scholarly journals STUDY ON THE PERFORMANCE OF WONOGIRI RESERVOIR AS FLOOD CONTROL STRUCTURE

2015 ◽  
Vol 1 (3) ◽  
pp. 85
Author(s):  
Alexander Armin Nugroho
Keyword(s):  
Water Level ◽  
Flood Control ◽  
Control Function ◽  
Flood Routing ◽  
Control Limit ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Catchment Areas ◽  
Flood Hydrograph ◽  
Reservoir Inflow

The Wonogiri reservoir was built with a primary function as flood control, especially in areas prone to flooding along the Bengawan Solo River. To find out the performance of the Wonogiri reservoir in flood control of Bengawan Solo, a study was conducted on flood hydrograph characteristics of the reservoir inflow by considering the contribution inflow from all sub-watersheds in the reservoir catchment area, at the end of December 2007. Calculation analysis flood hydrograph of Wonogiri Reservoir inflow is done with the calibration of Wuryantoro and Keduang sub-watersheds. Results of the calibration were then used reference to simulate flood hydrograph inflow in each sub-watershed catchment areas. Flood routing in the reservoir was done with the assumption that the inflow of the reservoir was left to face up a height of water in the reservoir 135.3 m (the lower flood control limit) and 138.3 m (the upper flood control limit) and then the spillway gates full-opening. Results of this research indicated that the maximum discharge inflow into the reservoir on the event of Wonogiri flood at the end of December 2007 was ranged from 3,331 to 4,993 m3/s; and it was occurred on December 26, 2007 at between 04:00 - 06:00 am. The most dominant flood hydrograph contribution into the reservoir was derived from Keduang sub-watershed. The flood in the reservoir was simulated as that the spillway gates were closed until water level of reservoir reached the minimum height of 135.3 m and 138.3 m and only until then the spillway gates full-opening. The reservoir water level reached 135.47 m on December 26, 2007 at 6:00 am and outflow was generated when the gates opened to reach 550 m3/s and then increased up to 642 m3/s at 14:00 after then it gradually decreases. The water level simulation was unable to reach 138.3 m because up to December 27, 2007 at 23:00 the water level reservoir reaches only 136.44 m. The Wonogiri reservoir flood control function still can run well and able to reduce the peak flood of 85%.

scholarly journals Research and Application of Key Technologies for Dynamic Control of Reservoir Water Level in Flood Season

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3576
Author(s):  
Jun Zhang ◽  
Yaowu Min ◽  
Baofei Feng ◽  
Weixin Duan
Keyword(s):  
Water Supply ◽  
Water Level ◽  
Real Time ◽  
Flood Control ◽  
Dynamic Control ◽  
Water Levels ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Danjiangkou Reservoir ◽  
Flood Season

In today’s reservoir operation study, it is urgent to solve the issues on improving flood resource utilization, maximizing reservoir impoundment, and guaranteeing water supply through real-time regulation optimization under the premise of ensuring flood control safety and taking risks properly. Based on previous studies, the key real-time operation technologies for dynamic control of reservoir water levels in flood season are summarized. The Danjiangkou Reservoir was taken as an example, the division of flood stages, reservoir water level requirements for improving water supply guarantee, dynamic control indexes of reservoir water level for beneficial use in stages during the flood season, and flood control dispatching indexes are proposed. Moreover, a practicable real-time flood forecast operation scheme for Danjiangkou Reservoir was compiled. Its application in 2017 indicated that the established scheme can provide strong technical support to ensure the overall benefits of Danjiangkou Reservoir, including flood control, water supply, and power generation.

scholarly journals Effect of Seasonality on the Quantiles Estimation of Maximum Floodwater Levels in a Reservoir and Maximum Outflows

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 519
Author(s):  
José Aranda ◽  
R. García-Bartual
Keyword(s):  
Water Level ◽  
Stochastic Approach ◽  
Flood Routing ◽  
Reservoir Water ◽  
Initial Water ◽  
Reservoir Water Level ◽  
Main River ◽  
Relevant Variables ◽  
Hydrological System ◽  
Operation Results

Certain relevant variables for dam safety and downstream safety assessments are analyzed using a stochastic approach. In particular, a method to estimate quantiles of maximum outflow in a dam spillway and maximum water level reached in the reservoir during a flood event is presented. The hydrological system analyzed herein is a small mountain catchment in north Spain, whose main river is a tributary of Ebro river. The ancient Foradada dam is located in this catchment. This dam has no gates, so that flood routing operation results from simple consideration of fixed crest spillway hydraulics. In such case, both mentioned variables (maximum outflow and maximum reservoir water level) are basically derived variables that depend on flood hydrograph characteristics and the reservoir’s initial water level. A Monte Carlo approach is performed to generate very large samples of synthetic hydrographs and previous reservoir levels. The use of extreme value copulas allows the ensembles to preserve statistical properties of historical samples and the observed empirical correlations. Apart from the classical approach based on annual periods, the modelling strategy is also applied differentiating two subperiods or seasons (i.e., summer and winter). This allows to quantify the return period distortion introduced when seasonality is ignored in the statistical analysis of the two relevant variables selected for hydrological risk assessment. Results indicate significant deviations for return periods over 125 years. For the analyzed case study, ignoring seasonal statistics and trends, yields to maximum outflows underestimation of 18% for T = 500 years and 29% for T = 1000 years were obtained.

scholarly journals The study on the time lag of water level in the Three Gorges Reservoir under the regulation processes

2021 ◽  
Author(s):  
Kunlong He ◽  
Hongwei Shi ◽  
Chenchen Chen ◽  
Yao Cheng ◽  
Jiao Liu
Keyword(s):  
Water Level ◽  
Three Gorges Reservoir ◽  
Flood Control ◽  
Time Lag ◽  
Three Gorges ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
The Three Gorges Reservoir ◽  
The Three Gorges ◽  
Operation Period

Abstract The identification of the water level time lag (WLTL) under the regulation processes is of great significance for environmental impact, flood control, and sediment transport of huge reservoirs. The traditional hydrodynamic method can calculate the flood inflow process and the water level change process along the river channel, but it is difficult to estimate the time difference of the reservoir water level fluctuation to the dispatching process. To quantitatively evaluate the reservoir regulation effect on the WLTL in the Three Gorges Reservoir (TGR), the daily water level data from 2011 to 2017 of five stations in the TGR are analyzed in this paper. The results revealed that there is a significant water level difference along the reservoir from April 1 to October 31. The gap between the end of the reservoir and the Three Gorges Dam (TGD) is the largest, reaching 23.67 m on July 2. The longer the distance from the TGD, the longer the time lag. Furthermore, the WLTL is also different at the four different operating periods of the reservoir in a year. During the low water level operation period and high water level operation period, the time lag is 3 days which is the greatest, while in the water level decline period and water level rise period, the time lag is within 2 days.

scholarly journals A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3461
Author(s):  
Hao-Han Tsao ◽  
Yih-Guang Leu ◽  
Li-Fen Chou ◽  
Chao-Yang Tsao
Keyword(s):  
Water Level ◽  
Reservoir Operation ◽  
Flood Control ◽  
Fuzzy Neural Networks ◽  
Water Levels ◽  
Hydropower Plant ◽  
Extreme Weather Events ◽  
Reservoir Water ◽  
Multi Stage ◽  
Reservoir Inflow

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

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.

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