Academic literature on the topic 'Netravati'

The salt water mixes with fresh water and forms brackish water. The brackish water contains some quantity of salt, but not equal to sea water. Salinity determines the geographic distribution of the number of marshes found in estuary. Hence salinity is a very important environmental factor in estuary system. Sand is one major natural aggregate, required in construction industry mainly for the manufacture of concrete. The availability of good river sand is reduced due to salinity. The quality of sand available from estuarine regions is adversely affected due to this reason. It is the responsibility of engineers to check the quality of sand and its strength parameters before using it for any construction purpose. Presence of salt content in natural aggregates or manufactured aggregates is the cause for corrosion in steel. In this study the amount of salinity present in estuary sand was determined. Three different methods were used to determine the salinity in different seasonal variations. The sand sample collected nearer to the sea was found to be high in salinity in all methods. It can be concluded that care should be taken before we use estuary sand as a construction material due to the presence of salinity.

An agro-meteorological investigation was undertaken during rabi, 2016 and 2017 at Farm, Department of Agricultural Meteorology, College of Agriculture, Pune, Maharashtra State (India). The experiment was laid out in split plot design with three replications. The treatment comprised of four varieties viz. V1: NIAW-301 (Trymbak ) V2: NIAW-917 (Tapovan), V3: NIAW-1415 (Netravati) and V4:NIAW-1994 (Phule Samadhan) as main plot and four sowing windows viz., S1: 43rd MW (22-28 October), S2: 45th MW (5-11November), S3: 47th MW (19-25 November) and S4: 49th MW (3-9 December) as sub plot treatments. The agrometeorological indices indicated more values for 45th MW (5-11November) and 47th MW (19-25 November) sown wheat crops and lowest values in late sown crop. Days to crown root stage, tillering stage, ear emergence stage, 50% flowering stage, milking stage, dough stage and physiology maturity matched closely with observed values for all sowing environments. It revealed that the grain yields were significantly higher in NIAW-1994 (51.07 and 48.52 qha-1) and significantly superior to the rest of the wheat varieties. This was followed by NIAW-917(45.72 and 43.43 q ha-1), NIAW-301(43.57 and 41.27 q ha-1). The variety NIAW-1415 recorded significantly lower grain yield (40.89 and 38.84 qha-1) during 2016 and 2017, respectively. The grain yield was maximum at 47th MW sowing window (50.40 and 47.88 qha-1), the grain yield of 45th MW (47.94 and 45.42 qha-1) were at par with 47th MW sowing window. This was followed by 43rd MW sowing window (43.88 and 41.68 q ha-1), 49th MW sowing window (39.04 and 37.07 q ha-1) during 2016 and 2017, respectively.

Wheat (Triticum aestivum L.) is a thermo-sensitive long-day crop. Temperature is a major determinant of its growth and productivity. Late sown wheat exposes preanthesis phenological events to high temperature that influence grain development and ultimately the yield [1]. Comprehensive assessments of the influence of climate variability on crop yields at local and regional scales can be highly beneficial. With an aim to assess the weather influences on wheat at local scale this study was taken up. An experiment was conducted at Department of Agricultural Meteorology Farm, College of Agriculture, Pune, Maharashtra State (India) in a split-plot design with three replications and sixteen treatment combinations of four different varieties and four sowing windows. Four varieties used were NIAW-301 (Trymbak ), NIAW-917 (Tapovan), NIAW-1415 (Netravati) and NIAW-1994 (Phule Samadhan). Four sowings were taken up on 43rd MW (22-28 October), 45th MW (5-11November), 47th MW (19-25 November) and 49th MW (3-9 December). The grain yield of wheat was influenced significantly by wheat varieties. The grain yields were significantly higher in NIAW-1994 (51.07 and 48.52 qha-1) and significantly superior to the rest of the wheat varieties. This was followed by NIAW-917(45.72 and 43.43qha-1), NIAW-301(43.57 and 41.27 q ha-1). The variety NIAW-1415 recorded significantly lower grain yield (40.89 and 38.84 qha-1) during 2016 and 2017, respectively. Correlation analysis with weather parameters e.g. Temperature (Maximum and Minimum), Relative humidity (Morning and Evening), Rainfall and bright sunshine hours and yield showed that from tillering to 50% flowering stage, maximum temperature (-0.962*) was significantly negatively correlated with grain yield (r = -0.980**), (r =-0.950**) during 2016 and 2017, respectively in NIAW-301 (Trymbak ). The same trend was observed in the remaining varieties also. Regression equations were developed to predict the yield.

Seawater Intrusion (SWI) into the fresh groundwater aquifers is one of the major hydrological problems in coastal regions. This global issue is aggravated by increasing demands for fresh groundwater due to on-going urbanization, population growth, and economic development in these regions. Freshwater and seawater mixing is by mechanical dispersion and hydrodynamic diffusion, which results in the formation of the transition zone. The position and width of the transition zone change with hydrogeological, hydrological circumstances, and anthropogenic activities such as excessive groundwater extraction, climate variations/and sea-level fluctuations, etc. The groundwater models provide a scientific and predictive tool for determining the extent of SWI at field-scale. But, the development of numerical models to determine the groundwater flow and SWI in the heterogeneous aquifers requires several hydrogeological field data. Due to incomplete knowledge of geological structures such as heterogeneity, anisotropy, and layering, the development of conceptual numerical models is often simplified. This indicates that there is a wide gap in understanding the hydrogeology characterization in three-dimensional (3D) modeling of the groundwater flow and SWI. With this motivation, the work presented in this thesis is aimed at estimating the anisotropic heterogeneous parameters in a layered coastal aquifer for the simulation of hydraulic head and SWI. The coastal plain in between River Swarna and River Netravati of Karnataka in India is selected as the study area. As most of the West flowing rivers of Karnataka are seasonal and tidal, due to this saline water intrudes into rivers during the non-monsoon period up to several kilometers contaminating the adjacent fresh groundwater aquifers. And due to the permeable geological formation, thousand million cubic feet of recharged rainwater is designated as subsurface groundwater discharge, thus, making it an interesting and challenging case study to pursue. The study area between the rivers extends over 1191 km2 (large-scale) with the Arabian Sea in the West. Applying the different parameter estimation approaches to this large-scale area requires an efficient iv machine and adequate field measurements. Due to insufficient field data and to reduce the computational cost, the parameter estimation approaches are applied on the small-scale coastal aquifer of 8 km2 areal extent, which lies within the large-scale area. The work reported in this thesis contributes towards developing a 3D, variable-density conceptual model integrated with the Geographic Information System (GIS). The developed basic conceptual model is constrained with real-field data such as layering, aquifer bottom topography, and appropriate initial conditions. The initial aquifer parameters are layered heterogeneous but spatially homogeneous. The developed basic conceptual model showed poor correlation with observed state variables (hydraulic head and solute concentration), signifying the importance of spatial heterogeneity of aquifer parameters in all the layers. A sensitivity analysis is carried out to determine the most critical aquifer parameters that affect SWI modeling. From the analysis results, it is inferred that the anisotropic heterogeneous hydraulic conductivity and longitudinal dispersivity are the most significant parameters to be estimated spatially. This investigation demonstrates the necessity of considering anisotropy and heterogeneity for effective modeling of the SWI in a layered coastal aquifer. An inverse modeling approach is used to estimate heterogeneous aquifer parameters. The solution for the inverse SWI problem has not been studied as extensively as forward modeling. Inverse modeling aims to estimate layerwise anisotropic heterogeneous hydraulic conductivity and longitudinal dispersivity. The inverse code minimizes the least square error of state variables and errors induced by regularization to estimate heterogeneous parameters at pilot points. It is found that the inverse model output for longitudinal dispersivity is mostly the same as that of the empirical equation given by Xu and Eckstein (1995). Thus, for further investigation, Xu and Eckstein equation (1995) is used to obtain heterogeneous longitudinal dispersivity. The calibrated inverse model showed substantial improvement in the simulated state variables compared to the basic conceptual model output. The validation for the simulated hydraulic head is performed at discrete validation wells and a novel methodology is developed to validate simulated solute concentration for Electrical Resistivity Tomography (ERT) data, which shows good results against field v measurements. The estimated heterogeneous hydraulic conductivity (K) is used to verify the layering and geological formation; by verifying the cross-sectional profiles with ERT profiles. To conclude, the results obtained from the calibrated inverse model are better, but the approach is computationally demanding, which motivated to estimate anisotropic heterogeneous K by a geostatistical approach. Estimation of aquifer parameters in coastal aquifers by conducting field experiments is a challenging task due to its complex hydrochemical conditions. Several studies have addressed 1D or 2D parameters estimation in coastal aquifers, based on geophysical methods but not many studies have addressed 3D parameter estimation with clay lenses being present. A methodology is proposed to estimate K and porosity in an aquifer containing clayey formation. The regression equations between K and bulk resistivity is established using resistivity and fluid conductivity data in petrophysical equations. The established equations are validated for the small-scale area as wells as for the extended area in between River Gurupura and River Pavanje which showed R2 of 0.83, indicating good reliability. The established equations enable the estimation of anisotropic K at 2D resistivity profiles. The horizontal K i.e., in both x and y-direction, varies from 0.37–50 m/d, which agrees with the inverse model results. At each K profile, equivalent vertical K and anisotropy ratio are also estimated. This significantly enriches the 3D K database for numerical modeling, which the earlier studies have failed to address. The geophysical data is not only used to estimate anisotropic K but also to characterize the aquifer. The locally estimated K data at ERT profiles are used for upscaling to aquifer-scale intrinsically, where boundary conditions and size of the domain are not considered. The intrinsic upscaling is used to estimate heterogeneous K field at an aquifer-scale where the field (resistivity) data is lacking. Earlier studies focused on generating the K fields by neglecting the anisotropy but here based on the major principal axes, the layerwise anisotropic heterogeneous K random fields are generated. The estimated K in x and y-direction at ERT profiles are upscaled by considering Gaussian covariance function with known layerwise mean, variance, and correlation length. The upscaled K is used as input to the numerical model for transient vi simulation of the state variables. The upscaled model output is compared with the calibrated inverse model and basic conceptual model output. The performance of the upscaled model is better than the basic conceptual model but showed a relatively high error when compared with the calibrated inverse model output and with observed data. The computational time and computational effort taken by intrinsic upscaling are less than that of the inverse modeling. Thus, this method which also accounts for parameter randomness is applied to estimate anisotropic heterogeneous K for the large-scale aquifer. The 3D large-scale conceptual model is constructed with real field data about layering, aquifer bottom topography along with appropriate initial and boundary conditions. The intrinsic upscaling method is used to generate multiple realizations spatially correlated K random fields at each layer in x and y-direction. And the other flow and solute transport parameters are considered as spatially homogeneous and values are taken from the literature. Spatio-temporal varying recharge rates are estimated from the water-table fluctuation method and pumping rates at wells are taken from the literature. The transient simulation of a 3D variable-density large-scale conceptual model is carried out for 1647 days. The model results showed a high error to observed state variables. This may be due to unknown locations of groundwater draft, neglecting tidal influence, land-use land cover change, evapotranspiration, etc. The performance can be improved by constraining the model with the above-mentioned inputs. This study is a building block towards large-scale 3D modeling in a coastal anisotropic heterogeneous aquifer. The improved conceptual model can be further used for the predictive analysis of groundwater flow and SWI.

Abstract In recent years, the use of unmanned vehicles has advanced because of a growing number of civil applications such as firefighting or non-military security work, such as surveillance of pipelines etc. The application of these technologies with decreased cost and size has received attention in both civil and military applications. Recent advances in sensors, modeling and simulation and availability of open-source software and hardware for data integration has created an environment of remotely monitoring that was not possible a few years ago. This paper examines a niche cost-effective, portable Unmanned Surface Vessel that has been designed to capture the bathymetric profile of shallow water basins using single beam echosounder. Bathymetry is the measurement of the depth of water in oceans, rivers, or lakes. Bathymetric maps look a lot like topographic maps, which use lines to show the shape and elevation of land features. Today, echo sounders are used to make bathymetric measurements. Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Accurate mapping of shallow bathymetry is critical for understanding and characterizing coastal environments providing a foundation for measuring underwater light density, mapping and monitoring and planning marine operations and transportation. Methods for estimating shallow water bathymetry have suffered from a variety of trade-offs and limitations. Conventional methods such as shipborne sounding or airborne LiDAR have limited spatial coverage. The unit described in this paper has been designed and has been trained to acquire data in a predefined set path, minimizing the human intervention and the associated errors. A successful trial run was done for mapping the bed profile of the river basin in India. The vessel has been upskilled for capturing sonar data sets, with water quality parameters and soil samples using an automated auger. The vessel functions using the combined various open-source software and hardware tools for data assimilation, while the captured data sets are real-time transferred using IOT to Ground Controlled Station. The tropical river basin chosen is a part of Netravati River located in Dakshina Kannada District, Karnataka, India. This area is a part of the monsoon belt, and the Netravati riverbed is subjected to heavy sand deposition during a part of the year. The data on the excessive sand deposition is of immense value to the district and state administration. This study has been carried out at a frequency of 30 days and is provided as an input during non-monsoon period for district administration for outlining removal of excessive sand deposition monitoring of water quality in the estuarine ecosystem. The work done is a one-of-a-kind pilot study developed in-house using the recent advances seen in the world of open-source platforms. This paper demonstrates a unique application that is of value to the state administration in decision making and in addition contributes to environmental monitoring of the riverbed.