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This paper presents a study of the variation of the elevation of Tongguan, which is located in the backwater zone of the Sanmenxia Reservoir, in response to changes in flow runoff. The analysis indicated that the rise of the elevation of Tongguan, which is defined as the stage corresponding to a discharge of 1000 m³/s at Tongguan station, is controlled by the stream energy. A close relationship existed between the elevation of Tongguan and the superimposed stream energy that integrates the current and the preceding years' flow and dam operation conditions. When the flow runoff remains relatively constant and the pool level of the dam has a relatively large range of variations, then the elevation of Tongguan is primarily controlled by the dam operation conditions. On the other hand, if the flow runoff has a relatively large range of variations and the pool level of the dam remains relatively constant, then the elevation of Tongguan is primarily controlled by the flow conditions. These findings are of importance for optimizing the dam operation in order to lower and control the elevation of Tongguan, and therefore to minimize the backwater effect of the dam operation. [ABSTRACT FROM AUTHOR]

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m3, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups. [ABSTRACT FROM AUTHOR]

Background. Zooplankton is an important component of aquatic organisms and has important biological and economical significance in freshwater ecosystems. However, traditional methods that rely on morphology to classify zooplankton require expert taxonomic skills. Moreover, traditional classification methods are time-consuming and labor-intensive, which is not practical for the design of conservation measures and ecological management tools based on zooplankton diversity assessment. Methods. We used DNA metabarcoding technology with two different markers: the nuclear small subunit ribosomal RNA (18S rRNA) and mitochondrial cytochrome c oxidase (COI), to analyze 72 zooplankton samples collected in 4 seasons and 9 locations from the Sanmenxia Reservoir. We investigated seasonal changes in the zooplankton community and their relationship with water environmental factors. Results. A total of 190 species of zooplankton were found, belonging to 12 phyla, 24 classes, 61 orders, 111 families, and 174 genera. Protozoa, especially ciliates, were the most diverse taxa. Richness and relative abundance of zooplankton showed significant seasonal changes. Both alpha and beta diversity showed seasonal trends: the diversity in summer and autumn was higher than that in winter and spring. The zooplankton diversity was most similar in winter and spring. By correlating metabarcoding data and water environmental factors, we proved that water temperature, chemical oxygen demand, total nitrogen and ammoniacal nitrogen were the main environmental factors driving the seasonal changes in zooplankton in the Sanmenxia Reservoir. Water temperature, followed by total nitrogen, were the most influential factors. This study highlights the advantages and some limitations of zooplankton molecular biodiversity assessment using two molecular markers. [ABSTRACT FROM AUTHOR]

The Sanmenxia Dam built in 1960 in the middle reach of the Yellow River has experienced severe sedimentation problems, not only in the reservoir area itself but also extending hundreds of kilometers in the backwater region. Morphological responses in the middle Yellow River and its tributary, the Wei River, upstream of the dam have generally lagged behind the causal changes in upstream and downstream controls. Based on the geometrical characteristics of a wedge-shaped sedimentation body, a method was derived for calculating equilibrium values of the accumulated sedimentation volume in the channel reaches. Combining the method for calculating with rate law models for channel morphological adjustment, a method for calculating the accumulated deposition volumes was derived. The results showed that the proposed method can accurately simulate sedimentation processes in the study reaches during 1960 and 2011, in the response to the integrated impacts of base level fluctuations and changes in incoming water and sediment discharges. The characteristics of the potential adjusted (equilibrium) morphology was analyzed and it is shown that while the nature of the adjusted (or equilibrium state) of the fluvial system varies dynamically with external conditions, it generally acts as the target for the observed channel adjustments, although the actual values of variables defining the fluvial system seldom adjust to their corresponding equilibrium values due to the effects of multiple disruptions, continuously varying water and sediment inputs, and delayed responses in the fluvial system. [ABSTRACT FROM AUTHOR]