Chander Kumar Singh

SPATIAL-TEMPORAL MONITORING OF GROUNDWATER USING MULTIVARIATE STATISTICAL TECHNIQUES IN BAREILLY DISTRICT OF UTTAR PRADESH, INDIA

Spatial-Temporal Monitoring of Groundwater Using Multivariate Statistical Techniques in Bareilly District of Uttar Pradesh, India Monitoring of groundwater quality in Bareilly district, Uttar Pradesh, India, was performed at 10 different sites during the years 2005-2006. Obtained quality parameters were treated using principal component analysis (PCA) and cluster analysis (CA). The study shows usefulness of multivariate statistical techniques for evaluation and interpretation of groundwater quality data sets. Monitorování Podzemní Vody V Čase A V Prostoru Pomocí Multivariačních Statistických Metod V Oblasti Bareilly, Uttar Pradesh, Indie V letech 2005-2006 byla na deseti odběrných místech v regionu Bareilly, Uttar Pradesh, Indie sledována kvalita podzemní vody. Zjištěné kvalitativní parametry byly zpracovány pomocí analýzy hlavního prvku (PCA) a pomocí shlukové analýzy (CA). Studie dokládá vhodnost multivariačních statistických metod pro vyhodnocení a interpretaci změřených výsledků.

Impact of land-use and land-cover change on groundwater quality in the Lower Shiwalik hills: a remote sensing and GIS based approach

Human activities have exerted small to large scale changes on the hydrological cycle. The current scenario regarding groundwater resources suggests that globally there is a water crisis in terms of quantity (availability) and quality. Therefore there is a great need for the assessment and monitoring of quality and quantity of groundwater resources at local level. This paper presents a case study of the lower Shiwalik hills, in Rupnagar, Punjab, India, to trace land-use and land-cover changes during the past 17 years, with an emphasis on groundwater quality and quantity. This study was performed in alluvial and hilly terrain. The results show that the quantity of groundwater increased with the help of natural and artificial recharge due to change in land-use and land-cover pattern (increased area of fallow land). The quality of groundwater deteriorated due to input of fertilizers for enhancing the short-term soil fertility. Using a Remote Sensing and GIS based approach, we show the final results in map form. In particular we highlight a potential groundwater exploration site, which could be useful for district level planning. Our research shows that the change in land-use and land-cover affects the quantity and quality of groundwater.

Identification and analysis of groundwater potential zones in Ken–Betwa river linking area using remote sensing and geographic information system

The use of remote sensing data with other ancillary data in a geographic information system (GIS) environment is useful to delineate groundwater potential zonation map of Ken–Betwa river linking area of Bundelkhand. Various themes of information such as geomorphology, land use/land cover, lineament extracted from digital processing of Landsat (ETM+) satellite data of the year 2005 and drainage map were extracted from survey of India topographic sheets, and elevation, slope data were generated from shuttle radar topography mission (SRTM) digital elevation model (DEM). These themes were overlaid to generate groundwater potential zonation (GWPZ) map of the area. The final map of the area shows different zones of groundwater prospects, viz., good (5.22% of the area), moderate (65.83% of the area) poor (15.31% of the area) and very poor (13.64% of area).

Geochemical characterization and heavy metal contamination of groundwater in Satluj River Basin

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. The aim of present study is to evaluate hydrogeochemical parameters and heavy metals in groundwater and to study their spatial distribution in the Rupnagar District of Punjab. The spatial distribution of physico-chemical parameters were studied using Arc GIS 9.2. It was observed that the concentration of parameters, such as NO3, Cd, Cr, Mn and Pb was above permissible limit (World Health Organization, WHO) in southern part of the study area. The heavy metal pollution index (HPI) was calculated for all sampling locations and it was found much above the critical limit of pollution. Geochemical reaction models of selected water groups were constructed using Phreeqc. Geochemical modeling suggests that sodium has source other than halite-albite and calcium has alternate source other than gypsum-carbonate or silicates. It also suggests that evaporites, ion exchange, dissolution along with anthropogenic activities are controlling the hydro-geochemistry of groundwater in the region. Various indices, such as heavy metal pollution index, permeability index, sodium adsorption ratio, were studied to verify suitability of groundwater for drinking and irrigation.

Geochemical modelling, ionic ratio and GIS based mapping of groundwater salinity and assessment of governing processes in Northern Gujarat, India

In semi-arid/arid regions, groundwater is the major source of irrigation, drinking and industrial requirements, water salinity and shortage are major problems of concern. North Gujarat, India, is one such area where highly saline groundwater is generally ascribed to rapid increase of population, agriculture and industries induced decline in water table by unplanned abstraction of groundwater. However, no effort has been made to discriminate the natural and anthropogenic influences on groundwater salinity. In this brief background, the present study attempts to identify the factors and processes controlling the groundwater salinity in the area, based on ionic ratios in integration with various graphical methods, saturation indices and geographical information system. Na+/Ca2+ > 1 indicates the deficiency of Ca2+ possibly due to CaCO3 precipitation or ion exchange process. Na+/Cl− > 1 and \( {\text{SO}}_{4}{}^{2 - } /{\text{Cl}}^{ - } \gg 0.05 \) suggest salinization is mainly due to wastewater infiltration and/or due to irrigation water return flow. Sea water intrusion in coastal parts, vertical and lateral mixing of water and anthropogenic inputs are also responsible for salinization of groundwater. USSL diagram, Na%, sodium adsorption ratio, residual sodium carbonate and magnesium hazard indicate unsuitability of groundwater for irrigation purposes. To prevent groundwater salinization, appropriate measures need to be taken to control further indiscriminate exploitation of groundwater for irrigation.

Identification of erosional and inundation hazard zones in Ken-Betwa river linking area, India, using remote sensing and GIS.

Ken–Betwa river link is one of the pilot projects of the Inter Linking of Rivers program of Government of India in Bundelkhand Region. It will connect the Ken and Betwa rivers through a system of dams, reservoirs, and canals to provide storage for excess rainfall during the monsoon season and avoid floods. The main objective of this study is to identify erosional and inundation prone zones of Ken–Betwa river linking site in India using remote sensing and geographic information system tools. In this study, Landsat Thematic Mapper data of year 2005, digital elevation model from the Shuttle Radar Topographic Mission, and other ancillary data were analyzed to create various thematic maps viz. geomorphology, land use/land cover, NDVI, geology, soil, drainage density, elevation, slope, and rainfall. The integrated thematic maps were used for hazard zonation. This is based on categorizing the different hydrological and geomorphological processes influencing the inundation and erosion intensity. Result shows that the southern part of the study area which lies in Panna district of Madhya Pradesh, India, is more vulnerable than the other areas.

Hydrogeochemical Evolution and Appraisal of Groundwater Quality in Panna District, Central India

Intense agricultural and mining/industrial activities make groundwater quality vulnerable to contaminants. This study conducted in one of the mining areas of Panna district evaluated the factors influencing the groundwater hydrogeochemistry using water quality parameters and multi-isotopic approach considering the fact that groundwater is the only major source of drinking water. Forty-five water samples comprising both shallow and deep aquifers were collected and analyzed for major ions, δ18O, and δD. The geochemical data were used to characterize and classify water samples based on a multitude of ion plots and diagrams. The groundwater in the region is found to be contaminated with fluoride and nitrate. The sources for fluoride are mostly geogenic in nature. The alkaline nature of groundwater triggers replacement of the exchangeable fluoride from minerals like biotite/muscovite and results in its enrichment. In addition, it is contributed through leaching of fluorides from granitic rocks, abundantly present in the study area. The weathering of these fluoride-bearing minerals releases fluoride into the groundwater. On the other hand, nitrate enrichment is mainly attributed to leaching from untreated sewerage system and agricultural runoff containing nutrients from excess use of fertilizers. The stable isotopic composition for most of the collected samples was found to be near the local meteoric water line (LMWL), i.e., origin of ground water is meteoric in principle; however, the point away from the LMWL might favor exchange with rock minerals and evaporation processes. This study sets an important background for decision makers to take the suitable countermeasures from the public health perspective for sustainable water resources management.

Clustering Data and Incorporating Topographical Variables for Improving Spatial Interpolation of Rainfall in Mountainous Region

This study was an attempt to quantify the improvement in the accuracy of rainfall interpolation in the mountainous terrain by clustering of rainfall data at the data preparation stage and incorporating topographical variables at interpolation stage. The univariate kriging techniques, ordinary kriging (OK), simple kriging and universal kriging (UK) were compared with multivariate kriging method of ordinary cokriging (OCK). The elevation, slope and terrain ruggedness index (TRI) computed from digital elevation model were incorporated as explanatory variable in OCK. These algorithms were applied to the normal annual and seasonal rainfall data points located in Central Himalayas of Uttarakhand region. The study area was divided into two different zones of lowland and upland based on the elevation variability. This zonation was then used as a basis for clustering the rainfall data. The performance of the interpolation techniques was assessed for subdivided regions and compared with the results obtained for complete region. The evaluation was based on absolute error metrics of root mean square error (RMSE) in combination with RMSE-observations standard deviation ratio. The absolute percentage error (APE) statistics was calculated for every observation points and then the percentage of data for with APE ≤ 30% (APE30) was analyzed. It was observed that performance efficiency of the interpolation methods improves by 5-20% if the rainfall data is clustered based on homogeneity of terrain elevation as against considering the complete set of data. In complex terrain, the inclusion of topographical variables improves the cokriging based rainfall prediction if it is correlated with rainfall.

Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County, New Jersey

Extracting high-quality building footprints is a basic requirement in multiple sectors of town planning, disaster management, 3D visualization, etc. In the current study, we compare three different techniques for acquiring building footprints using (i) LiDAR, (ii) object-oriented classification (OOC) applied on high-resolution aerial photographs and (iii) digital surface models generated from interpolated LiDAR point cloud data. The three outputs were compared with a digitized sample of building polygons quantitatively by computing the errors of commission and omission, and qualitatively using statistical operations. These findings showed that building footprints derived from OOC gave highest regression and correlation values with least commission error. The R2 and R values (0.86 and 0.92, respectively) imply that the footprint areas derived by OOC matched more closely with the actual area of buildings, while a low commission error of 24.7% represented a higher number of footprints as correctly classified.

Tree species biomass and carbon stock measurement using ground based-LiDAR

This study scrutinises the use of terrestrial laser scanning (TLS) to measure diameter at breast height (DBH) and tree height at individual tree species level. LiDAR point cloud scans are collected from uniformly defined control points. The result of processed TLS data demonstrates the precise measurements of tree height and DBH by comparing it with field data (DBH, tree height, tree species and location). The average tree height and DBH obtained through TLS measurements were 9.44 m and 43.30 cm, respectively. A linear equation between TLS derived parameters and field measured values were established, which gave the coefficient of determination (r2) of 0.79 and 0.96 for tree height and DBH, respectively. Further, these parameters were used to calculate above ground biomass (AGB) for individual tree species by considering a non-destructive approach. The total AGB and carbon stock from 80 different trees are computed to be 49.601 and 22.320 tonnes, respectively.