Numerical Modelling of Flow Over Aerator of Orifice Spillway

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Community wells for sustainable irrigation in tank commands: a case study

An optimization model has been formulated to maximize the net benefit from a tank command with conjunctive use of surface water from the tank and ground water from wells and community well in the tank area. The Kannangudi tank in Pudukkottai district, Tamil Nadu, India has been taken as the case study. Six crops were found in the command area and are considered for arriving the optimal cropping pattern. The study result shows that, the wells and community well in a tank command contributes to a sustainable irrigation and apparently maximize the net benefit from that tank command

Sediment Yield Assessment of a Large Basin Using PSIAC Approach in GIS Environment

Reservoirs are the key infrastructure for the socio-economic development of a country. The reservoirs are proven to be a remedial solution of highly erratic spatial and temporal availability of water. The growth in population and consequent developmental activities within a catchment area has shown to aggravate the problem of sedimentation which comprised of erosion, sediment transport and its deposition in these reservoirs. Among all above mentioned, reservoir sediment deposition is most important as it reduces its useful life and impairs the purposes of these vast water resource. The sediment yield has been considered as comprehensive index for assessing sustainability of such resources. The present study investigates the suitability of Pacific Southwest Inter-Agency Committee (PSIAC) model in determining the sediment yield rate for a drainage basin considering nine basin factors in geographical information system (GIS) environment. For the analysis, a large river basin at the foothill of Himalayas in India has been considered as case study. It was realized that the GIS approach made large basin characteristic sampling very easy and efficient for this hilly basin. A regression equation between specific sediment yield and effective model factors was established based on geomorphic features for this basin. It was observed that most of the basin area is falling under moderate to high sediment yielding potential zone, leading to high sediment yield

Estimation of Useful life of a Reservoir Using Sediment Trap Efficiency

The most important practical and critical problem related to the performance of reservoirs is the estimation of storage capacity loss due to sedimentation process. The problem to be addressed is to estimate the rate of sediment deposition and the period of time at which the sediment would interfere with the useful functioning of a reservoir. Fairly a large number of methods and models are available for the estimation, analysis and prediction of reservoir sedimentation process. However, these methods and models differ greatly in terms of their complexity, inputs and computational requirements. In the present study, the rate of sedimentation and useful life time of a reservoir were estimated using the trap efficiency (Te) approach. The empirical relationship suggested by Brune (1953) to estimate reservoir sediment Te and Gill (1979) approach to estimate useful life of a reservoir are modified to suit Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district, Himachal Pradesh, in the Himalayan region of Indi

Evaluation of reservoir sedimentation using data driven techniques

The sedimentation is a pervasive complex hydrological process subjected to each and every reservoir in world at different extent. Hydrographic surveys are considered as most accurate method to determine the total volume occupied by sediment and its distribution pattern in a reservoir. But, these surveys are very cumbersome, time consuming and expensive. This complex sedimentation process can also be simulated through the well calibrated numerical models. However, these models generally are data extensive and require large computational time. Generally, the availability of such data is very scarce. Due to large constraints of these methods and models, in the present study, data driven approaches such as artificial neural networks (ANN), model trees (MT) and genetic programming (GP) have been investigated for the estimation of volume of sediment deposition incorporating the parameters influenced it along with conventional multiple linear regression data driven model. The aforementioned data driven models for the estimation of reservoir sediment deposition were initially developed and applied on Gobindsagar Reservoir. In order to generalise the developed methodology, the developed data driven models were also validated for unseen data of Pong Reservoir. The study depicted that the highly nonlinear models ANN and GP captured the trend of sediment deposition better than piecewise linear MT model, even for smaller length datasets. (C) 2013 Elsevier B. V. All rights reserved

Re-look to conventional techniques for trapping efficiency estimation of a reservoir

All reservoirs are. subjected to sediment inflow and deposition up to a certain extent leading to reduction in their capacity. Thus, the important practical problem related to the life of reservoir is the estimation of sedimentation quantity in the reservoirs. Large number of methods and models are available for estimation of reservoir sedimentation process. However, each model differs greatly in terms of their complexity, inputs and other requirements. In the simplest way, the fraction of sediment deposit in the reservoir can be determined through the knowledge of its trap efficiency. Trap efficiency (T,) is the proportion of the incoming sediment that is deposited or trapped in a reservoir. Most of the T, estimation methods define a relationship of the T, of the reservoir to their capacity and annual inflow, generally through curves. In this study, the empirical relationships given by Brune and Brown were used and compared for estimating the trap efficiency of Gobindsagar Reservoir (Bhakra Dam) on Satluj River in Bilaspur district of Himachal Pradesh, in the Himalayan region of India. A new set of regression equations has been developed for Brune's method and compared with Brown and other available Brune's equations. It has been found that Brune's equations developed in the present study estimated better than the other Brune's equations reported in literature. Later, in the present study it was found that Brown's approach was over estimating the T, Hence it was again modified for Gobindsagar reservoir. It was also identified that sediments coming to this particular reservoir were mainly of coarse nature

Trap Efficiency Estimation of a Large Reservoir

Sediment deposited or trapped in a reservoir can easily be quantified by the simple knowledge of its trap efficiency (T e ). In the present study methods proposed by Brown and Brune have been adopted to estimate T of Pong Reservoir (Beas Dam) on the Beas River in Kangra district of Himachal Pradesh, India. The necessary modifications in the adopted methods have been made using the available data for this reservoir. This modification is basically to take into account the variation in trap efficiency with time

Artificial Neural Network Models for Sivajisagar lake Evaporation Prediction

Prediction of lake evaporation is very much essential for effective water resources planning, operation and management. In India, usually, the lake evaporation is estimated from the pan evaporation and the average water spread area. Accurate prediction of lake evaporation by conventional method is a cumbersome process, since it is in non-linear relationship with the storage and other meteorological parameters. The recently evolved soft computing techniques are proved to be efficient to model these non-linear hydrological processes. Thus in the present study, two artificial neural network algorithms (ANN) namely, multi-layer perceptron (MLP) and time lagged recurrent neural network (TLRN) are compared to predict the lake evaporation. The daily Shivajisagar lake evaporation data collected from the Koyna dam circle for a period of 49 years has been used in the modelling. About 70% of the dataset is used for training the ANN models and the remaining 30% is used for testing. It is found that both the ANN algorithms predicted the lake evaporation very well with a correlation coefficient around 0.99. This shows that, if the input data series exhibits good pattern with less noise, the soft computing techniques results in better performance

Comparison of Policies Derived from Stochastic Dynamic Programming and Genetic Algorithm Models

A comprehensive Genetic Algorithm (GA) model has been developed and applied to derive optimal operational strategies of a multi-purpose reservoir, namely Perunchani Reservoir, in Kodaiyar Basin in Tamil Nadu, India. Most of the water resources problem involves uncertainty, in order to see that the GA model takes care of uncertainty in the input variable, the result of the GA model is compared with the performance of a detailed Stochastic Dynamic Programming (SDP) model. The SDP models are well established and proved that it takes care of uncertainty in-terms of either implicit or explicit approach. In the present study, the objective function of the models is set to minimize the annual sum of squared deviation from desired target release and desired storage volume. In the SDP model the optimal policies are derived by varying the state variables from 3 to 9 representative class intervals, and then the cases are evaluated for their performance using a simulation model for longer length of inflow data, generated using a Thomas-Fiering model. From the performance of the SDP model policies, it is found that the system encountered irrigation deficit, whereas GA model satisfied the demand to a greater extent. The sensitivity analysis of the GA model in selecting optimal population, optimal crossover probability and the optimal number of generations showed the values of 150, 0.76 and 175 respectively. On comparing the performance of SDP model policy with GA model, it is found that GA model has resulted in a lesser irrigation deficit. Thus based on the present case study, it may be concluded that the GA model performs better than the SDP model

Sediment Yield Assessment of a Large Basin using PSIAC Approach in GIS Environment

Reservoirs are the key infrastructure for the socio-economic development of a country. The reservoirs are proven to be a remedial solution of highly erratic spatial and temporal availability of water. The growth in population and consequent developmental activities within a catchment area has shown to aggravate the problem of sedimentation which comprised of erosion, sediment transport and its deposition in these reservoirs. Among all above mentioned, reservoir sediment deposition is most important as it reduces its useful life and impairs the purposes of these vast water resource. The sediment yield has been considered as comprehensive index for assessing sustainability of such resources. The present study investigates the suitability of Pacific Southwest Inter-Agency Committee (PSIAC) model in determining the sediment yield rate for a drainage basin considering nine basin factors in geographical information system (GIS) environment. For the analysis, a large river basin at the foothill of Himalayas in India has been considered as case study. It was realized that the GIS approach made large basin characteristic sampling very easy and efficient for this hilly basin. A regression equation between specific sediment yield and effective model factors was established based on geomorphic features for this basin. It was observed that most of the basin area is falling under moderate to high sediment yielding potential zone, leading to high sediment yield