Eutrophication and climate change are currently the major concerns for surface water management (Xia et al. 2016), as nutrients and climate factors lead to more intensive and more frequent occurrence of algal blooms (Mariani et al. 2015). Extreme events, including heat waves, storms and flooding effects, are expected to increase across Europe (Beniston et al. 2007; IPCC 2007). According to Elliott (2010) and Carvalho et al. (2011), the most important factor affecting the mass growth of cyanobacteria, especially in deep stratified reservoirs, is water retention time. However, only little information is available about the effect of water-level fluctuations (WLF) and the flushing rate on the spread of cyanobacteria and their taxonomic variability (Padisák et al. 1999; Romo et al. 2013; Pawlik-Skowrońska & Toporowska 2016). In deep stratified reservoirs, where interannual changes have affected the strength of stratification, irregular hydrological events override the seasonal cycle of phytoplankton and cause irregular proliferation of fastgrowing species (Padisák 1991; Naselli-Flores 1999). Proliferation of cyanobacteria in lakes and water supply reservoirs is a growing issue due to the presence of many potentially toxic species (Carmichael 1992; Codd 2000), which may affect organoleptic properties of water (Watson 2003; Izaguirre & Taylor 2004), cause water treatment problems and, in the worst case scenario, pose a threat to public health (Falconer 1999).
In deep lakes, which stratify in summer, some planktonic cyanobacteria proliferate in the metalimnion and form deep chlorophyll maxima (DCM). This has been observed for
The objective of this study was to identify and describe the major drivers of
The Vrutci reservoir (43°50ʹ34ʹʹN, 19°41ʹ36ʹʹE) was created in 1984 by damming the Đetinja River in Western Serbia (Fig. 1). It is a temperate, mid-altitude reservoir. The normal operational level of the reservoir was set at 621.3 m a.s.l. The reservoir is elongated in shape and is 7 km long, with a maximum depth (located near the dam) of 60 m (Table 1). The geological substrate consists mainly of limestone and serpentines. The average retention time is estimated at 250 days (JCI 1971). Apart from the Đetinja River, there are 28 other permanent and intermittent tributaries. The Vrutci reservoir is the main source of water supply for the city of Užice. It is also used for flood protection, sediment retention, and low-flow management during periods of drought. The total area of the catchment is 160 km2. It is sparsely populated and has no industry (JCI 1971). Mixing patterns in the Vrutci reservoir are highly complex and dependent on meteorological conditions and water withdrawals. Monitoring of the reservoir has been insufficient over the last 20 years and carried out by the officials only once a year.
Vrutci reservoir on the Đetinja River (Western Serbia). The yellow marker indicates the sampling location referred to as Vodozahvat
Main morphological features of the studied Vrutci reservoir and characteristics of the catchment
Characteristics of the reservoir | Value | Unit |
---|---|---|
Area | 1.92 | km2 |
Volume | 40.2 × 106 | m3 |
NPL (Normal Pool Level) | 621.3 | m a.s.l. |
Max depth | 60 | m |
Mean depth | 20.9 | m |
Water retention time | 250 | days |
Characteristi cs of the catchment | ||
Area | 160 | km2 |
Max altitude | 1544 | m a.s.l. |
Mean altitude | 915 | m a.s.l. |
At the end of December 2013, the upstream part of the reservoir turned into a reddish pulp that spread all over the reservoir in just a few days, which was followed by the formation of red surface patches. The study period lasted from January 2014 to December 2015. Water temperature (WT), pH, conductivity (Cond) and chlorophyll
Phytoplankton samples were collected monthly throughout the study period with the exception of November 2014, January and February 2015, due to the lack of funding during these months. The sampling site was located near the dam and water intake and was referred to as Vodozahvat (Fig. 1). Representative samples were collected from stationary depths: 0, 3, 10, 14, 18, 27, 31, 35 and 43 m. Water samples for qualitative analysis of phytoplankton were collected using a plankton net (Ø 22–23 μm). A vertical (beta) Van Dorn bottle of 2.2 l was used to collect samples for quantitative analysis. Samples were put into plastic bottles and preserved with Lugol’s solution according to European Standards 15204 (2006).
The identification of taxa was performed using a light microscope (Karl Zeiss Imager M1), camera (Carl Zeiss AxioCam MRC5) and fluorescence microscope (Olympus BX 5) according to the standard literature (Huber-Pestalozzi et al. 1983; Starmach 1983; Krammer & Lange-Bertalot 1986; 1988; 1991; John et al. 2002; Komárek & Anagnostidis 1998; 2005; Komárek 2013). The Shannon diversity index (Hʹ) was calculated according to the standard formula, based on the number and biomass of species (Shannon & Weaver 1949).
The quantitative analysis of phytoplankton was based on the Utermöhl method (EN 15204 2006), using a Leica inverted microscope. The number of cells was converted into the phytoplankton biomass by geometric approximations, using the standard formulas (Hillebrand et al. 1999; Sun & Liu 2003), while the biomass data were expressed as mm3 l−1.
Meteorological data (air temperature – AT, wind speed – Wind, precipitation – Prec, insolation and cloudiness) were obtained from annual reports of the Republic Hydrometeorological Institute of Serbia (RHMZ), from the nearest weather station (Zlatibor). Data on the water outflow and water level were obtained from the Užice Public Utility Company.
Complex external drivers produced complex-shaped vertical temperature profiles that deviated from those commonly observed. In most cases, the distinction between seasonal and ephemeral thermocline was not obvious. A modified van Genuchten fitting curve (Rimmer et al. 2005; Jennings et al. 2012) was employed to model the observed temperature profiles. The thermocline was then positioned to match the depth at which the second derivative of the temperature plot equals zero.
To answer the question as to which environmental factors shaped the dynamics of the
Variation partitioning with three groups of variables was performed. The first group included physical and hydrological parameters (WT, Cond, Secchi, and Wflush), the second group included meteorological parameters (AT, Prec and Wind) and the third group was represented by nutrients or chemical parameters (NH4, NO3, ortho-P and TP). The objective of these analyses was to assess how much variability in our data is described by every group of variables, taking in account their simple and conditional effect. The significance of each fraction was also presented.
Species diversity of the phytoplankton in the Vrutci reservoir, expressed as the Shannon diversity index, was generally low (1.18 in 2014 and 1.20 in 2015) and higher in the epilimnion than in the metalimnion. The phytoplankton biomass was characterized by the dominance of the cyanobacterium
Volume-weighted averages of
Biomass of
Annual water-level fluctuations in the Vrutci reservoir in 2014 and 2015
Due to the exceptionally mild winter of 2013/2014, the ice cover was not formed, which induced the monomictic mixing behavior of the Vrutci reservoir. High air temperatures in summer induced warming of surface layers and formation of a stable vertical thermal structure. Maximum epilimnetic water temperatures were recorded in August 2014 and July 2015 (Tables 2 and 3). More pronounced differences in mean temperatures between the water layers were observed in 2015.
Mean, minimum and maximum values of hydrological, meteorological, physical and chemical (Chl-
Hydro- and meteorological parameters | Min. | Mean | Max | ||||||
---|---|---|---|---|---|---|---|---|---|
Water flushing (m3 s−1) | −3.71 | 0.86 | 6.79 | ||||||
Air temp. (°C) | −0.09 | 9.07 | 17.33 | ||||||
Wind speed (m s−1) | 1.19 | 1.85 | 4.66 | ||||||
Insolation (h) | 3.34 | 5.35 | 8.91 | ||||||
Precipitati on (mm) | 1.38 | 7.37 | 12.36 | ||||||
Physicochemical parameters | |||||||||
Secchi depth (m) | 1.2 | 1.9 | 2.8 | ||||||
Epilimnion | Metalimnion | Hypolimnion | |||||||
Min. | Mean | Max | Min. | Mean | Max | Min. | Mean | Max | |
Water temp (°C) | 4.9 | 14.5 | 22.7 | 5.2 | 10.7 | 15.9 | 4.9 | 8.5 | 12.5 |
Conductivity (μS cm−1) | 222.0 | 259.2 | 288.5 | 219.0 | 247.1 | 290.5 | 204.5 | 239.8 | 276.0 |
pH | 8.2 | 8.7 | 9.2 | 7.8 | 8.2 | 8.5 | 7.7 | 7.8 | 8.2 |
Chl- |
3.1 | 7.2 | 13.9 | 3.7 | 7.4 | 9.9 | 4.5 | 6.1 | 9.8 |
Mean, minimum and maximum values of hydrological, meteorological, physical and chemical parameters in 2015
Hydro- and meteorological parameters | Min. | Mean | Max | ||||||
---|---|---|---|---|---|---|---|---|---|
Water flushing (m3 s−1) | −1.10 | 0.15 | 3.38 | ||||||
Air temp. (°C) | −0.76 | 9.29 | 20.71 | ||||||
Wind speed (m s−1) | 1.00 | 1.41 | 2.20 | ||||||
Insolation (h) | 3.94 | 6.61 | 10.89 | ||||||
Precipitati on (mm) | 1.25 | 5.76 | 9.82 | ||||||
Physicochemical parameters | |||||||||
Secchi depth (m) | 1.1 | 2.8 | 4.3 | ||||||
Epilimnion | Metalimnion | Hypolimnion | |||||||
Min. | Mean | Max | Min. | Mean | Max | Min. | Mean | Max | |
Water temp. (°C) | 6.4 | 16.5 | 25.3 | 5.4 | 9.7 | 13.2 | 4.6 | 6.7 | 9.7 |
Conductivity (μS cm−1) | 145.0 | 267 | 320.5 | 223.2 | 244.4 | 256.5 | 222.6 | 251.5 | 301.0 |
pH | 8.3 | 8.6 | 9.0 | 7.3 | 8.2 | 9.0 | 7.2 | 7.6 | 8.6 |
Chl- |
3.6 | 4.8 | 6.5 | 3.3 | 11.2 | 32.8 | 2.9 | 3.8 | 5.0 |
N-NH4 (μg l−1) | 20 | 120 | 400 | 10 | 113 | 276 | 10 | 99 | 198 |
N-NO2 (μg l−1) | 5 | 6 | 9 | 14 | 35 | 90 | 5 | 14 | 90 |
N-NO3 (μg l−1) | 50 | 280 | 810 | 200 | 373 | 820 | 340 | 624 | 838 |
TN (μg l−1) | 450 | 920 | 1430 | 710 | 1101 | 1601 | 784 | 1228 | 1846 |
ortho-P (μg l−1) | 10 | 20 | 50 | 9 | 17 | 31 | 9 | 9 | 10 |
TP (μg l−1) | 30 | 120 | 470 | 34 | 135 | 417 | 34 | 156 | 324 |
TN:TP | 1.6 | 12.9 | 31.2 | 3.0 | 11.5 | 27.4 | 3.4 | 13.4 | 36.2 |
In both years, the location of the seasonal thermocline was observed from a depth of about 3 m in spring to a depth of about 12 m in autumn (Fig. 3b). As a consequence of the extreme inflow in May 2014, the temperature of hypolimnetic water increased to about 4°C and the thermocline shifted 3 m upward. The pH value was relatively constant, with slightly lower values in deeper strata of metalimnion and hypolimnion compared to the upper strata (Tables 2 and 3).
In May 2014, heavy precipitation was observed when during four consecutive days (from 13 to 16 May) 110 mm of rainfall was recorded at the watershed gauge station. Almost 25% of the reservoir volume was replenished with surface runoff (Fig. 4). The water level varied in a wide range between 615 and 626.7 m a.s.l., and in over 30% of the study period the water level was higher than the usual water level (621.3 m a.s.l.; Fig. 4). Minimum water flushing values were recorded in October 2014 and July 2015, which indicated a higher outflow than inflow (Tables 2 and 3).
N-NH4 concentrations were higher in epi- than in meta- and hypolimnion, and the maximum value was recorded in September 2015 (400 μg l−1; Table 3). N-NO3 mean concentrations were generally high, especially in meta- and hypolimnion (Table 3), but concentrations started to decrease rapidly in June, from the surface to a depth of 15 m (Fig. 5a). Ortho-P concentration was high in the epi- and metalimnion (Table 3) at a depth ranging from 0 to 10 m from July to September (Fig. 5b).
Nutrients in the Vrutci reservoir in 2014 and 2015: N-NO3 concentration (a) and ortho-P concentration (b)
According to data from two years,
RDA ordination diagram showing the relationships between the biomass of
The response curves that include the relationship between the
Response curves that include the relationship between
According to the variation partitioning, physical and hydrological variables (WT, Cond, Secchi and Wflush – fractions a + d + f + g) explained 21.8% of the variability in our data. Irrespective of the others, this group of variables explained 11.8% of the total variation (fraction “a”). Meteorological parameters (Prec, Wind and AT – fractions b + d + g + e) explained a total of 16.2%, and independently – 6.2% of the variability (fraction “b”). Nutrients (chemical parameters) used in the RDA analysis
Explained variation and significance tests of the variation partitioning that included three groups of variables – conditional (a, b, c) and simple effects (a+d+f+g, b+d+g+e, c+e+f+g) were tested
Tested fraction | Explained variation (%) | F | |
---|---|---|---|
a | 11.8 | 10.0 | 0.0002 |
b | 6.2 | 7.1 | 0.0002 |
c | 9.1 | 7.8 | 0.0002 |
a + d + f + g | 21.8 | 14.5 | 0.0002 |
b + d + g + e | 16.2 | 11.3 | 0.0002 |
c + e + f + g | 18.8 | 12.0 | 0.0002 |
a + b + c + d + e + f + g | 40.8 | 9.8 | 0.0002 |
Our results confirmed the hypothesis that physical (WT) and hydrological parameters (Wflush) presented in Table 4 were crucial for the proliferation of
The prolonged autumn-winter mixing favored the spread of
Although many studies emphasize the importance of phosphorus load management for cyanobacterial blooms, reducing its input is not always an effective way to prevent toxic blooms (Lewis & Wurtsbaugh 2008; Amano et al. 2010; Koreiviene et al. 2014) and our results are in accordance with this observation. As a result,