Gonghu Bay, the northeastern part of Lake Taihu, is the main urban drinking water source for Wuxi city of the Taihu Basin and also the first water-receiving area connecting the main inflow channel of the Wangyu River and the central lake of Lake Taihu (Figure 1). It covers about 150 km² and has a multi-year average water depth of 1.8 m (Zhong et al., 2012). Due to the frequent serious cyanobacterial blooms in the north part of Lake Taihu from May to December in a year, Gonghu Bay is always threatened by Microcystis scums (Dai et al., 2018, 2020). According to the Technical Specifications Requirements for Environmental Monitoring of Surface Water and Waste Water (HJ/T 91-2002) issued by the Ministry of Ecology and Environment of P.R. China, we distributed the monitoring sites in a uniform grid with a 2.50 km distance among two sites in Gonghu Bay. We also laid three monitoring sites in Wangyu River and the central lake of Lake Taihu, respectively, (Figure 1). In Gonghu Bay, there were three site lines from the river mouth of Wangyu River to the central lake, of which two were along the west and east sides of Gonghu Bay, respectively. The other was of the central line of Gonghu Bay. Each line had five sites.

Since WDYT is always operated according to the water level of Lake Taihu (TBA, 2013). Once the water level of Lake Taihu is lower than the control water level for water supply which is ruled by the Taihu Basin Authority (TBA), the hydro-junctions in the Wangyu River will open to transfer Yangtze River water to Lake Taihu. Additionally, if Lake Taihu faces serious cyanobacterial blooms, the WDYT will also be urgently operated to relieve bloom disasters (Qin et al., 2010). Due to this reason, the WDYT has been regularly operated during the dry period in Taihu Basin from November to February year by year (TBA, 2013, 2014). In this study, we constantly monitored in situ on the middle days of July, August, November, and January from 2013 to 2014. Among these sampling days, the days of November and January in 2013 and July in 2014 were in the period without water diversion, while the days in August of 2013 and November and January in 2014 were all in the water diversion period.

About a 2-L water sample (about 50 cm under the water surface) for each site was collected using the column water sampler. One liter was stored in a clean plastic bottle and used for measuring the physicochemical parameters in the laboratory in 24 h. Another 1-L water sample for each site was stored in a sterile plastic bottle and fixed with the 15 ml of 5% Lugol’s iodine reagent (Jin and Tu, 1990), which was transported to the laboratory for phytoplankton community identification.

Water temperature, pH, turbidity, and dissolved oxygen content were detected in situ using the Portable Multi-parameters Detection HQ30d (HACH, Shanghai, China) kit. The contents of total nitrogen (TN), total phosphorus (TP), ammonia (NH₃-N), nitrate (NO₃-N), soluble reactive phosphorus (SRP), permanganate index (COD_Mn), chlorophyll a (Chl a), and dissolved silicate (SiO₃-Si) were all measured in the laboratory according to the literature methods (Jin and Tu, 1990).

After over 24 h of standing, the 1-L Lugol’s fixed water sample for each site was concentrated to 50 ml in a sterile glass bottle using a separating funnel. Then, 0.1 ml of the concentrated sample was injected into the algae counting box and put in the Axiovert 200 (CARL ZEISS, Germany) optical microscope to uniformly capture 100 random views. This procedure was repeated three times for one sample. The cell abundances of phytoplankton species were identified according to the literature (Hu and Wei, 2006).

The investigated physicochemical parameters were differently sensitive to the water diversion in the three seasons (Figure 3). In the three seasons during 2013–2014, there were significant differences in the mean values of water temperature, DO, pH, Chl a, TN, NO₃-N, and TP in Gonghu Bay between the water diversion and non-diversion days. In some of the three seasons, significant differences were also found in the mean values of turbidity, COD_Mn, NH₃-N, SRP, and SiO₃-Si. Due to the lower contents of DO and pH (Figure 3) and the higher values of water temperature (Figure 3), TN, NH₃-N, NO₃-N, TP, and SiO₃-Si (Figure 3) in the Wangyu River on some of the water diversion days, the contents of water temperature, DO, pH, TN, NH₃-N, NO₃-N, TP, and SiO₃-Si were evidently different from those on the non-diversion days in the same season. Since the contents of turbidity in the Wangyu River and central lake were always higher than those in Gonghu Bay, this parameter was not sensitive to the water diversions in different seasons (Figure 3). The average contents of COD_Mn and Chl a in Gonghu Bay were more influenced by the growth of phytoplankton in Gonghu Bay, but the average content of Chl a was also lower on the water diversion days than that on the non-diversion days in autumn (Figure 3). The contents of SRP were also not sensitive to the water diversions in different seasons (Figure 3).

The collinearity analysis of the 12 monitored parameters on all six sampling days showed there were pronounced correlations among some of the physicochemical parameters (Figure 4). Based on the collinearities among the physicochemical parameters, seven physicochemical parameter groups were classified with the Pearson coefficient of 0.30: group A, COD_Mn, Chl a, and pH; group B, turbidity; group C: water temperature and TP; group D, DO; group E, TN and NO₃-N; group F, SiO₃-Si; group G, NH₃-N, and SRP (Figure 5). However, if selecting the higher Pearson coefficients, we could get more groups. Combining with the results of mean value comparisons and the convenience in data acquisition, seven physicochemical parameters, including water temperature, pH, DO, TN, TP, Chl a, and SiO₃-Si, were selected to be the sensitive physicochemical parameters used for sites distribution optimization and daily water quality monitoring. The selected parameters represent different physicochemical features of the lake water.

Water temperature, reflecting seasonal and climate changes, is the basic physical parameter influencing characteristics of hydrodynamics, circulation of substance and energy, and life processes in lakes (O'Reilly et al., 2003; Elçi, 2008; Gudasz et al., 2010). The pH value is the comprehensive physicochemical index which reflects the dynamics of hydrogen ion determined by the contents of carbon dioxide, bicarbonate ion, and carbonate in water (Charlson and Rodhe, 1982). Due to the normally lower pH values in rivers than in lakes (Feng et al., 2017), the pH value which is always increased by the cyanobacterial blooms (Cao et al., 2016) in the water-receiving lake regions were decreased significantly. Dissolved oxygen is also a regular and critical environmental monitoring parameter which is easily obtained in situ using automatic monitoring equipment. The Chl a index represents not only the primary production but also the level of cyanobacterial blooms. Additionally, N, P, and Si are the basic elements of natural waters, and the nutrient parameters are also the normal monitoring indexes for drinking water sources (Wu et al., 2017). Finally, the selected physicochemical parameters not only reflected the sensitive characteristics of the physicochemical habitat in the water-receiving lake region, but also covered the most important monitoring water quality indexes for the drinking water sources.

The NMDS results based on the Euclidean distance matrices of the sensitive physicochemical parameters from 2013 to 2014 showed the pronounced differences among sites, with the gradient from the inflow river mouth of Gonghu Bay to the adjacent region between Gonghu Bay and the lake center on the water diversion days (Figure 6). The classification of sampling sites was different with different distance thresholds. Along with the increase of Euclidean distance, the number of site clusters decreased. At the 1.0 level of Euclidean distance, most of the sampling sites differed from each other on each water diversion day, while at the 5.0 level of Euclidean distance, only two site clusters were classified on the three water diversion days. Totally, at the Euclidean distance range from 3.0 to 4.0, three site groups were found in the inflow river mouth of Gonghu Bay, bay center, and the adjacent region between Gonghu Bay and the lake center during the water diversion periods, respectively (Figure 6). The Euclidean distance is the most popular dissimilarity index for measuring the distances among different environmental matrices (Hadjisolomou et al., 2018). In this current study, the classification result at the Euclidean distance range from 3.0 to 4.0 could reflect the heterogeneity in the spatial effect induced by water diversion. Therefore, based on the environmental matrices of sensitive physicochemical parameters, the sites distribution could be optimized which could also reflect the impacts of water diversion on the drinking water sources in lakes.

The NMDS on the Bray-Curtis similarity matrices of the phytoplankton communities showed obvious spatial heterogeneity in phytoplankton community structures on the three water diversion days of 2013–2014 (Figure 7). At the similarity level of 80%, the phytoplankton community in different sites was significantly different. The ecological structure of Gonghu Bay is complicate with the algae-dominated west region and the macrophyte-dominated east region (Gao et al., 2017). Moreover, the lake currents of some water regions in Gonghu Bay are sensitive to the disturbance induced by wind and water diversion activities (Lv, 2013). Therefore, the phytoplankton habitat in Gonghu Bay presented obvious spatial heterogeneity, which might be an important reason for the significant differences in phytoplankton communities among different sites at the similarity of 80%. However, from 60 to 40% of the similarity levels, most of the adjacent sites were grouped together and shaped several site groups in the inflow river mouth of Gonghu Bay, center, and the adjacent region between Gonghu Bay and the central lake during the water diversion periods, respectively, (Figure 7). Consistent with the results of the NMDS analysis on sensitive physicochemical properties of water samples, the spatial distribution of the phytoplankton communities in Gonghu Bay during the water diversion days was similar. The similar distribution pattern for both the phytoplankton communities and their physicochemical habitats was demonstrated by some previous studies (Li et al., 2019; Kutlu et al., 2020). In this study, the thresholds for the cluster indices were determined to optimize the sampling sites distribution, which was alternative according to the monitoring requirements.

In view of the above NMDS analysis results, taking into account the spatial distribution of monitoring sites and ecological functions of monitoring areas, the optimization of monitoring sites for the ecological effects of the water diversion project was optimized. The representative monitoring sites that can reflect the ecological effects of water diversion in Gonghu Bay were initially selected. Among them, the distance between every point in Gonghu Bay was optimized from the original 2.5–5 km.

As the only monitoring point of the Wangyu River, site R3 should be preserved to represent the characteristics of inflow water. As the most sensitive water area to water diversion activities, site A1 that linked the Wangyu River inlet and Gonghu Bay is also preserved. During the periods of water diversion, the physicochemical and biological elements of Gonghu Bay showed an evident spatial gradient along the Wangyu River inlet to the mouth of Gonghu Bay, so three monitoring sites (A2, C2, and E2) are preserved along the center line of Gonghu Bay. As a reference water area, lake center regions retained the original L3 monitoring site. The east and west regions of Gonghu Bay are two different types of ecosystems, and the southeast monsoon has a greater impact on Lake Taihu in the summer. Therefore, the monitoring sites (C1 and C3) were preserved for the east and west regions, respectively (Figure 8).

From the monitoring costs perspective, the sites layout scheme could choose the optimizing results based on the Euclidean distance of 3.0–4.0 or the Bray-Curtis similarity from 40 to 60%. This scheme divided Gonghu Bay into three water regions with the distance gradient far from the inflow river mouth. In each of the three regions, one representative site could be located. However, if focusing on more details of each region, the standards with the Euclidean distance lower than 2.0 and the Bray-Curtis similarity higher than 60% should be considered. At the same time, the marginal effect of the biological diversity of the shoreline ecosystem in lakes and the impact of wind directions in different seasons in Lake Taihu should be taken into consideration. The most representative monitoring sites in the monitored area should be selected in different seasons.