scholarly journals Results of Chernovskoe Reservoir hydro botanical monitoring from 1974 to 2015

2017 ◽  
Vol 6 (1) ◽  
pp. 77-82
Author(s):  
Vera Valentinovna Solovieva
Keyword(s):  
Dynamic Equilibrium ◽  
Floristic Diversity ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Important Indicator ◽  
Vegetation Growth ◽  
The Past ◽  
Coastal Plant ◽  
Level Lowering ◽  
Water Vegetation

Features of reservoirs use make it necessary to collect, analyse and synthesise environmental information about the state of hydroecosystems with a purpose of their development forecast. Overgrowing processes are an important indicator of ecosystem. The following paper contains the results of the study of Chernovskoe reservoir flora and vegetation in different years. Floristic diversity is compared with other reservoirs and hydro botanic information about them has already been published in a number of the authors papers. The comparative analysis has shown that the overall composition of reservoirs flora is random, while there is some regularity in the environmental spectrum - each of them is characterized by a small number of aquatic species and by the dominance of coastal plant species. The study of Chernovskoe reservoir vegetation has shown that the composition of dominants has changed over the past 40 years, from 1974 to 2015. There is a dominance of air water vegetation above the water one, but the borders of the water vegetation growth have widened. Chernovskoe reservoir is currently in dynamic equilibrium. The lifetime of aquatic ecosystem at this stage may be unlimited if there is an unstable hydro regime and impulsive character of water use. The reservoir water level lowering may lead to overgrowth and accelerate activation of waterlogging.

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 Characterizing the Development Pattern of a Colluvial Landslide Based on Long-Term Monitoring in the Three Gorges Reservoir

2021 ◽  
Vol 13 (2) ◽  
pp. 224
Author(s):  
Xin Liang ◽  
Lei Gui ◽  
Wei Wang ◽  
Juan Du ◽  
Fei Ma ◽  
...  
Keyword(s):  
Time Variable ◽  
Development Pattern ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges ◽  
Long Term Monitoring ◽  
Term Monitoring ◽  
Colluvial Landslide

Since the impoundment of the Three Gorges Reservoir (TGR) in June 2003, the fluctuation of the reservoir water level coupled with rainfall has resulted in more than 2500 landslides in this region. Among these instability problems, most colluvial landslides exhibit slow-moving patterns and pose a significant threat to local people and channel navigation. Advanced monitoring techniques are therefore implemented to investigate landslide deformation and provide insights for the subsequent countermeasures. In this study, the development pattern of a large colluvial landslide, locally named the Ganjingzi landslide, is analyzed on the basis of long-term monitoring. To understand the kinematic characteristics of the landslide, an integrated analysis based on real-time and multi-source monitoring, including the global navigation satellite system (GNSS), crackmeters, inclinometers, and piezometers, was conducted. The results indicate that the Ganjingzi landslide exhibits a time-variable response to the reservoir water fluctuation and rainfall. According to the supplement of community-based monitoring, the evolution of the landslide consists of three stages, namely the stable stage before reservoir impoundment, the initial movement stage of retrogressive failure, and the shallow movement stage with stepwise acceleration. The latter two stages are sensitive to the drawdown of reservoir water level and rainfall infiltration, respectively. All of the monitoring approaches used in this study are significant for understanding the time-variable pattern of colluvial landslides and are essential for landslide mechanism analysis and early warning for risk mitigation.

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.

scholarly journals Reservoir Flood Routing Simulation for Dam Safety Management in Thailand

2021 ◽  
Vol 16 (4) ◽  
pp. 596-606
Author(s):  
Rangsarit Vanijjirattikhan ◽  
Chinoros Thongthamchart ◽  
Patsorn Rakcheep ◽  
Unpong Supakchukul ◽  
Jittiwut Suwatthikul ◽  
...  
Keyword(s):  
Safety Management ◽  
Simulation Software ◽  
Flood Routing ◽  
Dam Safety ◽  
Dam Construction ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Remote Monitoring System ◽  
Operation Rules ◽  
Gate Opening

A reservoir flood routing simulation software with spillway operation rules that are readable and configurable by the spillway operator is developed in this study. The software is part of the Dam Safety Remote Monitoring System used by the Electricity Generating Authority of Thailand. The flood routing simulation is implemented using a storage-indication routing method, which is a hydrologic method. The spillway operation rules are exhibited in a tree-based structure, in which the spillway gate opening is derived from the current reservoir water level (RWL), spillway gate opening, and flood situation if the peak inflow has passed. The simulation results show that the simulated RWL is similar to the RWL data in the dam construction manual. This verifies the accuracy of the reservoir flood routing simulation, which is useful for planning the spillway operation.

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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