Intelligent Simulation of Water Temperature Stratification in the Reservoir.

In order to fully make use of limited water resources, humans have built many water conservancy projects. The projects produce many economic benefits, but they also change the natural environment. For example, the phenomenon of water temperature stratification often occurs in deep reservoirs. Thus, effective ways are needed to predict the water temperature stratification in a reservoir to control its discharge water temperature. Empirical formula methods have low computational accuracy if few factors are considered. Mathematical model methods rely on large amounts of accurate hydrological data and cost long calculation times. The purpose of the research was to simulate water temperature stratification in a reservoir by constructing an intelligent simulation model (ISM-RWTS) with five inputs and one output, determined on the basis of artificial neural networks (ANN). A 3D numerical model (3DNM) was also constructed to provide training samples for the ISM-RWTS and be used to test its simulation effect. The ISM-RWTS was applied to the Tankeng Reservoir, located in the Zhejiang province of China, and performed well, with an average error of 0.72 °C. Additionally, the Intelligent Computation Model of Reservoir Water Temperature Stratification (ICM-RWTS) was also discussed in this paper. The results indicated that the intelligent method was a powerful tool to estimate the water temperature stratification in a deep reservoir. Finally, it was concluded that the advantages of the intelligent method lay in its simplicity of use, its lower demand for hydrological data, its well generalized performance, and its flexibility for considering different input and output parameters.

[Distribution Characteristics of Water Temperature and Water Quality of Fuxian Lake During Thermal Stratification Period in Summer].

In order to explore the distribution characteristics and change rule in daytime of the water temperature and water quality of the deep plateau lake Fuxian Lake during the thermal stratification period in summer, a typical point location was selected respectively in the south, middle and north of Fuxian Lake in July 2014 to carry out investigation and observation after stratified sampling at each point location and continuous stratified sampling of the point location in the north during daytime. The result showed that ①The distribution of water temperature of Fuxian Lake in summer had the temperature distribution characteristics of deep lake during stratification period. The segment from the surface to a depth of 15 meters was epilimnion with a range of 25.51-22.81℃. The segment from the depth of 15m to 40m was thermocline with a range of 22.81-14.72℃. The segment below 40m was hypolimnion with a range of 14.72-13.70℃. The max temperature difference between the surface and the lakebed was 11.8℃, which was smaller than that of lakes in temperate zone during the same period. The temperature of the lakebed was about 14℃ which was higher than that of the lakes in temperate zone,which reflected the characteristics of water temperature stratification of the deep plateau lake Fuxian Lake; ②The water temperature stratification determined the characteristics of chemical stratification and the ecological stratification:the pH, DO and conductivity presented the same layered structure as the distribution of water temperature. What is noteworthy was that the DO concentration of the lakebed was as low as 2-3mg·L-1. As an oligotrophic lake, the DO became lower and lower at the lakebed of Fuxian Lake, which indicated that it possibly faced ecological risks. Due to the separation of thermocline, the nutritive salt accumulative effect appeared in the hypolimnion. Chlorophyll a and permanganate index had a corresponding response relation with the water temperature stratification and showed the max value at the upper layer of the lake. ③During the thermal stratification period, the water temperature stratification of Fuxian Lake had a change during daytime. The increased thermal radiation in the noon caused the thermocline to dive, the strength to increase and the depth to narrow, which significantly influenced the dynamic distribution of pH, DO, conductivity and chlorophyll a of the epilimnion and the thermocline. The change rule of TP, TN and permanganate index during the daytime was not significant.

Analysis of numerical simulations and influencing factors of seasonal manganese pollution in reservoirs.

Seasonal manganese pollution has become an increasingly pressing water quality issue for water supply reservoirs in recent years. Manganese is a redox-sensitive element and is released from sediment under anoxic conditions near the sediment-water interface during summer and autumn, when water temperature stratification occurs. The reservoir water temperature and water dynamic conditions directly influence the formation of manganese pollution. Numerical models are useful tools to quantitatively evaluate manganese pollution and its influencing factors. This paper presents a reservoir manganese pollution model by adding a manganese biogeochemical module to a water quality model-CE-QUAL-W2. The model is applied to the Wangjuan reservoir (Qingdao, China), which experiences manganese pollution during summer and autumn. Field data are used to verify the model, and the results show that the model can reproduce the main features of the thermal stratification and manganese distribution. The model is used to evaluate the manganese pollution process and its four influencing factors, including air temperature, water level, wind speed, and wind directions, through different simulation scenarios. The results show that all four factors can influence manganese pollution. High air temperature, high water level, and low wind speed aggravate manganese pollution, while low air temperature, low water level, and high wind speed reduce manganese pollution. Wind that travels in the opposite direction of the flow aggravates manganese pollution, while wind in the same direction as the flow reduces manganese pollution. This study provides useful information to improve our understanding of seasonal manganese pollution in reservoirs, which is important for reservoir manganese pollution warnings and control.