Search Results (13)

Khyber Pakhtunkhwa launched the Billion Tree Afforestation Project (BTAP) in 2014. Pakistan also initiated a “10 billion trees in five years” project in 2018. The soil and water assessment tool (SWAT) model was used to forecast the impacts of LULC changes on water yield under three scenarios: before planting, after 1 billion trees planted, and after 10 billion trees planted. Model calibration and validation were undertaken at the Bisham Qila gauging station from 1984 to 2000 and 2001 to 2010. The Tarbela reservoir’s mean annual runoff declined from 53.70 mm to 45.40 mm after 1 billion trees planted, while under the third scenario it approximated 35.05 mm. Full article

Millions of people rely on river water originating from snow- and ice-melt from basins in the Hindukush-Karakoram-Himalayas (HKH). One such basin is the Upper Indus Basin (UIB), where the snow- and ice-melt contribution can be more than 80%. Being the origin of some of the world’s largest alpine glaciers, this basin could be highly susceptible to global warming and climate change. Field observations and geodetic measurements suggest that in the Karakoram Mountains, glaciers are either stable or have expanded since 1990, in sharp contrast to glacier retreats that are prevalently observed in the Himalayas and adjoining high-altitude terrains of Central Asia. Decreased summer temperature and discharge in the rivers originating from this region are cited as supporting evidence for this somewhat anomalous phenomenon. This study used remote sensing data during the summer months (July–September) for the period 2000 to 2017. Equilibrium line altitudes (ELAs) for July, August and September have been estimated. ELA trends for July and September were found statistically insignificant. The August ELA declined by 128 m during 2000–2017 at a rate of 7.1 m/year, testifying to the Karakoram Anomaly concomitant with stable to mass gaining glaciers in the Hunza Basin (western Karakoram). Stable glaciers may store fresh water for longer and provide sustainable river water flows in the near to far future. However, these glaciers are also causing low flows of the river during summer months. The Tarbela reservoir reached three times its lowest storage level during June 2019, and it was argued this was due to the low melt of glaciers in the Karakoram region. Therefore, using remote sensing data to monitor the glaciers’ health concomitant with sustainable water resources development and management in the HKH region is urgently needed. Full article

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (R_a), water inflow (I_w), minimum water reservoir level (L_r), and storage capacity of the reservoir (C_r) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of R_a, I_w, L_r, and C_r. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment. Full article

The live storage of Pakistan’s major reservoirs, such as the Tarbela reservoir, has decreased in recent decades due to the sedimentation load from the Upper Indus Basin, located in High Mountain Asia. The government of Khyber Pakhtunkhwa took the initiative in 2014 and introduced the Billion Tree Afforestation Project (BTAP). They planted one billion trees by August 2017, mostly in hilly areas. In 2018, the Government of Pakistan also launched a project of 10 billion trees in five years. We assessed the effect of different land-use and land-cover (LULC) scenarios on the water yield and sediment load in the Tarbela reservoir of Pakistan. The soil and water assessment tool (SWAT) model was used to predict the impacts of the LULC changes on the water yield and sediment load under three distinct scenarios: before plantation (2013), after planting one billion trees (2017), and after planting ten billion trees (2025). The model calibration and validation were performed from 1984 to 2000 and 2001 to 2010, respectively, using the SUFI2 algorithm in SWAT-CUP at the Bisham Qila gauging station. The statistical evaluation parameters showed a strong relationship between observed and simulated streamflows: calibration (R² = 0.85, P_BIAS = 11.2%, NSE = 0.84) and validation (R² = 0.88, P_BIAS = 10.5%, NSE = 0.86). The validation results for the sediment load were satisfactory, indicating reliable model performance and validity accuracy (R² = 0.88, P_BIAS = −19.92%, NSE = 0.86). Under the LULC change scenarios, the water yield’s absolute mean annual values decreased from 54 mm to 45 mm for the first and second scenarios, while the third scenario had an estimated 35 mm mean annual water yield in the Tarbela reservoir. The sediment load results for the second scenario (2017) showed a 12% reduction in the sediment flow in the Tarbela reservoir after 1 billion trees were planted. In the third scenario (2025), following the planting of 10 billion trees, among which 3 billion were in the Tarbela basin, the sediment load was predicted to decrease by 22%. The overall results will help to inform the water managers and policymakers ahead of time for the best management and planning for the sustainable use of the water reservoirs and watershed management. Full article

This study was conducted to design a small hydropower project at Dor River in Abbottabad, Khyber Pakhtunkhwa, Pakistan. The study area is part of the Hazara Basin and contains sedimentary rocks deposited in glaciofluvial, fluvial and marine environments. The suitable locations were chosen for the proposed hydropower project components and shown on geological map of the study area. Rock Mass Rating (RMR) studies were conducted to check the quality of rocks exposed at the selected sites. The rocks were classified as fair rocks with RMR ranging from 48 to 55, which shows that rocks are suitable for construction activities, e.g., tunneling, etc. The rocks of the area were also found suitable for their use as a construction material, which is an additional positive aspect of this study. For potential hydropower evaluation, the discharge of the Dor river was measured using the current meter method. Additionally, the sediment load of the river was determined using Whatman filter papers. The Dor River water discharge is variable, where the maximum water discharge was found in the months of July (6.79 m³ s⁻¹) and August (6.71 m³ s⁻¹). Hence, the construction of a small hydropower project on Dor River can be favorably undertaken to produce a plant with low (2.79 MW), average (5.37 MW) and high-power potential (13.16 MW). In suspended sediment load analysis, it was found highest in the months of July and August and lowest in December. Annually, the Dor River takes 7267 tons of sediment to Tarbela Reservoir, which is likely to adversely affect both the life and capacity of the country’s currently largest hydropower-producing reservoir located downstream. The construction of the hydropower project proposed in this study will effectively slow the deposition of sediment into Tarbela Reservoir, which in turn will enhance the life of the reservoir positively. Full article

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. Full article

Pakistan’s agriculture and economy rely heavily on the Tarbela Reservoir. The present storage capacity of Tarbela is 8.2 BCM and it has been depleted by more than 40% due to sedimentation since 1976. It also has had a 0.94 percent (0.134 BCM) decrease in gross reservoir capacity every year. Historically, the amount of sediment trapped in the Tarbela Reservoir during the period 1976–2020 was 198.5 million tonnes annually. Based on the current operation by the Water and Power Development Authority (WAPDA), the delta is expected to extend to 2.41 km from the dam in 2035. The reservoir will become a run-of-the-river reservoir with a gross storage capacity of 2.87 BCM. This rapid loss of storage capacity will significantly impact reservoir benefits while also putting turbine performance at risk due to abrasion. Slowing the sediment deposition phenomena by a flexible operational strategy is a worthwhile aim from the dam manager’s viewpoint to achieve Sustainable Development Goals (i.e., poverty and hunger alleviation, clean affordable energy, protecting ecosystem etc.). Therefore, for the safe and long-term operation of the turbines, the existing Standard Operating Procedures (SOPs) adopted by WAPDA need to be appraised to delineate their impact on future optimized operations. The aspect of considering static SOPs on the whole period of reservoir operation has not been attempted earlier. The Tarbela Reservoir was selected as a case study to enhance the existing reservoir operation. The methodology relies upon the use of a 1-D sediment transport model in HEC-RAS to study the impact of the operational strategy on sedimentation. In conjunction, the existing reservoir operation of Tarbela was modelled in HEC-ResSim using its physical, operational, and 10-daily time-series data for simulation of releases and hydropower benefits based on a revised elevation-capacity curve for sedimentation. After calibration and validation, the model was applied to predict future reservoir operation impacts on a 5-year basis from 2025 to 2035 for determining storage capacity, irrigation releases, power production and energy generation. It was predicted that as the storage capacity of the reservoir is depleted (by application of the WAPDA current SOPs in future years), the irrigation releases would be increased in the Kharif season (April–September) by 7% and decreased by 50% in the Rabi season (October–March) with a corresponding increase in power generation by 4% and decrease by 37%, respectively, and the average annual energy generation would be decreased by 6.5%. The results showed that a gradual increase in the minimum operating level will slow down delta movement but it will reduce irrigation releases at times of high demand. The findings may assist water managers to improve the Tarbela Reservoir operation to achieve sustainable development goals and to attain societal future benefits. Full article

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. Full article

Pakistan’s society and economy are highly dependent on the surface and groundwater resources of the Indus River basin. This paper describes the development and implementation of a daily Indus River System Model (IRSM) for the Pakistan Indus Basin Irrigation System (IBIS) to examine the potential impact of reservoir sedimentation on provincial water security. The model considers both the physical and management characteristics of the system. The model’s performance in replicating provincial allocation ratios is within 0.1% on average and the modeling of water flow at barrages and delivered to irrigation canal commands is in agreement with recorded data (major barrage NSE 0.7). The average maximum volumetric error for the Tarbela and Mangla reservoirs are respectively 5.2% and 8.8% of mean annual inflow. The model showed that a 2.3 km³ reduction in storage volume since 1990 equates to approximately 1.3 km³ i.e., a 4–5% reduction in irrigation deliveries, respectively, for Punjab and Sindh in the dry (Rabi) season. This decline indicates that without further augmentation of system storage, the Rabi season supplies will continue to be further impacted in the future. This paper demonstrates the suitability of IRSM for exploring long term planning and operational rules and the associated impacts on water, food and energy security in Pakistan. Full article

In this study, a model was created with the Water Evaluation and Planning (WEAP) System and used to explore the benefits of altering the operations of Tarbela Dam in terms of reliability, resilience, and vulnerability (RRV) for the three objectives of irrigation supply, hydropower generation, and flood control. Sensitivity analysis and logical reasoning with operators identified a feasible operational rule curve for testing using the integrated performance analysis. The reservoir performance for the altered operations was compared to the baseline performance following current operations for both historical and projected future climate and water demand conditions. Key simulation results show that the altered operations strategy tested under historical climate and water demand conditions would increase RRV by 17%, 67%, and 7%, respectively, for the water supply objective and 34%, 346%, and 22%, respectively, for hydropower generation. For projected future conditions, the proposed operations strategy would increase RRV by 7%, 219%, and 11%, respectively, for water supply and 19%, 136%, and 13% for hydropower generation. Synthesis of the results suggests significant benefits for reliability and resilience of water supply and hydropower are possible with slight operational adjustments. Overall, the integrated performance analysis supports the need to develop an optimized operations rule for Tarbela to adapt to projected climate and demand scenarios. Full article

Extensive research of the variability of flows under the impact of climate change has been conducted for the Upper Indus Basin (UIB). However, limited literature is available on the spatial distribution and trends of suspended sediment concentrations (SSC) in the sub-basins of UIB. This study covers the comparative assessment of flows and SSC trends measured at 13 stations in the UIB along with the variability of precipitation and temperatures possibly due to climate change for the past three decades. In the course of this period, the country’s largest reservoir, Tarbela, on the Indus River was depleted rapidly due to heavy sediment influx from the UIB. Sediment management of existing storage and future planned hydraulic structures (to tap 30,000 MW in the region) depends on the correct assessment of SSC, their variation patterns, and trends. In this study, the SSC trends are determined along with trends of discharges, precipitation, and temperatures using the non-parametric Mann–Kendall test and Sen’s slope estimator. The results reveal that the annual flows and SSC are in a balanced state for the Indus River at Besham Qila, whereas the SSC are significantly reduced ranging from 18.56%–28.20% per decade in the rivers of Gilgit at Alam Bridge, Indus at Kachura, and Brandu at Daggar. The SSC significantly increase ranging from 20.08%–40.72% per decade in the winter together with a significant increase of average air temperature. During summers, the SSC are decreased significantly ranging from 18.63%–27.79% per decade along with flows in the Hindukush and Western–Karakorum regions, which is partly due to the Karakorum climate anomaly, and in rainfall-dominated basins due to rainfall reduction. In Himalayan regions, the SSC are generally increased slightly during summers. These findings will be helpful for understanding the sediment trends associated with flow, precipitation, and temperature variations, and may be used for the operational management of current reservoirs and the design of several hydroelectric power plants that are planned for construction in the UIB. Full article

Setting precise sediment load boundary conditions plays a central role in robust modeling of sedimentation in reservoirs. In the presented study, we modeled sediment transport in Tarbela Reservoir using sediment rating curves (SRC) and wavelet artificial neural networks (WA-ANNs) for setting sediment load boundary conditions in the HEC-RAS 1D numerical model. The reconstruction performance of SRC for finding the missing sediment sampling data was at R² = 0.655 and NSE = 0.635. The same performance using WA-ANNs was at R² = 0.771 and NSE = 0.771. As the WA-ANNs have better ability to model non-linear sediment transport behavior in the Upper Indus River, the reconstructed missing suspended sediment load data were more accurate. Therefore, using more accurately-reconstructed sediment load boundary conditions in HEC-RAS, the model was better morphodynamically calibrated with R² = 0.980 and NSE = 0.979. Using SRC-based sediment load boundary conditions, the HEC-RAS model was calibrated with R² = 0.959 and NSE = 0.943. Both models validated the delta movement in the Tarbela Reservoir with R² = 0.968, NSE = 0.959 and R² = 0.950, NSE = 0.893 using WA-ANN and SRC estimates, respectively. Unlike SRC, WA-ANN-based boundary conditions provided stable simulations in HEC-RAS. In addition, WA-ANN-predicted sediment load also suggested a decrease in supply of sediment significantly to the Tarbela Reservoir in the future due to intra-annual shifting of flows from summer to pre- and post-winter. Therefore, our future predictions also suggested the stability of the sediment delta. As the WA-ANN-based sediment load boundary conditions precisely represented the physics of sediment transport, the modeling concept could very likely be used to study bed level changes in reservoirs/rivers elsewhere in the world. Full article

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. Full article