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ThesisItem Open Access Effect of soil amendments on runoff, sediment yield, biomass, soil physico-chemical properties and loss of major nutrients from sloping land under natural and simulated rainfall conditions(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Kushwaha, Daniel Prakash; Anil KumarIn this research, experimental study was made in three successive trials, viz. first and second trials were conducted under natural rainfall conditions in Mollisols soils of Pantnagar in the foothills of North Western Himalayas during monsoon season of 2018 and 2019, respectively; and the third trial was conducted in simulated rainfall conditions just after completion of natural experiments. During first trial under natural conditions, a pre-determined dose of soil amendments was applied, while in second trial an increased dose of amendments was applied to check their incremental effect. Three soil amendments viz. biochar, anionic PAM and gypsum were used to fix six treatments: control (C); gypsum (G); biochar (B); gypsum and polyacrylamide (G+PAM); biochar and polyacrylamide (B+PAM); and biochar and gypsum (B+G) and each treatment was replicated thrice on plots of size 3m × 3m using randomized block design on uniform land slope of 12%. Hypothesis was whether six treatments could reduce surface runoff, sediment yield, loss of major soil nutrients (N, P and K), and maintain and improve soil physico-chemical properties and above- and below-ground biomass or not? (B+PAM) treatment of second trial, in which biochar was applied @ 1500 g/m2 and PAM was applied @ 4 g/m2, was found to be the best treatment under natural conditions. Therefore, this particular treatment was considered again under simulated rainfall conditions, but this time variable land slopes of 0%, 6%, 12%, 18%, and 24% on plots of size 1m × 3m and variable application rates of amendments along with five simulated rainfall intensities viz. 7.06, 9.07, 11.05, 12.97 and 14.96 cm/h were applied. Trial under simulated rainfall conditions was completed in four consecutive stages, mainly control (no amendments), biochar mixed with soil @ 1000 g/m2, 1500 g/m2 and 2000 g/m2. In each stage, PAM was applied @ 3, 4 and 5 g/m2. Available N-P-K losses, biochar loss, sediment yield and runoff were taken into account in simulated rainfall conditions. In simulated experiment, low dose of anionic PAM as 3 g/m2 (0.03 t/ha) in all conditions and high dose of biochar as 2000 g/m2 (20 t/ha) was found to be acceptable for reduction of runoff, sediment yield and major nutrient losses. In the loss of biochar study, 1500 g/m2 (15 t/ha) was found acceptable instead of high dose 2000 g/m2 (20 t/ha) for reduction of biochar loss. In this study, three reasonable model scenarios of sediment yield, nutrient loss and biochar loss were also developed using the experimental data of both conditions. For these model scenarios, four modeling techniques viz. MLP-ANN, SVM-RKF, SVM-LKF and MLR were used. Under natural conditions, five variables of the best treatment viz. rainfall, runoff, sediment yield, biochar loss and nutrients (N, P and K) loss were selected, while under simulated conditions, seven variables viz. rainfall, runoff, sediment yield, biochar loss, nutrients (N, P and K) loss, application rate of amendments, and land slope were selected for the development of model scenarios. On the basis of modeling results under natural and simulated conditions, it was found that SVM-LKF model performed well in comparison to other models in simulating event based data.ThesisItem Open Access Modelling rainfall-runoff using various intelligent computing techniques for Haripura and Baur Dams in Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-07) Dumka, Basant Ballabh; Pravendra KumarWater resources are primary aspects of social, cultural and economic growth of a country. Water conservation is a basic idea for sustainable development of agricultural production, industrialization and high living standards of people. In this study, intelligent computing techniques like artificial neural network (ANN), wavelet coupled ANN (WANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and nonlinear regression (NLR) were used for rainfall-runoff modelling. The rainfall and runoff data of 20 years duration (1996-2015) for Haripura dam and 8 years duration (2006-2013) for Baur dam were used in this study. These dams are located in Uttarakhand. The data were used for prediction of present day runoff for both of the dams. Gamma test was used to select the best input combination for the development of models. Multilayer perceptron and various activation functions were used for ANN, various wavelet functions were used for WANN, various membership functions were used for ANFIS and radial kernel function was used for SVM techniques. The results showed that the Db-2 WANN model and SVM model were found to be the best among all developed models for Haripura and Baur dams. As the study areas are close to each other, their climatic and meteorological conditions and even soil characteristics are same. Hence both dams are comparable and a common model can be suggested for both Haripura and Baur dams.ThesisItem Open Access Predicting the Bhimtal lake water level fluctuations by using different machine learning techniques(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-11) Tripathi, Vaibhav; Kashyap, P.S.Lake water level forecasting at various time intervals using the records of past time series is an important issue in water resources planning, engineering, etc.. Variations in lake level are complex outcomes of many environmental factors, such as precipitations, direct and indirect runoffs. The future planning, management and prediction of water demand and usage should be preceded by long-term variation analysis for related parameters in order to enhance the process of developing new scenarios whether for surface-water or ground-water resources. Water level plays an important part in the community’s well-being and economic livelihoods. This study investigated the fluctuations in the water level of Bhimtal Lake in the Nainital district (India) by using different machine learning techniques. Different soft computing such as MLP based ANN, Support Vector Machine, Random Forest, Multilinear Regression and CatBoost were used to predict the daily stage. The following data required for the study spanned over 12 years (2009-2020). By using Gamma test, the best input combination of variables (rainfall and stage lagged by two days, rainfall and stage lagged by one day and present day rainfall) were determined. The performance of the calibrated models was assessed qualitatively by visual interpretation and quantitatively using statistical indicators such as coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE). The MLR and CatBoost models were found as the best models compared to ANN, SVR and RF models for prediction of daily stage of the study area.ThesisItem Open Access Modelling of standardized groundwater index using integrated remote sensing and machine learning techniques(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-11) Shomya Kumari; Deepak KumarGroundwater is an important natural freshwater reserve on which billions of habitants depend for their diverse utilization. Global water demand has far exceeded the total available water resources which in turn have put a serious concern on food security. India is one of the largest agricultural user of groundwater in the world where there has been a large scale revolutionary shift from surface water management to a widespread groundwater abstraction. Increased industrialization, rapid population growth, climate change, changes in the land use and land cover, has influenced the extensive use of groundwater which simultaneously affects the groundwater level. Groundwater drought occurs when this groundwater level falls below the critical level. In the present study, analysis of groundwater drought of the state of Bihar, India, has been carried using a drought index called Standardized Groundwater Index (SGI) and the spatial and temporal distribution of SGI has been reflected using Remote sensing and GIS approach. The rainfall and groundwater data of 38 districts of Bihar from 2002-2019 has been used and was divided seasonally into pre- monsoon, monsoon, post- monsoon and winter seasons. Further, SGI was modelled using Artificial Neural Network and Random Forest machine learning techniques with different input models. GRACE satellite water equivalent data along with rainfall and below groundwater level was used to predict SGI. Finally, the trend analysis of groundwater level data of 38 districts of Bihar for all the four seasons was studied using Mann- Kendall test statistics and Thein Sen's slope estimator. The results of SGI spatial and temporal distribution showed that districts like Aurangabad, Gaya, Buxar, Bhojpur, Kishanganj, Katihar, Kaimur, Rohtas, Nawada, Saran Chappra, Siwan, Samastipur, Supaul are prone to the critical groundwater drought condition. On comparing the performance of the two models to predict, SGI it was found that RF models performs superior than the ANN model with correlation coefficient value of (r) as 0.95. The trend analysis results showed that 45% of the districts are showing decline in the groundwater level particularly in pre-monsoon season.ThesisItem Open Access Application of machine learning techniques for rainfall-runoff modelling of Gola watershed(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-11) Singh, Abhinav Kumar; Pankaj KumarThe prediction of runoff has a significant role in water resource planning and management. There is a great need for good soil and water management system to overcome challenges of water scarcity and other natural adverse events like- floods, landslides, etc. Rainfall-runoff modelling is an appropriate approach for runoff prediction, which makes it possible to take preventive measures to avoid damage caused by natural hazards. In the present study, machine learning techniques namely: Multiple linear regression (MLR), Multiple adaptive regression splines (MARS), Support vector machine (SVM), and Random forest (RF) were used for runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall data for 12 years (2009-2020) of three rain-gauge stations (Nainital, Bhimtal & Kathgodam) and runoff data at the outlet of watershed i.e. Kathgodam were obtained from their respective irrigation departments for the analysis. Thiessen polygon method was used for the calculation of mean areal rainfall of the watershed. Gamma test was conducted to obtain the best inputs for the models. The complete dataset has been divided into training and testing datasets, where 80% of data was used in training and rest 20% was used for the testing period. The goodness of fit for the models was evaluated by root mean square error (RMSE), coefficient of determination (R2), Nash- Sutcliffe coefficient of efficiency (NSE), and percent bias (PBIAS). For runoff prediction, the overall performance-wise rankings of models were RF, MARS, SVM, and MLR. Among all four models, the RF model outperformed in training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for runoff prediction of the Gola watershed.ThesisItem Open Access Effect of rainfall dynamics on solute transport and groundwater recharge for rainfed semi-arid regions(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Vijay Kumar, P.; Deepak KumarIndia was the world's second largest fertilizer producer after China, and it also ranks first in fertilizer imports. Reducing fertilizer use cannot be considered a viable option in India since optimizing food output was the highest priority for the population's food and nutritional protection. Improving fertilizer usage quality by maintaining steps such as balanced nutrient distribution, good planning, and water conservation will significantly reduce fertilizer leaching beyond the root zone of crops. A detailed explanation of both water and solute flow through the vadose zone was expected to reliably forecast environmental impacts associated with human activities such as overuse of fertilizer and irrigation. This study investigated the effect of variation of climate change on solute transport and groundwater recharge for rainfed Cotton Crop using HYDRUS-1d and HYDRUS-2d in Semi-arid Region of Gopalapur in the Raichur district (India). The solute transport and reaction parameters were assessed using HYDRUS-2d at the depths of 50, 100 and 150 cm of soil horizon. while the potential groundwater recharge and cumulative bottom fluxes as well as soil water content were assessed using the HYDRUS-1D. the following input data required are Metrological data from 2015-2020, Soil data, Crop data, Soil hydraulic parameters, and Solute data respectively. The results pertaining to HYDRUS-1d showed that the potential groundwater recharge for average precipitation, 20% decreased precipitation and 20% increased precipitation were 16.66 cm, 4.33 cm, and 30.35 cm respectively. There was huge difference of groundwater recharge between 20% increased and decreased precipitation due to availability of water to percolate. Similarly HYDRUS-2d showed that solute transport for average precipitation, 20% decreased precipitation and 20% increased precipitation at 150cm depth of soil horizon was that for N is 3.21*10-5, 1.33*10-7, 6.969*10-4mmolcm-3, P is 0, 0, 2.9*10-7mmolcm-3 and K is 0, 0, 6.25*10-8 mmolcm-3 respectively. From this it can be infer that increased precipitation caused solute to move faster than at usual rate. Therefore, it is concluded that the HYDRUS model would aid in reducing fertilizer losses, improving groundwater efficiency, and ultimately lowering production costs.ThesisItem Open Access Effect of sediment particle characteristics on critical bed shear stress under different channel bed slopes and roughness conditions(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-06) Anuradha Kumari; Akhilesh KumarThe condition for incipient motion of sediment particle i.e., critical condition of motion occurs when magnitude of drag force (shear force) exerted by flowing fluid on the sediment particle exceeds the resistance offered by the particle. The analysis of movement of sediment particles along a channel bed has been one of the most challenging problems before the hydraulic engineers for a long time as accurate measurement of flow parameters in a channel at which the incipient motion exactly occurs is a subjective criterion. Sediment transport in gravel-bottomed streams is generally through surface creep as bed load transport due to presence of larger sized particles. Ample research work has been reported on incipient motion to describe the effect of some of these variables considering sediment particles as spherical. However, a very little work is reported in the available literature for non-spherical sediment particles, which is the normal situation in natural streams, where orientation of sediment particles plays a significant role on their incipient motion. Depending on the orientation of a non spherical sediment particle, the surface area of the particle exposed to the flow gets changed and accordingly the entire force dynamics on the sediment particle is altered. This experimental study was conducted in Soil and Water Conservation Engineering Laboratory, College of Technology at Pantnagar with the focus to investigate the effects of sediment particle shape, size, unit weight, orientation and flatness along with channel bed roughness and channel bed slope on the critical bed shear stress. The experiments were conducted by using a rectangular Hydraulic Tilting Flume of 7.0m×0.6m×0.3m size with precise water supply, regulation and measurement systems and can be adjusted a forward slope up to 5%. Incipient conditions for motion of sediment particle were created and observations were recorded analyzed for 252 different combinations. The critical bed shear stress was found to be higher for non- spherical sediment particles as compared to spherical sediment particles of the same nominal diameter and increasing almost linearly with size for all shapes of sediment particles. It was also observed that spherical sediment particles of the same size were having lesser critical bed shear stress as compared to rectangular prismatic sediment particles under similar conditions. As channel bed slope increased from 0.25% to 0.5%, there was almost a 100% increase in critical bed shear stress in case of rectangular prism shaped sediment particles of the same size but about 50% increase was observed in case of spherical and cubical sediment particles under similar situations. Almost a linear increase in critical bed shear stress was observed when the bed roughness height increased from 0mm to 8.000mm for all sized sediment particles at every channel bed slope. It was observed that critical bed shear stress did not change significantly with the change in orientation of the sediment particles, while total shear force acting on the sediment particle changed with orientation in the ratio of their exposed area of non-spherical sediment particles under similar flow and channel roughness conditions. With the increasing flatness ratio, the effect of orientation of sediment particle became an important consideration to determine the magnitude of the total shear force acting on the particle under particular set of conditions. The critical bed shear stress was found to be increasing with the increase in unit weight of the sediment particle at a particular bed slope and roughness height. Relationships between dimensionless critical shear stress ‘θ’ (also called Shields parameter) to the critical particle Reynolds number (Rep) of sediment particles of noncubical and non-spherical shape along with parameters like slope of channel bed and its roughness were developed. Mathematical relationships were developed to determine critical bed shear stress and tested for their estimation efficacy using scatter plots and statistical indices for different combinations of experimental variables. The developed mathematical relationships are likely to provide an effective tool to analyze critical shear stress at incipient conditions.ThesisItem Open Access Efficacy of roots and shoots of napier and lemon grasses to control runoff and sediment outflow under simulated rainfall and overland flow conditions(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Tamta, Sushma; Akhilesh KumarIn this study, extensive laboratory experiments were conducted under controlled environment of a laboratory with simulated rainfall and overland flow conditions using a soil filled hydraulic tilting flume as test plot. As per mandate of this study, the effects of root and shoot characteristics of Napier grass and Lemon grass on runoff and sediment outflow at different growth stages with four simulated rainfall intensities of 4.0 cm/h, 6.5 cm/h, 8.3 cm/h and 9.4 cm/h and four overland flow rates of 6.6 l/s/m, 9.0 l/s/m, 10.7 l/s/m and 12.4 l/s/m to observe runoff and sediment outflow at three land slopes of 1%, 2%, and 3% were observed. After each experiment, different morphological characteristics such as Leaf Area Index (LAI), Shoot Length (SL), Number of Leaves (NL), Number of Tillers (NT), Shoot Biomass (SB), Root Density (RD), Root Length (RL), Root Biomass (RB), and Total Biomass (TB) of grasses were measured at Stage-I (90DAP), Stage-II (120DAP) and Stage-III (150DAP) for both crops. Runoff samples were collected for whole plant plot and root plot for various combinations of input variables and in total 936 runoff samples were collected and analyzed to determine sediment concentration and sediment outflow rate. The analysis of findings revealed that Napier grass and Lemon grass were very effective to reduce runoff and sediment outflow and their efficacy increased with the extended growth stage. The reduction in runoff and sediment outflow at stage-I, i.e. 90 DAP, was approximately 56% and 85% for Napier grass, and 52% and 82% respectively for Lemon grass. At stage-II, i.e., 120 DAP, the reduction in runoff and sediment outflow was approximately 68% and 90% for Napier grass, and 59% and 87% respectively for Lemon grass while at stage-III, i.e., 150 DAP, it was observed as 74% and 96% for Napier grass, and 69% and 94% respectively for Lemon grass as compared to bare plot under simulated rainfall conditions.The sediment outflow rate reduction from roots was 9%, 17% and 33% more than the shoots at 90 DAP, 120 DAP and 150 DAP respectively under over land flow conditions. Napier grass with its higher values of above-ground biomass parameters (shoots) and below ground bio mass parameters (roots) as compared to Lemon grass was found to be more effective to reduce runoff and sediment outflow. It was also observed that the contribution of shoots in runoff rate reduction was higher than the roots and maximum reduction was observed at low rainfall intensity. Similarly, root part of the plant has more contribution in sediment outflow rate reduction as compared to shoot part of the plant. The relative contribution of the roots of Napier grass in sediment outflow reduction varied from 61% to 78%, and contribution of shoots was found to be 22-39%. On the other hand, for Lemon grass, the relative contribution of roots in sediment outflow reduction varied from 59% to 78%, and contribution of shoots varied in the range of 22% to 41% under selected rainfall intensities, land slopes and growth stages as compared to whole plant. Mathematical models were established and for runoff and sediment outflow in terms of root and shoot parameters. Developed models were found to be very satisfactory based on the values of various performance indicators.ThesisItem Open Access Trend analysis of climatic variables for Rajkot (Gujarat)(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Mainwal, Shivani; Singh, Praveen VikramClimatic variability, particularly rainfall, air temperature, wind, relative humidity and solar radiation, has received a great deal of attention worldwide. The magnitude of the variability or fluctuations of these variables varies according to location. In the present study the variability and trend of these climate variables have been examined for Rajkot (Gujarat) during the period of 35 years from 1979 to 2013. The variability of these climatic variables has been analyzed using statistical parameters while trend analysis has been studied using non-parametric approaches such as Mann- Kendall and Sen’s slope estimator test. Statistical and trend analysis has been done for various time scale i.e. monthly, seasonal (pre-monsoon, monsoon, post-monsoon and winter) and annual basis. The average minimum and maximum value of daily rainfall, maximum temperature, minimum temperature, wind, relative humidity and solar radiation were found as 0.00mm and 22.77mm, 26.60 0C to 43.61 0C, 11.370C to 27.630C, 1.98 m/s to 5.68 m/s, 0.19 to 0.90 and 9.89 MJ/m2 to 28.03 MJ/m2. For rainfall there was an increasing trend from February to June and December and decreasing trend for the month January and July to November on monthly basis. The seasonal rainfall shows an increasing trend for winter and pre-monsoon season and decreasing trend for post-monsoon and monsoon season while it shows an increasing trend on annual basis. For maximum temperature there was an increasing trend from January to March, July, August and December and decreasing trend for April to June and September to November on monthly basis. The seasonal maximum temperature shows an increasing trend for winter and annual season while there was a decreasing trend for pre-monsoon, monsoon and post-monsoon. The minimum temperature showed an increasing trend from January to April and December and decreasing trend for May to November on monthly basis. The seasonal minimum temperature shows an increasing trend for pre-monsoon, winter and annual season while there was a decreasing trend for monsoon and post-monsoon. For wind showed an increasing trend from January to March and September to December and decreasing trend for April to august on monthly basis. The seasonal wind showed an increasing trend for pre monsoon, post monsoon and winter season while there was a decreasing trend for monsoon and annual season. The relative humidity shows increasing trend from February to June, and decreasing trend for the month of January and July to December. The seasonal relative humidity shows an increasing trend for pre monsoon, winter and annual season and decreasing trend for monsoon and post monsoon season. The solar radiation shows an increasing trend from January to March, July, August, November and December and decreasing trend for April to June, September and October on monthly basis. The seasonal solar radiation shows an increasing trend for monsoon, post monsoon, winter and annual season and decreasing trend for pre monsoon.
