3.3 Cluster Analysis and ANOVA Analysis under the time of water
quality parameters
Cluster Analysis (CA) divides the sampling time into clusters depending
on similar characteristics of the water quality indicators, which in
turn makes an essential contribution to the overall analysis of the
water quality later on (Li et al., 2018; Singh et al., 2004; Varol,
2020). In this study, Temporal CA was plotted in a tree diagram based on
the changes in 10 metrics for the three reservoirs from 2019 to 2021
(Fig. S3). and clustered the 12 months at (Dlink / Dmax) × 100
< 15. Interestingly, all three reservoirs were categorized
into three statistically significant clusters, and the three clusters
corresponded closely to the stabilized water level period, rainy season
high flow period, and winter low flow period in Chuzhou City,
respectively. These two phenomena provide strong evidence of the
similarity in the variation of water quality indicators in the three
reservoirs. ANOVA results confirmed significant differences between
clusters. For example, all water quality parameters except SD, BOD,
NH3-N, and TP in the Shahe Reservoir showed significant
differences (P < 0.05) between clusters (Table 3), for
Huanglishu Reservoir, WT, pH, and DO were excluded (Table 4); and
compared to the Shahe Reservoir, the Chengxi Reservoir contains more
NH3-N and TP (Table 5).