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Theses

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2014 - 201912
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Subject: Soil and Water Engineering × End date: 2019 × Thesis Type: Ph.D ×
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Now showing 1 - 9 of 12
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    ThesisItemOpen Access
    Effective water management in Kesinga and Kotni Basins-application of WEAP model
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-11) Nivesh, Shreya; Kashyap, P.S.
    The relentless increase in population coupled with rising temperature, varying rainfall patterns, depleting groundwater, rising sea-levels, declining snowfall, retreating glaciers, intense tropical cyclones and resulting spurt in the demand for water is precipitating a major crisis for food security and rural economy, which require careful planning and management of limited non-renewable water resources. In this study an attempt has been made to develop Water Evaluation and Planning System (WEAP) model to analyse water balance, to achieve water security and sustainability in Kesinga and Kotni basins. FAO-CROPWAT 8.0 computer programme was used to compute crop irrigation requirements, reference evapotranspiration (ETo) and to develop scheme water supply for various districts of Kesinga and Kotni basins. Agroclimatic data were collected using FAO New_LocClim local climate estimator for each district. Meteorological data were collected from the India Meteorological Department (IMD), Pune and Hydrological data were collected from Central Water Commission (CWC), Mahanadi and Eastern Rivers division, Bhubaneswar, Odisha, India. The model was structured according to one scenario with a current accounts year (1990) and reference period (1991-2004) for external driving factors (irrigation demand, livestock, urbanization, and population) to predict their impacts on water balance or water supply system. Total annual water demand, unmet demand and streamflow for reference scenario in Kesinga basin were 77541 Mm3, 52631 Mm3 and 72818 Mm3 respectively while, in Kotni basin were 79174 Mm3, 48586 Mm3 and 27495 Mm3 respectively. The RMSE and NSE were 43.10 Mm3 and 99.40% respectively for Kesinga basin and 183.58 Mm3 and 72.02% respectively for the Kotni basin. Results indicated that tributaries of Mahanadi river do not have sufficient capacity to satisfy water demands in two basins and most of the tributaries will be under water stress conditions in all months of the year. Outcomes of the study demonstrated that WEAP model is a useful tool for integrated water resources management and suggested that dependence on surface water resources alone is not sufficient to satisfy water demands.
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    ThesisItemOpen Access
    Analysis of meteorological and hydrological droughts in Uttarakhand state
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-01) Malik, Anurag; Anil Kumar
    Drought is a natural disaster which disturbs the entire ecosystem and adversely affects various sectors, such as agriculture, hydropower generation, water supply and industry. Occurrence of drought and its forecasting are critical components of hydrology which play a major role in risk management, drought preparedness and mitigation. This study was conducted using monthly rainfall and streamflow data of Almora, Bageshwar, Chamoli, Champawat, Dehradun, Haridwar, Nainital, Pauri Garhwal, Pithoragarh, Rudraprayag, Tehri Garhwal, U.S. Nagar/Pantnagar, Uttarkashi, Naula and Kedar stations located in Uttarakhand State, India, with the specific objectives to determine the spatiotemporal trends in hydro-meteorological data, find the best fit probability distribution, characterize meteorological and hydrological drought and wet conditions using Standardized Precipitation Index (SPI), Effective Drought Index (EDI), and Streamflow Drought Index (SDI), demarcate the homogeneous areas using Agglomerative Hierarchical Clustering (AHC), and predict hydro-meteorological drought and wet conditions using soft computing and statistical techniques. The results of trend analysis revealed significant positive (rising) and negative (falling) trends with different magnitudes in monthly, seasonal and annual rainfall time series data at 1%, 5% and 10% significance levels for 13 stations, while negative trend in monthly, seasonal and annual streamflow time series data at 1%, 5% and 10% significance levels at Naula and Kedar stations. The Kolmogorov-Smirnov test (K-S) statistic showed gamma distribution fitted well to 1-, 3-, 6-, 9-, 12-1 and 24-month rainfall and streamflow data series at 1% and 5% significance levels. The gamma distribution was used for analysis of hydrometeorological drought and wet conditions based on SPI, EDI, and SDI at 1-, 3-, 6-, 9-, 12-, and 24-month time scales for study stations. The occurrence of severe and extreme hydrometeorological drought and wet conditions were minimum, while normal, moderate drought and wet conditions occurred most frequently at 1-, 3-, 6-, 9-, 12-, and 24-month time scales for all the stations. The AHC analysis showed minimum three clusters (1, 2 and 3) and maximum four clusters (1, 2, 3 and 4) of similar characteristics in the study region. The performance of CANFIS model, followed by MLPNN, was found to be the best for prediction of hydro-meteorological drought or wet conditions based on the multi-scalar SDI, SPI and EDI values for most of the stations. The results of trend analysis and prediction of hydro-meteorological drought and wet conditions would help the local stakeholders, hydrologists, water managers and policy maker to understand the risks and vulnerabilities related to climate change and anthropogenic activities in the study region.
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    ThesisItemOpen Access
    Swat based runoff and sediment yield modelling for Nagwan Watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-08) Roti, Vasantgouda; Kashyap, P.S.
    Physically based Soil and Water Assessment Tool (SWAT) model was setup and assessed for the runoff and sediment yield from Nagwan watershed (98.42 km2) situated in Hazaribagh district of Jharkhand. The runoff parameters namely effective hydraulic conductivity in the main channel, SCS curve number, base flow alpha factor for bank storage, manning’s “n” value for the main channel and saturated hydraulic conductivity of soil layers were found to be the most sensitive. The sediment yield parameters namely, sediment that can be re-entrainment during channel sediment routing and average slope steepness, exponential parameter for calculating sediment re-entrainment in the channel sediment routing and average slope length were found to be the most sensitive. The statistical indicators of R2, NSE, PBIAS and RSR for monthly runoff during calibration period were found to be 0.74, 0.84, 9.80 and 0.51 respectively. Similarly for validation period value R2, NSE, PBIAS and RSR were found to be 0.77, 0.66, 10.50 and 0.58 respectively. For monthly sediment yield the R2, NSE, PBIAS and RSR during calibration were 0.83, 0.82, 5.90 and 0.42 respectively. The values of R2, NSE, PBIAS and RSR during validation period were 0.71, 0.67, 17.10 and 0.58 respectively indicating the model performance was good for both runoff and sediment yield simulation which are adequate for SWAT model application for management planning. Such successful evaluation of SWAT model as illustrated in this study can widen model applicability into other ungauged basins. The critical sub-watersheds were identified on the basis of sediment yield annually from the SWAT simulated values during period of 2007 to 2013. Out of fifteen sub-watersheds four watersheds were found to be under very high soil loss, six under high soil loss, one under moderate and four under slight soil loss group.
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    ThesisItemOpen Access
    Data driven runoff modelling of a mid-himalayan watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-01) Pankaj Kumar; Devendra Kumar
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    ThesisItemOpen Access
    Wavelet-ANN and wavelet-ANFIS based runoff modeling
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2018-01) Sachan, Ashish; Devendra Kumar
    Land and water are two vital natural resources for sustaining life on earth. These resources are getting degraded by an alarming rate, due to over exploitation and unscientific management of the resources owing to increasing population and industrialization. For the scientific and proper management of these resources, it is needed to having a tool for precise prediction of future events. The present study is an attempt to develop a mathematical model which will be efficient in data correctness and prediction of future events. Present study has been carried out to simulate, forecast and compare runoff yield from a watershed using a hybrid artificial neural network modeling technique combined with wavelet and fuzzy logic analysis. In the present study the Nagwan and Vamsadhara watershed of the south-east monsoon were selected. The data of the entire monsoon period, starting from June 1st to September 30th of each year were used for the model development and verification of developed models. One computer progamme in C language was developed for resolving the observed daily data of runoff. For modeling of Artificial neural network and Adaptive neuro fuzzy inference system, a software NEUROSOLUTION was used. The model performance was checked through seven selected performance evaluation criteria, viz. Mean square error, Normalize mean square error, Correlation coefficient, Percentage error, Nash-Sutcliffe efficiency, Percent bias and RMSE-observations Standard deviation ratio. The results of the study reveal that, resolution of all input data makes the model more acceptable at the same time one should avoid increasing complexity of model which may adversely affect the performance of the model.
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    ThesisItemOpen Access
    Simulation of runoff and sediment yield from a hilly watershed using Soil and Water Assessment Tool (SWAT) and Wavelet Neural Network (WNN) models
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Jillani, Asima; Anil Kumar
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    ThesisItemOpen Access
    Meterological and hydrological drought characteristics of upper Ramganga Catchment in Uttarakhand
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-08) Mishra, Alok Kumar; Anil Kumar
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    ThesisItemUnknown
    Multilayer perceptron and single multiplicative neuron based artificial neural network rainfall - runoff models for a Himalayan watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2014-09) Singh, Praveen Vikram; Akhilesh Kumar
    Artificial Neural Network (ANN) has been widely used in the field of rainfall-runoff modeling. ANN models based on Multilayer perceptron and single multiplicative neuron were developed and verified for Khunt watershed in Almora, India to predict daily runoff. The daily data of rainfall and runoff of Khunt watershed of active monsoon period (18th June to 30th September) during years 2005-2009 were used for the training of both the models, whereas the data of years 2010 were used for validation and 2011 for the verification. The MLP-ANN was developed for various preprocessing techniques and the effect of preprocessing techniques on the performance of the model was considered. It was found that standardization technique provided the best results with smallest network topology for multilayer perceptron based ANN while using tan sigmoid function in the hidden layer and linear activation function in the output layer. It was also observed during the development of the models the multiple random generations of weights and biases are essential to reach the global minima along with practically feasible values of runoff. Model MLP4 having network structure as 6-5-1 with daily input series exhibits best results. Single multiplicative neural network is different from multilayer perceptron neural network as SMN-ANN is composed of multiplicative single neuron instead of multiple additive neurons. Single neuron model using multiplicative function strengthens non-linearity characteristic of the model and involves less parameters than those employed in MLP networks. SMN-ANN model was calibrated for different learning rates for daily input series and performed best for 0.5 learning rate. Qualitative performance of the model was assessed by the visual observation, whereas, quantitative performance was verified by estimating the values of various statistical indices such as coefficient of efficiency (CE), coefficient of determination (R2), index of agreement (d), normalized root mean square error (NRMSE), Integral square error (ISE), standardized mean absolute percentage error (SMAPE), peak difference percentage error (PDPE) and volumetric error (VE). SMN based ANN model performed very satisfactory for runoff prediction as the value of coefficient of efficiency (CE) was found to be 0.9511 while in case of MLP based ANN model it was found to be 0.8185 respectively. The value integral square error (ISE) was found to be less than 0.03 and the volumetric error was found within 10% for the whole data series. SMN-ANN model performed either at par or better than MLP-ANN model when applied on different data sets. At the same time, the SMN-ANN model is more user friendly as in its development the tedious and cumbersome process involved for the selection of appropriate network architecture is completely eliminated. Hence the SMN-ANN model provides an effective alternative to MLP-ANN model for rainfall–runoff modeling.
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    ThesisItemOpen Access
    Runoff disaggregation using artificial neural networks for a hilly watershed
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-07) Panwar, Rajdev; Devendra Kumar