Satiprasad Sahoo

Evaluation of Recharge and Groundwater Dynamics of a Shallow Alluvial Aquifer in Central Ganga Basin, Kanpur (India)

Evaluation of recharge and groundwater dynamics of an aquifer is an important step for finding a proper groundwater management scenario. This has been performed on the basis of statistical Kendall Tau test to find a relationship between groundwater levels and hydro-meteorological parameters (e.g., precipitation, temperature, evaporation). Recharge to the aquifer was estimated for identification of critical areas/locations based on the analytical Soil and Water Assessment Tool. Moreover, spatiotemporal variability of groundwater levels has been quantified using space–time variogram. The overall characterization method has been applied to the shallow alluvial aquifer of Kanpur city in India. The analysis was performed using groundwater level data from 56 monitoring piezometer locations in Kanpur from March 2006 to June 2011. Groundwater level shows relatively higher correlation with temperature. Performance of the geostatistical model was evaluated by comparing with the observed values of groundwater level from January 2011 to June 2011 for two scenarios: “with limited spatiotemporal data” and “without spatiotemporal data.” It is evident that spatiotemporal prediction of groundwater level can be performed even for the unmonitored/missing data. This analysis demonstrates the potential applicability of the method for a general aquifer system.

Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand

Land surface temperature (LST) is an important factor in global climate change studies, in estimating radiation budgets, in heat balance studies and as a control for the climate dynamics and modelling frame. This study analyses the land surface temperature distribution in the region of Gua, Chiria, Megataburu and Kiriburu. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data of the year 1994, 2004 and 2014 are used to effects of land use/land cover changes on the surface temperature distribution. The remote sensing technique is used to detect the land use changes, its impact on the land surface temperature and variation in mean LST from these hot spots. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. Results of the study show that the LST of different land use differs significantly. This study also indicates that the external temperature has an impact on surfaces of self-heating areas. This study demonstrates that the growth of rapid mining industrial area significantly decreases the vegetation areas, hence increased the surface temperature. This analysis demonstrates the potential applicability of the methodology for climate modelling frame.

Index-based groundwater vulnerability mapping using quantitative parameters

To evolve a proper management scenario for groundwater utilization, identification of groundwater vulnerability zones is a critical step. In the present study, an attempt has been made to identify plausible groundwater vulnerability zones based on DRASTIC, Agricultural DRASTIC, AHP (Analytic Hierarchy Process) DRASTIC and Modified DRASTIC methods in the Hirakud command area. The main objective is to determine vulnerability zones for groundwater pollution based on quantitative parameters with the help of geographic information system (GIS) platform. DRASTIC model is an integrated GIS based tool used to evaluate the groundwater vulnerability mapping. DRASTIC models use seven hydrogeological parameters: depth to water table (D), recharge rate (R), aquifer media (A), soil media (S), topography (T), impact of vadose zone (I) and hydraulic conductivity (C). Modified DRASTIC model is used to assess the groundwater vulnerability considering land use/land cover (LULC). Finally, vulnerability map is validated using water quality parameters (EC, Cl−, Mg2+ and SAR) over the study area. Moreover, DRASTIC vulnerability map indicate that the northern part of the study area is more vulnerable for groundwater pollution. Groundwater vulnerability is an important environmental concern that needs to be assessed for proper groundwater management. This analysis demonstrates the potential applicability of the methodology for a general aquifer system.

Grey analytic hierarchy process applied to effectiveness evaluation for groundwater potential zone delineation

Abstract An attempt has been made to identify plausible groundwater potential zones (GWPZ) based on Grey Analytic Hierarchy Process Method (Grey-AHP) using integrated remote sensing and geographic information system. Grey-AHP combines the advantages of classical analytic hierarchy process and grey clustering method for accurate estimation of weight coefficients. The method also examines the effectiveness of GWPZ identification process. The proposed methodology has been applied to the Hirakud canal command area, Odisha (India). Feature layers [e.g. soil type, geology] are utilized for groundwater potential index (GWPI) calculation. The resulting GWPI map has been classified into three GWPZ namely: good, moderate and poor. Effectiveness based on grey clustering method is found to be in between ‘better’ and ‘common’ classes. Value of coefficient of determination (R2 = 0.865) supports the obtained effectiveness evaluation result. This analysis demonstrates the potential applicability of the methodology for a general aquifer system.

Appraising the Accuracy of Multi-Class Frequency Ratio and Weights of Evidence Method for Delineation of Regional Groundwater Potential Zones in Canal Command System

A comparative study of probabilistic frequency ratio (FR) and weights of evidence (WofE) method is performed for delineation of regional groundwater potential zones (GPZ) in canal command system. In the present case study, delineation of GPZ in the Hirakud agricultural command area of Odisha, India. Field discharge data from borewells are utilized for the analysis along with remote sensing (RS) and geographic information system (GIS) techniques. Various influencing attributes responsible for occurrence and movement of groundwater, e.g., land use / land cover, soil type, groundwater depth, geology, elevation, geomorphology, slope, recharge rate, rainfall, normalized difference vegetation index, drainage density, crop intensity are integrated by using GIS platform. Model results from FR and WofE show similar trends. The middle portion of the study area covers the ‘Good’ GPZ. Sensitivity analyses are performed for FR and WofE methods.

Mapping the distribution of iron ore minerals and spatial correlation with environmental variables in hilltop mining areas

The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE ≤ 0.51 MRSFF RMSE ≤ 0.48), Goethite (SFF RMSE ≤ 0.047 MRSFF RMSE ≤ 0.438) and Desert (SFF RMSE ≤ 0.63 and MRSFF RMSE ≤ 0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.