Bhakra Reservoir Research Articles

Pixel-Based Soil Loss Estimation and Prioritization of North-Western Himalayan Catchment Based on Revised Universal Soil Loss Equation (RUSLE)

Land degradation is a noteworthy environmental risk causing water quality issues, reservoir siltation, and loss of valuable arable lands, all of which negate sustainable development. Analysis of the effect of land use changes on erosion rate and sediment yield is particularly useful to identify critical areas and define catchment-area treatment plans. This study utilized remote sensing and geographical information system/science (GIS) techniques combined with the Revised Universal Soil Loss Equation (RUSLE) on a pixel basis to estimate soil loss over space and time and prioritized areas for action. The methodology was applied to the Sutlej catchment from the perspective of sedimentation of the Bhakra reservoir, which is leading to the loss of active storage capacity and performance and of the safety and efficiency of many existing hydroelectric projects in the Sutlej and its tributaries that drain the Himalayas. Soil loss estimation using RUSLE was first calibrated using data from three sites, and the calibrated model was then used to estimate catchment soil loss for 21 years (1995–2015). The number of land use/land cover (LULC) classes as 14 and the C factor as 0.63 for agriculture land were optimized using the observed data for the Sutlej catchment. Further, the linkage between soil erosivity and annual precipitation was also established. It was concluded that extensive control treatment would be necessary from the soil and water conservation point of view. Structures like check dams, terraces, bunds, and diversion drains in the upstream can overcome the issue of fragmentation of soil in the Sutlej catchment.

Simulation of reservoir outflows using regression tree and support vector machine

Water stored in reservoirs has a lot of crucial function, including generating hydropower, supporting water supply, and relieving lasting droughts. During floods, water deliveries from reservoirs must be acceptable, so as to guarantee that the gross volume of water is at a safe level and any release from reservoirs will not trigger flooding downstream. This study aims to develop a well-versed assessment method for managing reservoirs and pre-releasing water outflows by using the machine learning technology. As a new and exciting AI area, this technology is regarded as the most valuable, time-saving, supervised and cost-effective approach. In this study, two data-driven forecasting models, i.e., Regression Tree (RT) and Support Vector Machine (SVM), were employed for approximately 30 years’ hydrological records, so as to simulate reservoir outflows. The SVM and RT models were applied to the data, accurately predicting the fluctuations in the water outflows of a Bhakra reservoir. Different input combinations were used to determine the most effective release. For cross-validation, the number of folds varied. It is found that quadratic SVM for 10 folds with seven different parameters would give the minimum RMSE, maximum R2, and minimum MAE; therefore, it can be considered as the best model for the dataset used in this study.

Large Losses in Glacier Area and Water Availability by the End of Twenty-First Century under High Emission Scenario, Satluj Basin, Himalaya

Glaciers in the Satluj river basin are likely to lose 53% and 81% of area by the end of the century, if climate change followed RCP 8.5 scenario of CNRMCM5 and GFDL-CM3 models respectively. The large variability in area loss can be due to difference in temperature and precipitation projections. Presently, Satluj basin has approximately 2000 glaciers, 1426 sq. km glacier area and 62.3 Gt glacier stored water. The current mean specific mass balance is –0.40 m.w.e. a–1. This will change to –0.42 and – 1.1 m.w.e. a–1 by 2090, if climate data of CNRM-CM5 and GFDL-CM3 are used respectively. We have used an extreme scenario of GFDL-CM3 model to assess the changes in the contribution of glacier melt to the Bhakra reservoir. Mass balance model suggests that glaciers are contributing 2 km3 a–1 out of 14 km3 of water. This will increase to 2.2 km3 a–1 by 2050, and then reduce to 1.5 km3 a–1 by the end of the century. In addition, loss in glacier area by the end of century, will also increase the vulnerability of mountain communities, suggesting need for better adaptation and water management practices.

Assessment of sedimentation in Pong and Bhakra reservoirs in Himachal Pradesh, India, using geospatial technique

All water storage structures constructed on natural rivers are subjected to reservoir sedimentation. The reservoir sedimentation is filling of the reservoir behind a dam with sediment carried into the reservoir by streams. The sediment particles which originate from erosion processes in the catchment are propagated along with the river flow. When the flow of a river is stored in a reservoir, the sediment settled down in the reservoir and reduces its capacity. Decrease in the storage capacity of a reservoir beyond a limit hampers the purpose for which it was constructed. Therefore, assessment of sediment deposition becomes very important for the management and operation of such reservoirs. Some conventional methods, such as hydrographic survey and inflow-outflow approaches, are being used to estimate the sediment deposition in a reservoir, but these methods are tedious, time-consuming and costly. Consequently, in this study, a remote sensing data based digital image processing technique was used to assess the sedimentation in Pong and Bhakra reservoirs, located in foothill of Western Himalayas, India. Seven dates of IRS-(P6) LISS-III satellite data from maximum to minimum reservoir level were used to assess temporal and spatial patterns of reservoirs. The water spread areas of the reservoirs were assessed by using a band rationing technique i.e. Normalized Difference Water Index (NDWI). Furthermore, the revised capacities of the reservoirs between minimum and maximum levels were computed using the trapezoidal formula. From the analysis, it has been observed that the live storage capacity of Pong Reservoir, due to sedimentation, was estimated as 632.84Mm3 in last 35 years (1974–2009), whereas it was determined as 802.34Mm3 for Bhakra reservoir in the span of 46 years (1963–2009). Further, the sedimentation rate in the Pong reservoir was calculated as 18.08Mm3/year, while for the Bhakra reservoir it was 17.46Mm3/year. These sedimentation rates are comparable with the hydrographic survey analysis.

Analysis of Temperature Trends in Sutluj River Basin, India

Surface air temperature is an important climatic variable that drives the hydrological cycle. Precipitation and runoff production are impacted by surface air temperature changes and are, therefore, important for water resources planning, irrigation and agriculture. Focusing on the Sutlej Basin in the Himalayan region, the present research is aimed at utilizing observational evidence to evaluate the response of the region to global warming through investigation of temperature trends. Trends in long-term average annual and seasonal surface air temperature, and several temperature indices at eight stations in the Sutlej River basin have been examined using Mann-Kendall non-parametric test. Six stations exhibited increasing trends in annual average maximum temperature with two being statistically significant. The trends in annual average minimum temperature were mixed; three of eight stations exhibited statistically significant decreasing trends. Some higher elevation stations exhibited clear warming trends both in maximum and minimum temperatures. Analysis of seasonal temperature data indicated that the warming was more pronounced in winter and spring than in summer and autumn. Trends in temperature indices considered in this paper were predominantly increasing. The predominant pattern of increased warming in the basin could have implications for water availability as the snow and glacier melt contribution to annual runoff at Bhakra reservoir is estimated at 59%. The analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Some of these variations could be partially attributed to data reliability.

Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India

The prediction of streamflow is required in many activities associated with the planning and operation of the components of a water resources system. Soft computing techniques have proven to be an efficient alternative to traditional methods for modelling qualitative and quantitative water resource variables such as streamflow, etc. The focus of this paper is to present the development of models using multiple linear regression (MLR), artificial neural network (ANN), fuzzy logic and decision tree algorithms such as M5 and REPTree for predicting the streamflow at Kasol located at the upstream of Bhakra reservoir in Sutlej basin in northern India. The input vector to the various models using different algorithms was derived considering statistical properties such as auto-correlation function, partial auto-correlation and cross-correlation function of the time series. It was found that REPtree model performed well compared to other soft computing techniques such as MLR, ANN, fuzzy logic, and M5P investigated in this study and the results of the REPTree model indicate that the entire range of streamflow values were simulated fairly well. The performance of the naïve persistence model was compared with other models and the requirement of the development of the naïve persistence model was also analysed by persistence index.

Predictability of Western Himalayan river flow: melt seasonal inflow into Bhakra Reservoir in northern India

Abstract. Snowmelt-dominated streamflow of the Western Himalayan rivers is an important water resource during the dry pre-monsoon spring months to meet the irrigation and hydropower needs in northern India. Here we study the seasonal prediction of melt-dominated total inflow into the Bhakra Dam in northern India based on statistical relationships with meteorological variables during the preceding winter. Total inflow into the Bhakra Dam includes the Satluj River flow together with a flow diversion from its tributary, the Beas River. Both are tributaries of the Indus River that originate from the Western Himalayas, which is an under-studied region. Average measured winter snow volume at the upper-elevation stations and corresponding lower-elevation rainfall and temperature of the Satluj River basin were considered as empirical predictors. Akaike information criteria (AIC) and Bayesian information criteria (BIC) were used to select the best subset of inputs from all the possible combinations of predictors for a multiple linear regression framework. To test for potential issues arising due to multicollinearity of the predictor variables, cross-validated prediction skills of the best subset were also compared with the prediction skills of principal component regression (PCR) and partial least squares regression (PLSR) techniques, which yielded broadly similar results. As a whole, the forecasts of the melt season at the end of winter and as the melt season commences were shown to have potential skill for guiding the development of stochastic optimization models to manage the trade-off between irrigation and hydropower releases versus flood control during the annual fill cycle of the Bhakra Reservoir, a major energy and irrigation source in the region.

Modeling of Suspended Sediment Concentration at Kasol in India Using ANN, Fuzzy Logic, and Decision Tree Algorithms

The prediction of the sediment loading generated within a watershed is an important input in the design and management of water resources projects. High variability of hydro-climatic factors with sediment generation makes the modelling of the sediment process cum- bersome and tedious. The methods for the estimation of sediment concentration based on the properties of flow and sediment have limitations attributed to the simplification of important parameters and boundary conditions. Under such circumstances, soft computing approaches have proven to be an efficient tool in modelling the sediment concentration. The focus of this paper is to present the development of models using Artificial Neural Network (ANN) with back propagation and Levenberg-Maquardt algorithms, radial basis function (RBF), Fuzzy Logic, and decision tree algorithms such as M5 and REPTree for predicting the suspended sediment concentration at Kasol, upstream of the Bhakra reservoir, located in the Sutlej basin in northern India. The input vector to the various models using different algorithms was derived con- sidering the statistical properties such as auto-correlation function, partial auto-correlation, and cross-correlation function of the time series. It was found that the M5 model performed well compared to other soft computing techniques such as ANN, fuzzy logic, radial basis function, and REPTree investigated in this study, and results of the M5 model indicate that all ranges of sediment concentration values were simulated fairly well. This study also suggests that M5 model trees, which are analogous to piecewise linear functions, have certain advantages over other soft computing techniques because they offer more insight into the generated model, are acceptable to decision makers, and always converge. Further, the M5 model tree offers explicit expressions for use by field engineers. DOI: 10.1061/(ASCE)HE.1943-5584.0000445. © 2012 American Society of Civil Engineers. CE Database subject headings: Suspended sediment; Neural networks; Fuzzy sets; Reservoirs. Author keywords: Suspended sediment concentration; Neural networks; Fuzzy Logic; M5; REPTree; Bhakra reservoir.

Assessment of sedimentation in Bhakra Reservoir in the western Himalayan region using remotely sensed data

Sediment particles originating from erosion processes in the catchment are propagated along with the river flow. When the flow of a river is stored in a reservoir, the sediment settles in the reservoir and reduces its capacity. Reduction in the storage capacity of a reservoir beyond a limit hampers the purpose for which it was designed. Thus assessment of sediment deposition becomes very important for the management and operation of such reservoirs. Some conventional methods, such as hydrographic survey and inflow-outflow approaches, are used for estimation of sediment deposition in a reservoir, but these methods are cumbersome, time consuming and expensive. There is a need for developing simple methods, which require less time and are cost effective. In this study, a remote-sensing approach has been attempted for assessment of sedimentation in Bhakra Reservoir, located on the Satluj River in the foothills of the Himalayas. Multi date remote sensing data (IRS-1B, LISS II) provided the information on the water-spread area of the reservoir, which was used for computing the sedimentation rate. The revised capacity of the reservoir between maximum and minimum levels was computed using the trapezoidal formula. The loss in reservoir capacity due to deposition of sediments for a period of 32 years (1965–1997) was determined to be 807.35 Mm3, which gives an average sedimentation rate of 25.23 Mm3 year−1. The average rate of sedimentation using hydrographic survey data for the same period was 20.84 Mm3 year−1. A comparison of the results shows that the rate of sedimentation assessed using the remote sensing based approach was close to the results obtained from the hydrographic survey.

Water and Sediment Yields into the Sutlej River from the High Himalaya

Extensive high-quality hydrologic and sediment yield data would greatly aid planning for the use of water resources of the Sutlej River system. The total catchment within the mountains is 47,480 km2, with the Spiti and Kinnaur tributaries accounting for 5,875 and 5,157 km2, respectively, in India. By far the largest part lies in Tibet. This paper attempts to utilize the available data to provide a first estimate of the magnitude of the problem facing development engineers. It is concluded that extensive sediment control treatment will be necessary to improve the safety and efficiency of the many hydroelectric projects that are being planned for the Sutlej and its tributaries which drain from the Himalaya. Nine years of discharge and sediment yield data collected daily from calibrated sections of the rivers form the basis for this study. While the monthly and annual runoff data show little variation throughout the period, sediment yield was extremely variable. About 80 percent of the annual flow occurs between May and September, mainly from snowmelt. Sediment yield increased exponentially with increased discharge, with especially high concentrations from June through August. The main sediment sources are gully erosion, stream-bank erosion, landslides, road construction, and glacier erosion. The sediment from this region accounts for half the annual accumulation in the Bhakra reservoir farther downstream.

Study of Bhakra reservoir operation

The study outlines the methodology for working out a multiobjective reservoir operation with conjunctive utilization of surface and groundwater. Irrigation and power have been considered the two objectives for operation of the reservoir. The results of this study are compared with those achieved with conventional operation in the past and show that there is an increase in irrigation by 7 to 8% due to conjunctive use of surface and groundwater. The trade-off between irrigation and power has been developed and discussed. The transformation surface between three objectivesviz., power, irrigation and carry-over storage has been developed and the merits of carry-over storage have been discussed.

Institutional Rationing of Canal Water in Northern India: Conflict between Traditional Patterns and Modern Needs

Previous articleNext article No AccessInstitutional Rationing of Canal Water in Northern India: Conflict between Traditional Patterns and Modern NeedsRichard B. ReidingerRichard B. Reidinger Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by Economic Development and Cultural Change Volume 23, Number 1Oct., 1974 Article DOIhttps://doi.org/10.1086/450771 Views: 7Total views on this site Citations: 24Citations are reported from Crossref Copyright 1974 The University of ChicagoPDF download Crossref reports the following articles citing this article:Fijnanda van Klingeren, Nan Dirk de Graaf Heterogeneity, trust and common-pool resource management, Journal of Environmental Studies and Sciences 11, no.11 (Nov 2020): 37–64.https://doi.org/10.1007/s13412-020-00640-7Grant Alan Burrier, Philip Hultquist Temples, travesties, or something else? 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