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23 pages, 7244 KiB  
Article
The Effect of Dam Break Speed on Flood Evolution in a Downstream Reservoir of a Cascade Reservoir System
by Huajiang Bo, Faxing Zhang, Liyuan Zhang, Xiaolong Zhang and Liang Yin
Water 2024, 16(20), 2993; https://doi.org/10.3390/w16202993 - 20 Oct 2024
Viewed by 956
Abstract
The dam break flood is one of the potential causes of catastrophic events in cascade hydropower hub groups. Investigating the movement patterns of dam break flooding among reservoir groups under different dam break speeds is crucial for flood prevention and emergency response. In [...] Read more.
The dam break flood is one of the potential causes of catastrophic events in cascade hydropower hub groups. Investigating the movement patterns of dam break flooding among reservoir groups under different dam break speeds is crucial for flood prevention and emergency response. In this study, the evolution characteristics of dam break floods were investigated in a cascading reservoir system, focusing on different break speeds of the upstream dam. The results indicate that the dam break speed determines the concavity or convexity of the water level curve changes in the upstream reservoir. Accordingly, dam breaks are classified into three modes: instant dam break, fast dam break, and slow dam break. An approximate critical speed has been identified to differentiate between the fast dam break and slow dam break. Further investigation into the evolution patterns of dam break floods in downstream reservoirs under different break modes was conducted. Correspondingly, the flood peak discharge and peak arrival time of the dam break floods vary differently with break speed under different break modes. Finally, a theoretical analysis for the flood peak discharge at the dam site during gradual dam break at a certain speed was established, which is able to predict the over-dam flood peak discharge in fast and slow dam break modes. This study is based on a combination of laboratory flume experiments and three-dimensional numerical simulations. This study has theoretical significance for the reinforcement of public infrastructure safety and the prevention of natural disasters. Full article
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Figure 1
<p>Experimental setup. (<b>a</b>) Three-dimensional model of the experimental system; (<b>b</b>) locations of water level monitoring points and discharge measurement sections.</p>
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<p>Comparison of water levels along the reservoir at <span class="html-italic">t</span> = 2.0 s after the instantaneous break of the upstream dam under different grid sizes in the z-direction.</p>
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<p>The simulation results of the dam breaking at different times. (<b>a</b>) <span class="html-italic">t</span> = <span class="html-italic">t</span><sub>1</sub>; (<b>b</b>) <span class="html-italic">t</span> = <span class="html-italic">t</span><sub>1</sub> + <tt>∆</tt><span class="html-italic">t</span>.</p>
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<p>Comparison of dam break flood evolution patterns at upstream instantaneous dam breaks (<span class="html-italic">u</span>* ≈ ∞). (<b>a<sub>1</sub></b>) <span class="html-italic">t</span> = 1.2 s (Experimental result); (<b>a<sub>2</sub></b>) <span class="html-italic">t</span> = 1.2 s (Simulation result); (<b>b<sub>1</sub></b>) <span class="html-italic">t</span> = 3.4 s (Experimental result); (<b>b<sub>2</sub></b>) <span class="html-italic">t</span> = 3.4 s (Simulation result); (<b>c<sub>1</sub></b>) <span class="html-italic">t</span> = 13.2 s (Experimental result); (<b>c<sub>2</sub></b>) <span class="html-italic">t</span> = 13.2 s (Simulation result).</p>
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<p>Comparison of water levels at point P<sub>4</sub> for different upstream break speeds: experiment vs. simulation.</p>
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<p>Water level time series at points P<sub>1</sub>–P<sub>4</sub> in the upstream reservoir for different upstream dam break speeds.</p>
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<p>Water level variation curves and corresponding decline rates for three break modes.</p>
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<p>Dam break flood evolution pattern under the instant dam break mode (<span class="html-italic">u</span>* ≈ ∞). (<b>a</b>) Experimental flood evolution patterns at different times; (<b>b</b>) leaping pattern of the dam break flood.</p>
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<p>Dam break flood evolution pattern under the fast dam break mode (<span class="html-italic">u</span>* = 0.0250). (<b>a</b>) Experimental flood evolution patterns at different times; (<b>b</b>) climbing pattern of the dam break flood.</p>
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<p>Dam break flood evolution pattern under the slow dam break mode (<span class="html-italic">u</span>* = 0.0042). (<b>a</b>) Experimental flood evolution patterns at different times; (<b>b</b>) lifting pattern of the dam break flood.</p>
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<p>Dam break flood evolution pattern under the slow dam break mode (<span class="html-italic">u</span>* = 0.0042). (<b>a</b>) Experimental flood evolution patterns at different times; (<b>b</b>) lifting pattern of the dam break flood.</p>
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<p>Flow rate variations over time for different dam break speeds. (<b>a</b>) <span class="html-italic">h</span><sub>d</sub>/<span class="html-italic">h</span><sub>u</sub> = 1.0; (<b>b</b>) <span class="html-italic">h</span><sub>d</sub>/<span class="html-italic">h</span><sub>u</sub> = 0.67; (<b>c</b>) <span class="html-italic">h</span><sub>d</sub>/<span class="html-italic">h</span><sub>u</sub> = 0.33; (<b>d</b>) <span class="html-italic">h</span><sub>d</sub>/<span class="html-italic">h</span><sub>u</sub> = 0.</p>
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<p>Variations in peak flood discharge <span class="html-italic">Q</span><sub>max</sub> and peak arrival time <span class="html-italic">t</span><sub>max</sub> with dam break speed at different cross-sections. (<b>a</b>) Peak flood discharge; (<b>b</b>) Peak arrival time.</p>
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<p>Peak flood discharge varies with water depth ratio (<span class="html-italic">h</span><sub>d</sub>/<span class="html-italic">h</span><sub>u</sub>). (<b>a</b>) CS<sub>1</sub>; (<b>b</b>) CS<sub>2</sub>; (<b>c</b>) CS<sub>3</sub>; (<b>d</b>) CS<sub>4</sub>.</p>
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<p>Variations in peak discharge coefficient <span class="html-italic">q</span> with dam break speed at different cross-sections. (<b>a</b>) CS<sub>1</sub>; (<b>b</b>) CS<sub>2</sub>; (<b>c</b>) CS<sub>3</sub>; (<b>d</b>) CS<sub>4</sub>.</p>
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<p>Variations in peak arrival coefficient <span class="html-italic">β</span> with dam break speed at the dam site.</p>
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<p>Schematic diagram of the dam head at the time of peak flood discharge.</p>
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<p>Schematic diagram of the simplified flow process at the dam site.</p>
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<p>Curve of peak process coefficient <span class="html-italic">γ</span>.</p>
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<p>Curve of peak arrival coefficient <span class="html-italic">β</span>.</p>
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<p>Comparison of calculated and simulated peak flood discharge values at the dam site for different break speeds.</p>
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20 pages, 1595 KiB  
Article
Multi-Dimensional Collaborative Operation Model and Evaluation of Cascade Reservoirs in the Middle Reaches of the Yellow River
by Xinjie Li, Qiang Wang, Yuanjian Wang, Hongtao Zhang, Jieyu Li and Donglin Li
Water 2023, 15(19), 3523; https://doi.org/10.3390/w15193523 - 9 Oct 2023
Viewed by 1598
Abstract
Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of [...] Read more.
Reservoir operation optimization is a technical measure for flood control and is beneficial owing to its reasonable and reliable control and application of existing water conservancy and hydropower hubs, while ensuring dam safety and flood control, as well as the normal operation of power supply and water supply. Considering the beneficial functions of reservoirs, namely flood control and ecological protection, this paper firstly established a two-objective optimal operation model for the reservoir group in the middle reaches of the Yellow River. We aim to maximize the average output of the cascade reservoir group and minimize the average change in ecological flow during the operation period under efficient sediment transport conditions, with the coordination degree of water and sediment as the constraints of reservoir discharge flows. The paper aims to construct an evaluation index system for reservoir operation schemes, apply a combined approach of objective and subjective evaluations, and introduce the gray target and cumulative prospect theories. By uniformly quantifying the established scheme evaluation index system, screening the reservoir operation schemes with the fuzzy evaluation method, and selecting the recommended scheme for each typical year, this paper provides a new scientific formulation of the operation schemes of reservoirs in the middle reaches of the Yellow River. The selected schemes are compared with actual data, demonstrating the effectiveness of joint reservoir operation and for multidimensional benefits in terms of power generation, ecology, and flood control. Full article
(This article belongs to the Special Issue China Water Forum 2023)
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<p>Overall program block diagram of SA-NSGA-II.</p>
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<p>Pareto frontier of the operation scheme for a high flow/sediment year.</p>
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<p>Pareto frontier of the operation scheme for a median water/sediment year.</p>
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<p>Pareto frontier of the operation scheme for a low flow/sediment year.</p>
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<p>Comprehensive benefit evaluation index system of operation schemes of the reservoir group.</p>
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<p>Water level process of the optimal operation schemes. (<b>a</b>) Wanjiazhai Reservoir, (<b>b</b>) Sanmenxia Reservoir, (<b>c</b>) Xiaolangdi Reservoir.</p>
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15 pages, 2148 KiB  
Article
The Optimization of Water Storage Timing in Upper Yangtze Reservoirs Affected by Water Transfer Projects
by Fan Wen, Wenhai Guan, Mingxiang Yang, Jixue Cao, Yibo Zou, Xuan Liu, Hejia Wang and Ningpeng Dong
Water 2023, 15(19), 3393; https://doi.org/10.3390/w15193393 - 27 Sep 2023
Cited by 2 | Viewed by 1284
Abstract
To alleviate regional disparities in water resource distribution and consequent scarcity, China has initiated and planned a series of inter-basin water transfer projects using the Yangtze River Basin as the source. These projects are expected to divert approximately 33.4 billion cubic meters of [...] Read more.
To alleviate regional disparities in water resource distribution and consequent scarcity, China has initiated and planned a series of inter-basin water transfer projects using the Yangtze River Basin as the source. These projects are expected to divert approximately 33.4 billion cubic meters of water annually from the Yangtze River Basin. The implementation of these water transfer projects will inevitably alter the hydrological conditions in the upper reaches of the Yangtze River, impacting the reservoir storage strategies of cascading hydroelectric stations under current end-of-flood-season operational plans. This study quantitatively assesses the impact of water transfer projects on end-of-flood-season reservoir storage in cascading systems using the reservoir fullness ratio as an indicator. Employing reservoir storage analysis models, optimization techniques, and flood risk assessment methods, we simulated reservoir storage processes to evaluate associated flood risks and derive an optimized timing strategy for cascading reservoir storage. The results indicate that advancing the reservoir filling schedule by five days for both the Baihetan and Three Gorges dams can offset the adverse impacts of water transfer projects on reservoir storage efficiency. This adjustment restores the reservoir fullness ratio to levels observed in scenarios without water transfers while still meeting flood control requirements. After optimizing the timing of reservoir filling, the electricity generation capacity for the Baihetan and Three Gorges dams increased by 1.357 and 3.183 billion kWh, respectively, under non-transfer scenarios. In water transfer scenarios, the electricity generation for the Baihetan and Three Gorges dams increased by 1.48 and 2.759 billion kWh, respectively. By optimizing reservoir filling schedules, we not only improved the reservoir fullness ratio but also enhanced the electricity generation efficiency of the cascading systems, offering valuable insights for future reservoir operation optimization. Full article
(This article belongs to the Special Issue China Water Forum 2023)
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<p>The upper reaches of the Yangtze River.</p>
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<p>The simulated and observed daily streamflow at (<b>a</b>) Pingshan, (<b>b</b>) Cuntan, and (<b>c</b>) Yichang.</p>
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<p>The 0.01% staged design floods for the Three Gorges Reservoir.</p>
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<p>The 0.01% staged design floods for the Baihetan Reservoir.</p>
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<p>Flood risk analysis chart for the Three Gorges Reservoir.</p>
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<p>Flood risk analysis chart for the Baihetan Reservoir.</p>
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17 pages, 6862 KiB  
Article
The Effect of Down-Cascade Re-Regulation on Alleviating the Flow Regime Alteration Induced by an Up-Cascade Reservoir
by Yongfei Wang, Xianxun Wang, Edgar Virguez, Kang Zheng, Ting Hu, Yadong Mei and Hao Wang
Water 2023, 15(12), 2166; https://doi.org/10.3390/w15122166 - 8 Jun 2023
Cited by 1 | Viewed by 1511
Abstract
An analysis of the effect on the flow regime caused by reservoir operation is crucial to balancing the exploitation and protection of water resources. The long-term effect of this on the intraday scale and small storage capacity is considerable, but rarely analyzed. This [...] Read more.
An analysis of the effect on the flow regime caused by reservoir operation is crucial to balancing the exploitation and protection of water resources. The long-term effect of this on the intraday scale and small storage capacity is considerable, but rarely analyzed. This study examines the world’s largest dual-cascade hydro-junction, the Three Gorges Dam and Gezhouba Dam junction, as a case study, adopting eight indices to characterize the reservoir’s inflow and outflow fluctuation. In doing this, we evaluate the alteration of the flow regime induced by an up-cascade reservoir and its alleviation caused by the down-cascade re-regulation. The results show: (1) an increment of the river flow fluctuation at the Three Gorges Dam, matched with hourly scale alleviation at the Gezhouba Dam; (2) a reduction (25.09~41.35%) in the quantitative indices of the river flow regime fluctuation; (3) perturbations on the power output. These findings provide references for developing methods to assess the re-regulation mechanisms in systems with upper- and lower-cascades. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>Flowchart of calculation of the river flow fluctuation.</p>
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<p>Hourly outflow of TGH reservoir on 1 January 2013.</p>
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<p>Geographical location of the Three Gorges Dam (TGD)–Gezhouba Dam (GD) hydro-junction.</p>
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<p>Hourly Power output, inflow, and outflow of the TGD reservoir in January 2013.</p>
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<p>SD of inflow and outflow and their reduction at the GD reservoir.</p>
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<p>Rates of mean fluctuation reductions in each month.</p>
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<p>Frequency, Kernel density estimation, and cumulative probability of the standard deviation reduction in inflow and outflow at the GD reservoir.</p>
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<p>Frequency, Kernel density estimation, and cumulative probability of normalized SD, FOD, CV, and RBF reduction in inflow and outflow at the GD reservoir.</p>
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<p>Frequency, Kernel density estimation, and cumulative probability of normalized RA, LT, NI, and SL reduction in inflow and outflow at the GD reservoir.</p>
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<p>Cumulative probability of normalized indices reduction in inflow and outflow at the GD reservoir.</p>
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<p>Scenarios of power output at down-cascade hydropower (power output process in re-regulation mode is the measured data at the GD, power output in run-of-river mode is the simulated data using fixed water head).</p>
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<p>Hourly normalized inflow and outflow of the Three Gorges Dam (TGD) reservoir (i.e., upper reservoir).</p>
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<p>Hourly normalized power output of the Three Gorges Dam (TGD) reservoir (i.e., upper reservoir).</p>
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<p>Hourly normalized water level of the Three Gorges Dam (TGD) reservoir (i.e., upper reservoir).</p>
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<p>Hourly normalized inflow and outflow of the Gezhouba Dam (GD) reservoir (i.e., lower reservoir).</p>
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<p>Hourly normalized power output of the Gezhouba Dam (GD) reservoir (i.e., lower reservoir).</p>
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<p>Hourly normalized water level of the Gezhouba Dam (GD) reservoir (i.e., lower reservoir).</p>
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17 pages, 1435 KiB  
Article
How Important for Society Is Recreation Provided by Multi-Purpose Water Reservoirs? Welfare Analysis of the Vltava River Reservoir System
by Kateřina Mácová and Zuzana Kozáková
Water 2023, 15(10), 1966; https://doi.org/10.3390/w15101966 - 22 May 2023
Cited by 3 | Viewed by 2438
Abstract
Contrary to the other functions of multi-purpose reservoirs, recreational use is not associated with a tangible social value, which hinders the search for new balances among optimal uses of water that will likely be needed under climate change. The objective of this study [...] Read more.
Contrary to the other functions of multi-purpose reservoirs, recreational use is not associated with a tangible social value, which hinders the search for new balances among optimal uses of water that will likely be needed under climate change. The objective of this study is to analyze visitation behavior and its patterns at a large-scale reservoir system on the Vltava River to quantify the total social benefits associated with recreation in monetary terms and to suggest how the magnitude of estimated recreation welfare relates to hydro-energy benefits, which are in usual practice taken much more into account than recreation in the strategic management of water dams. The elicited average consumer surplus per person and trip is EUR 55.7, which yields a total yearly recreation value of EUR 34 billion (ranging between 22 and 57). When compared to, e.g., the social value of hydro-energy generation, the actual yearly recreation welfare represents 1/3 of this nowadays more prioritized use. The results of the study bring new information for water management bodies that has been missing up to now, and they bring new arguments for reaching socially optimal water use in the strategic and operational management of the cascade of dams. From this perspective, the actual strategic relative prioritization of these two reservoir functions at the pilot site may be viewed as rational. Full article
(This article belongs to the Special Issue Water Governance and Sustainable Water Resources Management)
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<p>Flowchart of subsequent steps of the study.</p>
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<p>The Vltava River cascade and its dams and towns &gt;10,000 inhabitants in the vicinity; pilot area definition (LAUs) and its location within the Czech Republic (inset). Borders of NUTS3 regions are shown in grey; CZ borders are shown in black. Sources: The Czech Office for Surveying, Mapping and Cadastre; T. G. Masaryk Water Research Institute; background map: Esri; USGS; NOAA.</p>
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21 pages, 5996 KiB  
Article
Generation of Synthetic Series for Long-Term Analysis of Optimal Operation Policies of a Cascade Hydroelectric Dam System
by Rosa Valencia-Esteban, Maritza Liliana Arganis-Juárez, Ramón Domínguez-Mora, Alejandro Mendoza-Reséndiz, Eduardo Juan-Diego, Javier Osnaya-Romero, Eliseo Carrizosa-Elizondo and Rosalva Mendoza-Ramírez
Water 2023, 15(6), 1010; https://doi.org/10.3390/w15061010 - 7 Mar 2023
Viewed by 1574
Abstract
Stochastic Dynamic Programming (SDP) has been used to solve reservoir management problems in different parts of the world; specifically in Mexico, it has been used to obtain operating policies that optimize a given objective function. By simulating the operation of the system with [...] Read more.
Stochastic Dynamic Programming (SDP) has been used to solve reservoir management problems in different parts of the world; specifically in Mexico, it has been used to obtain operating policies that optimize a given objective function. By simulating the operation of the system with a comprehensive model, the behavior of such policies can be accurately evaluated. An optimal policy involves, on the one hand, the selection of the volume of water to extract from each reservoir of the system that guarantees the maximum expected benefit from electricity generation in the long term; and, on the other hand, an optimal policy should reduce the occurrence of unwanted events such as spills, deficits, as well as volumes exceeding the guide curves imposed by the operators of the dams. In the case of the Grijalva river dam system, SDP was applied to determine optimal operating policies considering three alternative guide curves proposed by different agencies; however, since the simulation of the operation of the system under the three alternatives with the historical record of dam inflows found that none of them showed deficits or spills, it was considered necessary to use synthetic series of inflows to increase the stress of the system. Records of synthetic biweekly series of 1000 years were then generated to simulate the behavior of the Grijalva river dam system using the optimal operation policies obtained for each alternative. By stressing the dam system by simulating its behavior with synthetic series longer than the historical record but preserving the same statistical characteristics of the historical series on the synthetic ones, it was possible to realistically evaluate each operating policy considering the frequency and magnitude of spills and deficits that occurred at each dam. For the generation of the synthetic series, a fragment method was used; it was adapted to simultaneously generate the inflow volumes to the two regulating dams (modified Svanidze method), which preserves the statistical characteristics of the historical series, including both the autocorrelations of each series and the cross-correlation. It was also verified that simulating the operation of the dam system with the generated series also preserves the average conditions, such as the average biweekly generation at each dam, which were obtained in the simulations with the historical record. Finally, an optimal policy was obtained (Test 4) by combining the guide curves used in the previous tests. Such a policy attained an average energy production of 474 GWh/fortnight, the lowest average total spills in the system (30,261.93 hm3), and limited deficits (5973.17 hm3) in the long term. This represents a relative increase of 16% in energy generated compared to the balanced historical operation scenario with respect to the few events of spills and deficits. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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<p>Location of dams in the Grijalva System. Source: Adapted with permission from [<a href="#B28-water-15-01010" class="html-bibr">28</a>].</p>
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<p>Guide curves tested for La Angostura and Malpaso.</p>
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<p>Probability distribution functions for the historical series and the 10 synthetic series generated with the modified Svanidze method.</p>
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<p>Comparison of the mean, average of the 10 synthetic series vs. historical series of La Angostura and Malpaso Dams.</p>
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<p>Comparison of standard deviation and average of the 10 synthetic series vs. historical series of La Angostura and Malpaso Dams.</p>
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<p>Comparison of the skewness coefficient and average of the 10 synthetic series vs. historical series of La Angostura and Malpaso Dams.</p>
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<p>Comparison of the autocorrelation coefficient and average of the 10 synthetic series vs. historical series of La Angostura and Malpaso Dams.</p>
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<p>Comparison of the cross-correlation coefficient (rxy) of the 10 synthetic series of La Angostura and Malpaso dams.</p>
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<p>Spill count in the policies tested for La Angostura dam and Malpaso dam.</p>
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<p>Deficit count in the policies tested for La Angostura dam and Malpaso dam.</p>
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<p>Guide curves tested for the policy with the combined guide curve, Test 4.</p>
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<p>Spill count with the combined guide curve policy.</p>
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<p>Deficit count with the combined guide curve policy.</p>
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12 pages, 240 KiB  
Opinion
Eutopian and Dystopian Water Resource Systems Design and Operation—Three Irish Case Studies
by J. Philip O’Kane
Hydrology 2022, 9(9), 159; https://doi.org/10.3390/hydrology9090159 - 6 Sep 2022
Cited by 1 | Viewed by 2323
Abstract
The Harvard Water Program is more than sixty years old. It was directed by an academic Steering Committee consisting of the professors of Government and Political Science, Planning, Economics, and Water Engineering. In 2022 we would add to the notional Steering Committee the [...] Read more.
The Harvard Water Program is more than sixty years old. It was directed by an academic Steering Committee consisting of the professors of Government and Political Science, Planning, Economics, and Water Engineering. In 2022 we would add to the notional Steering Committee the professors of Ecology, Sociology and Water Law, calling it the augmented Harvard eutopian approach to the design and operation of Water Resource Systems. We use the Greek word ‘eu-topos’ to mean ‘a good place’, figuratively speaking, and ‘dys-topos’ its antonym, ‘not a good place’. By opposing eutopia and dystopia (latin forms) (Utopian literature begins with Thomas More’s (1478–1535) fictional socio-political satire “Utopia”, written in Latin and published in 1516: “Libellus vere aureus, nec minus salutaris quam festivus, de optimo rei publicae statu deque nova insula Utopia”. “A little, true book, not less beneficial than enjoyable, about how things should be in a state and about the new island Utopia” [Wikipedia translation]. He coined the word ‘utopia’ from the Greek ou-topos meaning ‘no place’ or ‘nowhere’. It was a pun-the almost identical Greek word eu-topos means ‘a good place’), we pass judgement on three Irish case studies, in whole and in part. The first case study deals with the dystopian measurement of the land phase of the hydrological cycle. The system components are distributed among many government departments that see little need to cooperate, leading to proposition 1: A call for a new Water Law. The second case study deals with a project to restore a 200 km2 polder landscape to its condition in 1957. The project came to the University with an hypothetical cause of the increased flooding and a tentative solution: dredge the Cashen estuary of its sand, speeding the flow of sluiced water to the sea, and the status quo ante would be restored. The first scientific innovation was the proof that restoration by dredging is impossible. Pumping is the only solution, but it raises disruptive questions that are not covered by Statute. The second important innovation was the discovery in the dynamic water balance, of large leakage into the polders, either around or between sluiced culverts, when the flap valves are nominally closed, impacting both their maintenance and minimization of pumping. Discussions on our findings ended in dystopian silence. Hence proposition 2: Moving towards eutopia may only be possible with a change in the Law. The third case study concerns the protection of Cork City from flooding: riverine, tidal and groundwater. The government’s “emerging solution” consists of major physical intervention in the city centre, driven hard against local opposition, as the only possible solution. Two hydro-electric reservoirs upstream were largely ignored as part of a solution because the relevant Statute did not mandate their use for flood control. The Supreme Court has recently overturned this interpretation of the governing Statute. A new theory of flood control with a cascade of reservoirs, dams and weirs is the scientific innovation here. Once more these findings have been greeted by government with dystopian silence. Hence proposition 3: Re-open the design process to find several much better solutions, approximating a eutopian water world. Full article
(This article belongs to the Collection Feature Papers of Hydrology)
17 pages, 1027 KiB  
Article
Avoiding Buffer Tank Overflow in an Iron Ore Dewatering System with Integrated Control System
by Ênio L. Junior, Moisés T. da Silva and Thiago A. M. Euzébio
Sustainability 2022, 14(15), 9347; https://doi.org/10.3390/su14159347 - 30 Jul 2022
Cited by 1 | Viewed by 2767
Abstract
High water usage is necessary while ore passes through the many stages of a mineral processing plant. However, a dewatering system filters the final ore pulp product to remove the water, which is reutilized in the previous processes. This step is fundamental to [...] Read more.
High water usage is necessary while ore passes through the many stages of a mineral processing plant. However, a dewatering system filters the final ore pulp product to remove the water, which is reutilized in the previous processes. This step is fundamental to reducing the fresh new water consumption. Usually, several tanks, pumps, and filters form a dewatering system—any failure or shutdowns from those components disbalance the pulp flow. The waste of many tons of water and ore products for a tailing dam is the worst consequence of a mass disbalance in a dewatering system. This paper proposes an advanced regulatory control strategy composed of cascade and override loops for a dewatering system. The main purpose is to increase the production period, even under filter failure and changes in the inlet pulp characteristics. This control strategy is evaluated using a digital model of a large-scale Brazilian iron ore processing plant. Two scenarios are investigated: the simultaneous failure of two filters and disturbances in the flow and density of the thickener. The simulation results show that the proposed control strategy could extend the period of operation of the dewatering plant under failures in the disc filters and reject significant disturbances. For the considered simulation period, the proposed solution increases the time to overflow by 72% when compared to the previous control strategy. Thus, it is possible to avoid the waste of approximately 2448.36 tons of ore pulp that would be sent to the tailings dam. Full article
(This article belongs to the Topic Mining Safety and Sustainability)
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<p>The dewatering process.</p>
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<p>Closed-loop control system.</p>
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<p>Percentage of filter failures.</p>
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<p>Simplified diagram of the dewatering process.</p>
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<p>Proposed control strategy—pulp storage and transportation.</p>
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<p>Proposed control strategy—thickening.</p>
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<p>Schematic diagram of the simulation framework.</p>
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<p>Process variables—filter failure case, (<b>a</b>) density of the underflow, (<b>b</b>) flow rate of the underflow, (<b>c</b>) level—storage tank, (<b>d</b>) average level—buffer tanks.</p>
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<p>Manipulated variables—filter failure case, (<b>a</b>) setpoint of loop FIC02—proposed control, (<b>b</b>) Setpoint of loop FIC02—current control, (<b>c</b>) BP-005 frequency (<math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>)—proposed control, (<b>d</b>) BP-005 frequency (<math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>)—current control.</p>
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<p>Failure time of two filters.</p>
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<p>Thickener feeding disturbance.</p>
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<p>Process variables—thickener feeding disturbance, (<b>a</b>) density of the underflow, (<b>b</b>) flow rate of the underflow, (<b>c</b>) level—storage tank, (<b>d</b>) average level—buffer tanks.</p>
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<p>Manipulated variables—thickener feeding disturbance, (<b>a</b>) setpoint of loop FIC02—proposed control, (<b>b</b>) setpoint of loop FIC02—current control, (<b>c</b>) BP-005 frequency (<math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>)—proposed control, (<b>d</b>) BP-005 frequency (<math display="inline"><semantics> <mrow> <mi>T</mi> <msub> <mi>P</mi> <mn>1</mn> </msub> </mrow> </semantics></math>)—current control.</p>
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22 pages, 4031 KiB  
Article
Forecasting Water Temperature in Cascade Reservoir Operation-Influenced River with Machine Learning Models
by Dingguo Jiang, Yun Xu, Yang Lu, Jingyi Gao and Kang Wang
Water 2022, 14(14), 2146; https://doi.org/10.3390/w14142146 - 6 Jul 2022
Cited by 14 | Viewed by 2950
Abstract
Water temperature (WT) is a critical control for various physical and biochemical processes in riverine systems. Although the prediction of river water temperature has been the subject of extensive research, very few studies have examined the relative importance of elements affecting WT and [...] Read more.
Water temperature (WT) is a critical control for various physical and biochemical processes in riverine systems. Although the prediction of river water temperature has been the subject of extensive research, very few studies have examined the relative importance of elements affecting WT and how to accurately estimate WT under the effects of cascaded dams. In this study, a series of potential influencing variables, such as air temperature, dew temperature, river discharge, day of year, wind speed and precipitation, were used to forecast daily river water temperature downstream of cascaded dams. First, the permutation importance of the influencing variables was ranked in six different machine learning models, including decision tree (DT), random forest (RF), gradient boosting (GB), adaptive boosting (AB), support vector regression (SVR) and multilayer perceptron neural network (MLPNN) models. The results showed that day of year (DOY) plays the most important role in each model for the prediction of WT, followed by flow and temperature, which are two commonly important factors in unregulated rivers. Then, combinations of the three most important inputs were used to develop the most parsimonious model based on the six machine learning models, where their performance was compared according to statistical metrics. The results demonstrated that GB3 and RF3 gave the most accurate forecasts for the training dataset and the test dataset, respectively. Overall, the results showed that the machine learning model could be effectively applied to predict river water temperature under the regulation of cascaded dams. Full article
(This article belongs to the Section Ecohydrology)
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<p>Distribution map of main dams (blue circle) and hydrological monitoring points (red triangles) in the study area.</p>
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<p>Time series plot of water temperature (red lines), mean air temperature (black lines), and discharge (blue lines).</p>
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<p>Workflow summarizing the steps of the comparative analysis of the performance of the different ML methods.</p>
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<p>Permutation importance in DT and RF; DT, decision trees, RF, random forests. (DT on the <b>top</b>, RF on the <b>bottom</b>, WT: °C).</p>
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<p>Permutation importance in GB and AB; GB, gradient boosting regression, AB, adaptive boosting regression. (GB on the <b>top</b>, AB on the <b>bottom</b>, WT: °C).</p>
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<p>Permutation importance in SVR and MLPNN; SVR, support vector regression, MLPNN, multilayer perceptron neural networks. (SVR on the <b>top</b>, MLPNN on the <b>bottom</b>, WT: °C).</p>
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<p>Model fitting results—DecisionTree Regressor, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) two input variables (DOY and Flow), (<b>c</b>) all variables.</p>
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<p>Model fitting results—RandomForest Regressor, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) with two input variables (DOY and Flow), (<b>c</b>) with all variables.</p>
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<p>Model fitting results—GradientBoosting Regressor, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) with two input variables (DOY and Flow), (<b>c</b>) with all variables.</p>
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<p>Model fitting results—AdaptiveBoosting Regressor, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) with two input variables (DOY and Flow), (<b>c</b>) with all variables.</p>
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<p>Model fitting results—SupportVector Regression, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) with two input variables (DOY and Flow), (<b>c</b>) with all variables.</p>
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<p>Model fitting results—Multilayer Perceptron Neural Network, blue dot: X coordinate (observed data), Y coordinate (predicted data); black line: y = x; red dotted line: the regression curve of the blue dots. (<b>a</b>) only one input variable (DOY), (<b>b</b>) with two input variables (DOY and Flow), (<b>c</b>) with all variables.</p>
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24 pages, 6538 KiB  
Article
Drought Risk Assessment of Sugarcane-Based Electricity Generation in the Rio dos Patos Basin, Brazil
by Jazmin Campos Zeballos, Zita Sebesvari, Jakob Rhyner, Markus Metz and Vinicius Bof Bufon
Sustainability 2022, 14(10), 6219; https://doi.org/10.3390/su14106219 - 20 May 2022
Viewed by 2315
Abstract
Brazil has a large share of hydropower in its electricity matrix. Since hydropower depends on water availability, it is particularly vulnerable to drought events, making the Brazilian electricity matrix vulnerable to climate change. Starting in 2005, Brazil opened the matrix to new renewable [...] Read more.
Brazil has a large share of hydropower in its electricity matrix. Since hydropower depends on water availability, it is particularly vulnerable to drought events, making the Brazilian electricity matrix vulnerable to climate change. Starting in 2005, Brazil opened the matrix to new renewable sources, including sugarcane-based electricity. Sugarcane is known for its resilience to short dry spells. Over the last decades, its production area moved from the coastal plains of the Atlantic Forest biome to the savannahs of the Cerrado biome, which is characterised by a five- to six month-long dry season. The sugarcane-based electricity system is highly dynamic and complex due to the interlinkages, dependencies, and cascading impacts between its agricultural and industrial subsystems. This paper applies the risk framework proposed by the IPCC to assess climate-change-driven drought risks to sugarcane electricity generation systems to identify their strengths and weaknesses, considering the system dynamics and linkages. Our methodology aims to understand and characterize drought in the agriculture as well as industrial subsystems and offers a specific understanding of the system by using indicators tailored to sugarcane-based electricity generation. Our results underline the relevance of actions at different levels of management. Initiatives, such as regional weather forecasts specifically for agriculture, and measures to increase industrial water-use efficiency were identified to be essential to reduce the drought risk. Actions from farmers and mill owners, supported and guided by the government at different levels, have the potential to increase the resilience of the system. For example, the implementation of small dams was identified by local actors as a promising intervention to adapt to the long dry seasons; however, they need to be implemented based on a proper technical assessment in order to locate these dams in suitable places. Moreover, the results show that creating and maintaining small water reservoirs to enable the adoption of deficit-controlled irrigation technology contribute to reducing the overall drought risk of the sugarcane-based electricity generation system. Full article
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<p>Case study location. Database source: [<a href="#B23-sustainability-14-06219" class="html-bibr">23</a>,<a href="#B24-sustainability-14-06219" class="html-bibr">24</a>,<a href="#B25-sustainability-14-06219" class="html-bibr">25</a>,<a href="#B26-sustainability-14-06219" class="html-bibr">26</a>].</p>
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<p>Sugarcane system. Green boxes and lines represent the agriculture subsystem and water required in the industry, the yellow boxes and lines represent the industrial subsystem (SIN is an acronym for the National Interconnected System in Portuguese), and the grey boxes represent a zoom in of the industrial subsystem.</p>
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<p>Sugarcane system timeframe. Green boxes and lines represent the agriculture subsystem and water required in the industry, the yellow boxes and lines represent the industrial subsystem (SIN is an acronym for the National Interconnected System in Portuguese).</p>
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<p>Soil water content (SWC) variation (left axis) through the months in relation to the precipitation (right axis) and its impact on sugarcane crop yield. Data measured in the field.</p>
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<p>Agriculture hazard indicators, groups that were organised, and the mathematical values representing the hazards of the groups.</p>
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<p>Agriculture vulnerability indicators (first column) and groupings of indicators (second column).</p>
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<p>Monthly average river and ecological water flow, and available flow for processes after subtracting water flow (50% of the Q95) in cubic meters per second.</p>
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<p>Monthly hydrological drought thresholds in cubic meters per second.</p>
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<p>Yearly drought risk assessment results between 2004 and 2015 (risk values range from 0 to 1).</p>
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<p>Energy generation vs. risk assessment value between 2009 and 2015.</p>
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<p>Yearly hazard results between 2004 and 2015 (hazard values range from 0 to 1). Captions: industrial hazard (hazard (i)); agriculture hazard (hazard (a)).</p>
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<p>Land-use map used for the agriculture exposure.</p>
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<p>Yearly exposure results between 2004 and 2015 (exposure values range between 0 and 1). Captions: industrial exposure (exposure (i)); agriculture exposure—irrigated sugarcane (exposure (a-IS); and agriculture exposure—rain-fed sugarcane (exposure a-RS).</p>
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<p>Yearly vulnerability results (vulnerability results range between 0 and 1). Captions: industrial vulnerability (vulnerability (i)); agriculture vulnerability (vulnerability (a)).</p>
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<p>Normalised risk assessment comparisons for the years 2007 and 2015. Where “a” refers to the average of values, “b” refers to the highest value, and “c” refers to the weighted average results. * refers to the “Q” value detailed in Equation (2b).</p>
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22 pages, 13664 KiB  
Article
Influence of Hydrologic Alteration on Sediment, Dissolved Load and Nutrient Downstream Transfer Continuity in a River: Example Lower Brda River Cascade Dams (Poland)
by Dawid Szatten, Michał Habel and Zygmunt Babiński
Resources 2021, 10(7), 70; https://doi.org/10.3390/resources10070070 - 1 Jul 2021
Cited by 10 | Viewed by 3755
Abstract
Hydrologic alternation of river systems is an essential factor of human activity. Cascade-dammed waters are characterized by the disturbed outflow of material from the catchment. Changes in sediment, dissolved load and nutrient balance are among the base indicators of water resource monitoring. This [...] Read more.
Hydrologic alternation of river systems is an essential factor of human activity. Cascade-dammed waters are characterized by the disturbed outflow of material from the catchment. Changes in sediment, dissolved load and nutrient balance are among the base indicators of water resource monitoring. This research was based on the use of hydrological and water quality data (1984–2017) and the Indicators of Hydrologic Alteration (IHA) method to determine the influence of river regime changes on downstream transfer continuity of sediments and nutrients in the example of the Lower Brda river cascade dams (Poland). Two types of regimes were used: hydropeaking (1984–2000) and run–of–river (2001–2017). Using the IHA method and water quality data, a qualitative and quantitative relationship were demonstrated between changes of regime operation and sediment and nutrient balance. The use of sites above and below the cascade made it possible to determine sediment, dissolved load, and nutrient trapping and removing processes. Studies have shown that changes in operation regime influenced the supply chain and continuity of sediment and nutrient transport in cascade-dammed rivers. The conducted research showed that sustainable management of sediment and nutrient in the alternated catchment helps achieve good ecological status of the water. Full article
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<p>Sketch of the study area. (<b>A</b>) Longitudinal profile of LBC (<b>B</b>) on the background of the Brda catchment area.</p>
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<p>Examples of hourly water elevation of the Koronowski Reservoir on lacustrine section (<b>A</b>) and Brda River directly below the Smukała Reservoir (<b>B</b>) under different regimes. Regime I: hydropeaking (7–15 May 2000) and Regime II: run–of–river (9–17 September 2015).</p>
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<p>Comparison of changes in the value of hydrologic alteration (HA) for 33 Indicators of Hydrologic Alteration (IHA) for Tuchola (<b>A</b>) and Smukała (<b>B</b>) stations using RVA target range for a hydropeaking (1984–2000) and run–of–river (2001–2017) regime periods. Explanations for parameters 1.1–5.3 in <a href="#resources-10-00070-t002" class="html-table">Table 2</a>.</p>
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<p>The relationship between water discharge (Q, in m<sup>3</sup> s<sup>−1</sup>) with: (<b>A</b>) suspended sediment concentration (SSC, in mg L<sup>−1</sup>), (<b>B</b>) dissolved load concentration (DLC, in mg L<sup>−1</sup>), (<b>C</b>) total nitrogen (mgN L<sup>−1</sup>), (<b>D</b>) total phosphorus (mgP L<sup>−1</sup>) at the stations Tuchola (T) and Smukała (S) during Regime I (hydropeaking) and Regime II (run–of–river).</p>
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<p>The pattern loops for seasonal relationship for water discharge (Q, in m<sup>3</sup> s<sup>−1</sup>) for stations Tuchola (T) and Smukała (S) with: (<b>A</b>) suspended sediment concentration (SSC, in mg L<sup>−1</sup>), (<b>B</b>) dissolved load concentration (DLC, in mg L<sup>−1</sup>), (<b>C</b>) total nitrogen (mgN L<sup>−1</sup>) and (<b>D</b>) total phosphorus (mgP L<sup>−1</sup>), during Regime I (hydropeaking) and Regime II (run–of–river).</p>
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<p>The ability of yearly (β) and average (β<sub>av.</sub>) retention of river load and nutrients for LBC in the period 1991–2011. (<b>A</b>) suspended sediment load (SSL, in tones), (<b>B</b>) dissolved load (DL, in tones), (<b>C</b>) total nitrogen load (TNL, in tones) and (<b>D</b>) total phosphorus load (TPL, in tones). The green color line is for Regime I (hydropeaking), and the blue color line is for Regime II (run–of–river).</p>
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18 pages, 13822 KiB  
Article
Bathymetric Monitoring of Alluvial River Bottom Changes for Purposes of Stability of Water Power Plant Structure with a New Methodology for River Bottom Hazard Mapping (Wloclawek, Poland)
by Dariusz Popielarczyk, Marian Marschalko, Tomasz Templin, Dominik Niemiec, Isik Yilmaz and Barbara Matuszková
Sensors 2020, 20(17), 5004; https://doi.org/10.3390/s20175004 - 3 Sep 2020
Cited by 5 | Viewed by 2816
Abstract
The aim of this research was to produce a new methodology for a special river bottom hazard mapping for the stability purposes of the biggest Polish water power plant: Włocławek. During the operation period of the water power plant, an engineering-geological issue in [...] Read more.
The aim of this research was to produce a new methodology for a special river bottom hazard mapping for the stability purposes of the biggest Polish water power plant: Włocławek. During the operation period of the water power plant, an engineering-geological issue in the form of pothole formation on the Wisła River bed in the gravel-sand alluvium was observed. This was caused by increased fluvial erosion resulting from a reduced water level behind the power plant, along with frequent changes in the water flow rates and water levels caused by the varying technological and economic operation needs of the power plant. Data for the research were obtained by way of a 4-year geodetic/bathymetric monitoring of the river bed implemented using integrated GNSS (Global Navigation Satellite System), RTS (Robotized Total Station) and SBES (Single Beam Echo Sounder) methods. The result is a customized river bottom hazard map which takes into account a high, medium, and low risk levels of the potholes for the water power plant structure. This map was used to redevelop the river bed by filling. The findings show that high hazard is related to 5% of potholes (capacity of 4308 m3), medium with 38% of potholes (capacity of 36,455 m3), and low hazard with 57% of potholes (capacity of 54,396 m3). Since the construction of the dam, changes due to erosion identified by the monitoring have concerned approximately 405,252 m3 of the bottom, which corresponds to 130 Olympic-size pools. This implies enormous changes, while a possible solution could be the construction of additional cascades on the Wisła River. Full article
(This article belongs to the Special Issue Telemetry and Monitoring for Land and Water Ecosystems)
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<p>Cause of the problem on power plant Włocławek—motivation for research engineering-geological case of study: (<b>a</b>) Planned cascade of dams with optimal regime of sedimentation and erosion of the river bottom. (<b>b</b>) Realized dams with current problems of erosion of river bottom (only one power plant dam was constructed). (<b>c</b>) Planned dams in 1956. (<b>d</b>) Only one dam built in 1970. (<b>e</b>) Planned conditions. (<b>f</b>) Existing conditions. (<b>g</b>) Improved conditions as for river bottom erosion due to a threshold. (<b>h</b>) Erosion of the bottom continues and the threshold is endangered.</p>
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<p>Study area: (<b>a</b>) location of power station Włocławek, (<b>b</b>) power station construction, (<b>c</b>) cross sections of the measured potholes, (<b>d</b>) photo-documentation of the water power station.</p>
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<p>Geological cross section of the Włocławek water power plant.</p>
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<p>Bathymetric monitoring (<b>a</b>) measurements condition, (<b>b</b>) stages of measurements, (<b>a1</b>) study area measurements condition, (<b>a2</b>) bathymetric equipment, (<b>a3</b>) rough bottom and turbulent water flow, (<b>b1</b>–<b>b4</b>) hydrographic motorboat trajectories during measurement stages.</p>
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<p>Bathymetric monitoring (<b>a</b>) measurements condition, (<b>b</b>) stages of measurements, (<b>a1</b>) study area measurements condition, (<b>a2</b>) bathymetric equipment, (<b>a3</b>) rough bottom and turbulent water flow, (<b>b1</b>–<b>b4</b>) hydrographic motorboat trajectories during measurement stages.</p>
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<p>Study area. Potholes and cross sections.</p>
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<p>Changes in the river bed in (<b>a1</b>) cross-section A-A′, (<b>a2</b>) cross-section A′-A′′, (<b>a3</b>) detail No. 1, (<b>a4</b>) detail No. 2, (<b>b1</b>) cross-section B-B′, (<b>b2</b>) cross-section C-C′, (<b>b3</b>) detail No. 3, (<b>b4</b>) detail No. 4.</p>
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<p>Changes in river bottom erosion and new sediments (<b>a</b>) volume (m<sup>3</sup>), (<b>b</b>) volume in the number of Olympic-size swimming pools (3125 m<sup>3</sup>), (<b>c</b>) area (m<sup>2</sup>).</p>
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<p>The situation of changes in river bottom surface (river bottom erosion, new sediment), (<b>a</b>) between the first and second year of monitoring, (<b>b</b>) between the second and third year of monitoring, (<b>c</b>) between the third and fourth year of monitoring, (<b>d</b>) between the first and fourth year of monitoring, (<b>e</b>) between the first year of monitoring and 1970, (<b>f</b>) between the fourth year of monitoring and 1970.</p>
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<p>Graph of pothole quantification (<b>a</b>) volume (m<sup>3</sup>), (<b>b</b>) area (m<sup>2</sup>).</p>
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<p>River bottom hazard map (<b>a</b>) classification by factor of pothole depth, (<b>b</b>) classification by factor of distance from structure of dam and threshold, (<b>c</b>) final map classification by combination of these 2 factors (methodology is in risk matrix).</p>
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<p>Quantification of risk categories in the special river bottom hazard map for every pothole (<b>a</b>) classification by factor of pothole depth, (<b>b</b>) classification by factor of distance from structure of dam and threshold, (<b>c</b>) final map classification by combination of these two factors: (<b>a1</b>,<b>b1</b>,<b>c1</b>), volume (m<sup>3</sup>), (<b>a2</b>,<b>b2</b>,<b>c2</b>) area (m<sup>2</sup>).</p>
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<p>The sum of risk categories in the river bottom hazard map (<b>a</b>) classification by factor of pothole depth, (<b>b</b>) classification by factor of distance from structure of dam and threshold, (<b>c</b>) final map classification by combination of these 2 factors, (<b>a1</b>,<b>b1</b>,<b>c1</b>)—volume (m<sup>3</sup>), (<b>a2</b>,<b>b2</b>,<b>c2</b>)—area (m<sup>2</sup>).</p>
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20 pages, 4242 KiB  
Article
Numerical Simulation and Risk Assessment of Cascade Reservoir Dam-Break
by Liangming Hu, Xu Yang, Qian Li and Shuyu Li
Water 2020, 12(6), 1730; https://doi.org/10.3390/w12061730 - 17 Jun 2020
Cited by 19 | Viewed by 3828
Abstract
Despite the fact that cascade reservoirs are built in a large number of river basins nowadays, there is still an absence of studies on sequential embankment dam-break in cascade reservoirs. Therefore, numerical simulations and risk analyses of cascade reservoir dam-break are of practical [...] Read more.
Despite the fact that cascade reservoirs are built in a large number of river basins nowadays, there is still an absence of studies on sequential embankment dam-break in cascade reservoirs. Therefore, numerical simulations and risk analyses of cascade reservoir dam-break are of practical engineering significance. In this study, by means of contacting the hydraulic features of upstream and downstream reservoirs with flood routing simulation (FRS) and flood-regulating calculation (FRC), a numerical model for the whole process of cascade reservoir breaching simulation (CRBS) is established based on a single-embankment dam-break model (Dam Breach Analysis—China Institute of Water Resources and Hydropower Research (DB-IWHR)). In a case study of a fundamental cascade reservoir system, in the upstream Tangjiashan barrier lake and the downstream reservoir II, the whole process of cascade reservoir dam-break is simulated and predicted under working schemes of different discharge capacities, and the risk of cascading breaching was also evaluated through CRBS. The results show that, in the dam-break of Tangjiashan barrier lake, the calculated values of the peak outflow rate are about 10% more than the recorded data, which are in an acceptable range. In the simulation of flood routing, the dam-break flood arrived at the downstream reservoir after 3 h. According to the predicted results of flood-regulating calculations and the dam-break simulation in the downstream reservoir, the risk of sequential dam-break can be effectively reduced by setting early warnings to decrease reservoir storage in advance and adding a second discharge tunnel to increase the discharge capacity. Alongside the simulation of flood routing and flood regulation, the whole process of cascade dam-break was completely simulated and the results of CRBS tend to be more reasonable; CRBS shows the great value of engineering application in the risk assessment and flood control of cascade reservoirs as an universal numerical prediction model. Full article
(This article belongs to the Section Hydrology)
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<p>Hydraulic relations at the entrance of dam breach.</p>
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<p>The expansion process of the breach.</p>
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<p>Fundamental cascade reservoir.</p>
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<p>Flow chart of cascade reservoir breach simulation.</p>
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<p>The Tangjiashan barrier dam and Beichuan town.</p>
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<p>Water level-storage curve of reservoir II.</p>
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<p>Comparison between the recorded data and the computed results of DB-IWHR and BREACH, (<b>a</b>) outflow rate of the dam-break flood; (<b>b</b>) water level in the breach; (<b>c</b>) breach bottom elevation.</p>
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<p>Results of FRS in the simulation of flood routing.</p>
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<p>The water-level discharge curve of reservoir II.</p>
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<p>The water level, inflow and outflow rate of the downstream reservoir in the process of flood regulation.</p>
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<p>Computed results of the downstream dam-break simulation from DB-IWHR, (<b>a</b>) outflow rate of the dam-break flood and water level of the breach; (<b>b</b>) breach bottom elevation and width.</p>
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<p>Inflow, outflow rate and water level of the downstream reservoir in the process of flood regulation under operating scheme 1.</p>
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<p>Computed results of the downstream dam-break simulation from DB-IWHR under operating scheme 1, (<b>a</b>) outflow rate of the dam-break flood and water level of the breach; (<b>b</b>) breach bottom elevation and width.</p>
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<p>Discharge curve before and after adding the second discharge tunnel.</p>
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<p>Inflow, outflow rate and water level of downstream reservoir in process of flood regulation under operating scheme 2.</p>
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<p>Inflow, outflow rate and water level of downstream reservoir in process of flood regulation under operating scheme 3.</p>
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20 pages, 17388 KiB  
Article
Climate Change Impacts on Hydropower in Yunnan, China
by Benxi Liu, Jay R. Lund, Lingjun Liu, Shengli Liao, Gang Li and Chuntian Cheng
Water 2020, 12(1), 197; https://doi.org/10.3390/w12010197 - 10 Jan 2020
Cited by 16 | Viewed by 7448
Abstract
Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global [...] Read more.
Climate change could have dire effects on hydropower systems, especially in southwest China, where hydropower dominates the regional power system. This study examines two large cascade hydropower systems in Yunnan province in southwest China for 10 climate change projections made with 5 global climate models (GCMs) and 2 representative concentration pathways (RCPs) under Coupled Model Intercomparison Project Phase 5 (CMIP5). First, a back propagation neural network rain-runoff model is built for each hydropower station to estimate inflows with climate change. Then, a progressive optimality algorithm maximizes hydropower generation for each projection. The results show generation increasing in each GCM projection, but increasing more in GCMs under scenario RCP8.5. However, yearly generation fluctuates more: generation decreases dramatically with potential for electricity shortages in dry years and more electricity as well as spill during wet years. Average annual spill, average annual inflow and average storage have similar trends. The analysis indicates that a planned large dam on the upper Jinsha River would increase seasonal regulation ability, increase hydropower generation, and decrease spill. Increased turbine capacity increases generation slightly and decreases spill for the Lancang River. Results from this study demonstrate effects of climate change on hydropower systems and identify which watersheds might be more vulnerable, along with some actions that could help adapt to climate change. Full article
(This article belongs to the Special Issue System Dynamics Modelling for Water–Energy–Climate Nexus)
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<p>Location of Yunnan and hydropower plants of Lancang cascaded hydropower system (LCCHS) and Jinsha cascaded hydropower system (JSCHS).</p>
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<p>Yearly and monthly change in precipitation (mm) in Lancang River catchment between projected 2020–2100 and historical 1961–2016. (<b>a</b>) Yearly change in precipitation, and (<b>b</b>) monthly change in precipitation.</p>
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<p>Yearly and monthly change in precipitation (mm) in Jinsha River catchment between projected 2020–2100 and historical 1961–2016. (<b>a</b>) Yearly change in precipitation, and (<b>b</b>) monthly change in precipitation.</p>
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<p>The projected monthly inflow changes in Gongguoqiao between projected 2020–2100 monthly average and historical 1961–2016 monthly average.</p>
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<p>The projected monthly inflow changes in Liyuan between projected 2020–2100 monthly average and historical 1961–2016 monthly average.</p>
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<p>The projected yearly inflow of Gongguoqiao under different climate change projections.</p>
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<p>The projected yearly inflow of Liyuan under different climate change projections.</p>
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<p>The generation and output of LCCHS and JSCHS under climate change projections. (<b>a</b>,<b>b</b>) Monthly average output; (<b>c</b>,<b>d</b>) change in monthly average output compare to historical model simulation; (<b>e</b>,<b>f</b>) yearly generation distribution; (<b>g</b>,<b>h</b>) yearly generation. In each subfigure, the thick line represents the generation of the corresponding GCM, and the thin lines represent the generation of other GCMs.</p>
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<p>The spillage information of LCCHS and JSCHS. (<b>a</b>,<b>b</b>) Monthly average percentage of inflow, generation and spill; (<b>c</b>,<b>d</b>) peak generation and spill distribution; (<b>e</b>,<b>f</b>) yearly average spill at each station.</p>
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<p>Multi-year drought hydropower generation compared to historical yearly average (the x axis is consecutive years). (<b>a</b>) LCCHS, and (<b>b</b>) JSCHS.</p>
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<p>The generation and spill of JSCHS under different climate change projections with Longpan commissioned (does not contain the generation of Longpan hydropower station in order to compare the result). (<b>a</b>) Average monthly output, (<b>b</b>) average monthly percentage of inflow, generation and spillage, (<b>c</b>) peak generation and spill distribution, and (<b>d</b>) annual average spill of each station.</p>
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<p>The generation change with the addition of turbine capacity of Gongguoqiao. (<b>a</b>) Increased power generation of Gongguoqiao, and (<b>b</b>) increased power generation of LCCHS.</p>
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<p>The spill change with the addition of turbine capacity of Gongguoqiao. (<b>a</b>) Reduced spillage of Gongguoqiao, and (<b>b</b>) reduced spillage of LCCHS.</p>
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16 pages, 1469 KiB  
Article
Increasing River Temperature Shifts Impact the Yangtze Ecosystem: Evidence from the Endangered Chinese Sturgeon
by Hui Zhang, Myounghee Kang, Jinming Wu, Chengyou Wang, Junyi Li, Hao Du, Haile Yang and Qiwei Wei
Animals 2019, 9(8), 583; https://doi.org/10.3390/ani9080583 - 20 Aug 2019
Cited by 30 | Viewed by 4483
Abstract
The Yangtze River has the third greatest water flow and is one of the most human-influenced rivers in the world. Since 1950, this river system has experienced drastic human interventions, leading to various environmental changes, including water temperature. In this study, based on [...] Read more.
The Yangtze River has the third greatest water flow and is one of the most human-influenced rivers in the world. Since 1950, this river system has experienced drastic human interventions, leading to various environmental changes, including water temperature. In this study, based on observations during the past sixty years, we found that the seasonal temperature regime has been altered, both temporally (1–5 °C variation) and spatially (>626 km distance). Temperature shifts not only delay the timing of fish spawning directly, but also lead to degeneration in gonad development. Temperature regime alterations have delayed the suitable spawning temperature window by approximately 29 days over a decade (2003–2016). It confirmed that a period of lower temperature, higher cumulative temperature, and relatively higher temperature differences promoted the maturation of potential spawners based on the correlation analysis (p < 0.05). Also, thermal alterations were highly correlated with reservoir capacity upstream (R2 = 0.866). On-going cascade dam construction and global warming will lead to further temperature shifts. Currently, rigorous protection measures on the breeding population of the Chinese sturgeon and its critical habitats is urgently needed to prevent the crisis of the species extinction. Increasing river thermal shifts not only threaten the Chinese sturgeon but also affect the entire Yangtze aquatic ecosystem. Full article
(This article belongs to the Collection Effects of Pollutants on Fish)
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Graphical abstract

Graphical abstract
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<p>Study area of the Yangtze Basin. (<b>A</b>) Dams and five hydrological monitoring stations in the Yangtze main stem (Zhutuo, river km (rkm) 2474.2, rkm 0 is at the Yangtze estuary, Yichang, rkm 1669.2, Hankou, rkm 1043.2, Datong, rkm 553.9, and Nanjing, rkm 348.1). (<b>B</b>) Dams, hydrological monitoring stations, and longitudinal profile along the river.</p>
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<p>Increasing river temperature shifts threaten the spawning of Yangtze fish. (<b>A</b>) Temporal shift in water temperature at Yichang station (spawning area of the Chinese sturgeon) during 1981–2016. (<b>B</b>) Spatial shift of temperature deviations from Yichang (i.e., Gezhouba Dam, the lowermost dam on the main stem) to estuary (<a href="#app1-animals-09-00583" class="html-app">Supplemental Table S2</a>). (<b>C</b>) Spawning window delay in spring for the four major Chinese carps and autumn for the Chinese sturgeon due to the shifting temperature regime. (<b>D</b>) Gonad development failure of the Chinese sturgeon observed in 2014, probably due to the altered temperature regime.</p>
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<p>Increases in dam construction (water impounding) and increasing temperature shifts and how they potentially impact the whole Yangtze aquatic ecosystem. The equation between total reservoir capacity (TRC) and year (i.e., x) assumed x = 1 in 1992. According to the developing trends (Sen’s slopes), the cumulative alterations of water temperature in year, M-Apr and M-Dec during 1956–2030 are +1.73 °C, −3.98 °C, and +8.03 °C, respectively.</p>
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