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Article

Spatio-Temporal Variation of Trophic Status and Water Quality with Water Level Fluctuation in a Reservoir

by
Wenwen Liao
1,
Hsinan Chen
2,
Meijeng Peng
2 and
Tawei Chang
2,*
1
Northern Region Water Resources Office, Water Resources Agency, MOEA. 2, Jia’an Rd., Longtan Dist., Taoyuan City 325, Taiwan
2
Agricultural Engineering Research Center, 196-1 Chung Yuan Road, Zhongli Dist., Taoyuan City 320, Taiwan
*
Author to whom correspondence should be addressed.
Water 2023, 15(17), 3154; https://doi.org/10.3390/w15173154
Submission received: 26 July 2023 / Revised: 24 August 2023 / Accepted: 30 August 2023 / Published: 3 September 2023
(This article belongs to the Special Issue The Impact of Water Level Changes (Frequency and Amplitude) on Water Quality in Lakes)
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Abstract

:
Water level fluctuation (WLF) is one of the important factors that affect reservoir water quality, habitat, species, and ecosystems. In this study, an independent sample t-test was used to evaluate the trophic status and water quality of the spatial and temporal variations with WLF in Shihmen Reservoir, Taiwan. The results of this study show that the Shihmen Reservoir has the lowest mean water level and higher potential of showing eutrophic status in April and May. This may be attributed to a lower water level, water depth, and transparency in this period. However, although there is no statistically significant difference in mean algal abundance in spring compared with other seasons, seasonal mean algae abundance and the seasonal mean Carlson’s trophic status index (CTSI) show as highly and positively correlated. It means that the increase in the CTSI value may not only be caused by effects on the sediment increase but also by algal proliferation. Mean water depth seems to be one of the important key indexes for reservoir management regarding trophic status since it reflects water quality and can be easy to obtain. This study suggests that reservoir administration can use the water level as a reference threshold for controlling CTSI strategies. In proper hydrological conditions, administration should try to hold a higher water level in a reservoir to downgrade CTSI.

1. Introduction

Due to the impact of global climate change in recent years, the frequency of extreme rainfall or drought events has increased significantly. This not only brings a great challenge to the regulation and use of water resources but also results in water quality variations, which may be caused from WLF in a reservoir.
The extensive literature shows that WLF is a major factor in governing lake, river, and reservoir ecosystems. Leira and Cantonati (2008) [1] and Dinka et al. (2004) [2] illustrate different effects of WLF on the spatial and temporal differences of water chemistry, habitats, and indicators. However, most of the literature interest is on macrophytes (Hellsten et al., 1996, Casanova and Brock 2000, van der Valk 2005, Stefanidis and Papastergiadou 2013) [3,4,5,6], algae (Hawes and Smith 1993, Punning and Puusepp 2007, Coops and Hosper 2002, Bakker and Hilt, 2016) [7,8,9,10], zooplankton (Stefanidis and Papastergiadou 2013, Mageed and Heikal 2006) [6,11], invertebrates (Qu et al., 2023) [12], and fish (Stefanidis and Papastergiadou 2013) [6] rather than water quality. Conversely, there are still some studies trying to understand the impact of WLF on reservoir water quality. Not only regarding water quality, Zohary et al. (2014) [13] also underscores that water level fluctuations can act as a major environmental factor influencing habitat structure and availability, and related interactions. Kim et al. (2023) [14] demonstrated that the difference in the water quality for each water level scenario was larger at the lower location (downstream) of the reservoir than at the upper location (upstream). Liu et al. (2016) [15] also indicate that lower and higher water level periods differed in a number of water quality characteristics. The low water level period was associated with the peak concentrations of nitrogen and turbidity, while the higher water level period was mainly characterized by enhanced chlorophyll concentration. Rhodes and Wiley (1993) [16] report that declining water levels may cause contaminated sediments to be re-suspended and this will represent a potentially long-term environmental remediation problem. However, there are quite a few papers discussing the impact of WLF on the release of phosphorus from sediments. Ma et al. (2023) [17] indicated the sediment P distribution patterns responded differently to WLF, geographical location, and anthropogenic activities. Yuan et al. (2021) [18] demonstrated that a more significant desorption and replenishment effect of labile P to an aqueous solution would occur in lake regions with more dramatic water level variations.
Quantification of water quality is an integral part of scientifically based water resource management (Parparov et al., 2010) [19]. The water quality index (WQI) is a single number that expresses water quality by aggregating the physical, chemical, and biologic parameter measurement in water, which has been wildly applied around the world [20,21,22,23]. However, Carlson and Simpson (1996) [24] indicated that an unfortunate misconception concerning the term of the trophic state is synonymous with the concept of water quality. Although the concepts are related, they should not be used interchangeably. Consequently, a corresponding relation between trophic state and water quality has been established in lots of the literature. Parparov et al. (2006) [25] show how water quality can be quantified in relation to sustainable management in Lake Kinneret, Israel. El-Serehy et al. (2018) [26] applied the trophic state index (TSI) and water quality index (WQI) for the water quality quantitative assessment of Lake Timsah, Egypt. The corresponding relation between water quality, WQI, and CTSI is shown in Table 1.
Therefore, the main purpose of this study is to understand the spatio-temporal difference in trophic status and water quality with WLF of Shihmen Reservoir from 2011 to 2021. The result of this study can provide operational suggestions for reservoir administration to control and maintain CTSI under proper hydrological conditions.

2. Materials and Methods

2.1. Study Area and Sampling Site Description

Shihmen Reservoir is an important water facility for water supply in northern Taiwan. Owing to the task of supplying water for the livelihood of nearly 9 million people in Taipei City, New Taipei City, and Taoyuan City, the water quality of the reservoir is a matter of great concern to the government and the public in Taiwan. Shihmen Reservoir is located in the mid-lower stream of the Dahan Creek, which was the main tributary of Tamshui River in northern Taiwan. The catchment covers a total area of approximately 763 km2 between Daxi, Longtan, and Fuxing District in Taoyuan City. This reservoir integrates the function of power generation, public water supply, irrigation, flood control, and tourism, and has made a significant contribution to the increasing agricultural production, developing industry and commerce, improving people’s living, and preventing floods and droughts in northern Taiwan. Consequently, the water quality of this reservoir has attracted considerable attention from the government and the public. The Northern Region Water Resources Office (WRANB) of the Water Resources Agency is responsible for the management of Shihmen Reservoir, which has framed 5 water quality monitoring stations within the reservoir storage area. From the upstream to the downstream of the reservoir storage area, they are Chang Xin (CX), Amu Plain (AP), Dragon Ball Bay (DBB), Xian Island (XI), and Shihmen Channel Intake (SCI), respectively. Figure 1 shows the map of the study area and 5 water quality monitoring station locations in Shihmen Reservoir. The characteristic description of the 5 water quality monitoring stations is shown in Table 2. Regular water quality monitoring was conducted from the 5 monitoring stations every month to grasp the water quality variations in the reservoir storage area, while an algal abundance survey was conducted quarterly. In recent years, Shihmen Reservoir conducted ecologic surveys on fish and algae only. According to survey results, a total of fish in 2 orders, 5 families, and 18 genera were recorded in Shihmen Reservoir in 2019. The number and composition of fish species vary little between seasons. From 2016 to 2021, there is no annual difference in algae abundance, but there are seasonal changes between summer–autumn and winter–spring. This result may reflect the relatively stable water ecological environment in the reservoir. However, less variation in water temperature throughout the year is affected by global warming, which indirectly results in only two seasonal variations in algae.

2.2. Study Methods

2.2.1. Water Quality Monitoring

This study utilized monthly water quality and seasonal algal abundance monitoring data in Shihmen Reservoir from 2011 to 2021. It includes 151 water quality monitoring data values and 44 algal abundance survey data values (including irregular survey data), respectively.
All water samples were collected from the surface water at 0 to 50 cm using a Kemmerer water sampler. Portable instruments were used to analyze water temperature, pH, Electrical Conductivity (EC), Dissolved Oxygen (DO), and turbidity, while transparency (SD) was recorded using a Secchi Disk on-site. Suspended Solids (SS) content was measured using the weighing method. Nitrate (NO3−-N), ammonia nitrogen (NH4+-N), and Total Phosphorus (TP) were analyzed using a UV/VIS spectrometer (PerkinElmer Lambda 25), and chlorophyll a (Chl-a) was extracted with acetone and measured with the UV/VIS spectrometer as well. Total Organic Carbon (TOC) was analyzed using a TOC analyzer (O.I. Analytical, Aurora 1030), and Coliform groups (E. Coli.) were determined using the membrane filter technique.
The trophic status was calculated using CTSI according to Carlson (1977) [19]. The CTSI is the most popular index for evaluating the trophic status of a reservoir or lake worldwide, which is an arithmetic mean of the trophic status index (TSI) value of SD, TP, and Chl-a in the reservoir (Equation (1)). The WQI was calculated using Equation (5) with measurements of water quality parameters, which include DO, pH, NH4+-N, Coliform groups, SS, and TP.
CTSI = [TSI (SD) + TSI (Chl-a) + TSI (TP)]/3,
where TSI (SD) = 60 − 14.41 ln (SD),
TSI (Chl-a) = 9.8 ln (Chl-a) + 30.6,
TSI (TP) = 14.42 ln (TP) + 4.15,
WQI = 0.1   ∑ i = 1 n w i q i 1.5 ,
where i represents the number of water quality parameters.
  • w is the weighting factor ranging between 0 and 1;
  • q means the rating number assigned to a parameter ranging from 0 to 100.

2.2.2. Algal Abundance Monitoring

For the algal abundance survey, the Kemmerer water sampler was used to take three 500 mL water samples within 0 to 50 cm of the surface water at different locations around the monitoring station. After mixing three 500 mL water samples, 2.5 mL of Lugol’s solution was added to fix and preserve. Taking 100 mL of a mixed water sample for making slices, algae cells were collected by using a centrifuge at 3000× g for 10 min. One drop of a Coomassie blue gel dye solution was added to the precipitated algae cells after pouring off the supernatant. The algae sample stood at room temperature for 5 min and was filtered on a 0.45 μm nitrocellulose filter membrane using vacuum filtration. Finally, the number of algae cells was counted (×100) under an inverted optical microscope (Nikon, model A300) with a counting chamber.

2.2.3. Statistical Analysis

An independent samples t-test with a post hoc test of the Least-Significant Difference method (LSD) were used to evaluate the spatial and temporal variation differences in the water level, water depth, mean CTSI, and mean concentration of other water quality parameters. If the significance (p-value) is less than 0.05, it can be determined that there is a statistically significant difference.

3. Results and Discussion

3.1. Temporal Variation of Trophic Status and Water Quality in Reservoir

Based on Taiwan’s hydrological conditions, the period from May to October is the wet season with more rainfall, while the period from November to April of the following year is the dry season with less rainfall. With changes in rainfall, river inflow, and water usage, the water level within the storage area of Shihmen Reservoir also fluctuates accordingly. The monthly plots of long-term water quality parameters from 2011 to 2021 are shown in Figure 2. It shows that the mean CTSI, EC, NH4+-N, and SS raised when the water level decreased, particularly in 2015 and 2021 when the water level of Shihmen Reservoir decreased to 217.58 m and 202.89 m, respectively. During these periods, the increase in mean CTSI and nutrient concentrations was particularly evident.
CTSI is an indicator for assessing the trophic status of water bodies. Without a well-created definition of the corresponding relationship between CTSI and water quality, it should not be confused with good and worse water quality. In particular, different water uses have different water quality criteria. However, in terms of water quality parameters in Figure 2, water quality parameters of nutrients, SS, and transparency, etc., in the reservoir reflect WLF. Xiang et al. (2020) [27] pointed out that in China’s Three Gorges Reservoir, the water quality during the low water level period is the worst among the four stages of reservoir operation, including drainage, lower level, impoundment, and higher water level. Mosley (2015) [28] indicates that during a drought, water flow and volume decrease, typically leading to increased salinity due to a reduced dilution and concentration of mass. Dumitran et al. (2020) [29] and Chaves et al. (2013) [30] also found that the trophic state of a lake is directly influenced by hydrological variability and seasonal fluctuations in the hydrology of the system controlled by rainfall. Therefore, does the WLF of Shihmen Reservoir also affect the accumulation of nutrients and trophic status of the water body?
All monitoring data from 2011 to 2021 were divided by month (algal abundance was divided by season) to observe the variations in mean CTSI and water quality parameters. As shown in Figure 3, April and May are the times with the lowest mean water level each year, with mean water levels of 229.7 m and 230.1 m, respectively, which are statistically significant differences from the mean water levels of other months. April and May are the end of the dry season in Taiwan, and February to March is also the peak period of water demand for spring plowing in northern Taiwan. Therefore, April and May are often the times when the reservoir water storage and water level are lowest. Afterwards, a spring rain, monsoon, summer shower, and typhoon bring rainfall to replenish the water source, which gradually raise the water level of the reservoir. Consequently, during April and May each year, the mean CTSI, TP, chlorophyll a, EC, turbidity, and SS of the reservoir water reach the highest values while transparency is lowest. This can be attributed to a lower water storage and water level and is consistent with the results of Geraldes and Boavida (2005) [31]. They found that the highest values of TP, soluble reactive phosphorus, nitrate, water color, and chlorophyll a were found during the minimum level phase in S. Serrada Reservoir, Portugal. Stefanidis and Papastergiadou (2013) [6] also show trends of negative correlation in EC, and chloride strongly correlated with the water level in an eastern Mediterranean lake. Meanwhile, the mean CTSI values (Figure 3a) of Shihmen Reservoir in April and May were as high as 50.4 and 53.6, respectively, both of which are in the eutrophic status, and showed statistically significant differences from the mean CTSI in other months. From the perspective of water quality parameters composed of CTSI, the mean concentration of TP (Figure 3b) and chlorophyll a (Figure 3c) was the highest, while transparency (Figure 3d) was the lowest in May. At this time, the mean concentration of TP, chlorophyll a, and transparency had a statistically significant difference from the mean concentration in January to March and December, in January to April and October to December, and in January and June to December, respectively. It may imply that the lowest water level in Shihmen Reservoir in May is one of the important factors affecting CTSI. On the other hand, although the weather gradually warms in spring (March to May), the mean algal abundance in the reservoir reaches its peak (Figure 3e); however, there is no statistically significant difference in mean algal abundance among the four seasons. This indicates that the increase in CTSI may not be due to the proliferation of algae cells but the effect of the increase in higher SS content causing a decrease in transparency (Figure 3h,k). The reason is that when the water level is lower in May, precipitation, water flow, and anthropogenic and aquatic activities (creatures) are more likely to disturb the water, causing an increase in water turbidity. From the perspective of water quality, Figure 3l shows the lowest mean WQI in May; except in June to September, strong sunshine, a high temperature, and vigorous photosynthesis result in the pH rising and the WQI dropping significantly. Meanwhile, monthly mean CTSI and monthly mean WQI show a negative correlation (R2 = 0.397***) during the study period. It also may imply that water quality is affected by the constitutes of CTSI. However, according to Table 1, all of the mean WQI falls in the rank of 70–90 and a water quality of good criteria is suited for all purposes.
Carlson and Havens (2005) [32] used simple graphical methods to evaluate non-algal turbidity affecting trophic status with deviation of TSI (Chl-a), TSI (TP), and TSI (SD). Figure 4 shows the plot of the TSI (Chl-a)–TSI (TP) against the TSI (Chl-a)–TSI (SD) of the five monitoring stations of Shihmen Reservoir data during 2011 to 2021. According to Figure 4a, points on the X-axis to the left of the Y-axis indicate that light might have been scattered or absorbed by very small particles such as suspended clays or by dissolved color. Above 90% of data of each monitoring station showed the trophic status affected by clay turbidity. This finding is consistent with Mamun et al. (2021) [33]. They indicate that Total Suspended Solids (TSS) showed a highly significant influence on water clarity compared to TP and algal Chl-a in all reservoirs. Jones and Hubbart (2011) [34] also demonstrated that limnologists have long recognized that mineral turbidity reduces transparency in lakes and reservoirs and uncouples the relationship between Secchi depth and algal biomass. De Oliveira et al. (2020) [35] also reported that the decrease in Secchi depth from the lacustrine to the riverine zone may be related to the increase in nutrients and suspended matter. According to our experiences of the long period, water transparency could be interfered with when water turbidity is greater than 5 NTU. However, Figure 4b also shows the highly and positively correlated seasonal mean algae abundance and seasonal mean CTSI (R2 = 0.9688). This indicates that an increase in the CTSI value may not only be caused by a sediment increase but also by algal proliferation effects. After all, the increase in the algae abundance is also one of the main factors for the increase in CTSI and decrease in water transparency. Therefore, the combined effect of the increase in algae abundance and suspended matter is an important reason for the increase in CTSI.

3.2. Spatial Variation of Trophic Status and Water Quality of the Reservoir

In spite of the water quality parameters of Shihmen Reservoir vary on a temporal basis, and the spatial locations of the five monitoring stations also have an impact on the mean water quality. From 2011 to 2021, the mean CTSI at the five monitoring stations from the upstream to downstream was 49.0, 50.2, 48.1, 46.6, and 47.0, respectively. It is showing a trend of decreasing CTSI mean values as the stations move downstream. Carlson and Havens (2005) [32] analyzed data from Lake Rockwell Reservoir in Ohio, USA and found that as water passes down the reservoir, non-algal turbidity is apparently lost. The main reason for this is that the upstream watershed has a narrower river cross-section and shallower water depth, which causes water to have higher velocity, to be easily disturbed, and to carry sediments into the reservoir. When the water flows into the wider waters of the reservoir, water depth is deeper and water flow velocity drops significantly. It makes the sediments, which cause non-algal turbidity, have more hydraulic retention time and gradually settle down. Therefore, the decreasing trends of the mean transparency and CTSI of water become more obvious in the downstream of the reservoir. Since the water level at all monitoring stations is the same at a certain time, the water flow velocity in the wider waters is also quite similar, and the water depth of the monitoring station location seems to be one of the important factors affecting the CTSI variation. Figure 5 shows the mean value of water depth, CTSI, and the other water quality parameter variations at the five monitoring stations in Shihmen Reservoir from 2011 to 2021. The mean water depths of the five monitoring stations from the upstream to downstream are 17.3 m, 11.8 m, 23.0 m, 27.7 m, and 32.9 m, respectively (Figure 5a). It shows that the mean water depth of the downstream monitoring stations is deeper, showing a statistically significant difference in the water depth of the five monitoring stations. At the same time, the mean CTSI of each monitoring station also shows a statistically significant difference (Figure 5a), indicating that water depth is one of the important factors affecting CTSI indeed.
As a result of a tiny difference in other water quality parameters’ mean such as algal abundance (Figure 5e), EC (Figure 5f), TOC (Figure 5g), and NH4+-N (Figure 5i) among the five monitoring stations, there is no statistically significant difference as well. It is clear that the surrounding environment (such as tributary inflows) of monitoring stations does not affect the variation in water quality. However, the TP (Figure 5b), chlorophyll a (Figure 5c), transparency (Figure 5d), SS (Figure 5K), and WQI (Figure 5l) among the five monitoring stations had slight statistically significant differences, particularly between the two upstream CX and AP and the other three downstream monitoring stations. They have statistically significant differences due to more of a difference in water depth relative to the downstream monitoring stations, and thus are more obviously affected by non-algal turbidity. However, according to Table 1, all of the mean WQI also falls in the rank of 70–90 and a water quality of good criteria is suited for all purposes.
Since the water level is an important parameter for routine operation and management in a reservoir, the accurate water level data can be easy to obtain at any time, while the elevation of the sediments at the bottom of a reservoir varies slowly. Therefore, the water level can also reflect water depth variation. Figure 6 presents the regression analysis results of the water level and mean CTSI at the five monitoring stations in Shihmen Reservoir from 2011 to 2021.
Figure 6 shows monitoring stations in order from the upstream (CX) to the downstream (SCI) monitoring station. The regression analysis results indicate that the corresponding water levels for a CTSI of 50 at the CX, AP, DBB, XI, and SCI monitoring stations are 238.24 m, 237.07 m, 231.19 m, 218.83 m, and 219.28 m, respectively. This shows a trend of decreasing corresponding water levels as the monitoring station moves downstream. If these corresponding water levels are used as the assessment threshold for the trophic status of the water bodies, it can be observed, when the water level falls below the threshold, that the proportions of water showing the trophic status at the monitoring stations of CX, AP, DBB, XI, and SCI are 63.2% (36/57), 42.4% (14/33), 64.5% (20/31), 100% (2/2), and 100% (2/2), respectively. However, when the water level rises above the threshold, the proportion of eutrophic water bodies is relatively lower, ranging from 16.0% to 28.6%. This suggests that as the water level in the reservoir decreases, the probability of water bodies reaching the eutrophic status increases. Based on the recent record of the lowest water level (202.89 m in May 2021) in Shihmen Reservoir, the CTSI at all monitoring stations in May 2021 was greater than 50, with some upstream monitoring stations (CX, AP, and DDB) even reaching 60 or higher. The mean CTSI of Shihmen Reservoir in May 2021 was also high at 58.4. This indicates that, when the water level in the reservoir drops down, the water quality in the upstream area is more likely to be impacted than the downstream area.

3.3. Water Quality Difference with Water Level of Reservoir

As discussed above, there is a tendency for the mean CTSI of Shihmen Reservoir to vary with the water level. Figure 7 summarizes the variations in the water level and mean CTSI at 151 sampling times from 2011 to 2021, with CTSI being the average regarding the five monitoring stations on the sampling day. The regression analysis revealed that the corresponding water level threshold for a CTSI of 50 was 230.43 m. When the water level in the reservoir rose above 230.43 m, the proportion of water bodies showing eutrophication (CTSI > 50) was only 17.4% (21/121); however, when the water level falls below 230.43 m, the proportion of eutrophic water bodies significantly increases to 66.7% (20/30).
Based on the historical data of water quality parameters and a simple regression analysis, it was found that the water level of 230.43 m seems to be an important threshold affecting the trophic status of Shihmen Reservoir. In consequence, this study used the water level of 230.43 m as a threshold and grouped the monitoring data according to above or below the water level of 230.43 m. Two groups of data were then subjected to the independent samples t-test and post hoc test with the LSD method for the statistical analysis. The results of the test are shown in Table 3. There was a statistically significant difference in the mean value of a lot of water quality parameters when the water level was above or below 230.43 m, such as CTSI, pH, EC, DO, SD, SS, turbidity, TOC, water depth, and algal abundance. When the water level was lower (<230.43 m), the mean values of water quality parameters such as CTSI, EC, SS, turbidity, and algal abundance were much higher than when the water level was higher (>230.43 m), while the mean transparency decreased by 0.9 m. However, there was no statistically significant difference in the mean concentrations of nutrients such as TP, NH4+-N, NO3−-N, and chlorophyll a, as well as WQI, regarding above and below the water level of 240.43 m. Therefore, it can be inferred that the water level in Shihmen Reservoir is one of the important factors affecting the mean CTSI. When the water level in the reservoir is lower, although the nutrient (N and P) load increases, there is no statistically significant difference with the higher water level.
This shows that the main reasons for the decreasing water transparency and raise in eutrophic status are seemingly attributed to combined effects of algal proliferation and high suspended solids content in the water body. This can also be seen from the relationship between the water level, algal abundance, and transparency in Figure 8. From 2011 to 2021, there were eleven samples in which algal abundance exceeds 10,000 cells/mL, when the water level was higher than 230.43 m. The mean algal abundance and mean transparency were 6587 cells/mL and 1.7 m, respectively. In contrast, when the water level drops lower than 230.43 m, there is no algal abundance sample exceeding 10,000 cells/mL. The mean algal abundance was lower (2966 cells/mL) with mean transparency decreased to only 0.8 m. According to the study of Liu et al. (2016) [15], it is beneficial to increase the chlorophyll a concentration and algal abundance at a higher water level. Carlson (1991) [36] and Jin et al. (2021) [37] also demonstrated that inorganic particles (and sediment resuspension or suspended solids) have a negative impact on algal biomass. Smith (1990) [38] indicated that the growth of blue-green algae in reservoirs is reduced in the presence of high concentrations of non-algal turbidity. In turbid reservoirs, phosphorus is clearly adsorbed on these non-algal particles, and algal chlorophyll may also be very low, so turbid reservoirs are often mistakenly classified as belonging to the eutrophication level (Jones & Hubbart, 2011) [34]. Wang (1974) [39] also demonstrated that high turbidity conditions retard algal growth, which is a light-inhibition effect. According to the literature and the results of this study, they prove that transparency and CTSI may be slightly affected by algal proliferation in Shihmen Reservoir even when algal abundance raises at a higher water level.

4. Conclusions

In recent years, global climate change has led to a significant increase in the frequency of heavy rainfall and droughts. This makes changes in the reservoir trophic state and water quality a matter of great concern for governments and the public. This study analyzed the monthly water level and water quality data collected from Shihmen Reservoir from 2011 to 2021 to understand the temporal and spatial variations in CTSI and water quality. The results could serve as a reference for the reservoir management in formulating operational and management strategies.
The analysis revealed that the period of April and May is, regarding the water level, the lowest of Shihmen Reservoir. During this time, both the CTSI and several other water quality parameters reach their peak values, while water transparency and WQI are at their lowest throughout the year. However, all of mean WQI belongs to good criteria and is suitable for all-purpose use. Meanwhile, although the mean CTSI in each season was highly correlated with the mean algal abundance (Figure 4b), it was not statistically significant between seasons (Figure 3e). This suggests that the increase in CTSI values may not only be due to algal proliferation but rather the result of increased turbidity caused by disturbances when the water level is low in April and May. Based on the experience of the research team, water transparency could be affected when turbidity exceeds 5 NTU.
Regarding the spatial analysis of reservoir water quality, the study found that the surrounding environment of each monitoring station, such as inflows from tributaries, did not cause differences in water quality between the monitoring stations. Instead, the water depth of each monitoring station location seemed to be one of the important factors influencing CTSI and other water quality parameters’ variation. Furthermore, the upstream monitoring stations showed poorer water quality compared to the downstream stations and this difference was statistically significant. This may have resulted from a shallow water depth at upstream monitoring stations.
As the water level is an important and easily accessible parameter in reservoir operation and management, it can also reflect variations in water depth. According to the regression analysis of water level and CTSI variations, when the water level of Shihmen Reservoir drops below 230.43 m, there is a significant increase in the proportion of the eutrophic status (CTSI > 50). Moreover, water quality parameters at this low water level period such as pH, EC, DO, transparency, SS, turbidity, TOC, and algal abundance show statistically significant differences compared to water quality at a higher water level. Therefore, the reservoir management administration, WRANB, can consider a water level of 230.43 m as a reference threshold for controlling the trophic state. For example, under properly hydrological conditions, Shihmen Reservoir has mostly adopted a supercharge storage operation to keep a higher water level as much as possible for maintaining CTSI. This is quite consistent with the conclusion of Naselli-Flores and Barone (2005) [40]. They indicated that improving water quality in a reservoir could be achieved with two ways, to stop water drawdown above a threshold or to increase the capacity of the reservoir.
In conclusion, the water level is not only an important and easy to obtain operation parameter in Shihmen Reservoir but can also be an indicator for reflecting the water depth and water quality parameter variation of lakes, ponds, and reservoirs around the world. The management administration of these water bodies should formulate the water level management threshold and try to maintain the water level above the threshold, so as to reduce the probability of eutrophication.

Author Contributions

Conceptualization, W.L., H.C. and T.C.; investigation, M.P. and H.C.; data curation, H.C., M.P. and T.C.; writing—original draft preparation, T.C.; writing—review and editing, W.L. and T.C.; project administration, W.L., M.P. and T.C.; funding acquisition, T.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Northern Region Water Resources Office of Water Resources Agency (WRANB), MOEA, grant numbers: WRANB 100PM01, 101C14, 102C11, 102C55, 102C55-1, 102C55-2, 105C30, 105C30-2, 107C43, 107C43-2, 109C52 and 109C52-1.

Data Availability Statement

Restrictions apply to the availability of these data. Data were obtained from research project reports and with the permission of the WRANB, MOEA.

Conflicts of Interest

The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Figure 1. Map of the study area and 5 water quality monitoring station locations in Shihmen Reservoir. (The blue area shows storage area of the Shimen Reservoir, and the yellow dots represent the locations of water quality monitoring stations).
Figure 1. Map of the study area and 5 water quality monitoring station locations in Shihmen Reservoir. (The blue area shows storage area of the Shimen Reservoir, and the yellow dots represent the locations of water quality monitoring stations).
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Figure 2. Monthly variations of mean CTSI and other water quality parameters with the water level in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) EC, (c) NH4+-N, (d) SS. (J-11 represents January in 2011).
Figure 2. Monthly variations of mean CTSI and other water quality parameters with the water level in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) EC, (c) NH4+-N, (d) SS. (J-11 represents January in 2011).
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Figure 3. Variations of mean CTSI and other water quality parameters with the water level in each month in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) TP, (c) Chl-a, (d) transparency, (e) algal abundance (seasonal), (f) EC, (g) TOC, (h) turbidity, (i) NH4+-N, (j) NO3−-N, (k) SS, (l) WQI.
Figure 3. Variations of mean CTSI and other water quality parameters with the water level in each month in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) TP, (c) Chl-a, (d) transparency, (e) algal abundance (seasonal), (f) EC, (g) TOC, (h) turbidity, (i) NH4+-N, (j) NO3−-N, (k) SS, (l) WQI.
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Figure 4. The TSI deviation graph (a) and correlation of mean algae abundance and mean CTSI (b) of all Shihmen Reservoir data during 2011 to 2021.
Figure 4. The TSI deviation graph (a) and correlation of mean algae abundance and mean CTSI (b) of all Shihmen Reservoir data during 2011 to 2021.
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Figure 5. Variations of mean CTSI and other water quality parameters with water depth at the 5 monitoring stations in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) TP, (c) Chl-a, (d) transparency, (e) algal abundance, (f) EC, (g) TOC, (h) turbidity, (i) NH4+-N, (j) NO3−-N, (k) SS, (l) WQI.
Figure 5. Variations of mean CTSI and other water quality parameters with water depth at the 5 monitoring stations in Shihmen Reservoir from 2011 to 2021; (a) CTSI, (b) TP, (c) Chl-a, (d) transparency, (e) algal abundance, (f) EC, (g) TOC, (h) turbidity, (i) NH4+-N, (j) NO3−-N, (k) SS, (l) WQI.
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Figure 6. Regression analysis of mean CTSI with WLF in 5 monitoring stations of Shihmen Reservoir from 2011 to 2021. (○ represents data without algae abundance data; ● represents data with algae cells no. under 10,000 cells/mL; ● represents data algae cells no. between 10,000 ~ 20,000 cells/mL; ● represents data with algae cells no. above 20,000 cells/mL).
Figure 6. Regression analysis of mean CTSI with WLF in 5 monitoring stations of Shihmen Reservoir from 2011 to 2021. (○ represents data without algae abundance data; ● represents data with algae cells no. under 10,000 cells/mL; ● represents data algae cells no. between 10,000 ~ 20,000 cells/mL; ● represents data with algae cells no. above 20,000 cells/mL).
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Figure 7. Regression analysis of mean CTSI with WLF in Shihmen Reservoir from 2011 to 2021.
Figure 7. Regression analysis of mean CTSI with WLF in Shihmen Reservoir from 2011 to 2021.
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Figure 8. Relationship of mean CTSI and (a) algal abundance and (b) transparency with WLF in Shihmen Reservoir from 2011 to 2021.
Figure 8. Relationship of mean CTSI and (a) algal abundance and (b) transparency with WLF in Shihmen Reservoir from 2011 to 2021.
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Table 1. Descriptions corresponding to the use of water quality index (WQI) and trophic state index (TSI). El-Serehy et al. (2018) [26].
Table 1. Descriptions corresponding to the use of water quality index (WQI) and trophic state index (TSI). El-Serehy et al. (2018) [26].
Water QualityWQITSI
RankDescriptorRankDescriptor
Excellent91–100Eminently usable for all purposes<40Oligotrophic
Good71–90Suitable for all uses40–50Mesotrophic
Intermediate51–70Main use and/or some uses may be jeopardized50–60Eutrophic
Bad25–50Unsuitable for main and/or several uses60–80Eutrophic
Very Bad0–25Totally unsuitable for main and/or many uses>80Eutrophic
Table 2. Characteristic description of 5 water quality monitoring stations.
Table 2. Characteristic description of 5 water quality monitoring stations.
Monitoring StationStation Characteristic Description
Chang Xin (CX)
24°48′18″ N; 121°18′48″ E		The main entrance of the reservoir storage area (the middle of the river section)
Amu Plain (AP)
24°48′53″ N; 121°18′36″ E		The entrance of Sanmin Creek, a tributary of the reservoir. The upstream is agricultural and recreational areas (right bank of the reservoir storage area)
Dragon Ball Bay (DBB)
24°49′06″ N; 121°17′09″ E		The entrance of Nanzigou Creek, a tributary of the reservoir. The upstream is an agricultural and recreational area (right bank of the reservoir storage area)
Xian Island (XI)
24°48′20″ N; 121°15′07″ E		Small village area (left bank of the reservoir storage area)
Shihmen Channel Intake (SCI)
24°48′35″ N; 121°14′28″ E		Public water supply intake
Table 3. The water quality parameter statistical analysis results of independent samples t-test and LSD post hoc test with high and low water level in Shihmen Reservoir from 2011 to 2021.
Table 3. The water quality parameter statistical analysis results of independent samples t-test and LSD post hoc test with high and low water level in Shihmen Reservoir from 2011 to 2021.
Water Level
(m)		Water Depth (m)CTSIWQIWater Temp. (°C)EC
(μS/cm)		DO
(mg/L)		pHSS
(mg/L)
High Water Level (>230.43 m) n = 121Mean240.524.747.183.723.62039.18.574.3
SD4.44.24.36.74.9200.80.4510.3
Low Water Level (<230.43 m) n = 30Mean225.014.752.683.822.52528.08.1719.9
SD5.03.15.74.13.9191.20.3517.6
Significance (p-value)0.000 *0.000 *0.000 *0.9690.2030.000 *0.000 *0.000 *0.000 *
TP
(mg/L)		Turbidity (NTU)Trans-parency (m)NH4+-N
(mg/L) 		NO3−-N
(mg/L)		Chl-a
(μg/L)		TOC
(mg/L)		E. Coli.
(CFU/100 mL)		Algae no.
(cells)
High Water Level (>230.43 m) n = 121Mean0.0185.51.70.0500.2584.680.8852456587
SD0.00915.00.60.0610.1252.620.4866645810
Low Water Level (<230.43 m) n = 30Mean0.02225.20.80.0780.2584.990.6432622966
SD0.01323.60.50.0930.0973.300.1763032560
Significance (p-value)0.0790.000 *0.000 *0.1180.9820.5870.000 *0.8900.011 *
Note: Bold italics with a superscript “*” represent a statistically significant difference (p-value < 0.05).
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Liao, W.; Chen, H.; Peng, M.; Chang, T. Spatio-Temporal Variation of Trophic Status and Water Quality with Water Level Fluctuation in a Reservoir. Water 2023, 15, 3154. https://doi.org/10.3390/w15173154

AMA Style

Liao W, Chen H, Peng M, Chang T. Spatio-Temporal Variation of Trophic Status and Water Quality with Water Level Fluctuation in a Reservoir. Water. 2023; 15(17):3154. https://doi.org/10.3390/w15173154

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Liao, Wenwen, Hsinan Chen, Meijeng Peng, and Tawei Chang. 2023. "Spatio-Temporal Variation of Trophic Status and Water Quality with Water Level Fluctuation in a Reservoir" Water 15, no. 17: 3154. https://doi.org/10.3390/w15173154

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