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The groundwater resource is a multidimensional concept; it is defined by its location, its occurrence over time, its size, properties, conditions of accessibility, the effort required to mobilize it and therefore, all of which are to be considered in the context of demand. Groundwater, a renewable and finite natural resource, vital for man’s life, social and economic development and a valuable component of the ecosystem, is vulnerable to natural and human impacts. There is a great need for the assessment and monitoring of quality and quantity of groundwater resource required at local level to develop an exact scenario of watershed. In this study qualitative assessment of groundwater was done and a ground water quality index criterion was used to understand the suitability of groundwater for irrigation and drinking purpose in the study area. A GIS based multicriteria analysis was done by assigning weight to different water quality parameters. The water quality was grouped into six classes from very good to unfit for drinking. It was found that the in most part of the study area the water quality varied from moderate to good except in some areas where it is poor to unfit. An assessment of change in landuse and landcover was done from the year 1989 using Landsat data to year 2006 using LISS III satellite data. The change in LULC was correlated with water quality data and it was found that the areas around which rapid urbanisation as well as industrialisation is taking place showed poor to unfit groundwater in terms of quality.

The Lower Kosi Basin (LKB) in North Bihar is highly prone to floods and is influenced by upstream hydrology. A flood susceptibility index has been modelled by integrating eleven flood conditioning parameters (precipitation, elevation, slope, drainage density, distance from the river, ruggedness index, topographic wetness index, stream power index, curvature, normalized difference vegetation index, land use and land cover) derived from the satellite data, using a weighted linear summation model. The study uses Sentinel-1 synthetic aperture radar data to estimate flood frequency over a temporal scale of 2016–2020. The flood frequency was used to validate the flood susceptibility derived using multi-criteria decision making methods combined with geographical information system (MCDM-GIS). The study shows that ~ 66% of the area in LKB is susceptible to high to moderate flooding while the remaining ~ 34% is falls in the low flooding category. 15.24% of the area has high frequency (> 3 flood occurrences) of the flood, 9.66% has moderate (2 flood occurrences) and 9.72% of the area faced one-time flood during five years of period (2016–2020). The accuracy of MCDM-GIS derived flood susceptibility map was assessed using area under curve, confusion matrix, precision, recall, F1 score, weighted F1 score and overall accuracy.

The dependency of people on groundwater has increased in the past few decades due to tremendous increase in crop production, population and industrialization. Groundwater is the main source of irrigation in Shiwaliks of Punjab. In the present study the samples were collected from predetermined location as was located on satellite image on basis of spectral reflectance. Global positioning system was used to collect samples from specific locations. Principal components analysis (PCA) together with other factor analysis procedures consolidate a large number of observed variables into a smaller number of factors that can be more readily interpreted. In the present study, concentrations of different constituents were correlated based on underlying physical and chemical processes such as dissociation, ion exchange, weathering or carbonate equilibrium reactions. The PCA produced six significant components that explained 78% of the cumulative variance. The concentration of the few trace metals was found to be much higher indicating recharge due to precipitation as main transport mechanism of transport of heavy metals in groundwater which is also confirmed by PCA. Piper and other graphical methods were used to identify geochemical facies of groundwater samples and geochemical processes occurring in study area. The water in the study area has temporary hardness and is mainly of Ca–Mg–HCO 3 type.

Water is a fluctuating resource making it difficult to measure in time and in space. To demonstrate the efficiency of the geographic information system (GIS) for groundwater studies, information on the parameters controlling groundwater such as lithology, geomorphology and lineament analysis were analyzed. LISS-III and Landsat satellite image of the area was used to infer information on the geologic lineaments and geomorphology. To delineate linear features enhancement and direction, filtering was performed on single bands of Landsat images. Thematic maps for geology, slope, geomorphology and lineament were prepared and integrated in GIS by assigning the weights and ranking to various parameters controlling the occurrence of groundwater to generate the groundwater potential map for the study area. The results indicate that the floodplain of river and its adjoining areas have very good groundwater potential, whereas the steeply sloping area in the northern part having high relief and slope possesses poor groundwater potential.

Information about change is necessary for updating Land Use/ Land Cover LULC maps and the management of natural resources. The paper aims to map the changes in the LULC using hybrid classification methods and to quantify the land use/ land cover change that took place in the Rupnagar district of Punjab. The paper promotes the classification of LULC based on remote sensing information (obtained mainly through the utilization of Thematic Mapper TM) to generate data products that are both appropriate to, and immediately usable within different scientific applications. Satellite data provides the basis for geographically referenced land use/land cover characterization that is internally consistent, repeatable over time, and potentially more reliable. The main objective of this study is to quantify the change in the area of various LULC classes. Classification of four reflective bands of three Landsat images was carried out by using Isodata clustering algorithm with the aid of ground truth data. The second part focused on land use/ land cover changes by using the change detection comparison (pixel by pixel). The change analysis was performed by post image classification method, comparing the data from three different dates. The result indicates there was a rapid change in land use/land cover due to the increase in population. The results indicate that severe land cover changes have occurred in cropland (225.97 km2), dense forest (128.57 km2), settlement (93.5 km2), salt affected land (9.74 km2) and water body (11.69 km2) areas from 1989 to 2006.