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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar
After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960.
The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.
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ThesisItem Open Access Evaluation of Groundwater Storage Anomalies in Major River Basins of India using GRACE Satellite and In-Situ Groundwater Level Data(G. B. Pant University of Agriculture & Technology, Pantnagar-263145, 2024-06) Murudkar, Ankita Chandrakant; Kumar, DeepakGroundwater, a significant source of fresh water and often referred to as an invisible resource, plays a crucial role in sustaining various aspects of human life. However, excessive exploitation of groundwater can lead to adverse consequences such as droughts, water scarcity, and hindered social and economic development. Therefore, it is essential to analyze groundwater levels to effectively manage this vital resource. This study focuses on understanding groundwater depletion by investigating 14 different locations across five basins (Ganga, Krishna, Godavari, Mahanadi, and Narmada), strategically selected to be 300 km apart. In the Ganga Basin, the locations of Badarwas, Daryaganj, Prayagraj and Bahera were chosen, while Adlur and Amarapuram were selected in the Krishna Basin. For the Godavari River, Jalgaon, Dhoki, and Akolebazar selected as the study locations. Bodeli, Handia, and Banjari represented the Narmada River, and BLD-022-OW and Abhimanpur were selected in the Mahanadi Basin. A comprehensive dataset spanning 17 years from 2002 to 2019 was utilized, incorporating GRACE (Gravity Recovery and Climate Experiment) and GLDAS (Global Land Data Assimilation System) data to calculate groundwater storage anomalies (GWSA). The Mann-Kendall and Sen's slope methods were utilized to conduct trend analysis. Weaker correlations were observed between GWSAWELL and GWSAGRACE in the Ganga, Krishna, Narmada, and Godavari basins. To improve correlation with in-situ measurements (GWSAWELL), machine learning techniques, namely Artificial Neural Networks (ANN), Random Forest (RF), and Support Vector Machines (SVM), were employed. Spatial and temporal trend analysis revealed depletion patterns during specific seasons. Among the machine learning models, the RF model consistently demonstrated strong correlation levels (<0.90) with GWSAWELL. This research enhances groundwater estimation and facilitates effective water resource planning in densely populated areas through the application of machine learning techniques and analysis of long-term datasets.ThesisItem Open Access APPLICATION OF DATA-DRIVEN MACHINE LEARNING MODELS FOR RAINFALL PREDICTION: A CASE STUDY OF SUB-HUMID KONKAN REGION OF MAHARASHTRA(2023-03) Jadhav, Nikhi Kanta; Kumar, PankajRainfall is one of the most influential hydrologic variables required for number of applications in water resource management, specifically in the agriculture sector. Rainfall prediction has gained utmost importance in recent times due to its association with natural disasters such as floods, landslides, drought, etc. Rainfall prediction can help decision makers of a variety of fields in making decisions regarding important activities like crop planting, agricultural operations, sewer system operations, and managing natural disasters like floods and droughts. This study presents a comparative analysis of four data-driven machine learning models, namely, Multiple Linear Regression (MLR), Random Forest (RF), Categorical Boosting (CatBoost), and Extreme Gradient Boosting (XGBoost) for predicting daily rainfall of Dapoli station, located in the Ratnagiri district of Maharashtra. Historical daily meteorological observations starting from 2005 to 2021, for seventeen years, were collected for the analysis from Department of Agronomy, College of Agriculture, Dapoli. The meteorological parameters data include the parameters such as rainfall (R), minimum temperature (Tmin), maximum temperature (Tmax), relative humidity in the morning (RH1), relative humidity in the afternoon (RH2), wind speed (WS), sunshine hours (SS), vapor pressure in the morning (VP1), vapor pressure in the afternoon (VP2), and evaporation (E). The whole dataset was split into two parts, the training dataset and the testing dataset. The data were in the proportion of 80% and 20% for the training and testing phase, respectively for the prediction of rainfall. The qualitative and quantitative performance of the aforementioned models was assessed using four statistical properties, viz. coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean square error (RMSE), and index of agreement (d). After a detailed analysis, it was concluded that the RF model performed consistently well for predicting the daily rainfall at Dapoli station.ThesisItem Open Access REFERENCE EVAPOTRANSPIRATION PREDICTION USING VARIOUS HEURISTIC AND STATISTICAL APPROACHES(G. B. Pant University of Agriculture & Technology, Pantnagar-263145, 2024-02-01) Reang, Hamtoiti; Kumar, PravendraThe accurate estimation of reference evapotranspiration (ET0) has paramount importance and is crucial in irrigation planning and scheduling, watershed hydrology studies, drought forecasting and monitoring, water resource management and planning, etc. In the present study from Guwahati station (Assam), the standard FAO-56 based Penman-Monteith (PM) method was utilized to estimate daily ET0 which was considered an output to assess the models. The different soft computing and statistical techniques such as ANN, wavelet based ANN (WANN), ANFIS and MNLR models were used for the prediction of daily reference evapotranspiration in the study area. Gamma test (GT) was used to determine and select the best input combination of climatic parameters (i.e., mean temperature, mean relative humidity, wind speed and solar radiation) having the least gamma and V-ratio values. The qualitative and quantitative performance evaluation criteria were done by visual inspection and using statistical and hydrological indices such as coefficient of determination (R2), root mean square error (RMSE), coefficient of efficiency (CE) and Willmott index (WI) respectively, which were used for assessing the prediction accuracy of the developed models. Based on the comparison of the models, the results revealed that the WANN-11 model performed the best as compared to ANN-8, ANFIS-02 (trap-2) and MNLR models for prediction of reference evapotranspiration of the study area.The sensitivity analysis was also carried out for the best developed model to detect the most sensitive input parameter based on the performance of the model. It was found that mean relative humidity was the most sensitive input parameterfor daily reference evapotranspiration prediction of the study area. (ThesisItem Open Access Spatiotemporal analysis of standardized precipitation index for eastern Rajasthan, India(G. B. Pant University of Agriculture and Technology, Pantnagar, 2023-01) Aman Deep; Deepak KumarThe present study consists of spatiotemporal analysis of Standardized Precipitation Index (SPI) for Eastern Rajasthan, India. The total geographical study area is 42,857 km2 with a spatial extent from latitude 24°6' N to 27°15' N and longitude 75°83' to 77°54' E respectively. It covers 12.52% of Rajasthan’s total geographical area. The study area consists of seven districts namely Baran, Bharatpur, Kota, Jhalawar, Karauli, Dholpur and Sawai Madhopur. Using monthly precipitation data from 1991 to 2022, the Standardized Precipitation Index values were calculated by DrinC software. SPI values were calculated for the 3-Month (April-June), 3-Month (July-Sep), 6-Month (April-Sept), 6-Month (Oct-march) and for 12-Month. Further, trend between rainfall deviation and Standardized Precipitation Index was calculated. Spatial variation of drought was done by QGIS software for the study area. Drought was classified as mild, moderate, severe, or extreme category according to the value of SPI. Based on 3-Month SPI (April-June), it was noticed that all of the districts were severely and extremely affected by drought during the years 1991, 1992, and 1995. In the years 2006 and 2017 nearly all the districts experienced severe drought and all the districts experienced worst condition in the year 2002 as calculated by 3-Month SPI (July-Sept). Four districts out of seven districts experienced extreme drought in the year 2000 and 3 districts hit by drought severely in 2011 as calculated by 6-Month SPI (Oct-March). The drought faced in the state was of severe condition nearly in the entire study area in the year 2017 in April-Sept season as calculated by 6-Month SPI method. In the year 2017 nearly all the districts experienced severe drought and all districts were tremendously affected by extreme drought in 2002 (calculated using 12-M SPI). The value of coefficient of determination in April-June season was not good as in other seasons (calculated using 3-M, 6-M and 12-M SPI) for all the districts under study area. Drought may affect one district or group of districts depending on rainfall occurrence during the vegetative season. Thus, it is important to understand which area are prone to drought. So that adequate actions can be taken to resolve the problem.ThesisItem Open Access Hydrological modelling of Gola watershed using soil and water assessment tool(G. B. Pant University of Agriculture and Technology, Pantnagar, 2022-10) Mathpal, Himadri; Pankaj KumarWater is the most important natural resource for the existence of all living beings. However, as demand rises and supply decreases, the disparity between the two grows relatively high every day. Scientific water management is essential to meet the demands of irrigation and domestic use. An efficient strategy for managing, evaluating, and modeling significant water resources is the need of the hour. The advent of remote sensing and GIS technology has made it feasible to employ geographically and physically based hydrological models to mimic the operation of watershed systems as easily and realistically as possible. In reality, a major impediment to widespread adoption of these tools, particularly in developing countries, was a lack of or inaccessibility to data. In order to assess the hydrology of the Gola watershed in Uttarakhand, India, the current study incorporated remote sensing derived products, gridded precipitation and temperature data, and the Soil and Water Assessment Tool (SWAT) into a geographic information system modelling framework. Using the SWAT model, the whole basin was divided into 29 subbasins, which have been further divided into a total of 207 hydrological response units based on distinct land cover, soil, and slope classes. Runoff was modelled in this study by utilising input from 15 years (2006-2020) of meteorological data. The SUFI-2 algorithm of SWAT-CUP was used for model calibration and validation for a monthly time period. The first three years (2006–2008) were used as a warm-up phase, followed by the calibration period of eight years (2009–2016), after which the model was validated for the next four years (2017–2020). The effectiveness of the model was evaluated using the coefficient of determination (R2) and Nash-Sutcliffe (NSE). During calibration, R2 and NSE were 0.85 and 0.84, respectively while during validation, they were 0.65 and 0.65. The global sensitivity function in SWAT-CUP was used to rank the overall impact of each parameter after calibration. In the analysis, CN2, which simulates the stream flow of the Gola watershed, was discovered to be the most sensitive parameter with an absolute t-statistic of -15.56 and a p-value of 0.00. With a t-statistic and p-value of -0.17 and 0.85, respectively, it was determined that the SURLAG parameter was the least sensitive. The study found that linking the SWAT and SWAT-CUP made the calibration process for simulating local hydrology within the watershed faster and more accurate.ThesisItem Open Access Assessment of water quantity and quality through soil column under ordinary and neem coated urea application rates(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-09) Gupta, Shashi; Singh, P.V.Nitrogen one of the most important nutrient for agriculture production is usually supplied via application of ordinary and neem coated urea because of its high-water solubility and susceptibility to nitrogen loss through leaching. Its application at the soil surface may affect the infiltration, percolation through soil profile and leaching of nutrients in the soil profile may also deteriorate the ground water quality. Therefore, in the present study an attempt has been made to investigate the effect of application of two application rates i.e. 22 and 44 g/m2 of ordinary and neem coated urea on the infiltration and percolation behavior of soil and percolated water quality through a soil column after 24-hour, 48-hour, and 72-hour of saturation in the laboratory conditions on an experimental set up consisting of cylinder of 60 cm depth and 30 cm diameter with a 15 cm tall conical bottom with 2 cm hole. In total 54 experimental sets were performed having 3 replications of each of 18 treatment combinations of 2 factors time after saturation and applications rates of urea. Percolated water sample was collected after every 15 min to estimate percolated water quantity and quality was determined. The infiltration rates were found to 31.60 cm/h, 29.84 cm/h and 29.40 cm/h for the soil without urea application while the infiltration rate was found 29.26 cm/h, 26.64 cm/h and 21.76 cm/h for soil with application of 22 g/m2 of ordinary urea and 26.80 cm/h, 23.70 cm/h and 18.22 cm/h for soil with application of 22 g/m2 of neem coated urea respectively for 24, 48 and 72 h. The Percent volume reduction in percolation in case of ordinary and neem coated urea with application rate of 22 g/m2 as compared to soil without application of any urea was found 21.36 %, 25.03 % for 24 h after saturation 30.53 %, 34.21 % for 48 h after saturation and 44.44 %, 50.00% for 72h after saturation respectively. Maximum nutrient lost along with percolated water through after 24, 48 and 72h saturated soil column, under 22 g/m2 of ordinary and neem coated urea applications were found as 44.80, 42.56, 40.32 and 41.44, 39.20, 36.96 ppm respectively in case of nitrogen, 3.81, 3.77, 3.75 and 3.67, 3.61, 3.57 ppm respectively in case of phosphorous and for potassium as 7.10, 7.00, 6.70 and 6.40, 6.20, 5.90 ppm. The infiltration rates were determined to be 30.00 cm/h, 29.62 cm/h and 28.84 cm/h for the soil without application of urea while the infiltration rate was found 28.80 cm/h, 26.40 cm/h and 20.28 cm/h for soil with application of 44 g/m2 ordinary urea and 24.90 cm/h, 19.50 cm/h and 17.40 cm/h for soil with application of 44 g/m2 neem coated urea respectively for 24, 48 and 72 h. Application of ordinary and neem coated urea with application rate of 44 g/m2 as compared to soil without application of any urea were resulted in the percent volume reduction in percolation were found to be 28.33 %, 36.11 % for 24 h after saturation, 52.78 %, 61.11 % for 48 h after saturation and 54.29 %, 57.14 % for 72h after saturation respectively. Maximum nutrient lost along with percolated water through after 24, 48 and 72 h saturated soil column, under 44 g/m2 of ordinary and neem coated urea applications were found as 86.24, 81.76, 78.40 and 71.68, 68.32, 64.96 ppm respectively in case of nitrogen, 3.88, 3.85, 3.83 and 3.79, 3.71, 3.69 ppm respectively in case of phosphorous and for potassium as 7.60, 7.50, 7.30 and 6.90, 6.70, 6.50 ppm. The results shows that neem coated urea decrease all three-infiltration rate, percolation and the percent volume reduction more as compared to ordinary urea as well as in least nutrient losses through percolation. Infiltration rate, percolation and percent volume reduction decreases with increment in time after saturation. Rate of increase of cumulative infiltration is steady initially for a certain period of time and later on soil with neem coated urea resulted in the lowest value of cumulative infiltration and the rate while soil without urea in the highest value. Nutrient losses through percolation decreases with decrease in time duration after saturation.ThesisItem Open Access Watershed prioritization of Kosi watershed based on the morphometric and land use and land cover analysis(G.B. Pant University of Agriculture and Technology, Pantnagar, District Udham Singh Nagar, Uttarakhand. PIN - 263145, 2022-03) Sanodiya, Vishnu; Pankaj KumarWatershed prioritization has gained importance in natural resource management, especially watershed management. Kosi watershed is a constituent of the Kosi river, and it falls within the Almora and Nainital districts of the Uttarakhand, covering an 1819 km2 area. Using remote sensing and GIS application, prioritization of Kosi sub-watersheds have been done based on different morphological attribute lineage with soil erodibility and land use and land cover analysis. Prioritization has been performed to identify environmental stress areas. ArcGis 10.4.1 has been utilized for watershed delineation, morphometric analysis, and LULC supervised classification. The Kosi watershed is further delineated into 17 sub-watersheds for prioritization purposes. Various morphometric parameters (linear, areal, and relief) have been determined for each sub-watershed. Nine morphometric parameters has been selected for pairwise comparison matrix for prioritization based on the FAHP technique then suitable weights are assigned to the morphometric parameters. Further, these weights has normalized to assign final ranks to the sub-watersheds. Subwatersheds have been classified into five categories: very low, low, moderate, high, and very high. . In FAHP technique only Sub-watersheds SW1 and SW3 are found very high priority, whereas sub-watersheds SW8 and SW11 comes under very less priority. Land use and land cover mapping have been carried out with the help of Landsat 8 LISS III level II data with an overall accuracy of 84.98 % and a kappa coefficient of 0.81. Kosi watershed is classified into six classes forest, wasteland, waterbody, cultivated land, rocks and built-up area. Three classes wasteland, cultivated land, forest area are considered for prioritization, an average of the individual rank are determined for the final ranking of the sub-watersheds. Based on LULC sub-watersheds SW3, SW6 and SW9 comes under very high priority, and sub-watersheds SW14 and SW17 comes under very less priority.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.