Tarbela Dam Research Articles

Understanding the Effect of Hydro-Climatological Parameters on Dam Seepage Using Shapley Additive Explanation (SHAP): A Case Study of Earth-Fill Tarbela Dam, Pakistan

For better stability, safety and water resource management in a dam, it is important to evaluate the amount of seepage from the dam body. This research is focused on machine learning approach to predict the amount of seepage from Pakistan’s Earth and rock fill Tarbela Dam during 2003 to 2015. The data of temperature, rainfall, water inflow, sediment inflow, reservoir level collected during 2003 to 2015 served as input while the seepage from dam during this period was the output. Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and CatBoost (CB), have been used to model the input-output relationship. The algorithms used to predict the dam seepage reported a high R2 scores between actual and predicted values of average seepage, suggesting their reliability in predicting the seepage in the Tarbela Dam. Moreover, the CatBoost algorithm outperformed, by achieving an R2 score of 0.978 in training, 0.805 in validation, and 0.773 in testing phase. Similarly, RMSE was 0.025 in training, 0.076 in validation, and 0.111 in testing phase. Furthermore, to understand the sensitivity of each parameter on the output (average seepage), Shapley Additive Explanations (SHAP), a model explanation algorithm, was used to understand the affect of each parameter on the output. A comparison of SHAP used for all the machine learning models is also presented. According to SHAP summary plots, reservoir level was reported as the most significant parameter, affecting the average seepage in Tarbela Dam. Moreover, a direct relationship was observed between reservoir level and average seepage. It was concluded that the machine learning models are reliable in predicting and understanding the dam seepage in the Tarbela Dam. These Machine Learning models address the limitations of humans in data collecting and analysis which is highly prone to errors, hence arriving at misleading information that can lead to dam failure.

Floating solar photovoltaic as virtual battery for reservoir based hydroelectric dams: A solar-hydro nexus for technological transition

This paper presents a policy approach for the technological transition from hydropower to green energy in developing countries. Conventional electricity generation technologies have proven to be a burden on the economy of a country, as they have to depend on fossil fuels to meet their energy needs, such as diesel and gasoline. Pakistan has been taken as a case study to show the growing electricity demand of a developing country that implores major technological initiation. This serves as the assessment of suitability and resource regarding the installation of floating solar photovoltaic (FSPV) on reservoirs of hydroelectric dams, thus acting as a virtual battery. Generation from FSPV can complement the hydropower generation in the dry period, consequently meeting the peak demand. In real-time, by employing both technologies at the same substation, hydropower covers the intermittency issues of floating PV technology by improving power quality. Seven dams selected for this study have been analyzed based on their percentage surface area of the reservoir for electricity generation, water conservation, and emission reduction. Tarbela dam has been further analyzed for demonstrating the virtual battery effect by improving the electricity generation profile. The proposed approach meets the electricity needs of about 15.5% of the total population and serves the water need of 2.3 million people in the country.

Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan’s Tarbela dam, the world’s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN–LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions.

CFD Analysis to Predict Flow Behavior for Design and Operation of Tarbela Dam Reservoir for Different Environmental Conditions

Tarbela Dam is one of the world largest filled dams with a capacity of 13.69 km³. It is the backbone of Pakistan, and it is used for both power generation and irrigation. Like all dam reservoirs, sedimentation decreases the life of the Tarbela dam reservoir and affects the power generating unit and tunnel equipment’s and instruments. The Finite Volume Method was used to conduct a CFD analysis to investigate different parameters that influence sedimentation. The analysis was carried out using multiphase flow (air and water) considering the summer season for maximum reservoir inflow and discharge water through spillways and tunnels. Boundary conditions were applied to a 3D geometric model that had been meshed. The spillway outlet was found to have the highest velocity. Near the spillways outlet and tunnel inlets, strong vortex motion was observed. WAPDA were suggested to redesign tunnels 3 and 4 in order to reduce sediment inflow, as well as to improve the design of the spillways.

Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area

The mountainous catchments often witness contrasting regimes and the limited available meteorological network creates uncertainty in both the hydrological data and developed models. To overcome this problem, remotely sensed data could be used in addition to on-ground observations for hydrological forecasting. The fusion of these two types of data gives a better picture and helps to generate adequate hydrological forecasting models. The study aims at the improvement of ANN-based streamflow estimation models by using an integrated data-set containing, the satellite-derived snow cover area (SCA) with on-ground flow observations. For this purpose, SCA of three sub catchments of Upper Indus Basin, namely Gilgit, Astore and Bunji coupled with their respective gauge discharges is used as model inputs. The weekly stream-flow models are developed for inflows at Besham Qila located just upstream of Tarbela dam. The data-set for modeling is prepared through normalizing all variables by scaling between 0 and 1. A mathematical tool, Gamma test is applied to fuse the inputs, and a best input combination is selected on the basis of minimum gamma value. A feed forward neural network trained via two layer Broyden Fletcher Goldfarb Shanno algorithm is used for model development. The models are evaluated on the basis of set of performance indicators, namely, Nash–Sutcliffe Efficiency, Root Mean Square Error, Variance and BIAS. A comparative assessment has also been made using these indicators for models developed, through data-set containing gauge discharges, only and the data-set fused with satellite-derived SCA. In particular, the current study concluded that the efficiency of ANN-based streamflow estimation models developed for mountainous catchments could be improved by integrating the SCA with the gauge discharges.

Suitability of Gini moraines as natural pozzolanic material for Diamer Basha dam project

Diamer Basha dam is to be located on the mighty Indus River about 315 km upstream of Tarbela dam. The dam will be 272 m high and constructed from roller-compacted concrete (RCC). Earlier studies for the RCC mixture and placement conditions regarding heat development showed that extensive use of cement substitutes is essential for the construction of the RCC dam. The total amount of cementitious substitutes will be up to 2·0 Mt, which need to be supplied over a period of 4·5–5 years. Potential substitutes (blast-furnace slag from Pakistan Steel Mills, Karachi and fly ash from Lakhra, Pakistan and Bathinda, India) were considered unfeasible due to low production capacities, long distances from the project site and political reasons. In this study, locally available potential pozzolanic materials from Gini Gah, about 54 km upstream of the dam site, were investigated through physical, chemical and pozzolanic activity tests to assess their suitability for use in mass concrete. The results revealed that glacial moraines obtained from Gini are acceptable for use in RCC.

An Innovative Approach to Minimizing Uncertainty in Sediment Load Boundary Conditions for Modelling Sedimentation in Reservoirs

A number of significant investigations have advanced our understanding of the parameters influencing reservoir sedimentation. However, a reliable modelling of sediment deposits and delta formation in reservoirs is still a challenging problem due to many uncertainties in the modelling process. Modelling performance can be improved by adjusting the uncertainty caused by sediment load boundary conditions. In our study, we diminished the uncertainty factor by setting more precise sediment load boundary conditions reconstructed using wavelet artificial neural networks for a morphodynamic model. The model was calibrated for hydrodynamics using a backward error propagation method. The proposed approach was applied to the Tarbela Reservoir located on the Indus River, in northern Pakistan. The results showed that the hydrodynamic calibration with coefficient of determination (R2) = 0.969 and Nash–Sutcliffe Efficiency (NSE) = 0.966 also facilitated good calibration in morphodynamic calculations with R2 = 0.97 and NSE = 0.96. The model was validated for the sediment deposits in the reservoir with R2 = 0.96 and NSE = 0.95. Due to desynchronization between the glacier melts and monsoon rain caused by warmer climate and subsequent decrease of 17% in sediment supply to the Tarbela dam, our modelling results showed a slight decrease in the sediment delta for the near future (until 2030). Based on the results, we conclude that our overall state-of-the-art modelling offers a significant improvement in computational time and accuracy, and could be used to estimate hydrodynamic and morphodynamic parameters more precisely for different events and poorly gauged rivers elsewhere in the world. The modelling concept could also be used for predicting sedimentation in the reservoirs under sediment load variability scenarios.

Water Management in Tarbela Dam By using Bayesian Stochastic Dynamic Programming in Extreme Inflow Season

Existing method of forecasting inflows at Tarbela have some limitations, also system needs an adequate operating policy model to deal with highly volatile inflow of summer months of June, July, August and September. In this paper, historical data of inflows from 1986 to 2014 have been used to forecast upcoming inflows at dam. Bayesian predictive distribution is used to predict future inflows. These forecasted inflows were further incorporated into operating policy model to determine the optimal release during the prescribed months. Weather volatility is a major factor causing unstable inflows. High temperature during summer period cause high inflows at dam. Considering weather volatility, this policy model is proposed for the flood season (15th June to 30th September), in which inflows and outflows are higher than rest of the year. This model maximizes the expected profit from hydro power production, minimizes the expected loss from flood damage and updates the proper estimate of current stage of reservoir storage.

Possible options to slow down the advancement rate of Tarbela delta.

The pivot point of delta in Tarbela dam has reached at about 10.6km from the dam face which may result in blocking of tunnels. Tarbela delta was modeled from 1979 to 2060 using hec-6 model. Initially, the model was calibrated for year 1999 and validated for years 2000, 2001, 2002, and 2006 by involving the data of sediment concentration, reservoir cross sections (73 range lines), elevation-area capacity curves, and inflows and outflows from the reservoir. Then, the model was used to generate future scenarios, i.e., run-1, run-2, and run-3 with pool levels; 428, 442, and 457m, respectively, till 2060. Results of run-1 and run-2 showed advancement to choke the tunnels by 2010 and 2030, respectively. Finally, in run-3, the advancement was further delayed showing that tunnels 1 and 2 will be choked by year 2050 and pivot point will reach at 6.4km from the dam face.

CORRELATIONS BETWEEN FLUVIAL KNICKPOINTS AND RECURRENT LANDSLIDE DAMS ALONG UPPER INDUS RIVER

TThis article summarizes an investigation into the likely role of landsliding in the formation of knickpoints along the Indus River in northern Pakistan. The knickpoints and their related geomorphic parameters (channel profile, concavity, drainage area, steepness index, etc.) were extracted from ASTER Digital Elevation Models (DEMs) with 30m resolution using ArcGIS, and Matlab software. A total of 251 knickpoints were extracted from the longitudinal profile of the Indus River along a ~750km long reach upstream of Tarbela dam. The identified knickpoint locations, along with their respective normalized steepness index (ksn values), were compared with the lithologic contacts, mapped faults, a regional level landslide inventory, and the locations of prehistoric rockslides. The knickpoints identified adjacent to the prehistoric landslide dams (i.e. Katzarah and Gol-Ghone events, and Lichar Gah etc.), exhibited normalized steepness index (Ksn) in the range of 500-1800m0.9 at various locations along the river channel. The highest normalized ksn values (>1800m0.9) were observed in the tectonically-active Nanga Perbat Haramosh Massif (NPHM) region, where the river flows through narrow gorges, and/or where active thrust faults cross the river channel. The study reveals that the landslide dams appear to be one of the significant trigger factors in the formation of knickpoints along the Indus River.

Separating snow, clean and debris covered ice in the Upper Indus Basin, Hindukush-Karakoram-Himalayas, using Landsat images between 1998 and 2002

Summary The Hindukush Karakoram Himalayan mountains contain some of the largest glaciers of the world, and supply melt water from perennial snow and glaciers to the Upper Indus Basin (UIB) upstream of Tarbela dam, which constitutes greater than 80% of the annual flows, and caters to the needs of millions of people in the Indus Basin. It is therefore important to study the response of perennial snow and glaciers in the UIB under changing climatic conditions, using improved hydrological modeling, glacier mass balance, and observations of glacier responses. However, the available glacier inventories and datasets only provide total perennial-snow and glacier cover areas, despite the fact that snow, clean ice and debris covered ice have different melt rates and densities. This distinction is vital for improved hydrological modeling and mass balance studies. This study, therefore, presents a separated perennial snow and glacier inventory (perennial snow-cover on steep slopes, perennial snow-covered ice, clean and debris covered ice) based on a semi-automated method that combines Landsat images and surface slope information in a supervised maximum likelihood classification to map distinct glacier zones, followed by manual post processing. The accuracy of the presented inventory falls well within the accuracy limits of available snow and glacier inventory products. For the entire UIB, estimates of perennial and/or seasonal snow on steep slopes, snow-covered ice, clean and debris covered ice zones are 7238 ± 724, 5226 ± 522, 4695 ± 469 and 2126 ± 212 km2 respectively. Thus total snow and glacier cover is 19,285 ± 1928 km2, out of which 12,075 ± 1207 km2 is glacier cover (excluding steep slope snow-cover). Equilibrium Line Altitude (ELA) estimates based on the Snow Line Elevation (SLE) in various watersheds range between 4800 and 5500 m, while the Accumulation Area Ratio (AAR) ranges between 7% and 80%. 0 °C isotherms during peak ablation months (July and August) range between ∼ 5500 and 6200 m in various watersheds. These outputs can be used as input to hydrological models, to estimate spatially-variable degree day factors for hydrological modeling, to separate glacier and snow-melt contributions in river flows, and to study glacier mass balance, and glacier responses to changing climate.

Flow trends in river Chitral due to different scenarios of glaciated extent

Glaciers are considerably important machinery which is necessary to keep the equilibrium of the earth. The rapid boost in the mean temperatures of the globe and abnormal precipitations are the direct warnings for glaciers to survive. Mostly the perennial rivers of the world emerge from glaciers. In an agricultural country like Pakistan the existence of the glaciers is of prime importance. The major river of the country originates from the Himalayan and Karakorum glaciated mountains. River Indus is a vital example. Moreover the mega and world renowned Tarbela dam also exists on the same river. This can be surly concluded that the future of a country is directly associated with the flows available in the rivers and hence the existence of glaciers. The rapid disintegration of the glaciers has been reported throughout the world which is obviously not a healthy sign. Therefore, a careful study is required to estimate the variations of the flows with the receding glaciers. As a case study, Chitral watershed in Pakistan has been selected. The UBC watershed model has been used to forecast the flows. The model was calibrated for the daily flows observed in 2009 at Chitral stream gauging station. The percentage contributions to the total annual flow were simulated as 23%, 31%, 1% and 45% due to glacier melt, snow melt, rain-fall runoff and groundwater, respectively for year 2009. Model results were validated for the flows of year 2006 and 2007. The further analysis was performed by assuming reduction in glaciated areas and considering the climatological conditions same as observed in year 2009. The model results show that a significant decrease in the flows of Chitral River appears due to the assumed reduction of glaciated extent.

Monitoring spatial and temporal variability of suspended sediment in Indus River by means of remotely sensed data

Remote sensing has emerged as a useful tool for monitoring spatial and temporal distribution of sediment fluxes in the water bodies. The large quantity of sediment transported by the river waters create a number of water resources and environmental problems and could shorten the useful life of many downstream reservoirs and dams. The objective of the present article is to quantify the distribution of suspended sediment by means of Advanced Land Observing Satellite/Advanced Visible and Near Infrared Radiometer type 2 (ALOS/AVNIR-2) and Systeme Pour l'Observation de la Terre (SPOT) satellite data. Multispectral remotely sensed algorithm was developed to investigate the distribution of suspended sediment in the Indus River, Pakistan, and Tarbela dam reservoir. Reflectance of ALOS/AVNIR-2 band 3 and band 4 and SPOT-2 satellite band 2 and band 3 was found to be the best predictor of suspended sediment concentration in the surface waters. To deal with the lack of in situ data, remotely sensed data are coupled with optical modeling and the desired parameter is derived by model inversion technique. The developed methodology is an effective and efficient tool for monitoring erosion, deposition areas, and sediment distribution in large rivers.

SIMULATION OF TURBULENT FLOW THROUGH TARBELA DAM TUNNEL 3

Tarbela dam is one of the largest earth filled dam in the world. The sediments inflow in the Tarbela reservoir has resulted in reduction in water storage capacity. During the recent years, a reasonable increase of sediment particles in the tunnel is observed. This is damaging tunnels, power generating units and is a severe threat to the plant equipment. To the authors knowledge, to-date no comprehensive simulation studies are performed for flooding in the reservoir or turbulent flows in the tunnels. In this paper, turbulent flow using Reynolds Stress Model in Tunnel 3 of the Tarbela Dam is analyzed with and without considering the effect of sediments particle. Results are presented for three different water heads in the reservoir i.e. considering summer, winter and average seasons and for one-way and two-way/full coupling for sediments particle tracking/deposition. The effect of cavitation erosion and damage to the tunnels due to erosion is investigated and results are compared with the experimental erosion results for similar geometries and are found in good agreement. Sediments particulate analysis is also performed for the validation of the samples collected from WAPDA. Moreover, pressure, velocity and erosion rate results are discussed to get complete behavior of the turbulent flow of water in the tunnel.

A study of the factors and processes involved in the sedimentation of Tarbela reservoir, Pakistan

This study concerns Tarbela dam reservoir which is a major resource of Pakistan in terms of electricity generation and irrigation supplies. Rapid filling of reservoir due to sediment transported and deposited by Indus River is described and analysed in this article. Causes of sediment deposition and their impact on dam’s function and life are studied. The main characteristic parameters of the site are presented. The morphological evolution of the reservoir bed induced by dam construction, between the year 1974 when the River bed was in its original state before dam construction, and the situation of the river bed after the deposition of the sediment in the year 2006, is examined. A numerical model developed in this study (Khawaja and Sanchez in Tarbela dam: a numerical model for sediment management in the reservoir; coastal and maritime mediterranean conference, 1st edn, Hammamet, pp 111–114, 2009) allows obtaining the above-stated provisional evolution of the reservoir over the coming years. Finally, five potential remedial solutions are thoroughly discussed.

Multiphase Flow Simulations through Tarbela Dam Spillways and Tunnels

Tarbela dam is one of the largest earth filled dam in the world used for power generation and irrigation purposes. Like all reservoirs the sediments inflow in the Tarbela reservoir has resulted in reduction in water storage capacity and is also causing damage to the tunnels, power generating units and ultimately to the plant equipment. This numerical study was performed to predict the flow patterns and characteristics in Tarbela dam. Tunnel 3 and 4 inlets; originally on the bed level were raised in the 3-D model and meshed. Analysis was performed using multiphase flow (water and air) for maximum inflow in the reservoir, i.e., considering summer season and discharging water through different locations, i.e., tunnels and spillways. Pressure, velocities, flow rate and free surface height results obtained were found in good agreement with the analytical and existing results where available. Results show uneven discharge through each gate due to maximum velocity near exits and overall stagnant phenomena of water within the reservoir. Maximum velocity was observed along the spillways outlet. Strong vortex motion was observed near the spillways outlet and tunnel inlets. New design of Tunnel 3 and 4 were suggested to WAPDA in order to decrease the sediment inflow and improvements in design of the spillways were suggested.

Turbulent Flow Simulations through Tarbela Dam

Tarbela dam is one of the largest earth filled dam in the world. The sediments inflow in the Tarbela reservoir has resulted in reduction in water storage capacity. In addition damage to the tunnels, power generating units and ultimately to the plant equipment by the sediments particles carried by water is observed. To the authors knowledge, to-date no comprehensive simulation studies are performed for this dam reservoir and tunnels, especially at present when sediment delta and presence of sediment particles in the tunnels is observed to a reasonable extent. The aim of this study is to investigate the damage to the Tunnel 2 of the Tarbela Dam with and without considering the affect of sediment particles for one way and two way/full coupling during summer, winter and average seasons, using turbulent flows of water. Numerically calculated erosion results are compared with the experimental erosion results. Pressure, velocity and erosion rate density results are discussed in detail.

Turbulent Flow Simulations through Tarbela Dam

Tarbela dam is one of the largest earth filled dam in the world. The sediments inflow in the Tarbela reservoir has resulted in reduction in water storage capacity. In addition damage to the tunnels, power generating units and ultimately to the plant equipment by the sediments particles carried by water is observed. To the authors knowledge, to-date no comprehensive simulation studies are performed for this dam reservoir and tunnels, especially at present when sediment delta and presence of sediment particles in the tunnels is observed to a reasonable extent. The aim of this study is to investigate the damage to the Tunnel 2 of the Tarbela Dam with and without considering the affect of sediment particles for one way and two way/full coupling during summer, winter and average seasons, using turbulent flows of water. Numerically calculated erosion results are compared with the experimental erosion results. Pressure, velocity and erosion rate density results are discussed in detail.

Hydraulic model studies for the Ghazi-Barotha hydropower project, Pakistan

The $2200 million Ghazi-Barotha hydro-power project—one of the largest currently under construction worldwide and expected to be completed in about 2002—will have a peak generating capacity of 1450 MW and provide an estimated annual energy output of 6600 GWh. Its main components are a gated barrage and flow diversion structure on the Indus River at Ghazi, about 7 km downstream of Tarbela dam a 52 km long power channel with a discharge capacity of 1600 m3/s, bypassing a relatively steep reach of river, including Attock Gorge a five-unit powerhouse, discharging back to the Indus at Barotha via a 2 km long tailrace an ‘emergency’ spillway providing a bypass to the powerhouse two ‘headponds’ upstream of the power intake, to provide sufficient daily storage to support diurnal ‘peaking’ of the units. The project has a number of interesting hydraulic features, many of which were developed with the aid of about a dozen physical model studies. This paper includes a brief description of the project, outlining the role of most of the model studies and then describes two of them in greater depth.

HYDRAULIC MODEL STUDIES FOR THE GHAZI-BAROTHA HYDROPOWER PROJECT, PAKISTAN.

The $2200 million Ghazi-Barotha hydro-power project—one of the largest currently under construction worldwide and expected to be completed in about 2002—will have a peak generating capacity of 1450 MW and provide an estimated annual energy output of 6600 GWh. Its main components are a gated barrage and flow diversion structure on the Indus River at Ghazi, about 7 km downstream of Tarbela dam a 52 km long power channel with a discharge capacity of 1600 m3/s, bypassing a relatively steep reach of river, including Attock Gorge a five-unit powerhouse, discharging back to the Indus at Barotha via a 2 km long tailrace an ‘emergency’ spillway providing a bypass to the powerhouse two ‘headponds’ upstream of the power intake, to provide sufficient daily storage to support diurnal ‘peaking’ of the units. The project has a number of interesting hydraulic features, many of which were developed with the aid of about a dozen physical model studies. This paper includes a brief description of the project, outlining the role of most of the model studies and then describes two of them in greater depth.