17, 18, 19, 20, 21]. Nevertheless, many issues related to hydrobiological assessments of the ecological state of water bodies remain poorly studied. In particular, there are practically limitted studies that allow to assess the biological factors of the life of aquatic organisms in reservoirs under the influence of heated wastewaters from power plants. Protasov et al. [22] suggested change in the thermal regime and hydrodynamics in such water bodies significantly affect biotic parameters.
There are a number of studies aimed to study the distribution of a thermal plume from nuclear power plants using satellite data [23, 24]. The purpose of the work was (1) to identify change in thermal regime and the turbidity mode, i.e., the main conditions for the existence of plankton organisms in two reservoirs with different habitat conditions and peculiarities in composition and quantitative parameters of phytoplankton and zooplankton (the Oleksandrivske and the Tashlyk reservoir), and (2) make assumptions and predictions regarding abiotic factors and life in water bodies under conditions of global climatic changes and potential trends in lotic systems.
We have used water temperature measurements in the cooling pond of the Khmelnytsky nuclear power plant (NPP) in 2013–2021 (Fig. 1). Field studies of the Tashlyk and Oleksandrivske reservoirs were carried out in July 2018 at a number of stations (seven stations for each reservoir). The distributions of variables were analyzed according to conventionally identified longitudinal profiles (transects), Fig. 2A,B. The Oleksandrivske reservoir was created as a reservoir for a hydroelectric power plant (OHPP) with two hydraulic units with a total capacity of 9.8 MW, located in the canyon of the Southern Bug River. Tashlyk is a cooling reserve for South Ukraine NPP. It has three nuclear power units with a total installed capacity of 3000 MW.
Location map of study area on the territory of Ukraine. (A) Location of sites B and C on the territory of Ukraine. (B) A cooling reservoir of the Khmelnitsky nuclear power plant (NPP). (C) The Tashlyk and Oleksandrivske reservoirs.
Location of study stations (A) the Tashlyk reservoir, and (B) Oleksandrivske reservoir. The location of transects of (a–f) the Tashlyk reservoir, and (g–k) the Oleksandrivske reservoir on satellite image Sentinel-2 (for 30.08.2020).
The NPP is located on the left bank of the Tashlyk reservoir (TR) formed on the
inflow of the River. SUNPP is included as the basic part in the infrastructure
of the South-Ukrainian Power Complex (SUPC), the only company in Ukraine with
integrated use of basic nuclear and followmode hydroaccumulave capacies, as well
as water resources of the Southern Bug River. Today, the SUPC includes SUNPP,
Oleksandriske Hydroelectric Power Plant (OHPP) and Tashlyk Pumped-Storage Power
Plant (TPSPP) [5]. Pumping water for compensation of evaporation from the cooling
reservoir is used from the South .Bug River by a pumping station located on the
border with the Oleksandrivske reservoir. The amount of this water is more than
36 million m
If we differ the fundamental differences between the two studied water bodies, then the following should be pointed out: differences in the nature of the thermal regime and differences in the general hydrodynamics of waters [4]. Oleksandrivske reservoir is a typical reservoir on a middle river in which flow rates gradually decrease towards the dam, depth increases, sediments accumulate, based on the needs of water use, water flow from the reservoir can increase, however, basically there is a certain balance of inflow and outflow, water temperature at surface is practically unchanged throughout the water area. The Tashlyk reservoir has no flow through the dam; its natural replenishment due to a very small river is insignificant, the replenishment of water, a significant part of which evaporates, occurs due to pumping from the Southern Bug River. The so-called “internal water exchange”, that is, the ratio of the circulating waters of the nuclear power plant and the volume of the reservoir is very high: a volume of water equal to the volume of the entire reservoir could pass through the cooling systems, with the constant operation of three power units more than 90 times a year. Deep-water intake contributes to the mixing of the water mass, so heated, but oxygen-rich waters can move into the depths. In the reservoir, the water temperature is much higher than in the Oleksandrivske reservoir, in addition, there are significant differences in the water temperature in the reservoir itself.
Water bodies created specifically for technical purposes are complex techno-ecosystems [25, 26, 27, 28]. The long-term dynamics of plankton in the Tashlyk reservoir has been described [29]. It was found that the changes in plankton primarily occurred due to changes in the regime of temperature and exploitation of the reservoir.
Remote sensing data aid to solve problem related to surface temperature through
identification of patterns of changes in the state temperature of a resevior
[23, 30, 31, 32, 33, 34]. The Landsat Thermal Infrared Sensor (TIRS) data were collected for
the period of 2013 to 2020 to create distribution maps of surface temperature of
reservoir. The longwave infrared radiation (wavelength 8–14
The inverse Planck equation was used for thermal radiation of gray body. It converts the spectral density of energy brightness of thermal radiation provided by the TIRS sensor taking into account the thermal radiation coefficient [35]. Land surface temperature (LST) from satellite can be calculated by Eqn. 1:
Where T
(a) top of atmosphere radiance conversion to an at-satellite brightness temperature
(b) correction for atmospheric absorption and re-emission
(c) correction for surface emissivity
(d) correction for surface roughness
The first step is calculation process, as shown in Eqn. 1. The second to fourth steps of correction process are usually very complicated [37]. These steps can be simplified, and LST can be calculated using Eqn. 2:
Where LST is the land surface temperature; T
In this case, the ground surface emissivity was obtained from the normalized difference vegetation index (NDVI). The dependence is based on the density of vegetation in relation to the open soil, Eqn. 3:
Where P
Where NDVI is the value of normalized difference vegetation index in the current
pixel; NDVI0 is the maximum NDVI value for the open soil; NDVI
The temperature was measured by a mercury thermometer with an accuracy of 0.1
Standard methods were applied to determine the taxonomic composition, biomass, and abundance [39]. The lowest identified taxon (LIT) method was applied to describe taxonomic composition. The names and systematic affiliation of phytoplankton taxa are cited according to the Algae database [40]. Most phytoplankton (94%) and zooplankton (75%) LITs were identified to the species rank or higher. Phytoplankton communities were allocated by the similarity of dominant complex in biomass, and zooplankton communities by destruction. The share of the dominant species of phytoplankton accounted for 50% of the total biomass, and that for zooplankton was 50% of the total destruction. To plot the curves of phytoplankton dominance, we took species that biomass reached to 1% or more of the total. The coefficient of variation (CV) was used to assess the heterogeneity of distribution of plankton indicators over water area [41]. The diversity in abundance and biomass was evaluated using the Shannon index [42]. The similarity of the taxonomic composition was assessed by the Sеrensen coefficient [42]. Hydrochemical data were provided by the South-Ukraine NPP Ecological and Hydrochemical Laboratory.
Canonical Correspondence Analysis (CCA) was used [43] to determine the influence of some environmental factors (see Tables 1,2), as well as to identify the originality of individual communities, both by the nature of their habitat and by the parameters of their structure (Tables 3,4). CCA type of analysis was chosen from the models of gradient analysis because in our case it covered maximal number of row data in species and environmental variables for the direct unimodal calculation.
Station | Temperature, |
Transparency, m | Oxygen, mg O |
Depth, m | рН |
Tashlyk reservoir | |||||
60 | 32.7 | 1.50 | 7.28 | 30.00 | 8.63 |
61 | 34.3 | 0.70 | - | - | 8.60 |
61а | 41.2 | - | - | 8.00 | 8.64 |
63 | 34.5 | 0.90 | - | 5.90 | - |
64 | 34.1 | 1.10 | - | 7.00 | 8.77 |
65 | 35.4 | 0.70 | - | 8.00 | 8.67 |
68а | 34.5 | 0.85 | - | 1.15 | - |
IC1 | 31.4 | 8.75 | |||
Oleksandrivske reservoir | |||||
74 | 26.9 | 2.30 | 8.02 | 7.50 | 8.35 |
76 | 26.3 | 2.00 | 8.16 | 8.00 | 8.40 |
79 | 26.1 | 2.05 | 8.32 | 8.00 | 8.20 |
84 | 26.1 | 1.65 | 8.24 | 8.00 | 8.25 |
88 | 26.1 | 1.60 | 7.92 | 4.50 | 8.33 |
91 | 27.9 | 1.10 | 7.68 | 6.00 | 8.37 |
92 | 26.7 | 0.70 | 7.92 | 6.00 | 8.35 |
112 | 26.7 | 0.85 | 8.00 | 5.40 | 8.38 |
Station | General hardness, mmol/dm |
Dry residue, mg/dm |
Nitrogen of nitrates, mgN/dm |
Phosphorus of phosphates, mgP/dm |
Permanganate oxidizability, mg/dm |
Tashlyk reservoir | |||||
60 | 9.5 | 1090 | 0.66 | 0.016 | 6.56 |
61 | 9.6 | 1098 | 0.62 | 0.016 | 7.04 |
61а | 9.5 | 1089 | 0.62 | 0.023 | 7.52 |
IC1 | 9.4 | 1115 | 0.77 | 0.016 | 5.76 |
64 | 9.7 | 1084 | 0.54 | 0.016 | 7.52 |
65 | 9.8 | 1099 | 0.52 | 0.016 | 7.04 |
Oleksandrivske reservoir | |||||
74 | 5.7 | 616 | 0.66 | 0.15 | 7.84 |
76 | 5.7 | 628 | 0.58 | 0.17 | 8.00 |
79 | 5.8 | 611 | 0.77 | 0.19 | 7.36 |
84 | 5.6 | 635 | 0.83 | 0.21 | 6.88 |
88 | 6.2 | 618 | 0.76 | 0.19 | 7.44 |
91 | 6.2 | 610 | 0.52 | 0.16 | 7.52 |
92 | 6.2 | 609 | 0.49 | 0.19 | 7.12 |
112 | 6.2 | 596 | 0.45 | 0.15 | 7.36 |
Zone of reservoir | Community | Richness of LIT | Shannon index on abundance, НN, bit/ind | Shannon index on biomass, НВ, bit/mg | Abundance, N, mln cells/dm |
Biomass, B, mg/dm |
Tashlyk reservoir, discharge area, Station 61а | Scenedesmus magnus + Cosmarium sp.+ Nitzschia kuetzingiana (1phT) | 23 | 3.56 | 3.47 | 15.76 | 2.69 |
Tashlyk reservoir, Zones a–e, gulf (Stations 61, 63, 64, 65, and 68а) | Cosmarium sp.+ Scenedesmus magnus + Nitzschia kuetzingiana (2phT) | 39 | 2.65 |
3.23 |
32.88 |
3.09 |
Tashlyk reservoir, Zones e–f (Station 60) | Binuclearia lauterbornii + Cosmarium sp.+ Rhodomonas pusilla (3phT) | 25 | 3.48 | 3.45 | 20.02 | 2.53 |
Oleksandrivske reservoir, Zones g–h (Stations 74 and 76) | Scenedesmus armatus + Ankistrodesmus arcuatus + Cyclotella meneghiniana (1phA) | 19 | 3.22 |
2.97 |
3.64 |
0.28 |
Oleksandrivske reservoir, Zone I Station 79 | Rhodomonas pusilla + Cocconeis placentula (2phA) | 14 | 3.30 | 2.97 | 3.29 | 0.20 |
Oleksandrivske reservoir, Zone j Station 84 | Cryptomonas sp. (3phA) | 11 | 2.04 | 1.87 | 4.83 | 0.64 |
Oleksandrivske reservoir, Zone j Station 88 | Microcystis aeruginosa + Tetrachlorella alternans (4phA) | 20 | 2.70 | 2.80 | 8.51 | 0.75 |
Oleksandrivske reservoir, Zones j–k Stations 91, 92, and 112 | Microcystis aeruginosa (5phA) | 26 | 0.68 |
1.04 |
441.76 |
28.43 |
Zone of Reservoir | Community | Richness of LIT | Index Shannon on Abundance, НN, bit/ind | Index Shannon on Biomass, НВ, bit/mg | Abundance, N, thousand ind/m |
Biomass, B, g/m |
Destruction, R, kJ/m |
Tashlyk reservoir, Zones c–e (Stations 63, 65, and 61a) | Moina micrura + Nauplii (1zT) | 14 | 3.39 |
2.40 |
233.46 |
1.97 |
221.45 |
Tashlyk reservoir, Zones a–с, e–f (Stations 60 and 64) | Thermocyclops oithonoides + Cyclopoida juv. + Diaphanosoma dubium (2zT) | 14 | 2.40 |
2.38 |
589.87 |
5.56 |
576.46 |
Tashlyk reservoir, bay (Station 68а) | Cyclopoida juv. (3zT) | 10 | 0.64 | 0.30 | 159.52 | 1.02 | 197.73 |
Oleksandrivske reservoir, Zone g (Station 74) | Camptocercus rectirostris + Acroperus harpae (1zA) | 14 | 3.68 | 2.23 | 0.33 | 0.00975 | 0.34 |
Oleksandrivske reservoir, Zones g–j (Stations 76, 79, and 88) | Bosmina coregoni + Nauplii + Cyclopoida juv. + Diaphanosoma orghidani (2zA) | 23 | 2.22 |
2.91 |
74.01 |
0.83 |
42.72 |
Oleksandrivske reservoir, Zone j (Station 84) | Calanoida juv. + Daphnia cucullata + Bosmina coregoni (3zA) | 16 | 3.01 | 2.77 | 372.03 | 8.57 | 369.95 |
Oleksandrivske reservoir, Zone j (Station 91) | Diaphanosoma orghidani (4zA) | 19 | 1.67 | 1.60 | 647.94 | 6.94 | 292.82 |
Oleksandrivske reservoir, Zones g–k (Stations 92 and 112) | Diaphanosoma mongolianum + Cyclopoida juv. + Daphnia cucullata (5zA) | 17–21 | 2.72 |
2.80 |
519.75 |
11.04 |
498.13 |
The estimate of satellite based water temperature was compared with in-situ measurements [44]. We did not have a series of temperature measurements in the Tashlyk and Oleksandrivske reservoirs. Therefore, considering that within the temperate zone, the amendment could be the same in different localities. For verification, we have used data of cooling reservoir (Khmelnitsky NPP), in which a thermal regime was determined. Similarly, the Tashlyk reservoir, by discharge of heated water from the NPP. In-situ temperature measurements in the Khmelnitsky NPP cooling reservoir and technical reservoir were carried out on a monthly scale. Therefore, the closest date of Ladnsat-8 was selected and relationship between in-situ measurements and remote sensing data was determined. Errors and discrepancies between the calculated data and in-situ are associated with differences in timing of measurements and processing of remote sensing data.
Table 1 show information of water quality. The minimum transparency in lower
zone where heated water is discharged at the Tashlyk reservoir (Fig. 3A,B). The
maximum value in the Tashlyk reservoir reached at 1.6 m and in the Oleksandrivske
reservoir (2.5 m) respectively. The mineral content and organic suspensions,
including plankton organisms affect water transparency. Transparency indicators
in lentic water bodies are related to biotic factors (chlorophyll and biomass of
planktonic algae) by complex indirect dependence [45, 46, 47]. The in-situ
temperature distribution show that in the Tashlyk reservoir there are 20 classes
at intveral of 0.5 degrees (Fig. 4A,B). However, in the Oleksandrivske
reservoir there are only four classes. The temperature range in two water bodies
differed from 31.1
Field (in-situ measurements) based transparency distribution map. (A) the Tashlyk reservoir, and (B) the Oleksandrivske reservoir. The quantile form of displaying transparency with homogeneous value was selected (seven uneven classes).
Field (in-situ measurements) based temperature distribution map. (A) the Tashlyk reservoir, and (B) Oleksandrivske reservoir. The quantile form of displaying temperature with homogeneous value was selected (19 uneven classes for Tashlyk reservoir and five uneven classes for Oleksandrivske reservoir).
Satellite based temperature distribution map. (A, B, C) the Tashlyk reservoir, and (D, E) the Oleksandrivske reservoir.
In the Tashlyk reservoir during summer phytoplankton richness varied from 51 to
70 LITs in the period 1980–2018. Green algae and diatoms are dominant in the
region. The composition of phytoplankton at the phylum level was relatively
stable throughout the reservoir. At the same time, there are changes in biomass
of algae. In the initial stages of research, amid increasing power plant
capacity, an increase in biomass was reported from 3.98–4.75 mg/dm
In summer green alage and diatom was pre-dominated in the Oleksandrivske
reservoir. In early 1980s the biomass of phytoplankton was similar to the Tashlyk
reservoir: 0.363–4.994 mg/dm
Many studies have been carried out for zooplankton of Ukrainian reservoirs
[48, 49, 50, 51, 52, 53, 54]. In 1980–1984, the species composition of zooplankton in water bodies
consisted of 42 taxa, including Dreissena larvae, varying from 11 to 34
species in different seasons. Before the start-up of the nuclear power plant
(NPP) (1981–1982), in the Tashlyk reservoir, the biomass of summer zooplankton
averaged on the reservoir was 2.2 and 1.8 g/m
Changes in conditions, in particular, an increase in water temperature with an
increase in NPP power while two power units were already operating (1985), led to
a decrease in zooplankton biomass to 1.6 g/m
In the Tashlyk reservoir, according to field data, the sub-range of 33–35
Similarly, with abiotic characteristics, the distribution of biotic indicators can be obtained, which makes it possible to compare the diversity of conditions, the diversity of biotic indicators, and the diversity of phytoplankton abundance and biomass. Although the diversity of thermal conditions was lower in the Tashlyk reservoir, the diversity of biotic parameters was somewhat higher. The diversity of field distribution for zooplankton is higher than for phytoplankton, and it was noted for both reservoirs. Obviously, the distribution of phyto- and zooplankton was influenced by other factors—not just the distribution of surface temperature.
The distribution of temperature and transparency illustrated in Fig. 6. The distribution of temperature on transects shows two main differences in thermal regime. The difference in temperature is significant, whereas temperature in the Oleksandrivske reservoir is rather monotonous. However, in the middle part of Tashlyk, where area of heated water discharge, a certain increase in temperature is noticeable. Transparency shows a mirror image with temperature along the transect. For the Oleksandrivske reservoir, both variables are divided into two groups, dividing the transect almost in half (about 7 km south of the beginning of the transect). Transparency differed in these zones (g–i and j–k) by almost four times.
Changes of hydrobiological variables along transects (a-f, see Fig. 2A) in the Tashlyk reservoir and Oleksandrivske reservoir (g-k, see Fig. 2B). Changes of temperature along transects in the Tashlyk reservoir (A) and Oleksandrivske reservoir (B); the Secchi depth value in the Tashlyk reservoir (C) and Oleksandrivske reservoir (D); the abundance of phyto- and zooplankton in the Tashlyk (E) and Oleksandrivske (F) reservoirs, the biomass of phyto- and zooplankton along transects in the Tashlyk (G) and Oleksandrivske (H) reservoirs; the diversity indices by abundance and biomass of phyto- and zooplankton along transects in the Tashlyk (I) and Oleksandrivske (J) reservoirs. Colored lines for the Tashlyk reservoir (a, b, c, d, e, f) and for Oleksandrivske reservoir (g, h, i, j, k) show the characteristic sections of each transect, explained in the text.
There is a significant difference in distribution of values of biomass of phytoplankton and zooplankton in two reservoirs. When comparing distributions of different indicators, several characteristic zones on transects that can be distinguished as original zones of water bodies. In the Tashlyk reservoir, two are zones allocated: a,b and e,f. A decrease in temperature is reported in this zone. These zones are outside of direct influence of discharge. In these zones, there is an increase in transparency, number, and biomass of zooplankton. The abundance of phytoplankton in b–d zone has changed slightly, in contrast to zooplankton, the abundance and biomass decreased. In the c–d zone, slight increase in plankton diversity both in terms of abundance and biomass. In the d–f zone, an increase in abundance and biomass of zooplankton and an increase in diversity.
Another zonation takes place along a longitudinal transect in the Oleksandrivske reservoir. If the temperature indicator is distributed fairly evenly, those three zones are distinguished according to the transparency value: the high (g–i), intermediate (i–g), and low transparency zones (j–k). Algae biomass indices correspond well to the zonation: there was an inverse relationship between transparency and phytoplankton biomass values. Zooplankton biomass indicator also differ quite substantially in the three zones. High value in the g–i zone characterized the indicators of the phytoplankton LDL diversity and then decreased in the i–k zone. Certain rearrangements probably took place in phytoplankton communities, and there was a change from polydominant communities to monodominant ones. Zooplankton was characterized by changes in diversity along the transect similar to oscillation.
In 2018, 73 LITs of algae from 7 phyla were found in the phytoplankton of two
reservoirs. In the Tashlyk reservoir, there were 52 LITs, of which 30 belonged to
the phylum Chlorophyta, 15 to Bacillariophyta, 3 to Cyanobacteria, and 2 each to
Cryptophyta and Charophyta. In the Oleksandrivske reservoir, there are 47 LITs
(31—Chlorophyta, 8—Bacillariophyta, 4—Cyanobacteria, 2—Cryptophyta, and 1
each of Miozoa and Ochrophyta). Only 4 from 7 phyla were common for the
phytoplankton of both reservoirs. However, similarity of LIT composition reached
0.52 according to the Serensen index. The distribution of LIT richness over water
area was quite uniform. In the Tashlyk reservoir, the coefficient of variation
(CV) was 3.83%; and in the Oleksandrivske reservoir, it was 28.99%. The
similarity of the phytoplankton composition at different research stations was
higher in the Tashlyk reservoir and was 0.68
In the Tashlyk reservoir, the average value of phytoplankton is (25.82
Ratio of main phytoplankton phyla in two reservoirs represented as richness of LIT, abundance, and biomass. Note: TR, the Tashlyk reservoir; OR, the Oleksandrivske reservoir respectively in July 2018.
The graph of dominance–diversity of phytoplankton communities in the Tashlyk reservoir. Illustration (A) community Scenedesmus magnus + Cosmarium sp. + Nitzschia kuetzingiana, (B) Cosmarium sp. + Scenedesmus magnus + Nitzschia kuetzingiana, and (C) Binuclearia lauterbornii + Cosmarium sp. + Rhodomonas pusilla.
Graph of dominance–diversity of phytoplankton communities in the Oleksandrivske reservoir. Illustration (A) community Scenedesmus armatus + Ankistrodesmus arcuatus + Cyclotella meneghiniana, (B) Rhodomonas pusilla + Cocconeis placentula, (C) Cryptomonas sp., (D) Microcystis aeruginosa + Tetrachlorella alternans, and (E) Microcystis aeruginosa.
In both water bodies a total of 42 LITs of zooplankton were found, including Rotifera (13), Cladocera (17), Copepoda (11), and Dreissena veligers (Fig. 10). Tashlyk reservoir reported less number of zooplankton as a total of 17 LITs, including Rotifera Cladocera (6), and Copepoda (3). The amount of LITs of zooplankton in the Oleksandrivske reservoir was almost twice as large, i.e., 35, including Rotifera (8), Cladocera (15), Copepoda (11), and Dreissena veligers. In the composition of zooplankton of the Tashlyk resevior, only one species was found common with the studies in 1997 (Brachionus calyciflorus Pall.). At the same time, most of the species recorded in the Oleksandrivske reservoir were previously reported. Such eurythermic and thermophylic species as Conochiloides deltaicus Rud., Keratella tropica (Apstein), Tripleuchlanis plicata (Lev.), Diaphanosoma dubium Manuilova, and D. orghidani Negrea were presented as part of the LIT composition of the Tashlyk reservoir. The similarity of the taxonomic composition of the two reservoirs in 2018 was low—0.35 according to the Serensen index.
Ratio of the main zooplankton taxonomic groups in two reservoirs regarding the richness of LIT, abundance, biomass, and destruction (Respiration rate). TR, Tashlyk reservoir; OR, Oleksandrivske reservoir (July 2018).
The distribution of the zooplankton quantitative parameters over the Tashlyk
reservoir, in contrast to the composition, was less uniform (CV
In the Oleksandrivske reservoir, changes in structure of zooplankton communities were observed from the upper reaches to the lower reaches. The cluster of similarity of communities at stations according to the distribution of destruction showed significant isolation in the upper part (Station 74), where phytophilic species of cladocerans prevailed, and the level of abundance and destruction was lowest (Table 4). Downstream (Stations 76 and 79) and in the bay (Station 88), that is, in the g–j zones, the community in which, in addition to juvenile copepods, the main role belonged to Diaphanosoma orghidani Negrea and/or Bosmina coregoni Baird was formed (Table 4). In the middle part of the reservoir (Station 84), the community with dominant taxa Calanoida juv., Daphnia cucullata Sars, and B. coregoni was formed, and the level of quantitative development increased significantly. A special community was noted in the area of Station 91, which is influenced by the tributary of the river Bakshala and the reservoir on it. In the lower dam area (Stations 92 and 112), the zooplankton community reached its maximum development with the dominance of the large pelagic cladocera D. mongolianum Ueno (Fig. 11).
Graph of dominance–diversity of zooplankton communities in the Tashlyk reservoir. Illustration (A) community Moina micrura + Nauplii, (B) Thermocyclops oithonoides + Cyclopoida juv. + Diaphanosoma dubium, and (C) Cyclopoida juv.
Thus, in the Oleksandrivske reservoir, zooplankton communities were formed in accordance with the general zonation of the reservoir. However, the existence of some local communities have also been observed. The zooplankton of the Oleksandrivske reservoir consisted of five communities. Most of the zooplankton communities in the Oleksandrivske reservoir were characterized by a similar, relatively gradual decrease in the rates of relative destruction, and the proportion of the first rank did not exceed 23%. A more expressed role of the first rank (37%), expressed dominance, was characteristic of the community of the upper part of the reservoir, Camptocercus rectirostris + Acroperus harpae, and especially of the community Diaphanosoma orghidani (64%), formed under the possible influence of the tributary of the Bakshala river and the reservoir on it. As for the abundance value, in the lower part of the reservoir, the zooplankton biomass was the highest (Diaphanosoma mongolianum + Cyclopoida juv. + Daphnia cucullata) and was twice as high as the maximum biomass in the Tashlyk reservoir (in the community Thermocyclop soithonoides + Cyclopoida juv. + Diaphanosoma dubium). Not a single community of the same name in the two reservoirs has been reported (Fig. 12).
Graph of dominance–diversity of zooplankton communities in the Oleksandrivske reservoir. Illustration (A) community Camptocercus rectirostris + Acroperus harpae, (B) Bosmina coregoni + Nauplii + Cyclopoida juv. + Diaphanosoma orghidani, (C) Calanoida juv.+ Daphnia cucullata + Bosmina coregoni, (D) Diaphanosoma orghidani, and (E) Diaphanosoma mongolianum + Cyclopoida juv. + Daphnia cucullata.
Species and environmental data relationships can help to reveal most influenced biological variables and to define major influencing environmental factors. There are various approaches to identifying these relationships based on statistical correspondences [57]. In our case, the analysis was based on data on the distribution of the communities characterized by us (the structure and abundance of their constituent species) along the gradient of water parameters in which these communities existed. We deliberately separated the biological parameters of both phytoplankton and zooplankton communities in each of the studied reservoirs. Thus, four sets of biological data were identified and an analysis of their correspondence to the chemical parameters of water was carried out. Communities were identified according to their coenotic structure, which was done for the first time for this type of analysis. (Tables 3,4). In total, three types of zooplankton communities and five types of phytoplankton communities participated in the analysis for each of the reservoirs. Several analyzes were carried out previously and as a result, CCA type of analysis was chosen from the models of gradient analysis because in our case it covered maximal number of row data in biological and environmental variables for the direct unimodal calculation. As noted by Thrush et al. [57], the use of models to identify relationships between species and the environment can help explain various aspects of the variability in the structure of biological parameters under different conditions. Most importantly in such an analysis is assessment of model validity should focus on the variability of abundance, species ecology, and environmental influences, rather than how well the model predicts observed small-scale variability in fluctuations in environmental parameters.
In the Tashlyk reservoir both phyto- and zooplankton communities (Figs. 13,14) were very original and peculiar, this is especially significant for phytocommunities. For the phytoplankton community (Fig. 13) Scenedesmus magnus + Cosmarium sp. + Nitzschia kuetzingiana (1phT), the most significant factors were temperature and the content of organic matter and phosphorus of phosphates. The second (2phT) (Cosmarium sp. + Scenedesmus magnus + Nitzschia kuetzingiana) had salinity, and (3phT) the third, (Binuclearia lauterbornii + Cosmarium sp. + Rhodomonas pusilla) had nitrate nitrogenn and transparency.
Results of Canonical Correspondence Analysis for phytoplankton
communities of Tashlyk reservoir. p-value
Results of Canonical Correspondence Analysis for zooplankton
communities of the Tashlyk reservoir. p-value
For the 1zT zooplankton community (Fig. 14) (Moina micrura + Nauplii), the temperature factor was the significant factor. For the second (2zT) (Thermocyclops oithonoides + Cyclopoida juv. + Diaphanosoma dubium), water transparency was the significant factor. Salinity and organic matter content were significant for the third community (3zT), which was dominated by Cyclopoida juv. At the same time, the indicators of the structure of communities were not significantly associated with indicators of environmental conditions, these indicators were similar in the middle of the plot.
In the Oleksandrivske reservoir, in contrast to Tashlyk, it is rather difficult for phyto- and zooplankton communities to make a conclusion about their originality and uniqueness (Figs. 15,16). Community 5phA of phytoplankton (Microcystis aeruginosa) was somewhat more original, which could be assumed, based on the fact that it was mononodominant, had minimal values of the Shannon index, very large indices of quantitative parameters. Community 2phA (Rhodomonas pusilla + Cocconeis placentula) can also be considered quite original, with the lowest indices of both abundance and biomass. However, it is difficult to say which environmental factors were significant here. Most likely these were biogenic elements.
Results of Canonical Correspondence Analysis for phytoplankton
communities of the Oleksandrivskoye reservoir. p-value
Results of the Canonical Correspondence Analysis for the
zooplankton community of the Oleksandrivske reservoir. p-value
Among the zooplankton communities (Fig. 16) in the Oleksandrivske reservoir, the 1zA community (Camptocercus rectirostris + Acroperus harpae) can be considered the most original. Of all the factors, the most significant for him was the transparency of the water. This community was localized at the top river-like part of the reservoir. It was distinguished by very small indices of abundance and biomass, with a rather large wealth of LITs. The rest of the communities were quite similar in terms of their structural indicators. At the same time, the indicators of the structure of communities were not significantly associated with indicators of environmental conditions, they were close to the middle part of the plot.
In addition to abiotic factors, the potential composition and abundance of phytoplankton and zooplankton can be significantly influenced by factors such as biotic interactions with fish. We have not specifically studied this issue, but some remarks can be made. More than 40 fish species [58] inhabit the basin of the Southern Bug River, most of which can be considered planktophages, at least in the early stages of ontogenesis. Special studies of the ichthyofauna in the Oleksandrivske reservoir showed the presence of 32 fish species, of which 18 can be considered planktophages [59]. In the Tashlyk reservoir was introduction of invasive fish Tilapia sp. [60], which can affect the degree of plankton development. This issue requires further research.
A land polygon was selected near the water body locations (Object 3, Fig. 17),
and the average temperature of water surface in two reservoirs was determined
(Objects 1 and 2, Fig. 17). The minimum temperature decreased in investigated
period. At the same time, the maximum temperature had a steady upward trend. It
is obvious that the temperature on land had a well-pronounced upward trend, from
33 to 35
Objects on the Earth’s surface, within the boundaries of which the average temperatures were measured. 1, Tashlyk reservoir; 2, Oleksandrivske reservoir; 3, land polygon.
During the period of climate change, the situation may look like a paradox, when
the average temperature in Tashlyk did not change, while in a reservoir with a
natural thermal regime, an increase in average temperatures occurred. The fact is
that during the summer period, as a rule, all nuclear power units do not work, so
the technogenic thermal effect decreases during periods of high summer
temperatures. In addition, with an increase in the temperature of the circulating
water at the NPP water intake above 30
For the Oleksandrivske reservoir and land, the increase in the average
temperature was observed. If this trend continues for the Oleksandrivske
reservoir, in the next 10 years, it will be possible to observe an increase in
average summer temperature up to a level typical of the cooling reservoir at
present. As the comparative analysis of hydrobiological data shows, in the
Oleksandrivske reservoir, in the case of an increase in temperature, changes in
the composition and quantitative parameters indices and the coenotic structure of
hydrobionts will occur. In contrast to the Tashlyk reservoir, Oleksandrivske is a
flowing, non-closed reservoir, so changes can be expected in other water bodies
and watercourses of the basin. At the same time, attention should be paid to the
fact that, in the techno-ecosystem of the Tashlyk reservoir, there are certain
factors that can reduce the negative impact of temperature increases. In any case
we can pay attention to the reservoirs and their catchment basin interaction
because both, earth and water are influenced the biodiversity in the basins of
the rivers inputted to the Black Sea [61]. Thus, it was found that, in the
southern part of the reservoir, near the dam and to a depth of about 30 m, a
fairly favourable oxygen regime (more than 7 mg O
The remote sensing and GIS technologies has enormous applicability in hydrobiological researches. This comparative study shows that technical cooling reservoirs can be considered as model of climate change and temperature rise in water bodies. Also the study revealed the fact that the technical cooling reservoirs have additional to climate warming negative changing effects on aquatic ecosystems. Abundant plankton communities inhabit the Tashlyk reservoir with a high temperature is a consequence of a fairly wide variety of conditions that are created by technogenic factors. With a general increase in temperature, there will be no such factor in the Oleksandrivske reservoir, and the consequences can be much more adverse. Furthermore, certain technogenic factors in the Tashlyk reservoir and the structural and operational conditions in which the oxygen regime in the reservoir are improved should also be taken into account. Due to water temperature increase in the Oleksandrivske reservoir and factors related to the absence of oxygen regime improvement, the effects of warming can be catastrophic due to oxygen deficiency.
Noteworthy is the different level of development of cyanobacteria in reservoirs.
In Tashlyk reservoir, the biomass of cyanobacteria was 1.9% of the total
phytoplankton biomass, in the Oleksandrivske reservoir–92.8% and in general,
the phytoplankton biomass in the Oleksandrivske reservoir was several times
higher than in Tashlyk. We suppose the limiting factor for phytoplankton in
Tashlyk was the low content of phosphorus of phosphates in the water (0.017
mgP/dm
The analysis of the cenotic structure of plankton communities, including CCA, showed that aquatic organisms in the cooling pond live in more heterogeneous conditions, the structure of communities is less homogeneous than in the Oleksandrivskoye reservoir, where conditions are more continual. It let us to assume that the factors such as temperature or nutrients impact can be assessed as external factors related to the catchment area for the reservoirs with different type of using and that the cooling ponds are anthropogenic habitats which increase the basin heterogeneity and influence it from this point of view.
NPP, nuclear power plant; MW, Mega Watt; DO, dissolved oxygen; COD, chemical oxygen demand; BOD, biochemical oxygen demand; OHPP, The Oleksandrivske Hydroelectric Power Plant; TR, the Tashlyk reservoir; SUNPP, South-Ukrainian Nuclear Power Plant; SUPC, South-Ukrainian Power Complex; TPSPP, Tashlyk Pumped-Storage Power Plant; TIRS, Thermal Infrared Sensor; LST, Land surface temperature; NDVI, normalized difference vegetation index; LIT, lowest identified taxon; CV, coefficient of variation; CCA, Canonical Correspondence Analysis; LDL, Lower Determination Level of diversity; OR, the Oleksandrivske reservoir; GIS, Geographic Information System.
AP and TN designed the research study; AP, TN, OT, SKS, YG performed the research; SB and ACB provided help and advice on the conclusions; AP, TN, OT and SB analyzed the data; AP, TN, OT, SKS, YG wrote the manuscript. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript.
Not applicable.
Our thanks to the administration of the South Ukrainе NPP, employees of the Environmental Protection Department, and the Tashlyk PSP. We are also thanks to the Israel Ministry of Aliyah and Integration that partly supported this work. The authors also express their gratitude to Ecotur Sibiu Environmental Association (Sibiu, Romania, CUI 5841004), which paid the publication fee for this research paper.
This work was partially supported by the by the Ministry of Education and Science of Ukraine (Project No. M/22-2018) and partly by the program “Support for the development of priority areas of research” (project No 1230/ 0120U000172). The publication fee was paid by Ecotur Sibiu. CUI 5841004.
The authors declare no conflict of interest.
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