Status of arsenic contamination in potable water of Northern areas of Mizoram State and its adjoining areas of Southern Assam, India

A reconnaissance survey was conducted to examine the physicochemical properties of the potable water of Northern parts of the State of Mizoram, India, as well as the adjoining southern parts of the State of Assam, India. Groundwater samples were taken from those sources of water which were used as potable water source in the area. All the samples were analyzed for ionic concentrations of potassium (K⁺), sodium (Na⁺), calcium (Ca²⁺), magnesium (Mg²⁺), chlorine (Cl^?), sulfate (SO ₄ ^2? ), manganese (Mn), iron (Fe), and arsenic (As). Parameters such as pH, electrical conductivity, total dissolved solids, and total hardness were also measured in situ using digital instruments. The aim of the present work is to study the various physicochemical parameters following the recommendations of World Health Organization in order to test whether these sources are safe enough to be used as potable water resources. Furthermore, present work will throw light on the probable causes of presence of arsenic in Silchar City of southern Assam and total absence of it in neighboring state of Mizoram.     

Analysis of urban growth using Landsat TM/ETM data and GIS—a case study of Hyderabad, India

Hyderabad is one of the fastest growing mega cities in India and it is facing many economic, social and environmental problems due to rapid urban growth. For the better planning of resources and to provide basic amenities to its residents, it is necessary to have sufficient knowledge about its urban growth activities. Also, it is necessary to monitor the changes in land use over time and to detect growth activities in different parts of the city. To accomplish these tasks with greater accuracy and easiest way, remote sensing and geographic information system (GIS) tools proved to be very advantageous. This study makes an attempt towards the mapping of land use classes for different time periods and analysis of apparent changes in land use using the Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) data for the urban agglomeration of Hyderabad, India. In this study, three different time periods viz. 1989–2000, 2000–2005 and 2005–2011 are chosen for the analysis. The results have shown that high-density urban area had grown during 1989–2011 by encroaching into other land use classes. The urban growth has also affected water resources both, qualitatively and quantitatively in the region. The transformation of other land use types into urban area dynamically continued in the North-East and Southern parts of the city. In the North-East direction, the urban growth was mostly due to growth in industrial and residential area and in Southern part, mostly due to residential growth.     

Refinement of predictive reaeration equations for a typical Indian river

Dissolved oxygen mass balance has been computed for different reaches of River Kali in western Uttar Pradesh (India) to obtain the reaeration coefficient (K₂). A total of 270 field data sets have been collected during the period from March 1999 to February 2000. Eleven most popular predictive equations, used for reaeration prediction and utilizing mean stream velocity, bed slope, flow depth, friction velocity and Froude number, have been tested for their applicability in the River Kali using data generated during field survey. The K₂ values computed from these predictive equations have been compared with the K₂ values observed from dissolved oxygen balance measurements in the field. The performance of predictive equations have been evaluated using error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and correlation statistics. The equations developed by Smoot and by Cadwallader and McDonnell showed comparatively better results. Moreover, a refined predictive equation has been developed using a least‐squares algorithm for the River Kali that minimizes error estimates and improves correlation between observed and computed reaeration coefficients. Copyright © 2001 John Wiley & Sons, Ltd.     

A supplementary approach for estimating reaeration rate coefficients

Different commonly used predictive equations for the reaeration rate coefficient (K₂) have been evaluated using 231 data sets obtained from the literature and 576 data sets measured at different reaches of the River Kali in western Uttar Pradesh, India. The data sets include stream/channel velocity, bed slope, flow depth, cross‐sectional area and reaeration rate coefficient (K₂), obtained from the literature and generated during the field survey of River Kali, and were used to test the applicability of the predictive equations. The K₂ values computed from the predictive equations have been compared with the corresponding K₂ values measured in streams/channels. The performance of the predictive equations has been evaluated using different error estimation, namely standard error (SE), normal mean error (NME), mean multiplicative error (MME) and coefficient of determination (r²). The results show that the reaeration rate equation developed by Parkhurst and Pomeroy yielded the best agreement, with the values of SE, NME, MME and r² as 33·387, 4·62, 3·58 and 0·95, respectively, for literature data sets (case 1) and 37·567, 3·57, 2·6 and 0·95, respectively, for all the data sets (literature data sets and River Kali data sets) (case 2). Further, to minimize error estimates and improve correlation between measured and computed reaeration rate coefficients, supplementary predictive equations have been developed based on Froude number criteria and a least‐squares algorithm. The supplementary predictive equations have been verified using different error estimates and by comparing measured and computed reaeration rate coefficients for data sets not used in the development of the equations. Copyright © 2003 John Wiley & Sons, Ltd.     

Precipitation-runoff simulation for a Himalayan River Basin, India using artificial neural network algorithms

In the Himalayan regions, precipitation-runoff relationships are amongst the most complex hydrological phenomena, due to varying topography and basin characteristics. In this study, different artificial neural networks (ANNs) algorithms were used to simulate daily runoff at three discharge measuring sites in the Himalayan Kosi River Basin, India, using various combinations of precipitation-runoff data as input variables. The data used for this study was collected for the monsoon period (June to October) during the years of 2005 to 2009. ANNs were trained using different training algorithms, learning rates, length of data and number of hidden neurons. A comprehensive multi-criteria validation test for precipitation-runoff modeling has been undertaken to evaluate model performance and test its validity for generating scenarios. Global statistics have demonstrated that the multilayer perceptron with three hidden layers (MLP-3) is the best ANN for basin comparisons with other MLP networks and Radial Basis Functions (RBF). Furthermore, non-parametric tests also illustrate that the MLP-3 network is the best network to reproduce the mean and variance of observed runoff. The performance of ANNs was demonstrated for flows during the monsoon season, having different soil moisture conditions during period from June to October.     

Time-series analysis of monthly rainfall data for the Mahanadi River Basin, India

Time series analysis has two goals, modeling random mechanisms and predicting future series using historical data. In the present work, a uni-variate time series autoregressive integrated moving average (ARIMA) model has been developed for (a) simulating and forecasting mean rainfall, obtained using Theissen weights; over the Mahanadi River Basin in India, and (b) simulating and forecasting mean rainfall at 38 rain-gauge stations in district towns across the basin. For the analysis, monthly rainfall data of each district town for the years 1901-2002 (102 years) were used. Theissen weights were obtained over the basin and mean monthly rainfall was estimated. The trend and seasonality observed in ACF and PACF plots of rainfall data were removed using power transformation (α=0.5) and first order seasonal differencing prior to the development of the ARIMA model. Interestingly, the ARIMA model (1,0,0)(0,1,1) 12 developed here was found to be most suitable for simulating and forecasting mean rainfall over the Mahanadi River Basin and for all 38 district town rain-gauge stations, separately. The Akaike Information Criterion (AIC), goodness of fit (Chi-square), R 2 (coefficient of determination), MSE (mean square error) and MAE (mea absolute error) were used to test the validity and applicability of the developed ARIMA model at different stages. This model is considered appropriate to forecast the monthly rainfall for the upcoming 12 years in each district town to assist decision makers and policy makers establish priorities for water demand, storage, distribution, and disaster management.     

Non‐point source pollution estimation using a modified approach

Non‐point source (NPS) pollution from agricultural land is increasing exponentially in many countries of the world, including India. A modified approach based on the conservation of mass and reaction kinetics has been derived to estimate the inflow of non‐point source pollutants from a river reach. Two water quality variables, namely, nitrate (NO₃) and ortho‐phosphate (o‐PO₄), which are main contributors as non‐point source pollution, were monitored at four locations of River Kali, western Uttar Pradesh, India, and used for calibration and validation of the model. Extensive water quality sampling was done with a total of 576 field data sets collected during the period from March 1999 to February 2000. Remote sensing and geographical information system (GIS) techniques were used to obtain land use/land cover of the region, digital elevation model (DEM), delineation of basin area contributing to non‐point source pollution at each sampling location and drainage map. The results obtained from a modified approach were compared with the existing mass‐balance equations and distributed modelling, and the performances of different equations were evaluated using error estimation viz. standard error, normal mean error, mean multiplicative error and correlation statistics. The developed model for the River Kali minimizes error estimates and improves correlation between observed and computed NPS loads. Copyright © 2007 John Wiley & Sons, Ltd.     

Flood estimation in Mahanadi river system,India using partial duration series

Flood frequency analysis is a pre-requisite for setting up and safeguarding of many hydraulic structures, such as dams, barrages, check-dams, culverts and urban drainage systems. In the flood frequency analysis, partial duration series (PDS) may be considered when dealing with values exceeding certain limits causing floods. In fact, the PDS is capable of getting more information about extreme events than the annual maximum series (AMS). Additionally, an assumption that, the magnitude of the extreme events of a PDS is best described by a generalized Pareto (GP) distribution. The present work investigates the at-site flood frequency analysis to find the average number of peaks (λ) for modelling the PDS on the basis of the PDS/GP assumption and variability in the GP parameters coupled with the quantile estimation with an increase in the value of average number of peaks (λ) each year in the Mahanadi river system, Odisha, India. Also, to verify the PDS/GP assumption we tested seven different frequency distributions (Exponential, Gumbel, logistics, generalized extreme value (GEV), Lognormal (LN), generalized logistics (GL) and Pearson Type 3). Extensive daily discharge data collected from 23 gauging sites were used for the analysis. The results indicate precision and stability of GP distribution parameters for λ?=?4 for almost all the discharge sites. The peak flood estimated for various return periods in the Mahanadi river system using GP distribution is endowed with high correlation statistics for this λ value.