scholarly journals Copula-based modeling of hydraulic structures using a nonlinear reservoir model

2021 ◽  
Author(s):  
Qiaofeng Tan ◽  
Yunze Mao ◽  
Xin Wen ◽  
Tian Jin ◽  
Ziyu Ding ◽  
...  
Keyword(s):  
Water Level ◽  
Return Period ◽  
Flood Risk ◽  
Hydraulic Structures ◽  
Reservoir Model ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Volume ◽  
Discharge Water ◽  
Linear Reservoir

Abstract Multivariate flood frequency analysis has been widely used in the design and risk assessment of hydraulic structures. However, analytical solutions are often obtained based on an idealized linear reservoir model in which a linear routing process is assumed, and consequently, the flood risk is likely to be over- or underestimated. The present study proposes a nonlinear reservoir model in which the relationships of reservoir water level with reservoir volume and discharge are assumed to be nonlinear in order to more accurately describe the routing process as it takes into consideration the interactions between hydrological loading and different discharge structures. The structure return period is calculated based on the copula function and compared with that based on the linear reservoir model and the bivariate return period based on the Kendall distribution function. The results show that the structure return period based on the linear model leads to an underestimation of the flood risk under the conditions of high reservoir water level. For the same reservoir, linear and nonlinear reservoir models give quite different reservoir volume-water level and discharge-water level curves; therefore, they differ substantially in the sensitivity to flood events with different combinations of flood peak and volume. We also analyze the effects of the parameters involved in the reservoir volume-water level and discharge-water level relationships on the maximum water level at different return periods in order to better understand the applicability and effectiveness of the proposed method for different hydraulic projects.

Consequences of seasonal variation in reservoir water level for predatory fishes: linking visual foraging and prey densities

2016 ◽  
Vol 73 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Stephen L. Klobucar ◽  
Phaedra Budy
Keyword(s):  
Water Level ◽  
Lake Trout ◽  
Late Summer ◽  
Field Measurements ◽  
Early Summer ◽  
Predatory Fish ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Volume ◽  
Visual Foraging

In reservoirs, seasonal drawdown can alter the physical environment and may influence predatory fish performance. We investigated the performance of lake trout (Salvelinus namaycush) in a western reservoir by coupling field measurements with visual foraging and bioenergetic models at four distinct states (early summer, mid-summer, late summer, and fall). The models suggested that lake trout prey, juvenile kokanee (Oncorhynchus nerka), are limited seasonally by suitable temperature and dissolved oxygen. Accordingly, prey densities were greatest in late summer when reservoir volume was lowest and fish were concentrated by stratification. Prey encounter rates (up to 68 fish·day−1) and predator consumption are also predicted to be greatest during late summer. However, our models suggested that turbidity negatively correlates with prey detection and consumption across reservoir states. Under the most turbid conditions, lake trout did not meet physiological demands; however, during less turbid periods, predator consumption reached maximum bioenergetic efficiency. Overall, our findings demonstrate that rapid reservoir fluctuations and associated abiotic conditions can influence predator–prey interactions, and our models describe the potential impacts of water level fluctuation on valuable sport fishes.

scholarly journals The Monitoring-Based Analysis on Deformation-Controlling Factors and Slope Stability of Reservoir Landslide: Hongyanzi Landslide in the Southwest of China

Geofluids ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Bing Han ◽  
Bin Tong ◽  
Jinkai Yan ◽  
Chunrong Yin ◽  
Liang Chen ◽  
...  
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Monitoring Program ◽  
Significant Risk ◽  
Controlling Factors ◽  
Seepage Water ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Reservoir Area ◽  
Reservoir Landslide

Reservoir landslide is a type of commonly seen geological hazards in reservoir area and could potentially cause significant risk to the routine operation of reservoir and hydropower station. It has been accepted that reservoir landslides are mainly induced by periodic variations of reservoir water level during the impoundment and drawdown process. In this study, to better understand the deformation characters and controlling factors of the reservoir landslide, a multiparameter-based monitoring program was conducted on a reservoir landslide—the Hongyanzi landslide located in Pubugou reservoir area in the southwest of China. The results indicated that significant deformation occurred to the landslide during the drawdown period; otherwise, the landslide remained stable. The major reason of reservoir landslide deformation is the generation of seepage water pressure caused by the rapidly growing water level difference inside and outside of the slope. The influences of precipitation and earthquake on the slope deformation of the Hongyanzi landslide were insignificant.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

scholarly journals Large-Scale Landslide Displacement Rate Prediction Based on Multi-Factor Support Vector Regression Machine

2021 ◽  
Vol 11 (4) ◽  
pp. 1381
Author(s):  
Xiuzhen Li ◽  
Shengwei Li
Keyword(s):  
Support Vector Regression ◽  
Water Level ◽  
Large Scale ◽  
Displacement Rate ◽  
Support Vector ◽  
Single Factor ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Depth Analysis ◽  
Three Factor

Forecasting the development of large-scale landslides is a contentious and complicated issue. In this study, we put forward the use of multi-factor support vector regression machines (SVRMs) for predicting the displacement rate of a large-scale landslide. The relative relationships between the main monitoring factors were analyzed based on the long-term monitoring data of the landslide and the grey correlation analysis theory. We found that the average correlation between landslide displacement and rainfall is 0.894, and the correlation between landslide displacement and reservoir water level is 0.338. Finally, based on an in-depth analysis of the basic characteristics, influencing factors, and development of landslides, three main factors (i.e., the displacement rate, reservoir water level, and rainfall) were selected to build single-factor, two-factor, and three-factor SVRM models. The key parameters of the models were determined using a grid-search method, and the models showed high accuracies. Moreover, the accuracy of the two-factor SVRM model (displacement rate and rainfall) is the highest with the smallest standard error (RMSE) of 0.00614; it is followed by the three-factor and single-factor SVRM models, the latter of which has the lowest prediction accuracy, with the largest RMSE of 0.01644.

Seasonal succession and long-term stability of pelagic community in a productive reservoir

2005 ◽  
Vol 56 (8) ◽  
pp. 1137 ◽  
Author(s):  
V. F. Matveev ◽  
L. K. Matveeva
Keyword(s):  
Time Series ◽  
Water Level ◽  
Seasonal Succession ◽  
Perca Fluviatilis ◽  
Taxonomic Composition ◽  
Stationary Time Series ◽  
Introduced Fish ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Murray River

In Lake Hume, a reservoir located in an active agricultural zone of the Murray River catchment, Australia, time series for the abundances of phytoplankton and zooplankton taxa, monitored from 1991 through to 1996, were stationary (without trends), and plankton taxonomic composition did not change. This indicated ecosystem resilience to strong fluctuations in reservoir water level, and to other potential agricultural impacts, for example eutrophication and pollution. Although biological stressors such as introduced fish and invertebrate predators are known to affect planktonic communities and reduce biodiversity in lakes, high densities of planktivorous stages of alien European perch (Perca fluviatilis) and the presence of carp (Cyprinus carpio) did not translate into non-stationary time series or declining trends for plankton in Lake Hume. However, the seasonal successions observed in the reservoir in different years did not conform well to the Plankton Ecology Group (PEG) model. Significant deviations of the Lake Hume successional pattern from the PEG model included maxima for phytoplankton abundance being in winter and the presence of a clear water phase without large zooplankton grazers. The instability of the water level in Lake Hume probably causes the dynamics of most planktonic populations to be less predictable, but did not initiate the declining trends that have been observed in some other Australian reservoirs. Both the PEG model and the present study suggest that hydrology is one of the major drivers of seasonal succession.

Dam Safety Reliability Analysis Based on Artificial Neural Network

2011 ◽  
Vol 255-260 ◽  
pp. 3620-3625
Author(s):  
Hai Wei ◽  
Hua Shu Yang ◽  
Liang Wu ◽  
Yue Gui
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Level ◽  
Reliability Index ◽  
Safety Evaluation ◽  
Mathematic Model ◽  
Dam Safety ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Artificial Neural

There are many factors, such as climate, flood, material, geology, structure, management, to influence dam safety. So dam safety evaluation, involving many fields, is very complicated, and very difficult to establish mathematic model for assessment. Artificial Neural Network (ANN) has many obvious advantages to deal with these problems influenced by multi-factor, consequently is widely used in engineering fields. This paper considered water level, temperature, main factors influencing dam deformation, as random variables, employed ANN and statistical model to establish performance function of dam hidden trouble deformation and abnormal deformation. Then reliability theory was used to analyze dam safety reliability and sensitivity. The results show that temperature has great effect on probability of dam hidden trouble deformation and abnormal deformation than reservoir water level, due to great variability of temperature. Change of Reliability index of dam is contrary to reservoir water level. Temperature, especially average temperature in 10 days and 5 days, has great effect on sensitivity of reliability index than water level.

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