Seasonal variation of mixing depth and its influence on phytoplankton dynamics in the Zeya reservoir, China

Abstract

In reservoirs or lakes, mixing depth affects growth and loss rates of phytoplankton populations. Based on 1-year data from the Zeya reservoir, China, we scaled the mixing depth throughout a whole year by utilizing cluster analysis, and then investigated its influence on phytoplankton dynamics and other physical and chemical parameters. Over the whole year, all physical and chemical parameters except TN and temperature had significant correlations with mixing depth, indicating that mixing depth is one of the important driving factors influencing water environment. According to mixing depth, a year can be divided into three different periods, including the thermally stratified period, isothermally mixed period, and transition period between them. When considering the former two different periods separately, mixing depth had no correlation with the phytoplankton biovolume. However, over the whole year a significant correlation was observed, which indicated that the influence of mixing depth on phytoplankton growth in the Zeya reservoir still followed Diehl's theory. Furthermore, according to the steady-state assumption, a unimodal curve (mixing depthphytoplankton biovolume) with a significant peak appearing at a mixing depth of 2 m was observed, closely agreeing with Diehl' prediction.

Thermal structure and response to long‐term climatic changes in Lake Qiandaohu, a deep subtropical reservoir in China

“…2d-e). Lake Qiandaohu was stratified for most of the year, with only a short mixing period in winter, which is consistent with many similar subtropical or tropical reservoirs (Chen et al 2009;Wang et al 2012). Therefore, Lake Qiandaohu is a warm monomictic water body.…”

“…A long-term scale study from the meta-analysis of over 200 paleolimnological records from Northern Hemisphere lakes showed a strong correlation between long-term increases in air temperature and ice-out records and compositional changes in diatom communities (Rü hland et al 2008). In addition, the decrease in mixing depth, resulting from increased stratification, is the causal factor for the spring algal bloom in certain reservoirs (Berger et al 2006;Chen et al 2009). Therefore, further studies should be carried out to investigate the effects of the thermal structure of Lake Qiandaohu on water-quality parameters, including dissolved oxygen, nutrients, and suspended solid concentrations, and the phytoplankton and zooplankton communities.…”

“…These density gradients are often observed as a region of sharp changes in water temperature (metalimnion) that delineate an upper well-mixed region (epilimnion) from a relatively quiescent deep zone (hypolimnion). This vertical partitioning of the water column has important implications for the availability of dissolved oxygen, nutrients, light, and microbial substrates (Becker et al 2009;Wang et al 2012), as well as the seasonal dynamics, vertical distribution, and migration of phytoplankton and zooplankton (Chen et al 2009;Becker et al 2010;Cantin et al 2011), and the feeding behavior of higher-trophic-level organisms such as zooplankton and fish (Cantin et al 2011). Density stratification suppresses vertical transfer between surface and bottom waters and often results in a nutrient-poor, lightrich epilimnion that contrasts with a nutrient-rich, lightlimited hypolimnion (Macintyre et al 1999).…”

Biological Assessment of Water Quality: Ecological Status of Three Aegean Reservoirs Based on Phytoplankton Metrics

“…With regard to nutrients, the TP values which ranged from 105.8 µg L -1 to 203.5 µg L -1 at studied reservoirs were higher than those the mean TP reported by Marchetto et al (2009) for deep Reservoirs in Italy such as Cedrino, Cuga, Sos Canales, Pattada and Temo Reservoirs during the year 2006 and Valparáıso Reservoir (Negro et al, 2000) Spain. The values of TN ranged from 681.8 µg L -1 to 1012.0 µg L -1 were too higher than the mean TN reported by Chen et al (2009) for Zeya reservoir in China. These high nutrients coming from especially anthropogenic activities such as agriculture, sewage discharge in surrounding areas and excessive net-cage fish farming in reservoir mainly impacted the reservoirs (Wetzel 2001).…”

Using Clustering Analysis to Predict Outflowing Water Quality of Selectively Drawn Water from Water Sources and Reservoirs

“…Currently, the cluster analysis method has been used to study morphological characteristics of the water environment both at home and abroad. The studies carried out by Smolinski et al described the application of hierarchical cluster analysis method in the environmental system in detail [3]; Xuechu Chen et al analyzed the reservoir vertical temperature distribution using the hierarchical clustering method and calculated the depth of the mixed reservoir layer accurately [4];Jin Lu classified the evaluation water bodies of 15 lakes and reservoirs into four ecological types using the cluster analysis [5];Laimin Zhu et al divided the water masses in the South China Sea vertically into five types using the Q-type clustering analysis, and verified it further using the thermohaline point poly graph. The conclusions were completely consistent with one another [6].…”