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

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

Evaluation of Rainwater Harvesting Methods and Structures using Analytical Hierarchy process for a Large Scale Industrial Area

In India, with ever increasing population and stress on natural resources, especially water, rejuvenation of rainwater harvesting (RWH) technique which was forgotten over the days is becoming very essential. Large number of RWH methods that are available in the literature are demand specific and site specific, since RWH system depends on the topography, land use, land cover, rainfall and demand pattern. Thus for each and every case, a detailed evaluation of RWH structures is required for implementation, including the analy-sis of hydrology, topography and other aspects like site availability and economics, however a common methodology could be evolved. The present study was aimed at evaluation of various RWH techniques in order to identify the most appropriate technique suitable for a large scale industrial area to meet its daily wa-ter demand. An attempt is made to determine the volume of water to be stored using mass balance method, Ripple diagram method, analytical method, and sequent peak algorithm method. Based on various satisfying criteria, analytical hierarchy process (AHP) is employed to determine the most appropriate type of RWH method and required number of RWH structures in the study area. If economy alone is considered along with hydrological and site specific parameters, recharging the aquifer has resulted as a better choice. However other criteria namely risk, satisfaction in obtaining required volume of water for immediate utilization etc. has resulted in opting for concrete storage structures method. From the results it is found that AHP, if used with all possible criteria can result in a better tool for evaluation of RWH methods and structures. This RWH structures not only meets the demand but saves transportation cost of water and reduces the dependability of the industry on irrigation reservoir. Besides monetary benefits it is hoped that the micro environment inside the industry will improve due to the cooling effect of the stored water

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

Short-term rainfall prediction using ANN and MT techniques

Most of the rainfall prediction models use atmospheric weather data, which are somewhat difficult to access by common water resources managers. On the other hand, data-driven techniques are finding wider application in forecasting many hydrological variables. The data-driven technique predicts the future variable better if there is a well-defined pattern with or without noise in the data set. In the present study, this ability of data-driven techniques, such as artificial neural networks (ANNs) and model tree (MT), has been applied to predict the next time step rainfall using lagged time series of observed rainfall data. The models were trained and tested with 47 years of daily rainfall measurements at the Koyna Dam, Maharashtra, India. Among various available training algorithms, multilayer perceptron, radial basis function (RBF) and time lagged recurrent networks (TLRN) have been attempted. It is found that TLRN has captured the pattern in a better way. In case of MT, various trials on pruning and smoothening have been carried out and found that un-pruned and un-smoothed MT performed better. It is found that both ANN and MT models have performed equally better, indicating that they are promising techniques for short-term rainfall prediction. However, the agreeable result shows that the data-driven techniques may be explored further for prediction of short-term rainfall data from the observed rainfall series

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