Kishan Singh Rawat

Hydrogeochemical characterization of groundwater of peninsular Indian region using multivariate statistical techniques

Groundwater quality of Chennai, Tamil Nadu (India) has been assessed during different seasons of year 2012. Three physical (pH, EC, and TDS) and four chemical parameters (Ca2+, Cl−, TH, Mg2+ and SO42−) from 18 bore wells were assessed. The results showed that pH of majority of groundwater samples indicates a slightly basic condition (7.99post-monsoon and 8.35pre-monsoon). TH was slightly hard [322.11xa0mg/lpre-monsoon, 299.37xa0mg/lpost-monsoon but lies under World Health Organization (WHO) upper limit]. EC, TDS, Ca2+ and Mg2+ concentrations were under WHO permissible limit during post-monsoon (1503.42xa0μS/cm, 1009.37, 66.58 and 32.42xa0mg/l respectively) and pre-monsoon (1371.58xa0μS/cm, 946.84, 71.79 and 34.79xa0mg/l, respectively). EC shows a good correlation with SO42− (R2xa0=xa00.59pre-monsoon, 0.77post-monsoon) which indicates that SO42− plays a major role in EC of ground water of bore wells. SO42− has also showed positive correlations with TDS (R2xa0=xa00.84pre-monsoon, 0.95post-monsoon) and TH (R2xa0=xa00.70pre-monsoon, 0.75post-monsoon). The principal component analysis (PCA)/factor analysis (FA) was carried out; Factor1 explains 59.154 and 69.278xa0% of the total variance during pre- and post-monsoon, respectively, with a strong positive loading on Ca2+, Mg2+, SO42−, TDS and a negative loading on pH. Factor2 accounts for 13.94 and 14.22xa0% of the total variance during pre- and post-monsoon, respectively, and was characterized by strong positive loading of only pH and poor/negative loading of EC, Ca2+, Mg2+, SO42−, TDS and TH during pre- and post-monsoon. We recommend routine monitoring and thorough treatment before consumption. Further, this study has demonstrated the effectiveness of PCA/FA to assess the hydrogeochemical processes governing the groundwater chemistry in the area.

Assessment and validation of evapotranspiration using SEBAL algorithm and Lysimeter data of IARI agricultural farm, India

Evapotranspiration (ET) is a vital process in land surface atmosphere research. In this study, Surface Energy Balance Algorithm for Land (SEBAL) for the assessment of ET (for 23 December 2010, 8 January 2011, 24 January 2011, 9 February 2011, 25 February 2011, 29 March 2011 and 14 April 2011) from LANDSAT7-ETM+ and validation with Lysimeter data set is illustrated. It is based on the evaporative fraction concept, and it has been applied to LANDSAT7-ETM + (30 m resolution) data acquired over the Indian Agricultural Research Institute’s agricultural farm land. The ET from SEBAL was compared with Lysimeter ET using four statistical tests (root-mean-square error (RMSE), relative root-mean-square error (R-RMSE), mean absolute error (MAE), and normalized root-mean square error (NRMSE)), and each test showed a good correlation between the predicted and observed ET values. Results from this study revealed that the RMSE of crop-growing period was 0.51 mm d−1 for ETSEBAL, i.e. ETSEBAL having good accuracy with respect to observed ETLysimeter. Results were also validated using R-RMSE test, which also proved that ETSEBAL data are having good accuracy with respect to observed ETLysimeter as R-RMSE of crop-growing period is 0.19 mm d−1. MAE (0.19), NRMSE (0.21) and r2 (0.91) tests indicated that model prediction is significant, and model can be effectively used for the estimation of ET from SEBAL as input of remote sensing data sets. Finally, the SEBAL has been useful for remote agricultural land where ground-based data (Lysimeter data) are not available for daily ET (ET24 h) estimation. The temporal study of the ET24 h values analysed has revealed that the highest ET24 h values are owing to the higher development (high greenness) of crops, whereas the lower values are related to the lower development (low greenness) or null crop.

Soil erosion risk assessment and spatial mapping using LANDSAT-7 ETM+, RUSLE, and GIS—a case study

This paper discusses the application of the Revised Universal Soil Loss Equation (RUSLE) in conjunction with LANDSAT-7 ETM+ remote sensing data, and geographical information system (GIS) to the spatial mapping of soil erosion risk in Jhagrabaria Watershed Allahabad, U.P., India. Soil map and topographical data were used to develop the soil erodibility factor (K), and a digital elevation model (DEM) image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a LANDSAT Enhanced Thematic Mapper Plus (LANDSAT-7 ETM+) image, and support practice factor (P) was developed by crossing operation between land use/land cover classification map and slope map. Assuming the same climatic conditions in the study area, the rainfall-runoff erosivity (R) factor was not used. The value of K for the study area lies between 0.25 and 0.485, LS values were less than 1.4, and C and P values were less than 1. A soil erosion risk map with five classes (very low, low, medium, medium high, and high) was produced based on the simplified RUSLE within the GIS environment and was linked to land use/land cover (LULC) image to explore the relationships between soil erosion risk (SER) and LULC distribution. The results indicated that the land use/land cover (LULC) having the most succession and mature vegetation are in low-erosion-risk areas, while the barren and fallow lands are usually associated with high- to medium-erosion-risk areas. The spatial maps of the SER has been generated that can be utilized in the policy matter and planning for the watershed studied. This research also implied that the remote sensing and GIS tools and techniques provide the highly promising and important tools for evaluating and mapping soil erosion risk in the Jhagrabaria Watershed.

Mapping of groundwater quality using Normalized Difference Dispersal Index of Dwarka sub-city at Delhi National Capital of India

Abstract In this study, geo-statistical tool was used to map groundwater quality parameters. Groundwater samples were collected from 30 wells. In order to quantify the site-specific variations, Normalized Difference Dispersal Index (NDDI) was used to map five parameters namely pH, EC, TH, Ca, and Mg. Maximum NDDI value enrichment was exhibited by EC (−0.01) and Mg (−0.009 ≈ −0.01) it reflects accretion. In addition, for pH (−0.012), TH (−0.027), and Ca (−0.025), it outlined by dilution or minor accretion, whereas median NDDI value for EC, Ca, Mg, TH and pH is −0.02, −0.02, −0.03, −0.029, and −0.04, respectively. NDDI mapping is a functional tool for evaluation and comparative assessment of spatio-temporal variations in groundwater. Further, frequency distribution and inter correlation analysis of the data-set has displayed coherence. The results of study will help in management of water resources.

Groundwater quality evaluation using numerical indices: a case study (Delhi, India)

Groundwater is one of the major sources of water in arid and semi-arid regions. Hence, its qualitative assessment and knowledge about spatial variations are important for the purposes of planning and management in agricultural and domestic consumption. In this study we applied the Normalized Difference Dispersal Index (NDDI) and the Normalized Difference Index (NDI) over pre- and post-monsoon data set for Dwarka city of Delhi, India. NDDI maps for five environmentally sensitive ions were generated by integrated geochemical analysis in the Geographical Analysis System (GIS). NDDI plots highlights spot specific enrichment (accretion or attrition and dilution) in the study area. The NDI values suggest that World Health Organization (WHO) standards are a little more stringent as compared to Bureau of Indian Standards (BIS).

Water Quality Indices and GIS-based evaluation of a decadal groundwater quality

Abstract The water quality data of fixed 44 wells were monitored during post- and pre-monsoon season 2005–2013 of Kanchipuram district of state Tamil Nadu, India. Water Quality Indices (WQIs) methodology was applied to categorize groundwater on the basis of rating. In total, 13 groundwater quality parameters: calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), bicarbonate (HCO3 −), nitrate (NO3 −), chloride (Cl−), fluoride (F−), sulphate (SO4 2−), total hardness (TH), total dissolved solids (TDS), hydrogen ion concentration (pH) and electrical conductivity (EC) were taken for statistical analysis. Three different WQI models were computed using a decadal physicochemical water quality data mainly for drinking (WQI1 and WQI2) and irrigation (WQI3) need. The WQIs variability thematic maps were generated in the Geographical Information System environment through ArcGIS software. These spatial distribution maps of WQI provide a detailed overview. The work outlined that the groundwater of the area is impaired by the anthropogenic activities and needs proper management plan to control further contamination and pollution of the groundwater of the area. Further, the groundwater of few monitoring wells needs a proper treatment before its consumptive uses. The alternative water supply should be provided to local inhabitants for daily uses.

Estimation of Surface Runoff from Semi-arid Ungauged Agricultural Watershed Using SCS-CN Method and Earth Observation Data Sets

In the present study, Soil Conservation Service -Curve Number (SCS-CN) method, Earth Observation (EO) data sets and Geographic Information System (GIS) have been used in order to estimate the surface runoff from Jhagrabaria an agricultural watershed of Allahabad district, Uttar Pradesh (India). LANDSAT-7ETM+, NOAA data and hydrologic soil groups have been used to prepare land use/land cover, rainfall and soil map. The traditional SCS-CN method for calculating the composite CN is very tedious and time demanding process of the hydrologic modeling. Therefore, GIS is now being used in combination with the SCS-CN method. The outcome of work showed 79.35 (CNII) of normal condition, of dry condition 61.76 (CNI) and of wet condition 89.84 (CNIII), respectively. This investigation outline that ungauged watershed exhibits an annual average of 14xa0years runoff volume as 3.58xa0×xa0106xa0m3 from an average annual rainfall of 14xa0years 110.77xa0cm and the average annual surface runoff of 14xa0years was 23.83xa0cm and annual average runoff coefficient of 14xa0years was 0.22. The correlation analysis suggests that the strong correlation as R2 (0.91) was observed between satellite drive rainfall and runoff from SCS-CN method. The developed rainfall–runoff model for the region will be useful to understand the watershed and its runoff flow characteristics.

Appraisal of long term groundwater quality of peninsular India using water quality index and fractal dimension

A review has been made to understand the hydrogeochemical behaviour of groundwater through statistical analysis of long term water quality data (year 2005–2013). Water Quality Index (WQI), descriptive statistics, Hurst exponent, fractal dimension and predictability index were estimated for each water parameter. WQI results showed that majority of samples fall in moderate category during 2005–2013, but monitoring site four falls under severe category (water unfit for domestic use). Brownian time series behaviour (a true random walk nature) exists between calcium

Appraisal of Soil Conservation Capacity Using NDVI Model-Based C Factor of RUSLE Model for a Semi Arid Ungauged Watershed: a Case Study

(mathrm{Ca}^{2+})