Introduction

Acting as the backbone of human civilization, freshwater resources have sustained agricultural practices throughout history and currently fuel diverse human developments worldwide. Encompassing both groundwater and surface water, they only constitute only nearly one percent of the total water available on Earth, distributed unevenly across the globe and rapidly diminishing due to changing global climate in many regions such as Iran and Iraq in the Middle East (Moshir Panahi et al. 2020; Salman et al. 2021) and Egypt in North Africa (Abd Ellah 2020). It is estimated that per capita freshwater availability has been halved since the mid-twentieth century (Buta et al. 2023). Furthermore, large-scale remote sensing analysis revealed a decline in the quality of inland water worldwide, affecting over 60% of the 2000 major global inland waters (Wang et al. 2018). The concurrent decline in both quantity and quality emphasizes the urgent need for strict management and conservation of freshwater resources to meet the growing demands of human activities, especially in arid countries witnessing high human pressures (Kazemi et al. 2019).

To take full advantage of freshwater resources, humans have constructed artificial reservoirs to store surface freshwater, providing reasonable allocations and further expansion of developments all across parts of the world, especially in arid regions (Moshir Panahi et al. 2020). First, these constructions can bring about hydromorphological and geomorphological alterations to basins. Moreover, while the water quality in these reservoirs may be inconsequential for certain applications such as flood protection and hydropower, it becomes salient when humans are direct or indirect users of the water (Wassie and Melese 2017). Consequently, freshwater in these constructions should remain unpolluted or reach required standards for human use, safeguarding against disturbances arising from natural or anthropogenic alterations in chemical characteristics, such as pH, phosphorus, nitrite, nitrate, ammonia, and dissolved oxygen (Singh et al. 2019; Patale and Tank 2022). Due to the influx of organic compounds enriched with phosphorus and nitrogen, reservoirs also experience increased fertility, also known as eutrophication (Saturday et al. 2023), allowing undesirable species such as cyanobacteria, diatoms, and green algae to dominate the water and lead to water bloom (Li et al. 2022). This process impairs the functioning and resilience of aquatic ecosystems and the suitability of water for human activities (Buta et al. 2023), occurring not only as a result of oxygen consumption for photosynthesis but also the release of toxic metabolic products by some species (Glibert 2017).

The degradation of water quality hinges on the most important controlling factor, its temporal variation and relation to other factors such as climate parameters, hydrological events, and the intensity of human activities such as agriculture (Shahradnia et al. 2021, 2022). This underscores the significance of spatio-temporal analyses and the continuous monitoring of water quality parameters. Illustratively, Ni et al. (2018) explored the connections between water quality and the phytoplankton community in an intertidal mudflat pond in Hangzhou Bay. Their findings revealed significant correlations between most phytoplankton species and environmental factors such as temperature, pH, and dissolved oxygen. Examining the spatio-temporal evolution of eutrophication and water quality in the Turawa dam reservoir, Poland, Buta et al. (2023) confirmed the ongoing eutrophication, classifying the reservoir as either eutrophic or mesotrophic based on trophic status indices. Asadian et al. (2020) also classified the Zayandeh River dam lake as oligotrophic during spring and summer, possibly attributed to floods, runoff, and farmland drainage in the region. Investigating phytoplankton communities in 30 rubber dams over a 2-year field monitoring, Bao et al. (2022) observed dominance of Bacillariophyta in natural areas throughout the year, with Cyanophyta and Chlorophyta showing significant increases during warm seasons. Gecheva et al. (2020) found elevated nutrient levels in three standing water bodies in Bulgaria during summer, categorizing them as either eu- or hypertrophic. Moreover, they reported the highest phytoplankton species count and functional diversity in the meso-eutrophic condition.

The Karun-4 Dam Lake stands out as a pivotal water reservoir in western Iran, playing a crucial role in the regulation and distribution of a substantial share of surface freshwater within the Zagros mountain chain. Due to the extensive utilization of the lake water for various human activities, it becomes imperative to meticulously monitor and conserve its water quality. To address this objective, the present study conducted an assessment encompassing a range of water quality parameters throughout the four seasons of the year 2019. In addition to scrutinizing the chemical attributes of the water, efforts were made to identify the key chemical characteristics governing the water quality in the region. Furthermore, an analysis of phytoplankton communities was undertaken for each season to gauge their diversity and dominance. Ultimately, the trophic status of the lake was calculated for each season, and the factors influencing the presence of each species were measured and presented.

Material and methods

Experiment sites

The Karun-4 Dam Lake (hereafter Karun Lake) is located in Chaharmahal-va-Bakhtiari Province, western Iran. It spans the main branches of the Karun River and currently stands as the largest double-arch dam in the Middle East (Fig. 1). The construction of the Karun Dam (31° 36′ N, 50° 24′ E) concluded in March 2010, resulting in a water level of 1025 m and a capacity of 2.2 billion m3 (Ashjari et al. 2019). The primary objectives of the dam include the provision of agricultural water for downstream lands, the generation of hydropower electricity (2100 gigawatt hours), and the regulation of potentially devastating seasonal floods. The average annual precipitation in the reservoir's surrounding areas is estimated to be approximately 680 mm. The minimum and maximum air temperatures at the site are recorded as 8.0 and 32.4 °C, respectively (Statistical Center of Iran 2016).

Fig. 1
figure 1

Spatial representation of the Karun-4 Dam lake in SW Iran and distribution of water sampling stations

Full size image

Sample collection

In this investigation, 26 sampling stations were selected systematically and evenly distributed across the entire expanse of the lake's surface water (Fig. 1) for the assessment of water phytoplankton communities and chemical parameters. Sample collection occurred on four occasions throughout the year 2019, precisely on the midpoint of each season. All samples were acquired between 9:00 and 11:00 a.m. For this purpose, around 1 L of water was withdrawn from a depth of 50 cm, utilizing polyethylene containers that were pre-rinsed with 10% nitric acid, and care was taken to avoid any contact with the hands of the sampling personnel.

Measurement of water chemical characteristics and chlorophyll-a

Water temperature (T), dissolved oxygen (O2), electrical conductivity (EC), and pH were measured on site using the portable HQ40D Digital multi-meter kit. The measurement of lake trophic conditions necessitated the assessment of water transparency, accomplished through the utilization of the Secchi disk depth device. Turbidity (TUR) was also determined with a turbidity meter (2100N HACH Turbidity meter). Alkalinity (ALK) and the ions calcium (Ca2+) and chloride (Cl−) were determined through the titration method (Rice et al. 2012). Sodium (Na+) and potassium (K+) were measured using a photometer. Fluoride (F−), magnesium (Mg2+), ammonium (\({\text{NH}}_{4}^{+}\)), iron (Fe2+), potassium (K+), sulfate (\({\text{SO}}_{4}^{2-}\)), nitrate (\({\text{NO}}_{3}^{-}\)), and phosphate (\({\text{PO}}_{4}^{3-}\)) together with the compound silicon dioxide (SiO2) were quantified using the powder pillow procedures with a spectrophotometer (DR5000 HACH).

To quantify the concentration of chlorophyll-a (Chl-a), the water sample underwent initial agitation. Subsequently, a defined volume of water, determined by its color, was subjected to filtration using Whatman filter papers. Following this, Chl-a was extracted by crushing the filter paper, along with the algae adhering to it, utilizing a mortar and pestle in the presence of 90% acetone. The resultant mixture was then subjected to centrifugation. In the final step, the sample containing the Chl-a extract was transferred to a cuvette, and its absorbance at wavelengths 630, 647, 664, and 750 nm was determined using a spectrophotometer. The concentration of Chl-a was computed using Eq. 1 in which L represents the circumference line on the circular filter in cm, \(\text{Vf}\) denotes the volume of filtered water in L, and \(\text{Ve}\) indicates the volume of the extract in mL (Tunali et al. 2020).

$$\text{Chl}-\text{a }= (11.85\times (\text{E}664-\text{E}750)-1.54\times (\text{E}647-\text{E}750)-0.08 (\text{E}630-\text{E}750))\times \text{Ve}/\text{L}\times \text{Vf}$$
(1)

Identification of phytoplankton species

To collect phytoplankton samples at each station, 250 mL of water was also retrieved from a depth of 50 cm using sample bottles. The obtained water was then preserved in plastic containers with Lugol's solution. Following this, a sedimentation method was applied to concentrate the samples. The sample preparation process included the systematic transfer of sedimented materials from larger to progressively smaller containers, utilizing cylindrical containers with clean and thin glass bottoms. Careful attention was paid to filling the sedimentation chamber to prevent the formation of bubbles. Moreover, to prevent the dispersion of settled materials, the sample was meticulously transferred, and any excess liquid was gently decanted. After the sample underwent thorough shaking and homogenization, 5 cc of it was scrutinized and identified under an inverted microscope using a cavity slide in three replicates. Photographic documentation of the samples was conducted under the microscope after examination. Equation 2 was then utilized following the microscopic study to calculate the abundance of phytoplankton in 1 L of the specified water source, where D is the species count per L, N is the count of organisms observed in the microscopic sample (5 cc), v is the volume of concentrated water from 1 L of the sample (m3), and V is the volume of the sample observed under the microscope (m3). The species were identified using the available key identification sources (Varol and Fucikova 2015; Varol et al. 2018).

$$D=(N \times v) / V$$
(2)

Phytoplankton diversity and dominance indices

The diversity and dominance of the phytoplankton species were investigated using four indices, Dominance Index (Y), Margalef Diversity Index (D), Shannon–Wiener Index (H′), and Pielou’s Evenness Index (J′) (Ni et al. 2018; Gogoi et al. 2019). The Dominance Index (Eq. 3) was calculated by dividing the number of individuals of the ith species (\({n}_{i}\)) by the mutiplication of total number of individuals of phytoplankton (N) and the frequency of the ith species (\({f}_{i}\)). Margalef Diversity Index was calculated using Eq. 4 where S is the total number of phytoplankton species. The proportion of the ith species in total individuals (\({P}_{i}\)) was also caculated to measure the Shannon–Wiener Index using Eq. 5. Pielou’s Evenness Index was measured by dividing the Shannon–Wiener Index by the natural logarithm of the total number of phytoplankton species (Eq. 6).

$$Y= {n}_{i}/N\times {f}_{i}$$
(3)
$$D= (S-1)/\text{ln}\,N$$
(4)
$$ H^{\prime } = \sum \left( {P_{i} \times \ln \,P_{i} } \right) $$
(5)
$$ J^{\prime } = { }H^{\prime } /\ln \,S $$
(6)

Measurement of the trophic state index

The Trophic State Index (TSI) (Carlson 1977) was utilized to assess the trophic condition of the lake. In general, TSI serves as an indicator of the nutritional equilibrium, and an elevation in nutrient concentrations induces variations in phytoplankton biomass, resulting in a decrease in water clarity and an increase in water turbidity. Accordingly, TSI offers a practical means of quantifying these associations, assigning a numerical value between 0 and 100 to each parameter based on their respective equations (Lyu et al. 2022). The parameters incorporated in this index encompass total phosphorus (TP), Chl-a, and Secchi disk depth (SD) (Eq. 7–9), evaluated using the Carlson Index.

$$\text{TSI }\left(\text{TP}\right)= 10\times \left(2.46+ \frac{6.71+1.15 \times \text{ ln}\,(\text{TP})}{\text{ln}2.5}\right)$$
(7)
$$\text{TSI }(\text{Chl}-\text{a}) = 10\times \left(2.46+ \frac{\text{ln}\,(\text{Chl}-\text{a})}{\text{ln}2.5}\right)$$
(8)
$$\text{TSI }(\text{SD}) = 10\times \left(2.46+ \frac{3.69+1.52 \times \text{ ln}\,(\text{SD})}{\text{ln}2.5}\right)$$
(9)
$$\text{Total }(\text{TSI}) =(0.297\times \text{TSI }\left(\text{SD}\right))+(0.540\times \text{TSI }\left(\text{Chl}-\text{a}\right))+(0.163 \times \text{TSI }(\text{TP}))$$
(10)

Equation 10 serves as the total TSI, amalgamating the individual indices obtained from Eqs. 7–9. According to this index, the reservoir's condition was categorized into four states: oligotrophic (0 < TSI < 30), mesotrophic (30 < TSI < 50), eutrophic (50 < TSI < 70), and hypertrophic (70 < TSI) where oligotrophic and mesotrophic states are generally considered healthier, while eutrophic and hypertrophic states may indicate water quality issues, such as excessive nutrient loading and potential ecological imbalances (Lyu et al. 2022).

Statistical analysis

The water quality parameters measured at each station were reported as Mean ± Standard Error and underwent one-way analysis of variance (ANOVA) with Duncan LSD All-Pairwise Comparison Test to measure their statistical differences based on a p-value of 0.05. Principal Component Analysis (PCA) (Greenacre et al. 2022) was used to characterize the most important water quality parameters in each season, revealing the highest variation in the dataset. The Canonical Correspondence Analysis (CCA) (Guo and Wu 2019) also found that the diversity of Bacillariophyceae species decreases notably in ponds characterized by turbidity and higher alkalinity. Similarly and in agreement with the results of Smucker et al. (2021), Cyanobacteria exhibited a strong positive association with temperature, indicating a preference for warmer temperatures during both spring and winter.

During summer, Cyanobacteria thrived in nutrient-rich conditions, showcasing positive associations with Mg2+, SIO2, and K+. Furthermore, Cyanobacteria were consistently found to favor well-oxygenated environments and areas with higher iron concentrations across all seasons. Chlorophyceae, on the other hand, displayed positive associations with O2, pH, and \({\text{NO}}_{3}^{-}\), indicating a preference for well-oxygenated and slightly alkaline waters with nitrate availability, particularly in spring. This group also demonstrated a propensity for nutrient-rich conditions throughout the year, as evidenced by positive associations with Mg2+, \({\text{NH}}_{4}^{+}\), Na2+, and K+. Despite the shared affinity for nutrient-rich conditions, the specific preferences for individual nutrients and ions varied by season and species groups. This variability underscores the dynamic nature of the water environment in the region, influencing the presence and abundance of phytoplankton species. Notably, water temperature emerged as a crucial factor in spring and summer, while lower favorability was observed in environments with higher bicarbonate and alkalinity levels during winter and fall. These consistent patterns across species and seasons underscore the importance of understanding the interplay between environmental variables and phytoplankton dynamics in Karun Lake.

Conservation implications

The distinct patterns observed across species and seasons underscore the dynamic nature of the water environment in Karun Lake, emphasizing the need for adaptive management strategies that consider seasonal fluctuations in environmental conditions. Particularly, spring and summer were found to harbor a nutrient-rich environment, indicative of increased runoff and nutrient availability during these seasons, thereby facilitating the proliferation of certain species such as Bacillariophyceae. Additionally, seasons characterized by reduced alkaline and turbid conditions are prevalent in the region, resulting in diminished abundance of sensitive species and highlighting the necessity of maintaining water quality to support diverse phytoplankton communities. Consequently, adopting adaptive management approaches that account for temporal fluctuations in environmental conditions emerges as a more effective strategy for this region. Specifically, during summer, more stringent strategies aimed at mitigating nutrient inputs and managing algal blooms may be warranted to uphold ecosystem health and water quality in Karun-4 Lake.

Limitations and suggestions

The study primarily focused on surface water, limiting the depth of insights into potential vertical variations within the lake. A more comprehensive analysis of water layers could offer a nuanced understanding of the lake's dynamics and ecological interactions. Additionally, the research did not consider the potential impact of extreme weather events, which can play a pivotal role in sha** the lake's ecosystem. Future investigations should prioritize incorporating these factors to provide a more holistic perspective and strengthen the overall robustness of the findings. Moreover, it is crucial to assess the ubiquitous effect of human pressures and pollution on lake aquatic species, as substantially demonstrated in Iranian ecosystems (Nourouzi et al. 2018). To address these limitations, it is crucial to implement water conservation measures and establish ongoing monitoring programs for Karun Lake. Specifically, the adoption of adaptive management strategies is recommended, taking into account the dynamic nature of the ecosystem, particularly during critical seasons. By doing so, we can enhance our ability to respond effectively to environmental changes and contribute to the sustainable management and preservation of Karun Lake.

Conclusion

This study provides valuable insights into the complex interplay of factors influencing water quality, phytoplankton dynamics, and ecological responses in Karun Lake. The evident variations in water parameters underscore the lake's vulnerability to environmental shifts, emphasizing the impact of temperature, sunlight, and nutrient influx. The adaptability of the phytoplankton community, particularly the prevalence of Chrysophyceae (38%) and Bacillariophyceae (32%), highlights the lake's nutrient-rich conditions, particularly during the spring season. Ecological indices also mirrored the lake's dynamic nature, with heightened biological activity during spring (TSI total = 59.435) and summer (TSI total = 53.964). However, the study emphasizes the need for more in-depth analyses of vertical variations and the consideration of extreme weather events in the future investigations. To address these gaps, the conclusion advocates for adaptive management strategies, ongoing monitoring initiatives, and water conservation measures tailored to the dynamic ecosystem of Karun Lake.