Assessing different sources of uncertainty in hydrological projections of high and low flows: case study for Omerli Basin,Istanbul, Turkey

This study investigates the assessment of uncertainty contribution in projected changes of high and low flows from parameterization of a hydrological model and inputs of ensemble regional climate models (RCM). An ensemble of climate projections including 15 global circulation model (GCM)/RCM combinations and two bias corrections (change factor (CF) and bias correction in mean (BC)) was used to generate streamflow series for a reference and future period using the Hydrologiska Byråns Vattenbalansavdelning (HBV) model with the 25 best-fit parameter sets based on four objective functions. The occurrence time of high flows is also assessed through seasonality index calculation. Results indicated that the inputs of hydrological model from ensemble climate models accounts for greater contribution to the uncertainty related to projected changes in high flows comparing to the contribution from hydrological model parameterization. However, the uncertainty contribution is opposite for low flows, particularly for CF method. Both CF and BC increases the total mean variance of high and low flows. The variability in the occurrence time of high flows through RCMs is greater than the variability resulted from hydrological model parameters with and without statistical downscaling. The CF provides more accurate timing than BC and it shows the most pronounced changes in flood seasonality.     

Composite use of numerical groundwater flow modeling and geoinformatics techniques for monitoring Indus Basin aquifer, Pakistan

The integration of the Geographic Information System (GIS) with groundwater modeling and satellite remote sensing capabilities has provided an efficient way of analyzing and monitoring groundwater behavior and its associated land conditions. A 3-dimensional finite element model (Feflow) has been used for regional groundwater flow modeling of Upper Chaj Doab in Indus Basin, Pakistan. The approach of using GIS techniques that partially fulfill the data requirements and define the parameters of existing hydrologic models was adopted. The numerical groundwater flow model is developed to configure the groundwater equipotential surface, hydraulic head gradient, and estimation of the groundwater budget of the aquifer. GIS is used for spatial database development, integration with a remote sensing, and numerical groundwater flow modeling capabilities. The thematic layers of soils, land use, hydrology, infrastructure, and climate were developed using GIS. The Arcview GIS software is used as additive tool to develop supportive data for numerical groundwater flow modeling and integration and presentation of image processing and modeling results. The groundwater flow model was calibrated to simulate future changes in piezometric heads from the period 2006 to 2020. Different scenarios were developed to study the impact of extreme climatic conditions (drought/flood) and variable groundwater abstraction on the regional groundwater system. The model results indicated a significant response in watertable due to external influential factors. The developed model provides an effective tool for evaluating better management options for monitoring future groundwater development in the study area.     

Response Surface Method Coupled with First-Order Reliability Method Based Methodology for Groundwater Flow and Contaminant Transport Model for the Uranium Tailings Pond Site

Groundwater flow and contaminant transport modelling is carried out to predict the concentrations of radionuclides in groundwater beneath the tailings pond. This is used as a tool for radiological impact assessment studies. The geo-hydrological parameters involved in the modelling exhibit inherent uncertainty associated with their values. This uncertainty may get propagated to the model output, i.e. the dose to the public. The propagation of the uncertainty in the input parameters to the output is modelled using suitable methods of probabilistic analysis. Response surface method coupled with first-order reliability method is used to develop a methodology for estimating the probability that the dose rate value through drinking water pathway at a location around the tailings pond exceeds the WHO guidelines for drinking water, termed as the probability of exceedance of acceptable levels. This method also gives the estimate of sensitivity of the probability of exceedance to the different input parameters. It is observed that the probability of failure decreases as the distance from tailings pond centre is increased and beyond a distance of around 0.5 km from tailings pond centre, the probability reduces to zero. The work also brings out the importance of quantifying the uncertainty in case of actual field problems where there is wide variation in the values of the various parameters within the domain under study.     

Assessment of groundwater quality: a fusion of geochemical and geophysical information via Bayesian neural networks

Deplorable quality of groundwater arising from saltwater intrusion, natural leaching and anthropogenic activities is one of the major concerns for the society. Assessment of groundwater quality is, therefore, a primary objective of scientific research. Here, we propose an artificial neural network-based method set in a Bayesian neural network (BNN) framework and employ it to assess groundwater quality. The approach is based on analyzing 36 water samples and inverting up to 85 Schlumberger vertical electrical sounding data. We constructed a priori model by suitably parameterizing geochemical and geophysical data collected from the western part of India. The posterior model (post-inversion) was estimated using the BNN learning procedure and global hybrid Monte Carlo/Markov Chain Monte Carlo optimization scheme. By suitable parameterization of geochemical and geophysical parameters, we simulated 1,500 training samples, out of which 50 % samples were used for training and remaining 50 % were used for validation and testing. We show that the trained model is able to classify validation and test samples with 85 % and 80 % accuracy respectively. Based on cross-correlation analysis and Gibb’s diagram of geochemical attributes, the groundwater qualities of the study area were classified into following three categories: “Very good”, “Good”, and “Unsuitable”. The BNN model-based results suggest that groundwater quality falls mostly in the range of “Good” to “Very good” except for some places near the Arabian Sea. The new modeling results powered by uncertainty and statistical analyses would provide useful constrain, which could be utilized in monitoring and assessment of the groundwater quality.     

Groundwater vulnerability assessment using DRASTIC and Pesticide DRASTIC models in intense agriculture area of the Gangetic plains,India

Delineating areas susceptible to contamination from anthropogenic sources form an important component of sustainable management of groundwater resources. The present research aims at estimating vulnerability of groundwater by application of DRASTIC and Pesticide DRASTIC models in the southern part of the Gangetic plains in the state of Bihar. The DRASTIC and Pesticide DRASTIC models have considered seven parameters viz. depth to water level, net recharge, aquifer material, soil material, topography, impact of vadose zone and hydraulic conductivity. A third model, Pesticide DRASTIC LU has been adopted by adding land use as an additional parameter, to assess its impact on vulnerability zonation. The DRASTIC model indicated two vulnerable categories, moderate and high, while the Pesticide DRASTIC model revealed moderate, high and very high vulnerable categories. Out of the parameters used, depth to water level affected the vulnerability most. The parameter caused least impact was topography in DRASTIC, while in case of Pesticide DRASTIC and Pesticide DRASTIC LU models, the parameter was hydraulic conductivity. A linear regression between groundwater NO₃ concentrations and the vulnerability zonation revealed better correlation for Pesticide DRASTIC model, emphasising the effectiveness of the model in assessing groundwater vulnerability in the study region. Considering all three models, the most vulnerable areas were found to be concentrated mainly in two zones, (i) in the south-western part along Ekangarsarai-Islampur patch and (ii) around Biharsharif-Nagarnausa area in the central part. Both zones were characterised by intensive vegetable cultivation with urban areas in between.     

Modelling of spatial contaminant probabilities of occurrence of chlorinated hydrocarbons in an urban aquifer

In this study, a 3D urban groundwater model is presented which serves for calculation of multispecies contaminant transport in the subsurface on the regional scale. The total model consists of two submodels, the groundwater flow and reactive transport model, and is validated against field data. The model equations are solved applying finite element method. A sensitivity analysis is carried out to perform parameter identification of flow, transport and reaction processes. Coming from the latter, stochastic variation of flow, transport, and reaction input parameters and Monte Carlo simulation are used in calculating probabilities of pollutant occurrence in the domain. These probabilities could be part of determining future spots of contamination and their measure of damages. Application and validation is exemplarily shown for a contaminated site in Braunschweig (Germany), where a vast plume of chlorinated ethenes pollutes the groundwater. With respect to field application, the methods used for modelling reveal feasible and helpful tools to assess natural attenuation (MNA) and the risk that might be reduced by remediation actions.     

Why Use One Model? An Approach for Encompassing Model Uncertainty and Improving Best Practice

There has been some discussion within the UK modelling community regarding the preferred choice of dispersion model for assessing the air quality impact of elevated point sources, each of which is reported to perform better than the other in certain circumstances. Coupled to this is the growing acceptance within the modelling community to include some recognition of model uncertainty in its reporting. As computer processing speeds increase further, this paper considers the possibility of using more than one model and highlights some of the advantages in doing so.     

Modified Grey Model and its application to groundwater flow analysis with limited hydrogeological data: a case study of the Nubian Sandstone, Kharga Oasis, Egypt

Groundwater flow at Kharga Oasis, located in the western desert of Egypt, was previously analyzed using numerical models; however, the lack of basic data often limits the implementation of these models, as well as introducing a problem for model calibration and validation. The Grey Model (GM) was used to overcome these difficulties of data limitation and uncertainty of hydrogeological conditions. However, no clear theories exist for selecting the number of input model trends and the most suitable values of input parameters. Therefore, in the current study, a modification of the GM is newly proposed and called the Modified Grey Model (MGM) in an attempt to determine a process for selecting the best input models' trends with the appropriate values of input parameters to achieve acceptable fitting to observations. The sensitivity analysis results showed that the MGM produced more stable results than the GM using a wide range of values for input parameters. Moreover, the MGM reduced the calculation time required for fitting the measured piezometric level trends by 99.8 %. Three development scenarios of groundwater withdrawal were proposed that involved either expanding the present extraction rate or redistributing the groundwater withdrawal over the recent working production wells (RWPWs). The results concluded that the groundwater table in the northern part of the oasis could be temporally recovered to an economical piezometric level; however, the table in the southern part is severely decreased. Therefore, new production wells are recommended to be constructed in the southern part far enough from the RWPWs.     

Implications of depletion of groundwater levels in three layered aquifers and its management to optimize the supply demand in the urban settlement near Kahota Industrial Triangle area, Islamabad, Pakistan

This study deals with the implications of depletion of groundwater levels in three layered aquifers and its management to optimize the supply demand in the urban settlement near Kahota Industrial Triangle area, located adjacent to the Soan River, Islamabad Pakistan. Initially, a groundwater 3-D steady-state flow model has been developed, calibrated to the known observed heads of 24 water wells, verified, and confirmed that convergence has actually arrived and hydraulic heads are no more changing. Later, the transient simulation was carried out with the constant discharge rates of groundwater by means of pumping wells, storage factor, porosity, and observed drawdown matched with the simulated drawdown that appears to fall in close agreement with a difference of 0.25 m. As such, the developed groundwater model has facilitated to understand, evaluate, and to predict regional trends of groundwater flow regimes and their ultimate utilization at a maximum rate of 4.5 million gallons/day for the growing urban settlement. The calibrated and verified model was then used to simulate the depletion of groundwater level, annual water balance, discharge versus time drawdown, and a temporal behavior of the system over an extended period of pumping. The modeling results indicate that, due to the pumping, the direction of flow has changed: first from groundwater regimes to the Soan River and then it is entirely reversed from the Soan River to the groundwater regimes as the drawdown started to deepen.     

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.     

Improving Efficiency Of Uncertainty Analysis In Complex Integrated Assessment Models: The Case Of The Rains Emission Module

Abstact Ever since the Regional Acidification Information and Simulation Model (RAINS) has been constructed, the treatment of uncertainty has remained an issue of major interest. In a recent review of the model performed for the Clean Air for Europe (CAFE) programme of the European Commission, a more systematic and structured uncertainty analysis has been recommended. This paper aims at contributing to the scientific debate how this can be achieved. Because of its complex structure on the one hand and limited research resources (time, computational capacities) on the other hand a full-blown uncertainty analysis in RAINS is hardly feasible. Therefore, all types of uncertainty require more efficient ways for uncertainty analysis. With respect to parameter uncertainty, we propose to focus research efforts for uncertainty analysis on key parameters. Among different approaches to select key parameters that have been discussed in the literature screening methods seem to be particularly appropriate for complex, deterministic Integrated Assessment models such as RAINS. Surprisingly, in Integrated Assessment modelling for air pollution problems of screening design have not been taken up so far. As a case study we consider the emission module of RAINS. We show that its structure allows for a straightforward and effective screening procedure     

Use of multivariate indicator kriging methods for assessing groundwater contamination extents for irrigation

Multivariate geostatistical approaches have been applied extensively in characterizing risks and uncertainty of pollutant concentrations exceeding anthropogenic regulatory limits. Spatially delineating an extent of contamination potential is considerably critical for regional groundwater resources protection and utilization. This study used multivariate indicator kriging (MVIK) to determine spatial patterns of contamination extents in groundwater for irrigation and made a predicted comparison between two types of MVIK, including MVIK of multiplying indicator variables (MVIK-M) and of averaging indicator variables (MVIK-A). A cross-validation procedure was adopted to examine the performance of predicted errors, and various probability thresholds used to calculate ratios of declared pollution area to total area were explored for the two MVIK methods. The assessed results reveal that the northern and central aquifers have excellent groundwater quality for irrigation use. Results obtained through a cross-validation procedure indicate that MVIK-M is more robust than MVIK-A. Furthermore, a low ratio of declared pollution area to total area in MVIK-A may result in an unrealistic and unreliable probability used to determine extents of pollutants. Therefore, this study suggests using MVIK-M to probabilistically determine extents of pollutants in groundwater.     

Delimitation of arsenic-contaminated groundwater using risk-based indicator approaches around blackfoot disease hyperendemic areas of southern Taiwan

This work determined scopes of arsenic(As)-contaminated groundwater using risk-based indicator classification approaches in blackfoot disease hyperendemic areas of southern Taiwan. Indicator kriging was first used to establish a conditional cumulative distribution function at each cell. Three approaches--the p-quantile estimate, the E-type estimate and the minimization of the expected loss--were then adopted to delimit contaminated regions for a regulated standard of As concentrations in groundwater. According to a risk assessment model established in our previous research, the standard was set to 250 microg/l for aquacultural use, corresponding to the 77.1th percentile of observed concentrations. Misclassification risks and uncertainty were examined for the classification approaches. The analyzed results reveal that contaminated areas are the largest using the 0.771-quantile estimate, whereas they are the smallest using the minimization of the expected loss. Proportions of credible polluted areas with low risks to false positives maintain a constant, 12.9-13.2%, for the classification approaches. To reduce a great impact on human health, As-polluted groundwater should be strictly prohibited to cultivate fish in credible polluted zones and monitored persistently in polluted zones with high risks to false positives.     

有机污染物环境逸度模型构建及软件化

目前研究人员已经可以通过建立模型来评估有机物的环境风险并研究其在多介质环境中的迁移与转化。针对多介质逸度模型中的参数繁多,不利于研究人员模拟有机污染物的归趋状况的问题,现基于Ⅲ级多介质环境逸度模型,采用Python编程语言进行软件设计,完成有机物在环境各介质中的分布模拟功能,并在此基础上集成参数灵敏度和模型不确定度分析功能,且对用户提供了友好的图形界面。以北京地区地表水环境中的药物和个人护理品（PPCPs）为例,研究结果表明该软件得到的模拟值与实测值有较好的吻合度,可为有机物的区域环境污染和风险评估提供参考。     

Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon

Fisheries and water resource managers are challenged to maintain stable or increasing populations of Chinook salmon in the face of increasing demand on the water resources and habitats that salmon depend on to complete their life cycle. Alternative management plans are often selected using professional opinion or piecemeal observations in place of integrated quantitative information that could reduce uncertainty in the effects of management plans on population dynamics. We developed a stochastic life cycle simulation model for an endangered population of winter-run Chinook salmon in the Sacramento River, California, USA with the goal of providing managers a tool for more effective decision making and demonstrating the utility of life cycle models for resource management. Sensitivity analysis revealed that the input parameters that influenced variation in salmon escapement were dependent on which age class was examined and their interactions with other inputs (egg mortality, Delta survival, ocean survival). Certain parameters (river migration survival, harvest) that were hypothesized to be important drivers of population dynamics were not identified in sensitivity analysis; however, there was a large amount of uncertainty in the value of these inputs and their error distributions. Thus, the model also was useful in identifying future research directions. Simulation of variation in environmental inputs indicated that escapement was significantly influenced by a 10% change in temperature whereas larger changes in other inputs would be required to influence escapement. The model presented provides an effective demonstration of the utility of life cycle simulation models for decision making and provides fisheries and water managers in the Sacramento system with a quantitative tool to compare the impact of different resource use scenarios.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Multi-scale Atmospheric Dispersion Modelling by Use of Adaptive Gridding Techniques

An accurate prediction of the transport-reaction behaviour of atmospheric chemical species is required to fully understand the impact on the environment of pollution emissions. Elevated levels of secondary pollutants such as ozone in the lower atmosphere can be harmful to the health of both plants and animals, and can cause damage to property present in the urban environment. Detailed models of pollution mechanisms must therefore be developed through comparisons with field measurements to aid the selection of effective abatement policies. Such models must satisfy accuracy requirements both in terms of the number of species represented, and the spatial resolution of species profiles. Computational expense often compels current models to sacrifice detail in one of these areas. This paper attempts to address the latter point by presenting an atmospheric transport-reaction modelling strategy based upon a finite volume discretisation of the atmospheric dispersion equation. The source terms within this equation are provided by an appropriate reduced chemical scheme modelling the major species in the boundary layer. Reaction and transport discretisations are solved efficiently via a splitting technique applied at the level of the non-linear equations. The solution grid is generated using time dependant adaptive techniques, which provide a finer grid around regions of high spatial error in order to adequately resolve species concentration profiles. The techniques discussed are applied in two dimensions employing emissions from both point and area sources. Preliminary results show that the application of adaptive gridding techniques to atmospheric dynamics modelling can provide more accurately resolved species concentration profiles, accompanied by a reduced CPU time invested in solution. Such a model will provide the basis for high resolution studies of the multiple scale interactions between spatially inhomogeneous source patterns in urban and regional environments.     

Correlated Parameters Uncertainty Propagation in a Rainfall-Runoff Model,Considering 2-Copula; Case Study: Karoon III River Basin

Hydrological models are widely used to investigate practical issues of water resources. Parametric uncertainty is considered as one of the most important sources of uncertainty in environmental researches. Generally, it is assumed that the parameters are independent mutually, but correlation within the parameter space is an important factor having the potential to cause uncertainty. The objective and innovation of this study was to address the effects of parameters correlation on a continuous hydrological model uncertainty. HEC-HMS with soil moisture accounting (SMA) infiltration method was used to model daily flows and simulate certainty bounds for Karoon III basin, southwest of IRAN, in two scenarios, independent and correlated parameters using 2-copula. The parameters were represented by probability distributions, and the effect on prediction error were evaluated using Latin hypercube sampling (LHS) on Monte Carlo simulation (MCS). Saturated hydraulic conductivity (K), Clark storage-coefficient (R), and time of concentration (tc) were chosen for investigation, based on observed sensitivity analysis of simulated peak over threshold (POT). One hundred runs were randomly generated from 100 parameter sets captured from LHS of parameters distributions in each sub-basin. Using generated parameter sets, 100 continuous hydrographs were simulated and values of certainty bounds calculated. Results showed that when 2-copula correlated R and tc, with 0.656 Kendall’s Tau and 0.818 Spearman’s Rho coefficients, were propagated, decreasing of outputs’ sharpness was more than when considering K and R (K-R), with 0.166 and 0.262; therefore, incorporation of correlations in the MCS is important, especially when the correlation coefficients exceed 0.65. The model was evaluated at the outlet of the basin using daily stream flow data. Model reliability was better for above-normal flows than normal and below-normal. Reliability increases of simulated flow when considering correlated R-tc was more than K-R because of the correlation values. Incorporation of copula for K-tc not only did not improve the model reliability but also decreased it. Results showed improvement of model reliability, by decreasing predicted error of hydrologic modeling, when dealing with correlated parameters in the system.     

Numerical modelling of the groundwater inflow to an advancing open pit mine: Kolahdarvazeh pit,Central Iran

The groundwater inflow into a mine during its life and after ceasing operations is one of the most important concerns of the mining industry. This paper presents a hydrogeological assessment of the Irankuh Zn-Pb mine at 20 km south of Esfahan and 1 km northeast of Abnil in west-Central Iran. During mine excavation, the upper impervious bed of a confined aquifer was broken and water at high-pressure flowed into an open pit mine associated with the Kolahdarvazeh deposit. The inflow rates were 6.7 and 1.4 m³/s at the maximum and minimum quantities, respectively. Permeability, storage coefficient, thickness and initial head of the fully saturated confined aquifer were 3.5?×?10^?4 m/s, 0.2, 30 m and 60 m, respectively. The hydraulic heads as a function of time were monitored at four observation wells in the vicinity of the pit over 19 weeks and at an observation well near a test well over 21 h. In addition, by measuring the rate of pumping out from the pit sump, at a constant head (usually equal to height of the pit floor), the real inflow rates to the pit were monitored. The main innovations of this work were to make comparison between numerical modelling using a finite element software called SEEP/W and actual data related to inflow and extend the applicability of the numerical model. This model was further used to estimate the hydraulic heads at the observation wells around the pit over 19 weeks during mining operations. Data from a pump-out test and observation wells were used for model calibration and verification. In order to evaluate the model efficiency, the modelling results of inflow quantity and hydraulic heads were compared to those from analytical solutions, as well as the field data. The mean percent error in relation to field data for the inflow quantity was 0.108. It varied between 1.16 and 1.46 for hydraulic head predictions, which are much lower values than the mean percent errors resulted from the analytical solutions (from 1.8 to 5.3 for inflow and from 2.16 to 3.5 for hydraulic head predictions). The analytical solutions underestimated the inflow compared to the numerical model for the time period of 2–19 weeks. The results presented in this paper can be used for developing an effective dewatering program.     

Assessment of the impact of landfill on groundwater quality: A case study of the Pirana site in western India

In present study focus has been given on estimating quality and toxicity of waste with respect to heavy metals and its impact on groundwater quality, using statistical and empirical relationships between different hydrochemical data, so that easy monitoring may be possible which in turn help the sustainable management of landfill site and municipal solid waste. Samples of solid waste, leachate and groundwater were analyzed to evaluate the impact of leachates on groundwater through the comparison of their hydrochemical nature. Results suggest the existence of an empirical relationship between some specific indicator parameters like heavy metals of all three above mentioned sample type. Further, K/Mg ratio also indicates three groundwater samples heavily impacted from leachate contamination. A good number of samples are also showing higher values for and Pb than that of World Health Organization (WHO) drinking water regulation. Predominance of Fe and Zn in both groundwater and solid waste samples may be due to metal plating industries in the area. Factor analysis is used as a tool to explain observed relation between numerous variables in term of simpler relation, which may help to deduce the strength of relation. Positive loading of most of the factors for heavy metal clearly shows landfill impact on ground water quality especially along the hydraulic gradient. Cluster analysis, further substantiates the impact of landfill. Two major groups of samples obtained from cluster analysis suggest that one group comprises samples that are severely under the influence of landfill and contaminated leachates along the groundwater flow direction while other assorted with samples without having such influence.