Reservoir Level Fluctuation Research Articles

Landslide Displacement Prediction Affected by the Periodic Precipitation, Reservoir Level and Groundwater Level Fluctuations

Since the initial impoundment of the Three Gorges Reservoir in June 2003 and approximately 30 m of reservoir level fluctuation, numerous preexisting landslides have been reactivated. To mitigate disastrous landslides, the Baishuihe landslide in the Three Gorges region was selected as a case study in predicting such displacement using the monitoring dataand a radial basis function-support vector machine (RBF-SVM) model.The landslide displacement was strongly influenced by periodic precipitation, reservoir level and groundwater level fluctuations. Primary landslide influencing factors were used as independent variables to predict the displacement using several kernel function types including sigmoid function, polynomial function, and RBF based on SVM model.Prediction results demonstrated that the RBF-SVM with the optimal parameters c

Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China

Landslides occur frequently in the Three Gorges in China, posing threats to human life and the normal operation of the Three Gorges Dam. A number of preexisting landslides have been reactivated since the initial impoundment of the Three Gorges Reservoir in June 2003. An effective and accurate method of predicting landslide displacement is necessary to mitigate the effects of these disastrous landslides. This study carries out a landslide displacement prediction for the Shuping landslide using 7 years of monitoring data, wavelet analysis, and a particle swarm-optimized support vector machine (PSO-SVM) model. The landslide’s displacement is strongly influenced by periodic precipitation and reservoir level fluctuations, and the cumulative displacement curve versus time indicates a step-like character. Based on the deformation characteristics of this landslide, the total displacement is divided into its trend and periodic components by means of the wavelet analysis. An S-curve estimation is used to predict the trend displacement via the curve fitting of the historical displacement versus time. Five primary factors are used as the input variables for a PSO-SVM model to predict periodic displacement. These factors include cumulative precipitation over the previous month, cumulative precipitation during a two-month period, maximum continuous decrement in the reservoir level during the current month, and cumulative increments and decrements in the reservoir level during the current month. The mean squared error, squared correlation coefficient, and Akaike’s information criterion of the wavelet-PSO-SVM model at GPS monitoring points ZG85 and ZG87 are 2.45, 0.945, and 20.80 and 10.46, 80.981, and 36.38, respectively. This method can be applied to the prediction of displacement in colluvial landslides in the Three Gorges. This study may provide useful information to engineers and planners involved in landslide prevention and reduction.

Survey and cause analyses of ground surface deformation near a foundation pit slope: a case study in the Three Gorges area, China

It is common that the deformation of an excavation slope leads to the displacement or even destruction of the surrounding buildings. An effective way to put an end to such accidents is to have adequate knowledge on the trigger factors of the deformation and failure of excavation slopes. Herein, taking the foundation pit slope at the Cultural Square in Badong County, China as an example, we carried out a site survey, including the engineering geology condition, the terrain change process, the spatial distribution characteristics of the surface crack and the construction process of the foundation pit. A further study was performed on the trigger factors, such as geological environment, earthquake, rainfall, fluctuation of reservoir level, human engineering activities, on ground surface deformation. The results show that the complex engineering geological condition and weak geological environment are the internal factors that lead to the deformation of the foundation pit slop. Earthquakes and fluctuations of reservoir level have little effect on this deformation, while rainfall and the excavation have a significant influence on it. The excavation process destroys the original geological environment and is thus the critical external factor inducing the formation and growth of crack, and rainfall intensifies the deformation.

Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (ELM) ensemble learning paradigm] is proposed to analysis the monitoring data for landslide displacement prediction. The rainfall data and reservoir level fluctuation data are also integrated into the study. The rainfall series, reservoir level fluctuation series and landslide accumulative displacement series are all decomposed into the residual series and a limited number of intrinsic mode functions with different frequencies from high to low using EEMD technique. A novel neural network technique, ELM, is employed to study the interactions of these sub-series at different frequency affecting landslide occurrence. Each sub-series extracted from accumulative displacement of landslide is forecasted respectively by establishing appropriate ELM model. The final prediction result is obtained by summing up the calculated predictive displacement value of each sub. The EEMD–ELM model shows the best accuracy comparing with basic artificial neural network models through forecasting the displacement of Baishuihe landslide in the Three Gorges reservoir area of China.

Study on Phreatic Line of Reservoir Slope under the Rainfall and Reservoir Level Fluctuation

The determination of the phreatic line under the action of rainfall and reservoir water level fluctuation together is of great importance for reservoir slope stability analysis. However, there are no unified Formulas for the calculation of phreatic line. Homogeneous reservoir slope seepage model with inclined impermeable bed is built. Then, a conceptual model is set up based on the basic principle of seepage theory, and the approximate analytic solutions of phreatic line are derived with the Boussinesq Differential Equation of unsteady infiltration and initial value calculated in steady flow condition. And the simplified Formula, which is convenient for engineering application, is obtained by Polynomial Fitting Method.

Factor Analysis of Liangshuijing Landslide Deformation

The evolution of landslide displacement is affected by many factors. Therefore, it has great significance to find out the factors which play the main role for the landslide dynamic evolution and inducing mechanism. This paper takes Liangshuijing Landslide as an example, combining with the displacement monitoring data, and selects the rainfall of current month, cumulative rainfall of anterior two months, monthly maximum reservoir water level and the monthly average reservoir water level as influencing factors. According to factor analysis, two main factors and rotated component matrix are extracted. And the main influencing factors for the landslide displacement of every year are found. With the comprehensive analysis of the factor loadings in the four factors, we can get the influencing degree order for Liangshuijing Landslide displacement evolution as follows: cumulative rainfall of anterior two months> rainfall of current month> the average reservoir level of current month>reservoir level fluctuation of current month.

Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes in landslide displacement and inducing factors. In this paper, a novel neural network technique called ensemble of extreme learning machine (E-ELM) is proposed to investigate the interactions of different inducing factors affecting the evolution of landslide. Grey relational analysis is used to sieve out the more influential inducing factors as the inputs in E-ELM. Trend component displacement and periodic component displacement are forecasted, respectively; then, total predictive displacement is obtained by adding the calculated predictive displacement value of each sub. Performances of our model are evaluated by using real data from Baishuihe landslide in the Three Gorges Reservoir of China, and it provides a good representation of the measured slide displacement behavior.

Aquatic macrophytes in a tropical African reservoir: diversity, communities and the impact of reservoir-level fluctuations

ABSTRACT Variation in species composition, distribution and abundance of macrophytes in the Malilangwe reservoir, a tropical African reservoir, was investigated at four sites in three seasons; March (hot-wet season), June (cool-dry season) and September (hot-dry season) over a nine-month period. Thirteen macrophyte species, representing nine families, were recorded during the study period. Submerged macrophytes had a patchy distribution and the mean transect cover per species was below 3% with the exception of Ceratophyllum demersum and Potamogeton pusillus. Canonical Correspondence Analysis (CCA) identified four distinct macrophyte groups closely associated with the three seasons; hot-wet, cool-dry and hot-dry and environmental factors; pH, dissolved oxygen, phosphorus and water level. Change of seasons and subsequent fluctuations in water levels resulted in changes in macrophyte community structure from the dominant emergent macrophytes (Cyperus involucratus, Panicum repens, Ludwigia stolonifera, Phragmites mauritianus and Schoenoplectus corymbosus) in the hot-wet season to submerged macrophytes (Najas horrida, Potamogeton crispus, Potamogeton pusillus, Potamogeton tricarinatus and Persicaria decipiens) in the hot-dry season. Although changes in water level variations seemed to influence macrophyte structure and composition changes, further research is needed to evaluate to what extent water level fluctuations interact with other seasonal factors acting independently.

Deformation Characteristics of a Preexisting Landslide in Reservoir Area during Reservoir Filling and Operation

A monitoring system was implemented to monitor the hydrodynamic and deformation response of a preexisting landslide in the Three Gorges Reservoir area. The monitoring results indicate that the deformation is mainly caused by reservoir impounding and is mainly composed of the deformation of the slip band. The effect of reservoir level fluctuation on landslide stability mainly depend on two aspects, one is the change of shear stress and shear strength on the slip surface due to the change of slide body weight and pore water pressure, the other is seepage pressure.

Hydrodynamic Change and Failure Mechanism of Qianjiangping Landslide, the Three Gorges Reservoir

Reservoir landslide is a severe geological hazard which is caused by the hydrodynamic change due to rainfall and fluctuation of reservoir level. The hydrodynamic change and its influence on landslide stability of Qianjiangping landslide before and after impoundment of the Three Gorges Reservoir are studied combining with its hydrogeological structure. There are two relatively independent hydrogeological units on both sides of the slipband. Before impoundment, the outlet of groundwater under slipband is above the river level, the supply and discharge of groundwater is in equilibrium, and the excess pore water pressure at the bottom of slipband is smaller or not exists. After impounding, the outlet of groundwater is under reservoir level and the discharge of groundwater is hindered, so the excess pore water pressure at the bottom of slipband will rises and the safety factor of the landslide will decreases. Combing with other factors, the stability of the landslide will further decreases and lead to landslide failure finally.

Displacement prediction in colluvial landslides, Three Gorges Reservoir, China

The prediction of active landslide displacement is a critical component of an early warning system and helps prevent property damage and loss of human lives. For the colluvial landslides in the Three Gorges Reservoir, the monitored displacement, precipitation, and reservoir level indicated that the characteristics of the deformations were closely related to the seasonal fluctuation of rainfall and reservoir level and that the displacement curve versus time showed a stepwise pattern. Besides the geological conditions, landslide displacement also depended on the variation in the influencing factors. Two typical colluvial landslides, the Baishuihe landslide and the Bazimen landslide, were selected for case studies. To analyze the different response components of the total displacement, the accumulated displacement was divided into a trend and a periodic component using a time series model. For the prediction of the periodic displacement, a back-propagation neural network model was adopted with selected factors including (1) the accumulated precipitation during the last 1-month period, (2) the accumulated precipitation over a 2-month period, (3) change of reservoir level during the last 1 month, (4) the average elevation of the reservoir level in the current month, and (5) the accumulated displacement increment during 1 year. The prediction of the displacement showed a periodic response in the displacement as a function of the variation of the influencing factors. The prediction model provided a good representation of the measured slide displacement behavior at the Baishuihe and the Bazimen sites, which can be adopted for displacement prediction and early warning of colluvial landslides in the Three Gorges Reservoir.

Coupling analysis of unsteady seepage and stress fields in discrete fractures network of rock mass in dam foundation

The drag force of water flow through single fracture and the coupling characteristics of seepage and stress in single fracture surface are analyzed, and a three dimensional model of coupled unsteady seepage and stress fields is proposed. This model is used to the analysis of foundation rock mass of a high dam. If the coupling effects are considered, the changes of boundary heads have less influence on the inner head of rock mass, and the strong permeability of main fractures appears. If the coupling effects are not considered, the fractures distribution affects the inner head more greatly. When the upstream water head declines, the inner head of dam foundation slightly declines and the hydraulic gradient distribution becomes smoother. A bigger upstream water level declining velocity has a stronger lag effect, meanwhile the values of stress components change more greatly. Therefore the upstream water level declining velocity directly affects the stability of rock mass in dam foundation and we should take into account the above factors to make sure the safety of the dam during reservoir level fluctuation period.

Movement behaviour of the Little Chief Slide

The Little Chief Slide has been extremely slow moving at a rate of 4–14 mm/year since the late 1960s. The 800 million m3slide lies about 3 km upstream of Mica Dam in British Columbia. The extremely slow rate was not affected by the Mica Dam reservoir filling in the 1970s. Although the Little Chief Slide is classified as a rock slide, the extremely slow movements take place on seams of soft materials composed mainly of micaceous clay gouge. The characteristics of the movement trends with time were investigated at six zones using in-place inclinometers (IPIs). The trends of the movement with the reservoir level fluctuations and pore pressure changes were investigated to quantify the contribution of steady-state creep to the total in situ movements. The creep behaviour of the clay gouge materials forming the movement zones was also investigated in the laboratory. The laboratory and field creep behaviours were then compared. The movement could be partitioned into steady-state creep and seasonally induced creep movements. This partitioning identified the locations where the seasonal movements are the highest, and, hence, where drainage measures could have the biggest impact.

Landslide prediction based on wavelet analysis and cusp catastrophe

During the monitoring engineering of landslides, the monitoring data of accumulated displacement are usually affected by the external factors. Therefore, the displacement curve always has step-like character, which brings some difficulties to the accurate prediction of landslides. In order to solve this problem, based on the wavelet analysis and cusp catastrophe, a new kind of analysis method is proposed in this article. First, Fourier transform method can be used to extract the frequency component of the curve of monitoring displacement. Second, the wavelet transform was adopted to inspect the breakpoints of signals, which can be used to analyze the cause of the occurrence of the step-like character in the curve of landslide monitoring. Based on the cusp catastrophe theory, a nonlinear dynamic model was established to conduct the simulation calculation of time forecasting of landslides. According to a case study of landslide, the periodical rainfall and reservoir level fluctuation are the main factors leading to the step-like changes in the curve of monitoring displacement. In addition, the results of simulation calculation are in agreement with the fact of local failure of landslides. This method can provide a new analysis way for the time prediction of landslides.

The hydraulic management of the Barra Bonita reservoir (SP, Brazil) as a factor influencing the temporal succession of its fish community

The temporal succession of fish communities allows evaluating the environmental conditions and the adaptation capacity of the fish species to anthropogenic stress in reservoirs. The fish community at Barra Bonita reservoir was sampled in two different periods of the year (dry and rainy) and in three different areas of the reservoir (fluvial, transition, and lentic). The species list was compared to another four lists, trying to detect the transformations of the fish community for the last 15 years. In order to evaluate the adaptation of the present fish community to the hydraulic management of reservoir, the trophic and reproductive structures were studied. Temporal succession analysis shows little change in fish richness of the communities. The number of fish species varies between 23 and 39 for a total of 68 registered species. From this, 27 can be considered constant, 14 accessory and 27 accidental; the main differences observed were for Anostomidae, Loricariidae and Characidae families. In relation to the hydraulic management, we found a fish community stabilized and adapted to environmental stress. This is characterized by the dominance of small-sized fish species of opportunistic diet and high reproductive compensation (r-strategists). The overlap of biological cycles of the most abundant species with the reservoir level fluctuations points to the period from September to March-April as critical for reproductive success and only the species with partial reproductive strategy or parental care are best succeeded. These results, interpreted in the context of the reservoir aging process, indicate that Barra Bonita reservoir is entering a transition phase, between the colonization and aging stages.

Ladon dam (Greece) deformation and reservoir level fluctuations: evidence for a causative relationship from the spectral analysis of a geodetic monitoring record

Three different spectral analysis techniques, namely Discrete Fourier Transformation (DFT), Lomb normalized periodogram analysis and fitting of trigonometric/polynomial functions, were used to analyze a long (>30 years) geodetic monitoring record consisting of the horizontal and vertical displacements of six control stations established on the crest of the Ladon concrete dam, in order to identify whether reservoir level fluctuations are responsible for dam deformation. The displacements of the dam were very small (up to 7 mm), statistically significant against random errors and maximum at the middle of the dam. There was no apparent linear correlation between dam displacements and the reservoir level. On the contrary there was evidence of fluctuation in the amplitude of displacement. The presence of periodicity in our data was confirmed using the autocorrelation function. The DFT, Lomb normalized periodogram and trigonometric/polynomial fitting revealed that horizontal and vertical displacements of three control stations at the middle of the crest of the dam correspond to a 12 month period, similar to that of the fluctuations of the reservoir level, indicating a causative relationship between hydraulic load and dam deformation.

Fault stability changes induced beneath a reservoir with cyclic variations in water level

The stress and pore pressure changes produced by a steady periodic variation of water level on the surface of a uniform porous elastic half‐space are evaluated using the fully coupled (Biot) equations of elastic deformation and pore fluid flow. Diverse choices of material properties all give a coupled stress field differing from the elastic stress field by at most 0.035 po, where po is the water pressure at the bottom of the reservoir. Peak coupled pore pressure change can lag peak water level in the depth range 0 < (ω/2c)1/2z < π, where ω is frequency of the cyclic change in water level, c is diffusivity, and z is depth. The maximum lag increases as B decreases, where B is the ratio of pore pressure increase to mean compressive stress increase under undrained conditions. Directly beneath the reservoir, for example, peak pore pressure in an annual cycle can lag peak water level by at most 10 days if B = 0.80, but can lag by up to 122 days if B = 0.11. When cyclic water level changes are superimposed on the steady state reservoir level, the time during the cycle at which a fault is most destabilized depends on whether the weight of the reservoir stabilizes or destabilizes the fault, which, in turn, depends on its orientation and location relative to the reservoir. B and c also influence the timing of the greatest destabilization. If B and c are low, maximum destabilization at low water level is possible for faults that are stabilized by the weight of the reservoir; this mechanism may have operated at Lake Mead. The analysis suggests that induced seismic events should be separated into groups having a common focal mechanism and occurring in similar locations relative to the reservoir before studying the time at which the events occur relative to the water level. The fully coupled solution is compared with an uncoupled solution, with a solution that is coupled but which assumes incompressible solid and fluid constituents (consolidation) and with a decoupled solution in which the difference between the pore pressure field and B times the elastic mean compressive stress obeys a homogeneous diffusion equation. The uncoupled and consolidation solutions respectively underestimate and overestimate pore pressure during short‐term reservoir level fluctuations as well as at times short compared to that required to achieve steady state. In contrast, the decoupled solution agrees closely with the fully coupled solution for the problem studied here.