GIS and Urban Groundwater

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Environmental Modelling & Software 19 (2004) 11111124www.elsevier.com/locate/envsoft

Borehole Optimisation System (BOS)A GIS based risk analysistool for optimising the use of urban groundwater

N.G. Tait a,,1, R.M. Davison b,2, J.J. Whittaker b,3, S.A. Leharne a, D.N. Lerner b

a School of Earth and Environmental Sciences, University of Greenwich, Medway Campus, Pembroke, Chatham Maritime, Kent ME4 4TB, UKb Department of Civil and Structural Engineering, Groundwater Protection and Restoration Group, University of Sheffield, Mappin Street,

Sheffield S1 3JD, UK

Received 30 October 2002; received in revised form 14 August 2003; accepted 21 November 2003

Abstract

Urban groundwater is generally an underused resource, partially due to the perceived risk of pollution and the strategic diffi-culties in placing boreholes in built-up areas. The development of a probabilistic risk based management tool that predictsgroundwater quality at potential new urban boreholes is beneficial in determining the best sites for future resource development.The Borehole Optimisation System (BOS) is a custom Geographic Information System (GIS) application that has been developedin the ArcView 3.1 environment with the objective of locating the optimum locations for new boreholes in urban areas. It couplesthree component models, the Catchment Zone Probability Model (CZPM), the Land-use Model (LM) and the Pollution RiskModel (PRM). The CZPM produces probabilistic catchment zones for a user-defined abstraction borehole location under uncer-tain and variable hydrogeological parameters. The LM identifies current and historical industries located within the selected prob-abilistic catchment zone. The PRM uses these industrial and the associated hydrogeological and contaminant data to predictprobabilistic contaminant concentrations in a particular analysis year. This paper outlines the methodologies employed in thedevelopment of BOS and attempts to validate the approach by presenting a simulation that forecasts PCE concentrations at anactual borehole location in the Nottingham urban aquifer. The results predict contaminant levels in the abstracted water that arein agreement with observed values, both being above the UK Drinking Water Standard of 10 lg/l. These demonstrate the appli-cability of BOS as a tool for informing decision-makers on the development of urban groundwater resources.# 2004 Elsevier Ltd. All rights reserved.

Keywords: Borehole Optimisation System; Coupling; GIS; Model integration; Probabilistic risk modelling; Urban groundwater

1. Introduction

Urban groundwater is a largely underused resource

in the UK partially due to the perceived contamination

risks, associated clean-up costs and logistics of placingboreholes in built-up areas. Consequently the recent

trend of rising water tables in urban areas, resulting

from reduced industrial abstraction since the 1960s,has led to well documented basement and tunnel flood-

ing, and geotechnical problems (Greswell et al., 1994;Lerner and Tellam, 1997; Lerner and Barrett, 1996).

Similarly the tendency of water suppliers to favourgroundwater from rural locations has led to environ-mental problems, such as depleted aquifers and riversexperiencing low flow conditions. A balance needs tobe attained whereby pressures on rural groundwaterare reduced and the continuing rise in urban watertables is arrested.

However, the required increase in urban ground-water exploitation is complicated by complex interac-

tions between cities and the underlying resource.Research has shown that leaking water mains, sewersand soakaways act as major sources of recharge in

Corresponding author. Tel.: +44-1932-357784; fax: +44-1932-349983.

E-mail address: [email protected] (N.G. Tait).1 Present address: Veterinary Laboratories Agency, Epidemiology

Department, New Haw, Addlestone, Surrey KT15 3NB, UK.2 Present address: Golder Associates, Landmere Lane, Edwalton,

Nottingham NG12 4DG, UK.3 Present address: Environmental Simulations International, Pri-

ory House, Priory Road, Shrewsbury SY1 1RU, UK.

1364-8152/$ - see front matter # 2004 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsoft.2003.11.014


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urban areas. When combined, these combined can beequivalent to the recharge in rural locations (Lerner,1986). Additionally, the long industrial history of manyUK cities results in complex patterns of pollution thatimpact on the underlying groundwater quality (Rivettet al., 1990; Burston et al., 1993; Ford and Tellam,1994). Therefore there is a high likelihood that abstrac-tion water quality in urban areas will be affected byborehole position. This is a critical consideration in thefuture efficient use of urban groundwater as it wouldbe advantageous to abstract groundwater from specificareas of a city depending on the water quality needs ofuses such as public supply, industrial supply or riveraugmentation.

Predicting the best locations for new urban bore-holes that will deliver groundwater of a quality thatmeets the specific needs of the user can only beachieved through informed decision making. The useof models to simulate environmental conditions and

provide information on which to base decisions is com-monplace. However a deterministic model cannot accu-rately simulate the complex interactions between a cityand its underlying groundwater due to the inherentuncertainties of the system and limited data avail-ability. The main objective of this paper is to presentthe methodologies employed in the development of amulti-component risk based probabilistic tool to sup-port the decision making process of optimising thepositioning of new urban boreholes. The tool hasrecently been applied to case studies in the TrassicSandstone aquifer around Nottingham, in the English

East Midlands. An example simulation from an actualborehole located in the aquifer under the city is illu-strated along with a discussion of results. The full casestudies include the analysis of the entire urban area forthe production of contaminant risk maps. The meth-odologies and data employed in these case studies andthe comprehensive set of results are presented in a laterpublication.

2. Concept

The Borehole Optimisation System (BOS) forecasts

water quality at new abstraction borehole locations.The conceptual model is the reverse of the conven-tional source, pathway and target risk assessmentmethodology. BOS begins with a user specified bore-hole (the target), and retraces the flow lines of the cap-ture zone in order to identify the multiple potentialcontaminant sources situated upstream (Fig. 1).

In order to undertake the risk analysis, BOS utilisesthree discrete component modules (Fig. 2). The Catch-ment Zone Probability Model (CZPM) module isbased on a three-dimensional finite-difference MOD-FLOW groundwater flow model (McDonald and

Harbaugh, 1988) and identifies the probabilistic surface

expression of a borehole catchment. The Land-useModel (LM) module utilises spatial land-use infor-mation and associated Microsoft Access land-use andcontaminant databases to identify the potential current

and historical contaminant sources within the catch-

ment area. The Pollution Risk Model (PRM) moduleemploys a stochastic analytical solute transport modelbased in a Microsoft Excel spreadsheet with CrystalBall probabilistic extension (Decisioneering, 1997) andcustom PRM add-ins. The PRM estimates the com-

bined threat posed by the identified potential contami-nant sources to the groundwater quality at theabstraction borehole in a specified year.

The integration of these independent componentmodules (CZPM, LM and PRM) within a single

Graphical User Interface (GUI) was central to the suc-cessful development of BOS as a powerful tool for

addressing the issues regarding the best use for urbangroundwater under conditions of high uncertainty. Sys-tem integration problems such as those discussed byAbel et al. (1994) include the identification of a frame-

work within which the external component models canbe linked. A Geographic Information System (GIS) isan established natural resource management tool (Timet al., 1996). GIS presents a consistent environment for

data management and visualisation for building riskanalysis and decision support systems that provide

integrated access to otherwise incompatible externalanalytical tools. In this case GIS offers the ideal plat-

Fig. 1. BOS conceptual model.

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form for coupling the diverse components that makeup BOS.

The integration of environmental models with GIShas been discussed generally by numerous authors(Goodchild et al., 1992, 1993, 1996; Fotheringham andRogerson, 1994; Fischer et al., 1996; Longley and

Batty, 1996; Fotheringham and Wegener, 2000). Themost common integration strategy classifications aretermed loose and tight coupling (Nyerges, 1993; Fischeret al., 1996; Karimi and Houston, 1996; Longley andBatty, 1996). Loose coupling relies on the transfer ofdata files between the GIS and external models forintegration. Information transfer is undertaken auto-matically either via pre- and post-processor routinesadded to the external model or through a commonGUI (Lieste et al., 1993; El-Kadi et al., 1994; Tim andJolly, 1994; Ramanarayanan et al., 1996; Sui andMaggio, 1999).

Tight coupling can be either full or close. Full coup-

ling entails embedding the model within the GISthereby removing the need for data file transfers. Thisprocess can be achieved through the GIS software ven-dors provision of macro and script programmingcapabilities enabling model development directly in theGIS (Huang and Jiang, 2002). However GIS languagessuch as Environmental Systems Research Institutes(ESRI) Avenue and Arc Macro Language (AML) areoften not powerful enough to implement sophisticatedmodels that rely on complex algorithms. Thereforeclose coupling is a more realistic option in manyinstances. This involves the development of models

within Dynamic Link Libraries (DLLs) using advancedprogramming languages such as C/C++ and Fortran.The DLLs are linked to the GIS through function callsfrom the GIS programming language (Ding andFotheringham, 1992; Batty and Xie, 1994).

Alternative integration strategy classifications aretermed one-way, loose, shared, joined and tool coupling(Brandmeyer and Karimi, 2000). This is a hierarchicalsystem in which each layer is segmented by the extentof the modeller interaction with the component models,automation of data transfer, existence of a GUI, shar-ing of data storage and presence of integration andmodelling tools. One-way coupling provides the lowestlevel of integration where the GIS and external modelremain completely separate and are linked only bymanual data transfer. In comparison loose couplinghas automated data transfer between the system com-ponents. These simple integration techniques involveminimal programming requirement. However they are

considered inefficient and unfriendly to the user.Shared coupling can be either GUI or data storage

sharing. GUI sharing provides a single virtual environ-ment where the GIS, external model and associatedcoupling method are hidden (Blodgett et al., 1995; Kimet al., 1995). Such applications tend to be user friendlyand are therefore more likely to be used (Berry et al.,1997). Data coupling involves user interaction directlywith each system component, although all componentsshare the same data files (Djokic et al., 1996). This typeof integration is less common because most environ-mental models are independently developed with each

using specific optimised data structures that are rarelycompatible. Other methods of sharing data are beingincreasingly used. The Dynamic Data Exchange (DDE)protocol is used to exchange data through active linksbetween Microsoft Windows applications. Open Data-base Connectivity (ODBC) employs Structured QueryLanguage (SQL) (ISO/IEC, 1992) to provide client/server access to data stored in SQL compliant data-bases. Object Linking and Embedding (OLE) extendsODBC to non-SQL tabular data and componentobjects.

Joined coupling utilises both a common GUI anddata storage in an embedded or integrated model struc-

ture. In the embedded method one system componentcontains another in a master/slave relationship. Inter-action is through the GUI with the master componentonly (Gorokhovich and Janus, 1996; Wu, 1996). Thesuccess of embedding models is dependent on the func-tionality of the programming language used. Integratedcoupling allows interaction with any model through theGUI. Each component model is a peer of every othermodel with functionality used in common sharedlibraries.

Tool coupling offers the highest level of integration.This is identified when component models are

Fig. 2. BOS modular components.

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contained within an overall modelling framework thatutilises both joined and shared coupling in a singleGUI operating shared data storage in a networked,heterogeneous computing environment (Leavesley et al.,1996; Hodgkin et al., 1997).

BOS seamlessly integrates CZPM, LM and PRMmodules within a GIS based GUI. A number of thecoupling methodologies outlined previously are used tointegrate control throughout the application interface.According to the classifications presented BOS is GUIsharing, with the CZPM module being loosely coupledto the GIS. Conversely both LM and PRM modulesuse data sharing techniques (ODBC and DDE, respect-ively) to link with the system. DLL external functioncalls including Win32API support are also utilised.Using these procedures the GUI contains all of thenecessary functions with which to undertake theabstracted groundwater risk analysis while shieldingthe user from the complexities of the background pro-

cess controlling the component modules. The inte-grated working environment offers either full control tothe experienced user or minimal interaction to the nov-ice and creates a powerful decision making tool forestablishing the best locations for new boreholes inurban areas.

3. Development

BOS was developed in the ArcView GIS 3.1 environ-ment using the Avenue programming language. Avenue

is an object oriented programming language thatallows the creation and customisation of new and exist-ing interfaces, automation of repetitive tasks and devel-opment of complete query and analysis applications. Inaddition, ArcView Dialog Designer provides theAvenue developer with dialog tools and controls to aidin the customisation of ArcView where the applicationrequires significant user interaction through visualcomponents. An ArcView application comprising cus-tom GUIs, dialogs and Avenue scripts can be storedand distributed as an extension. The major benefit ofan extension is the ability to load/unload it into thebase application as and when required. The BOS appli-

cation is deployed as an ArcView extension containing18 custom dialogs and 334 custom Avenue scriptstotalling approximately 50,000 lines of code.

3.1. Graphical user interface

When loaded into the base ArcView application theBOS extension introduces the GUI as a new optioninto the menu bar of the view document. The system iscontrolled entirely from the 25 menu items and accom-panying dialogs associated with the BOS menu option(Fig. 3). The GUI design was guided by the rigid linear

structure of the BOS process (i.e. CZPM results areinput into the LM that in turn produces data for thePRM). Progress through the sequence of eventsrequired each modular stage to be completed in thecorrect order. This type of sequential process initiallyencouraged the development of the application inter-

face as a series of steps through wizards. However thisapproach proved to be too restrictive. The final menubased system is more flexible, with options beingenabled and disabled as progression through eachmodule occurs. This results in instantaneous access tospecific dialogs depending on progression through theanalysis as opposed to cycling through numerous wiz-ard windows to find the functions required.

Successful implementation of the BOS processthrough the GUI is partly reliant on imported func-tionality. A critical dependency exists between the BOSinterface and the ArcView Spatial Analyst extension.Spatial Analyst contains functionality for accessing and

working with grid objects (raster data). Therefore, BOScan only be used if Spatial Analyst is loaded into thebase ArcView application. Similarly the external appli-cations on which the component modules are based(MODFLOW, Microsoft Access, Microsoft Excel andCrystal Ball) need to be installed on the operating com-puter.

BOS also depends on the user to supply the specificmodular datasets for the urban region to be analysed.Thus a MODFLOW groundwater flow model, histori-cal land-use shapefiles, Microsoft Access land-use andcontaminant property databases and surface elevation

Fig. 3. BOS graphical user interface.



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coverages are essential. Background shapefiles (roads,rivers, railways etc) of the study area are not necessarybut can be utilised by BOS, for visualisation purposes,if present. The reliance of BOS on the user to providethese representative datasets means that the applicationis not restricted to the analysis of a single urban area.Indeed the BOS application can be applied to anyregion given the appropriate data in the correct format.

3.2. Data management

The BOS application interface is underpinned by acore data management structure that is required tocontrol the significant amounts of diverse data usedand generated by the component modules. A BOS pro-ject is created at the beginning of each BOS simulation.This takes the form of a dictionary data structure. Adictionary is an unordered collection of associationsbetween keys and values. Each association is an

element in the dictionary. Both the key and value arearbitrary objects and the key uniquely identifies thevalue. In the BOS project dictionary a key is a stringreferring to a particular dataset name and the value is astring indicating the location of the dataset. Theadvantage of using the dictionary data structure is theprovision of very fast and efficient access to a large col-lection of objects (Razavi, 1999).

During every stage of the BOS process the projectdictionary is utilised in order to record data sourcesand trace data conversions and movements that are rel-evant to the current active simulation. The speed of

access provided by this data structure means that theactive project dictionary is referenced in almost everyAvenue routine controlling BOS. Therefore, the activeproject dictionary is queried for GUI status updates,error handling, data retrieval and processing proce-dures.

The GUI provides menu options that control thestatus of the active project dictionary and hence thestatus of BOS. These controls include the creation of anew BOS simulation that prompts for selection of theurban region to be analysed. Each time a new simula-tion is started, a corresponding project dictionary iscreated. There are options to save a copy of the active

project dictionary that is written to file thus enablingBOS simulations to be stored at any time. This capa-bility is essential as a single simulation can take severalhours to complete. A saved project dictionary can beloaded into BOS so that a simulation can be restartedwith previous data. Conversely the active project dic-tionary can be closed resulting in the removal of theassociated datasets from the BOS application. Finallythe contents of the active project dictionary can also beviewed.

The importance of the active project dictionary inthe BOS application cannot be overstated. Not only

does it act as a central reservoir for all of the infor-mation sources and data processing in BOS, it alsoenables the application to retrieve appropriate datasetsand other relevant information as and when they arerequired with minimal input from the user.

3.3. Catchment zone probability model (CZPM)module

The first modular component of BOS is the uncer-tainty based CZPM (Davison et al., 2002). The objec-tive of this module is to predict the spatial distributionof the probability that the groundwater originatingfrom a given location in the aquifer reaches a user-defined pumping borehole. The basis of the CZPMmodule is a three-dimensional finite-difference ground-water flow model constructed using MODFLOW.Numerous researchers describe the application ofMODFLOW groundwater flow models within a GIS

framework (Flugel and Micht, 1995; San Juan andKolm, 1996; Bonomi and Cavallin, 1999; Brodie,1999). These efforts utilise loose coupling techniques tointegrate the two components. BOS uses loose couplingtechniques in this case although automated data filetransfers are used in conjunction with pre- and post-processor routines that introduce probabilistic simula-tion capabilities.

Additional CZPM module components are the exter-nal processors that couple the MODFLOW ground-water flow model to the GUI. The MODSUMprocessor interrogates the selected model and retrieves

the essential hydrogeological data. Features such as thegeometric boundaries, hydrogeological features andphysical processes represented in MODFLOW areimported into BOS. Key model parameters (recharge,hydraulic conductivity, porosity, storage coefficient andspecific yield) are retrieved as either homogeneousmodel values or property zones. In addition, the uncer-tainty of the recharge parameter with time is also con-sidered, being defined as a constant for a given timewithin the total model time span (a MODFLOW stressperiod). The imported uncertain hydrogeological para-meters can then be assigned probability distributionstypes (constant, uniform, triangular, normal, log-

normal) and associated values. A new boreholelocation is selected with accompanying pumping attri-butes.

The CZPM processor controls the probabilisticsimulation. Details of the selected groundwater flowmodel (name, location, resolution, number of layersand zones), the user-defined probabilistic hydro-geological parameters, the new borehole location andassociated pumping attributes, and the type and num-ber of simulations are passed to the CZPM whichoperates 2 probabilistic simulation types, Monte-Carloand Generalised Likelihood Uncertainty Estimation



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(GLUE). The Monte-Carlo method in its simplest formaccepts all simulations with equal weighting. A modifi-cation of this method allows equal weighting of onlythose simulations meeting the calibration criteria withthe rest being neglected. The GLUE method weightsthe individual probability distributions according tohow well the flow simulation fulfils the calibration cri-teria (Bevan and Binley, 1991).

On execution, the CZPM processor inserts the speci-fied borehole and hydrogeological attributes into theMODFLOW files and proceeds to undertake the typeand given number of simulations. Every simulation isbased on a randomly generated parameter set, but eachensemble must fulfil the probability distributions speci-fied by the user in BOS. For each parameter set a flowfield is calculated. If the flow field is accepted under thecalibration criteria the advective transport of a hypo-thetical contaminant originating from the midpoint ofeach cell of the flow model is simulated using particle

tracking in MODPATH (Pollock, 1989). The catch-ment probability distribution P[x] is defined as theprobability that a particle starting at position x arrivesat the user-defined borehole. For a single simulationP[x] is either 0 (the particle does not reach the well) or1 (the particle does reach the well). After n specifiedsimulations the probability of a particle reaching theborehole is given as P[x/n].

BOS is currently designed to operate with steadystate MODFLOW groundwater flow models althoughfurther revisions of the code are planned to incorporatetransient systems. There are no limits to the number of

layers or zones within the steady state model that theapplication can process. BOS does not permit less than200 CZPM simulations (0.5% output resolution) inorder to produce a smooth probability grid that iseasier to contour in the LM module. The disadvantageof this increased accuracy is the time taken for theCZPM processor to complete. This will vary dependingon the complexity of the chosen groundwater flowmodel and the available hardware. A typical completesimulation for Nottingham took 40 min to complete ona PII 450 MHz computer with 512 Mb of RAM. Themain output of the CZPM is a catchment probabilitygrid and additional datasets indicating the travel time

mean and standard deviation for each cell to thepumping borehole. The GUI incorporates the full pro-cess of running the CZPM through menu items andcorresponding custom dialogs controlled by Avenuecode. This loose coupled integration can be sum-marised in six stages (Fig. 4) with the proceduresdescribed (Table 1).

3.4. Land-use Model (LM) module

The second modular component of BOS is the LM.The task of the LM is to identify all of the past and

present surface activities located within a specifiedcatchment probability zone that may be potential con-taminant sources and therefore affect groundwaterquality. The LM builds a catchment specific dataset ofthese industries and their properties. The basis of theLM module are two significant sets of land-use data

associated with the urban study area. The first are aseries of ArcView LM shapefiles representing historicalsurface activity in the urban area. Each shapefile pro-vides a snapshot of the city in a particular year andclassifies the land-uses present into residential, rec-reational, institutional, industrial, agricultural, com-mercial, waste, transport and water categories. In orderto create these datasets, land-uses need to be digitisedfrom high-resolution historical and present day maps.Each land-use then needs to be identified and cate-gorised into the appropriate group. This process canonly be achieved with the use of additional referencesfrom historical texts, industrial directories, local

records and societies. The greater the number of snap-shot land-use shapefiles constructed the more accuratethe representation of the historical patterns of industryin the study area.

The second dataset is the Microsoft Access LMdatabase that contains the relevant information aboutall of the land-uses in the industrial category of theland-use shapefiles. Only the industrial land-uses areutilised by BOS as these are considered to be associa-ted with contaminants. These data are held withinthree unique tables indicating specific industry, generalindustry and contaminant property information. The

Fig. 4. BOS/CZPM integration.



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specific industry dataset provides the cross-reference to

the spatial ArcView shapefile dataset with unique

industry numbers corresponding to the individual

shapes representing each industry. This table also

includes information on the industry name, type code,

start and stop date, whether there is known contami-

nation at the site, and any other relevant details. The

type code attributed to each industry is important as itdescribes the possible contaminants associated with

that land-use. The general industry table links the

range of industry types and their corresponding con-

taminant codes. The process of identifying the con-

taminants related to each industry type was aided by

documents such as the DOE industry profiles (Depart-

ment of the Environment, 1995). The contaminant pro-

perty table then links the specific contaminant code to

the associated properties. This table includes contami-

nant name, organic carbon partition coefficient (Koc),

degradation rate, aqueous solubility and mole fraction

data. The database can incorporate as many con-taminants as required but BOS is currently restricted to

the analysis of organics such as solvents and hydro-

carbons.Storing the LM module information in this way

serves to protect the spatial land-use records whilst

enabling the associated properties to be accessed and

edited through the Microsoft Access platform. The

manual construction of these datasets as described is

an extremely time consuming procedure. However, it is

anticipated that a great deal of these data will become

available under the requirement of Local Authorities to

record land-uses under Part IIa of the EnvironmentalProtection Act (1990).

The coupling of the Microsoft Access LM databaseand GUI is based on a data sharing technique. Inorder that BOS can connect to the database to retrievethe general, specific and associated contaminant pro-perty information for each industry located within theborehole catchment, the application requires a SQL

database connection to be set-up. The Microsoft Win-dows version of ArcView uses the Microsoft ODBCstandard. Using the ODBC administrator the Micro-soft Access ODBC driver for Windows needs to beinstalled and configured for the appropriate land-usedatabase source. The GUI incorporates the process ofrunning the LM through menu items and correspond-ing custom dialogs controlled by Avenue code. Thisdata sharing integration can be summarised in sixstages (Fig. 5) with the procedures described (Table 2).

3.5. Pollution Risk Model (PRM) module

The third and final modular component of BOSis the PRM. The role of the PRM is to predict thecumulative effects of all sources of a chosen contami-nant at the pumping borehole for a given year by themultiple land-uses identified by the LM component.The basis of the PRM module is a stochastic analyticalsolute transport model incorporating a one-dimensionalsolution to simulate advection, dilution, retardation andbiodegradation processes.

The PRM consists of an unsaturated zone modeland a saturated zone model that are interconnected by

Table 1BOS/CZPM integration

BOS Action Method

Import groundwater flow model Interrogates selected MODFLOW groundwaterflow model and imports datasets at a specifiedresolution

Input Model, ResolutionProcess MODSUMOutput Boundary, Head, Hydrogeology, Well

Edit hydrogeological parameters Edits hydrogeology data and assigns probabilitydistributions and values to uncertain modelparameters

Input HydrogeologyProcess BOSOutput Hydrogeology

Select borehole location Selects new borehole location within modelboundary and defines associated pumping attributes

Input Boundary, HeadProcess BOSOutput Borehole

Specify simulation options Specifies either Monte Carlo or GLUE optionsfor CZPM probabilistic simulation

Input Process BOSOutput Simulation

Run CZPM Executes CZPM probabilistic simulationand imports results

Input Borehole, Hydrogeology, SimulationProcess CZPMOutput Probability, Travel Time

Analyse CZPM results Summarises probabilistic catchment propertiesand statistics

Input ProbabilityProcess BOSOutput



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the condition that the contaminant flux through thebottom of the unsaturated zone equals the contaminantflux at the top of the saturated zone. This model isdifferent from many other solute transport models as itincludes contaminant dilution by groundwater at thepumping borehole. The PRM assumes that transport in

the unsaturated zone has vertical flow, and in the satu-

rated zone has horizontal flow. The contaminantconcentration Cs at the pumping borehole is given byEq. (1) where n refers to the number of sources, i is thesite number, Fu is the flux through the unsaturatedzone, Q is the pumping rate, ks is the first order decayrate in the saturated zone, ts is the travel time through

the saturated zone and Rf is the retardation factor(Prabnarong, 2000):

Cs Xn

i1

Fu;iQexpksts;iRf

1

BOS builds the PRM for a specified contaminant in aMicrosoft Excel spreadsheet using the hydrogeological,industrial and contaminant data defined in the earlierCZPM and LM modules for the specified borehole.The PRM is solved analytically within the spreadsheetusing the Crystal Ball probabilistic software add-in thatoperates two probabilistic simulation types, Monte-

Carlo and Latin-Hypercube. Thus the uncertainty ofsome hydrogeological, industrial and contaminantinput parameters can be handled by assignment of uni-form, triangular, normal and lognormal probabilitydistributions that represent the range of possible valuesdefined by the user.

The PRM undertakes the specified number of simu-lations. Before each simulation every contaminantworksheet is recalculated. In this process all probabil-istic parameters are sampled and the resulting valuespopulated into the relevant cells. These values are thenused to calculate the contaminant flux at the boreholefrom each industry. After each simulation the com-

bined borehole contaminant concentration from the

Fig. 5. BOS/LM interaction.

Table 2BOS/LM integration

BOS Action Method

Open MS Access LM database Opens Microsoft Access LM databaseassociated with current urban areaunder analysis

Input Process Win32 APIOutput

Plot probability contours Plots selected multiple catchment probabilitycontour lines for user-defined borehole usingCZPM results

Input Borehole, Boundary, Probability, WellProcess BOSOutput Contour

Select catchment zone Converts single selected probability contourline to catchment area

Input Borehole, Boundary, Contour, WellProcess BOSOutput Catchment

Specify land-use themes Clips historical land-use data to catchmentand filters industrial land-uses with associatedcontaminants

Input Catchment, Land-useProcess BOSOutput Industry

Run LM Links industry data to Microsoft Access LMdatabase to retrieve industry and relatedcontaminant properties

Input Industry, Land-use, ContaminantProcess ODBC, SQLOutput Industry

Analyse LM results Summarises industrial and contaminantproperties and statistics

Input IndustryProcess BOSOutput



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contributing sources is computed. The PRM under-takes this calculation by summation of the individualconcentrations from contributing industries in theanalysis year. The PRM decides which industries arecontributing to the borehole contamination at thespecified analysis date by testing whether the contami-nant flux duration contains the analysis year (the targetyear falls between the industry start date plus the traveltime to the borehole and the industry stop date plusthe travel time to the borehole). This calculation is con-trolled by VBA macros at runtime as the groundwatertravel time from each source to the pumping boreholeis described by a probability distribution. Therefore,during a multiple simulation run the variation in traveltimes can result in differing contributing sources for ananalysis year in each simulation. Once the simulation iscomplete, the borehole contamination statistics arereturned. These include information on the contami-nation mean, median, standard deviation, variance,

skewness and 97.5, 95, 50, 5 and 2.5 percentile confi-dence limits.

The coupling of PRM component and GUI is basedon a data sharing technique. DDE is a client/servercommunication mechanism that enables the two appli-cations to interact by exchanging data. ArcView sup-ports DDE and can communicate with other supportedapplications such as Microsoft Excel by defining a DDEconversation. In a conversation, one application is theserver and the other application is the client. In the caseof the BOS application, ArcView is always the client.There are three different ways to interact in a DDE con-

versation. Using the execute command the client asksthe server to perform some function via a directive inthe servers command language. In the case of a DDEconversation between ArcView and Microsoft Excel, theclient may manipulate the spreadsheet by using theMicrosoft Excel Version 4 Macro Function language. Inthe request operation, the client asks the server to returnthe value of an identified item. The poke command pas-ses data from the client to the server. The GUI incorpo-rates the full process of running the PRM through menuitems and corresponding custom dialogs controlled byAvenue code. This data sharing integration can be sum-marised in six stages (Fig. 6) with the procedures

described (Table 3).

4. Case study

BOS has recently been applied to a case study area inthe City of Nottingham (Fig. 7). Studies by Barrett et al.(2001) revealed the groundwater quality beneath thisarea to be poorer than nearby rural locations. This ismainly attributed to widespread chlorinated solvent andBTEX contamination from fuel and solvent spillages(Table 4). With the exception of localised pollution epi-

sodes, no significant concentrations of inorganic con-taminants were found. However, nitrate concentrationswere found to exceed the drinking water standards inboth urban and rural areas. As a consequence, two ofthe three main groundwater abstractions in Nottinghamhave been abandoned in recent years due to concerns

regarding quality. Most water is now withdrawn in thesurrounding rural areas.

4.1. Data

In order to run sample BOS simulations in the studyarea, representative datasets were constructed for theCZPM and LM modules. The CZPM module com-prises a steady state groundwater flow model for theNottingham urban aquifer region that was constructedusing MODFLOW. This single layer, multi-zone modelrepresents the Sherwood Sandstone Group in the East

Midlands that is bounded to the west by the LowerMagnesian Limestone Formation along the RiverLeen. In the east the relatively impermeable MerciaMudstone Group is the confining unit. The regionalhydraulic gradient is steepest in the northwest, fromwhere it drives groundwater flow southeasterly to dis-charge at wells in and around the Nottingham urbanarea, as baseflow to the River Trent (Trowsdale, 2002)and to the confined zone of the aquifer (Fig. 8).

The LM module comprises a series of six ArcViewland-use themes for the region encompassing Notting-ham. These coverages provide individual snapshots of

Fig. 6. BOS/PRM integration.



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the land-use characteristics of the urban area over the

last century in the years 1901, 1920, 1939, 1955, 1974

and 1991. An associated Microsoft Access LM data-

base was also assembled containing the relevant indus-

trial and contaminant specific data (Davison et al.,

2002).

4.2. Simulation

Using the constructed CZPM and LM datasets, asample BOS simulation was run for an actual boreholelocation. The Hicking Pentecos (Dyers Ltd) borehole issituated in the southern part of the Nottingham urbanarea where the Triassic Sandstones are unconfined.

This borehole has historical data recorded for a num-ber of organic contaminants. The probabilistic CZPMwas applied to the borehole through the BOS interfaceusing actual parameters (Table 5). The resultant catch-ment probability grid indicated the shape of the overallborehole catchment and the uncertainty of capture ofthose areas within the catchment (Fig. 9).

The catchment probability data were then used asinput for the LM module interaction process. Com-posite databases describing the past and present indus-tries and potential associated contaminants within theselected probabilistic catchment zones were created.

The parameters used in this process are listed (Table 6).The 10% probability zone representing a worst casescenario was selected for land-use analysis as lower cer-tainty zones result in larger catchment area and thusmore potential contaminant sources. Industries locatedwithin this area over the last century were then ident-ified (Fig. 10).

The catchment specific industry data were then usedin the PRM module procedure to predict contaminantconcentrations in abstracted groundwater from theborehole at a specified time. The parameters used inthis procedure are listed (Table 7). The chosen con-

Table 3BOS/PRM integration

BOS Action Method

Open MS Excel PRM spreadsheet Opens Microsoft Excel PRMspreadsheet associated with currenturban area under analysis

Input Process Win32 APIOutput

Set-up PRM spreadsheet Installs Crystal Ball, PRM addinsand inserts status worksheet intoMicrosoft Excel PRM spreadsheet

Input Process DDE, VBAOutput

Select contaminant worksheets Inserts contaminant worksheetsinto Microsoft Excel PRM spreadsheet and builds PRM withassociated data

Input Borehole, Hydrogeology,Industry, Travel Time

Process DDE, VBAOutput

Specify simulation options Specifies either Monte Carlo orLatin Hypercube options for PRMprobabilistic simulation

Input Process BOSOutput Simulation

Run PRM Executes PRM probabilisticsimulation in Microsoft Excel PRspreadsheet and imports results

Input SimulationProcess DDE, VBAOutput Contamination

Analyse PRM results Summarises abstractiongroundwater contaminatiostatistics

Input ContaminationProcess BOSOutput

Fig. 7. City of Nottingham region.



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taminant of interest was the chlorinated solvent tetra-chloroethene (PCE) which is used predominantly in thedry cleaning and metal cleaning industries. This wasanalysed for predicted impacts at the target boreholeduring the year 1996. The results of the PRM simula-tions indicate that 91 industries are potential contami-nant sources contributing to the PCE concentration at

the borehole in the analysis year. In this example therewas a 50% probability that the PCE concentration inthe abstraction water was below 48.82 lg/l in the year1996 (Fig. 11). This compares with an actual observedPCE concentration of 30.13 lg/l.

5. Discussion

The borehole location in the case study has a signifi-cant risk of the water quality abstracted in 1996 beingcontaminated with PCE, with concentrations wellabove the UK drinking water standard of 10 lg/l both

predicted and observed. Similar agreement existsbetween observed and predicted results at five otheractual borehole locations in the Nottingham urbanaquifer region. In addition, the performance of the

model has been tested manually by comparing predic-tions for multiple solutes against observations in twoexisting pumped boreholes in the area. In these casesthe simulated results were consistent with the availablefield data with only one prediction in 14 outside the90% confidence interval (Prabnarong, 2000). Thus themodel has been shown to give accurate results and,therefore, given the choice of locating a new borehole

Table 4Analyses of chlorinated solvents in the Nottingham area

Aquifer CTC TCA TCE TCM TeCE

Deep urban % Detection 0 48 67 6 82Mean (lg/l) 6.3 24.6 NQ 296Max (lg/l) 16.3 134.8 NQ 2563

Shallow urban % Detection 0 64 27 0 45Mean (lg/l) 27.2 49.7 15.8Max (lg/l) 90.4 92.5 57.8

NQ: Not quantified.After Barrett et al. (1996).

Fig. 8. Study area.

Table 5Borehole case study/CZPM parameters

Parameter Borehole case study

Groundwater flow model Nottingham steady stateResolution 17,500 CellsConductivity zones 6Recharge zones 3Storage zones 2Parameter distribution UniformParameter values Default 10%

Borehole location 457750, 339150Pump rate 1000 m3/dSimulation type Monte CarloSimulations 200

Fig. 9. Borehole case study/CZPM output.



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BOS provides a reasonable guide on which to base aninitial decision.

The sample simulation presented is intended to pro-

vide an insight into the BOS process and a sense of theresults that can be produced. It is recommended that

there are various applications of BOS that may be of

interest to water companies and other stakeholders.

The first is to identify potential locations for new bore-holes in the urban area using multiple location con-taminant predictions. This is considered the most

powerful and useful application of BOS as the results

of undertaking simulations for boreholes arranged in a

regular grid pattern over the urban area can be used

to build up a map indicating the predicted spatialdistribution of the chosen contaminant in abstracted

groundwater at a specified time. Such a case study has

been undertaken in the Nottingham urban aquifer

region where 1353 potential boreholes located at 100 m

intervals over a 4000 m by 3200 m grid covering

Nottingham city centre were simulated for a variety ofcontaminants. The results of this exercise will be pre-

sented in a later paper.Following the identification of low risk locations for

potential new boreholes using the above method, BOS

can then be used to analyse these sites in more detailusing single location contaminant predictions. Thevalue of this type of analysis is to conduct multiple

simulations at the desired location over different analy-sis years to predict contaminant concentrations over

time. This process can further aid the decision makingprocess by identifying whether the proposed borehole

location has the potential to become polluted in thefuture.

However, there are limitations to BOS, with variousassumptions being made throughout the process. In the

CZPM module, the calculation of the probabilisticcatchment zones under steady state conditions assumesthat all of the abstraction points have been present and

operating at the same rate throughout the duration ofthe model period. The LM module assumes that all of

the historical land-uses within the selected catchmentzone can be identified. However the land-use datasets

can never be entirely comprehensive and therefore large

numbers of potential pollution sources may be missed.The main assumption of the PRM is that all of theindustries associated with a selected contaminant arecontinuous sources of contamination. BOS does not

incorporate the concept of single pollution events such

Table 6Borehole case study/LM parameters


Probability contours 50, 40, 30, 20 and 10%Probability catchment 10%Land-use years 1901, 1920, 1939, 1955, 1974 and 1991Catchment industries 155

Associatedcontaminants

Benzene, Ethylbenzene, MTBE,Napthalene, Phenol, Tetrachloroethene,Toluene, Trichloroethene and Xylene

Fig. 10. Borehole case study/LM output.

Table 7Borehole case study/PRM parameters


Contaminant Tetrachloroethene (PCE)Analysis year 1996Simulation type Monte CarloSimulations 1000Total industries 155Potential sources 9150 Percentile 48.82 lg/l

Fig. 11. Borehole case study/PRM output.



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as spillages or leaks. Thus, each industry contributes acontaminant flux that is a probability function of theindustry area and the recharge rate at that particularlocation. The PRM also assumes that there is no tran-sient movement of existing contaminant plumes intothe new borehole catchment area.

The major difficulty anticipated in the application ofBOS to real world situations is the volume of data andthe amount of time required to be invested in gatheringthis information into the modular formats. The CZPMmodular component needs a groundwater flow modelof the urban area under investigation to be constructedand calibrated. The land-use databases and associatedshapefiles are at best difficult and time consuming toconstruct due to the historical nature of the majority ofthe data. Indeed, the LM module used in the Notting-ham case study in this paper took around two years toconstruct.

6. Conclusions

The BOS application is a potentially powerful tool inmaking decisions on the best use of urban groundwaterin uncertain conditions. Seamlessly linking uncertaintybased borehole catchment analysis, comprehensiveland-use and contaminant information and a probabil-istic pollution risk model under a single user interfacein the ArcView GIS environment, the application isable to predict multiple contaminant concentrations ata user specified borehole location within an urban area.BOS is designed to be completely portable and there-

fore any location can be analysed given the appropriateunderlying modular components.Main recommendations for the further development

of BOS would include the development of the CZPMas a closely coupled module. This would remove theneed for information transfer through data files, there-fore decreasing the programming requirement andincreasing the speed. An increase in the resolution ofthe CZPM would also be beneficial particularly at lowpumping rates where small catchment zones are pro-duced. In these cases the accuracy of the catchmentzone can be currently called into question. Furtherexpansion of the historical LM database would be

valuable, although as mentioned above the benefitsmay be small when the time taken to collect the data istaken into consideration.

A major consideration for any future development ofBOS concerns the application development platform.The future of ArcView GIS and the avenue program-ming language is in some doubt at the moment due toESRIs development of a new suite of GIS productsbased on Microsoft Component Object Model (COM)technology. Therefore the application may have to beported to the new platform to receive continuingsupport.

Acknowledgements

The authors gratefully acknowledge the Engineeringand Physical Sciences Research Council (EPSRC) forfunding this work as part of the Protection and Man-agement of Groundwater in the Urban Environmentproject. The authors would also like to thank DonMorley of the Nottinghamshire Industrial ArchaeologySociety (NIAS) for collecting the Nottingham land-usedata, Charles Vulliamy of the University of Sheffieldfor digitising the land-use and other topographicaldatasets, Paul Dewsbury of the University of Sheffieldfor completing the quality assurance on the land-usedatasets and Nottingham Library for patiently provid-ing the historical maps used for the land-use analysis.

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