Sensitivity of Recreational Access to Reservoir Water Level Variation: An Approach to Identify Future Access Needs in Reservoirs

2011 ◽  
Vol 31 (1) ◽  
pp. 63-69 ◽  
Author(s):  
Daniel J. Daugherty ◽  
David L. Buckmeier ◽  
Praveen K. Kokkanti
Keyword(s):  
Water Level ◽  
Level Variation ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Water Level Variation

Monitoring Model for Dam Seepage Based on Lag Effect

2013 ◽  
Vol 353-356 ◽  
pp. 2456-2462 ◽  
Author(s):  
Shao Wei Wang ◽  
Teng Fei Bao
Keyword(s):  
Water Level ◽  
Daily Variation ◽  
Level Variation ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Water Level Variation ◽  
Monitoring Model ◽  
Lag Effect ◽  
Monitoring And Forecasting ◽  
The Impact

The traditional dam seepage monitoring model is based on the relationship of seepage elements with the upstream reservoir water level and rainfall, which includes the impact of base value, this model will weaken the effects of reservoir water level variation and rainfall on the variation of seepage elements, especially under the condition of high reservoir water level and smaller head fluctuation, so components separated by this model are not normally the practical. Based on the theory of unsteady seepage, the lag effect function has been introduced into the seepage monitoring model to take the lag effect of reservoir water level variation and rainfall into account, and the daily variation monitoring model of dam seepage elements is established. Case studies are in good agreement with original observations, so the proposed model can be used in the daily variation monitoring and forecasting of dam seepage elements.

Study on Relationship between Deformation of Reservoir Water Level Fluctuation Change of the Shuping Landslide

2014 ◽  
Vol 556-562 ◽  
pp. 881-884
Author(s):  
Xue Qing Yan ◽  
Zong Hua Zhou ◽  
Peng Fei Tu
Keyword(s):  
Water Level ◽  
General Rule ◽  
Water Level Fluctuation ◽  
Level Fluctuation ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Water Level Variation ◽  
The Three Gorges ◽  
Landslide Deformation ◽  
Reservoir Water Level Fluctuation

The change of reservoir water level fluctuation has an important influence on the landslide deformation. In this paper, taking Zigui County of the Three Gorges Reservoir Shuping landslide as an example, based on the deformation and water level of reservoir deformation related analysis of landslide, summarizes the general rule by reservoir water level variation effect on deformation of landslide, which can provide some reference for the monitoring and analysis of landslide similar.

The Design of Movable Landslide Physical Model Testing Frame

2011 ◽  
Vol 201-203 ◽  
pp. 1433-1438 ◽  
Author(s):  
Dai Peng Zhao ◽  
Shi Mei Wang ◽  
Yun Zhi Tan ◽  
Xiao Ling Liu
Keyword(s):  
Water Supply ◽  
Water Level ◽  
Physical Model ◽  
Model Testing ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Water Level Variation ◽  
Physical Model Tests ◽  
Uniform Sample ◽  
Mechanism Of Landslide

The small landslide physical model testing frame was designed and made in order to analyze the induction mechanism of landslide by rainfall and reservoir water level variation by adopting the physical model tests. It is consisted by exterior frame, internal body box, water supply and discharge pipelines, hoisting jacks and steel rollers. Some functions such as uniform sample preparation, confined effect reduction, lifting flexibility as well as water level change simulation and so on are realized through some design technologies like removable model frame, adjustable width, rotatable frame body ,mobile model frame and water supply and discharge pipeline and so on.

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 Analysis of the Water Level Variation in the Polish Part of the Vistula Lagoon (Baltic Sea) and Estimation of Water Inflow and Outflow Transport through the Strait of Baltiysk in the Years 2008–2017

Water ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1328
Author(s):  
Michał Szydłowski ◽  
Wojciech Artichowicz ◽  
Piotr Zima
Keyword(s):  
Statistical Analysis ◽  
Numerical Model ◽  
Baltic Sea ◽  
Water Level ◽  
Vistula Lagoon ◽  
Level Variation ◽  
The Baltic Sea ◽  
Water Level Variation ◽  
The Baltic ◽  
The Vistula Lagoon

The Vistula Lagoon is located in both Poland and Russia along the southern coast of the Baltic Sea. It is connected to the Baltic Sea in the Russian part by the Strait of Baltiysk. The purpose of the paper is to identify the dominant factors underlying the water level variation mechanism at Tolkmicko in the Vistula Lagoon, revealed by a statistical analysis of the measured data and a discussion on the inflow and outflow transport variation through the strait, estimated by numerical modeling. Seawater transport is exceptionally valuable in terms of the hydrological water balance in the lagoon. Historical research on the hydrology of the lagoon shows that the water exchange in the lagoon is quite complex due to the presence of several different sources of water balance, such as seawater inflow, river inflow, groundwater inflow, precipitation, and evaporation. Unfortunately, there are no current data on seawater inflow and outflow through the Strait of Baltiysk due to the lack of continuous flow measurements in the strait. A novelty of the current work is an in-depth statistical analysis of the water level variation in the Polish part of the lagoon over a long time period and an estimation of water transport through the Strait of Baltiysk by use of a numerical model. The model reproduces well the water level variation responding to variations in the sea level outside the lagoon and the wind action over the lagoon. The years 2008–2017 were chosen as the analysis period. A two-dimensional free surface shallow water numerical model of the lagoon was adapted to simulate the water level variation in view of the wind over the lagoon and the sea level variation at one open boundary. Finally, it was concluded that the water level variation on the Polish side of the Vistula Lagoon is dominated by two factors: the water level in the Gulf of Gdańsk and the wind over the lagoon. The average annual marine water inflow into the Vistula Lagoon was estimated to be equal to 15.87 km3.

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.

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