Hydrodynamic and environmental characteristics of a tributary bay influenced by backwater jacking and intrusions from a main reservoir

The water depth and flow velocity of a river may be temporarily disturbed by the water level fluctuations connected with the operation of artificial dam reservoirs located downstream (so-called backwater fluctuations (BF)). In this research, we use the two-dimensional hydrodynamic model iRIC MFlow_02 to quantify the effects of BF on the lowermost section (ca. 1.5-km length) of a small (channel width ≤ 20 m) mountain stream, the Smolnik Stream, which flows into the Rożnów Dam Reservoir, in Southern Poland. To reproduce the hydrological conditions generally observed in the stream, six scenarios were simulated, considering three steady flow discharges at the inlet, with recurrence intervals of 1 year (1.8 m3 s−1, small flood), 2 years (24.5 m3 s−1; medium flood), and 20 years (89.5 m3 s−1; large flood), and two reservoir levels at the outlet: 265 m a.s.l. (normal reservoir water level) and 270 m a.s.l. (maximum reservoir water level). In these simulations, sediment transport and morphodynamics are not considered. The average modelled water depth was increased by backwater fluctuation in the channel, from 28% (from 1.62 m vs. 2.07 m) during a small flood, up to 59% (from 3.46 m to 5.50 m) during a large flood. Contrastingly, the average modelled flow velocity was decreased in the channel (from −8% 0.52 m s−1 vs. 0.47 m s−1) during a small flood to −78% (0.49 m s−1 vs. 0.11 m s−1) during a large flood. Our results demonstrate that backwater fluctuations substantially disturb the hydrodynamics of the studied stream, which is interpreted as a triggering factor of the previously documented alterations in its sedimentology, morphology, and riparian vegetation pattern.

Section: Discussionsupporting

confidence: 91%

Modelling the Effects of Dam Reservoir Backwater Fluctuations on the Hydrodynamics of a Small Mountain Stream

Liro

Nones

Mikuś

et al. 2022

“…In recent years, a large number of reservoirs and dams have been constructed to satisfy the need of human society for an adequate water supply, flood control, efficient shipping, agricultural irrigation and hydroelectric generation; eventually, water resource utilization became more efficient, water allocation and shortage were addressed and huge socioeconomic benefits were attained [33,34]. The environment of the water in a reservoir is affected directly by the river that flows into it, which is one of the major pollutant inputs to the reservoir.…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Distribution of Phytoplankton and Role of Environment Factors in the Shending River Backwater in the Danjiangkou Reservoir Area

Wang,

Luo,

Mei

et al. 2024

The Danjiangkou Reservoir supplies drinking water to most residents in northern China. However, signs of eutrophication have been observed in the inlet tributaries of the reservoir, including the Shending River backwater. This research used data from the Sentinel-2 Multispectral instrument and findings from a 2021 aquatic ecological survey to analyze the spatial and temporal characteristics of phytoplankton distribution in the Shending River backwater region. The average chlorophyll a (Chl-a) concentrations by season, ranked from largest to smallest, are as follows: summer (63.96 μg/L) > autumn (41.26 μg/L) > spring (27.47 μg/L) > winter (16.21 μg/L); the upstream of the backwater area and the near-shore tributary bay had relatively higher Chl-a concentration. Bacillariophyceae (Cyclotella meneghiniana and Synedra sp.) and Cryptophyceae species (Chroomonas acuta) were dominant in spring, whereas Chlorophyceae (Scenedesmus sp. and Chlorella vulgaris) and Cyanophyceae (Dactylococcopsis acicularis, Microcystis aeruginosa and Oscillatoria tenuis) species were dominant in summer. The seasonal succession characteristics of the phytoplankton community were consistent with those of the Plankton Ecology Group model. The average phytoplankton cell density was 4.80 × 107 cells/L, and the Shannon–Wiener average diversity index was 1.95, indicating that the Shending River backwater area was moderately eutrophic. According to Pearson correlation analysis and Mantel test, the main factors causing temporal and spatial differences in phytoplankton production in the Shending River’s backwater were water level (WL), water temperature (WT), ammonia nitrogen (NH3-N) and total nitrogen (TN). In particular, WL was significantly positively correlated with Bacillariophyceae, Chlorophyceae and Cyanophyceae, whereas WT was significantly correlated with Cryptophyceae and Chlorophyceae. NH3-N and TN were significantly correlated with Cyanophyceae. Therefore, intensive nitrogen removal from the tailwater of sewage treatment plants may be considered a feasible measure to prevent cyanobacterial bloom in the Shending River backwater of the Danjiangkou Reservoir.

“…Therefore, many hydropower stations use the combination of the physics-driven model and the statistical method in practical application to improve the calculation accuracy and efficiency. Through statistical calculations of historical operation data, the empirical curve describing the correlation of variables is summarized to calculate the variables that cannot be accurately described by the equation, such as the water level-storage capacity curve and the TWL-outbound flow rate curve [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tail Water Level under the Influence of Backwater Effect Based on Deep Learning Models: A Case Study in the Xiangjiaba Hydropower Station

Zhang,

Xie,

Wang

et al. 2023

Accurate forecasting of the tail water level (TWL) is of great importance for the safe and economic operation of hydropower stations. The prediction accuracy is significantly influenced by the backwater effect of downstream tributaries and the operation of adjacent hydropower stations, but the explicit quantification method of the backwater effect is lacking. In this study, a deep-learning-model-based forecasting method for TWL predictions under the backwater effect is developed and applied in the Xiangjiaba (XJB) hydropower station, which is influenced by the backwater effect of downstream tributaries, including the Hengjiang River (HJR) and the Minjiang River (MJR). Firstly, the random forest algorithm was used to analyze the influence of HJR and MJR flows with different lag times on the TWL prediction error of the XJB hydropower station. The results show that the time lags of the backwater effect of HJR and MJR run offs on the TWL of the XJB are 5~7 h and 1~2 h, respectively. Then, the run off thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained through scenario comparison, and the results show that the run off thresholds of the HJR and the MJR are 700 m3/s and 7000 m3/s, respectively. Finally, based on the analysis of the time lag and the threshold of the backwater effect, a deep learning model (LSTM)-based TWL forecasting method is established and applied to predict the TWL of the XJB station. The results show that the forecasting model has a good predictive performance, with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm, and the maximum absolute error is 63.35 cm. Compared with the LSTM-based prediction model without considering the backwater effect, the mean absolute error decreased by 31%, and the maximum absolute error decreased by 71%.