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ThesisItem Open Access Runoff prediction from Gaula river using Heuristic approaches(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Bhatt, Gaurav; Pravendra KumarRunoff is the most complex and important elements of hydrologic cycle which needs to be understood well and is to be predicted in a very efficient manner. Runoff prediction is very important for countries which are very much prone to floods in a short period of time. It can cause various famines and diseases if not controlled in a proper way. Considering these facts, a study has been carried out to assess the daily monsoon runoff prediction from Gaula river, Kathgodam, Nainital, Uttarakhand, India. The daily monsoon meteorological data of 11 years (1st June, 2008 to 30th Sept, 2018) were collected from Gaula barrage located at Kathgodam, Nainital, Uttarakhand, India. In the present study, multilayer perceptron artificial neural network (MLP-ANN) and Wavelet based artificial neural network (WANN) techniques were used to predict the daily monsoon runoff. The daily data for monsoon period (1st June to 30th September) of years 2008-2015 and 2016-2018 were used to train and test the models respectively. The lags were decided on the basis of Minitab statistical approach and all the possible input combinations were put to back-propagation algorithm and tan sigmoid activation function for training and testing of models. The performance of the models was evaluated qualitatively by visual observations and quantitatively using various performance indices viz. RMSE, correlation coefficient, coefficient of efficiency and Willmott index. The WANN model performed better than the MLP-ANN model.ThesisItem Open Access Spatio-temporal rainfall analysis of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-05) Rana, Saroj; Kashyap, P.S.Analysis of rainfall and rainy days are important issues for all mountainous states of India due to their varying topography and extreme rainfall events are the reason for quick surface runoff which is dangerous for safety of large structures and other natural resources. In this study, spatial and temporal variability and trend of rainfall and rainy days was analyzed for Uttarakhand sate. Long-term characteristics of rainfall and rainy days were checked using statistical parameters. Mean, coefficient of variation, standard deviation and skewness in the rainfall and rainy days time series were checked on the seasonal and annual basis. Lag-1 serial autocorrelation using student’s t-test confirmed the randomness in the time series data and on the basis of t-test, Mann-Kendall (MMK) test and modified Mann-Kendall (MK) test has been used to analyze the trend in rainfall and rainy days data of all districts of Uttarakhand. Theil-Sen’s slope estimator method has been used to determine the magnitude of trend change and the percentage change over the study period. The change point in the trend and beginning of the change was detected using Sequential Mann-Kendall (SQMK) test. The inverse distance weighting (IDW) technique of interpolation in QGIS 3.4 has been used for detecting the spatial variability and trend in rainfall and rainy days time-series. A moderate variation in annual rainfall and rainy days and high variation in seasonal rainfall and rainy days was observed in Uttarakhand. Both significant and non significant increasing and decreasing trends was obtained from the results depending upon the districts. A decreasing trend of rainfall and rainy days concentration was also observed in most of the districts on seasonal as well as annual basis. The analysis manifested the high spatial and temporal variability due to diverse precipitation-generating mechanisms and the need for an improved monitoring network.ThesisItem Open Access Hydrological characterization of Nagavali river basin under varying land use scenario(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-05) Naik, Bijayalaxmi; Kashyap, P.S.Soil and water are the two contrary sides of a coin that need to be monitored for watershed management point of view. In the present scenario with increase in population, a huge demand for water as well as land space is raising as a big question mark. Taking this issue into account, the impact on hydrological parameters due to change in land use & land cover has been studied in Nagavali River Basin of Odisha covering a total area of 9510 km2. SWAT is a semi distributed, continuous, deterministic hydrological model which was utilized for determining the changes on hydrological components due to variation in land use for specific years within the desired study area. During the total process the watershed was delineated to 29 subbasins with 249 HRUs. The developed SWAT model was calibrated for 11 years (2000-2010) and validated for 3 years (2011-2013) using SUFI-2 algorithm using SWAT-CUP. NSE and R2 for calibration were 0.91 in both cases where as in validation it was 0.79 and 0.96 respectively. The change in hydrological components due to land use scenarios was studied for five years i.e. 1985, 1995, 2005, 2015 and 2018. Evapotranspiration, precipitation, streamflow, lateral flow and groundwater contribution to stream were the five hydrological parameters that were considered for each year for the ongoing study. Evapotranspiration, precipitation and lateral flow were resulted maximum variation over the years.ThesisItem Open Access Comparison of MLP-ANN and W-ANN for SPI forecasting to assess meteorological drought(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Amit Kumar; Singh, Pravin VikramThe accurate assessment of drought is an essential component for effective water resource management planning to mitigate adverse consequences of drought. The Standardized Precipitation Index (SPI) is a widely used index to characterize meteorological drought on a varying time scale. Information about Standardized Precipitation Index (SPI) at a place is vital for the assessment of drought. In this study, an approach to forecast Standardized Precipitation Index (SPI) has been attempted to assess meteorological drought in drought prone area of the country at different time scales. This approach involved application of Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) and Wavelet Artificial Neural Network (W-ANN) to generate Standardized Precipitation Index values for different scales and denoted as, SPI-1, SPI-3, SPI-6, SPI-9, SPI-12 and SPI-24. To generate SPI values using these two models, the data set of Prabhani district in the state of Maharashtra was considered. The total data set of calculated values of SPI during 1971 to 2014 at various time scales was divided into three sets; (i) a training set, consisting of first 36 years data from January, 1971 to December, 2006; and (ii) a testing set, consisting of 4 years data from January, 2007 to December, 2010; and (ⅲ) a validation set, consisting of remaining 4 years data from January, 2011 to December 2014 for both the approaches. The SPI values at previous six-month lag were used to forecast current month SPI values and gamma test was used to decide the best combination of inputs for SPI forecasting. Both MLP-ANN and W-ANN models trained with the Levenberg Marquardt (LM) back propagation algorithm were developed using single hidden layer. The Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Efficiency (CE) statistical indices were adopted to evaluate the performance of these models. The SPI values generated by using best developed MLP-ANN and W-ANN models were compared with calculated values of SPI. The forecasted results indicate that for SPI-1, the performance of both MLP-ANN and WANN models was not satisfactory, however, MLP-ANN based model performed better than W-ANN model. For SPI-3, 6 and 9, the performance of W-ANN model was found to be better than MLP-ANN based model. In case of SPI-12 hand SPI-24, both the models were found to be performing satisfactorily, however, WANN model has a little bit edge over MLP-ANN. Interestingly, it was observed that the performance of both these models was found to be improving with increasing SPI time scale.ThesisItem Open Access Monthly rainfall modelling using Artificial Neural Network for Almora(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-07) Rawat, Devendra Singh; Devendra KumarArtificial Neural Networks model using different inputs were developed for monthly rainfall modelling of Almora. The 70% of monthly rainfall data from 1964 to 2018 was used for calibration and 30% for validation. Different topologies of models were constructed with change in number of hidden layers, processing elements and activation functions. The numbers of hidden layers varied from 1 to 3 and numbers of neurons from 1 to 10. Log-Sigmoid Axon & Tanh Axon transfer functions with back propagation algorithm and Levenberg-Marquardt learning rule were used. The performance of the models was evaluated qualitatively using rainfall time series graphs and scatter plots and quantitatively by employing Correlation coefficient, Root mean square error, Coefficient of efficiency, Integral square error and Pbias indices. The models having higher value of Coefficient of efficiency, Correlation coefficient and lower values of Root mean square error, Integral square error and Pbias were considered to be the best fit model. Based on the selected criteria, the performance of ANN model with 6-8-8-1 architecture with Log-SigmoidAxon transfer function with back propagation and Levenberg-Marquardt learning rule having past 1, 2, 3, 4, 5 and 6 months rainfall as inputs was found better than the other models.ThesisItem Open Access Spatio-temporal water spread mapping of various lakes in Nainital district of Uttarakhand using GIS and satellite data(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-05) Deoli, Vaibhav; Deepak KumarSurface water has been using regularly for drinking purpose as well as for agriculture, domestics and industrials uses. Hence, identification and mapping of waterbodies may be important to estimation of water quantity, changes detection, flood estimation and so on. In this study, mapping of water spread areas of Nainital Lake, Bhimtal Lake, Sattal Lake and Naukuchiatal Lakes have been studied. All lakes are situated in Nainital District of Uttarakhand State of India. To study the water spread areas for duration of 17 years from 2001-02 to 2017-18 of these lakes, Landsat-8 OLI and Landsat-7 ETM satellites have been used. To calculating areas of the studied lakes, each study year has been divided into three periods such as October to February, March to June and July to October. Water spread areas of all lakes have been calculated based on the 4 water indices namely NDWI, MNDWI, WRI and NDVI. To know the performance of these water indices and accuracy analysis physically GPS survey of all lakes has been conducted. Based on this GPS survey, WRI has been determined most accurate water index and based on this correct areas of all lakes has been calculated. To know the trend in water spread areas, Mann-Kendall test has been used to know the trend and San’s slope estimator test has been used to calculating magnitude of trend and percentage changes in water spread areas of all studied lakes. The depth of the lakes has been measured also at 10 different location for each lake and based on these 10 depths average depth of all lakes has been determined in pre-monsoon season of year 2019. After knowing the depths and areas of all lakes volume of water for all lakes has been calculated. This type of study, may be very helpful in state like Uttarakhand where physical mapping is difficult due to tough topography.ThesisItem Open Access Investigation of rainfall variability in southern part of Uttarakhand using entropy theory and soft computing technique(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-05) Singh, Shekhar; Deepak KumarIndia is naturally gifted with varying environment in which rainfall is a major indicator to climatic change and it plays a significant role in deciding the country‘s economy. It is very important to study about the variation of rainfall pattern. The estimation of rainfall variability helps in understanding the depletion found in various water resources, climatic change, weather and flood forecasting. In the present study, seven stations of southern part of Uttarakhand state namely Naintal, Almora, Pauri, Pantnagar, Landsdown, Kashipur and Mukteswar were taken into consideration for studying variability of rainfall pattern. The monthly rainfall data of seven rain gauge stations were selected for this study. Out of Seven stations, six stations have 116 years data i.e. 1901 to 2016 were obtained from IMD (Indian metrological Department) and for Pantnagar station, the monthly rainfall data was available for 1960 to 2018. These stations were selected on the basis of availability of data present. In the study, statistical analysis of monthly rainfall data was carried out for selective stations. Moreover, to study the variability in rainfall pattern, entropy theory has been used. The entropy technique is helpful in solving the problem related to the uncertainty or disorderliness in the dataset. Also, a hybrid model of Wavelet coupled with ANN was developed for modelling the seasonal rainfall pattern. For evaluating the performance of designed model Root Mean Square Error (RMSE), Coefficient of determination (R2) and Coefficient of Efficiency (CE) were used. This hybrid model helps in predicting the rainfall pattern in time series data. Furthermore, Mann-Kendall non-parametric test was employed to detect monotonic significant decreasing or increasing trend in time series data. The monthly, seasonal and annual analysis of rainfall data has been studied for the selected area of Uttarakhand. The results based on the statistical analysis of seasonal rainfall data concluded that the highest mean rainfall was observed in the winter season for the Landsdown station and it was found to be highest among all stations. In the seasonal analysis, highest variability in rainfall pattern was found at Pantnagar station (in terms of disorder index) during the winter season followed by Landsdown, Kashipur, Mukteswar, Almora, Nainital and Pauri stations. The results suggested that in monthly analysis, at Nainital station, the MMDI (Mean Marginal Disorder Index) was found to be high in the January and February month of winter season among all the stations. Furthermore, in seasonal analysis of rainy days, highest variability was observed in all the seasons of Nainital station. The high range in the disorder index signifies the inconsistency in rainfall pattern. For Kashipur station, WANN-10 model of Pre-monsoon season was found to be best model among all the stations and seasons. The results also indicated that, out of seven station, five stations showed significant decreasing trends and no significant trends were noticed in remaining stations i.e. Pantnagar and Mukteswar stations at 5% level of significance.ThesisItem Open Access Operational optimization of water release from multipurpose reservoir using genetic algorithm(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-05) Chauhan, Mansi; Singh, Praveen VikramIn India, most reservoirs are predominately managed for irrigation and hydropower that meets the needs of water to people living in the command area of the reservoir. The sustainable utilization of water is required to meet the present as well as the future demands. Water allocation to different stake holders causes conflict of interest for water utilization among various uses that complicates the reservoir water management. An efficient optimization technique is required to obtain optimal operational policy for complex reservoir system, so that non-linear relationships of objectives and constraints involved in reservoir operation can be dealt effectively. Modern optimization techniques such as evolutionary algorithms are found to be quite efficient in obtaining optimal operational policies for complex reservoir system. In the present study, an optimization technique i.e. Genetic Algorithm (GA) is used to obtain optimal operational policy for Tehri dam reservoir. The Genetic Algorithm based code first applied to a benchmark problem namely four reservoir problem and its performance is compared with the global solution. It was found that the generated code using Genetic algorithm provides satisfactory performance (more than 99% accuracy) for four reservoir operation system. Subsequently, for a practical problem, Tehri dam reservoir system located in Uttarakhand state is considered as a case study. Tehri dam reservoir system is a multi-purpose reservoir system serving mainly for irrigation and hydropower production. A mathematical model is formulated for minimization of sum of squared difference of actual releases and irrigation demands as an objective, subject to satisfying continuity equation, storage bounds, irrigation demands and minimum hydropower as constraints. The constraints of the reservoir operation problem are handled by using penalty function approach. Generated computer code used precisely for optimization of water release for 10-daily operation of Tehri dam reservoir system.ThesisItem Open Access Pollution assessment of river Ganga segment in Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2017-08) Dabral, Ashish; Kashyap, P.S.Soil erosion due to rain and wind action is a serious problem in India. Its negative impacts include reduction in soil productivity, silting of dams and reservoirs, deficits in water availability, pollution of water courses, serious damages to properties by soil-laden runoff, and desertification of natural environments. In the present study, chemical and physical parameters of the river were observed. The river Ganga segment in Uttarakhand was taken as study area from Devprayag (30.140N, 78.590E) to Balawali (29.640N, 78.10E). From the study area, a total of twelve locations were selected and water samples were taken in February and June. The classification of the locations of the samples of both the months for the utilization of water for various purposes like drinking water source without conventional treatment but after disinfection, fish culture and wild life propagation and irrigation and industrial cooling or controlled waste disposal was done for useful interpretation of which water could be used for what purposes according to desirable and permissible limits of pH, EC, TDS, free CO2, chloride, total acidity, calcium hardness, total hardness and magnesium hardness.