Modelling of recharge and pollutant fluxes to urban groundwaters

www.elsevier.com/locate/scitotenv

Science of the Total Environm

Modelling of recharge and pollutant fluxes to urban groundwaters

Abraham Thomas a, John Tellam b,*

a Department of Earth Sciences, University of the Western Cape, P Bag X17, Bellville 7535, Cape Town, South Africab Hydrogeology Research Group, Earth Sciences, School of Geography, Earth and Environmental Sciences, University of Birmingham,

Birmingham B15 2TT, UK

Available online 1 December 2005

Abstract

Urban groundwater resources are of considerable importance to the long-term viability of many cities world-wide, yet prediction

of the quantity and quality of recharge is only rarely attempted at anything other than a very basic level. This paper describes the

development of UGIf, a simple model written within a GIS, designed to provide estimates of spatially distributed recharge and

recharge water quality in unconfined but covered aquifers. The following processes (with their calculation method indicated) are

included: runoff and interception (curve number method); evapotranspiration (Penman–Grindley); interflow (empirical index

approach); volatilization (Henry’s law); sorption (distribution coefficient); and degradation (first order decay). The input data

required are: meteorological data, landuse/cover map with event mean concentration attributes, geological maps with hydraulic and

geochemical attributes, and topographic and water table elevation data in grid form. Standard outputs include distributions of:

surface runoff, infiltration, potential recharge, ground level slope, interflow, actual recharge, pollutant fluxes in surface runoff, travel

times of each pollutant through the unsaturated zone, and the pollutant fluxes and concentrations at the water table. The process of

validation has commenced with a study of the Triassic Sandstone aquifer underlying Birmingham, UK. UGIf predicts a similar

average recharge rate for the aquifer as previous groundwater flow modelling studies, but with significantly more spatial detail: in

particular the results indicate that recharge through paved areas may be more important than previously thought. The results also

highlight the need for more knowledge/data on the following: runoff estimation; interflow (including the effects of lateral flow and

channelling on flow times and therefore chemistry); evapotranspiration in paved areas; the nature of unsaturated zone flow below

paved areas; and the role of the pipe network. Although considerably more verification is needed, UGIf shows promise for use: in

providing input for regional groundwater solute transport models; in identifying gaps in knowledge and data; in determining which

processes are the most important influences on urban groundwater quantity and quality; in evaluating existing recharge models; in

planning, for example in investigation of the effects of landuse or climate change; and in assessing groundwater vulnerability.

D 2005 Elsevier B.V. All rights reserved.

Keywords: GIS; Urban; Aquifer; Recharge; Pollutants; Groundwater; Birmingham; Urgent

1. Introduction

Urban aquifers are of considerable importance to the

long-term viability of many cities across the world (e.g.,

0048-9697/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.scitotenv.2005.08.050

* Corresponding author. Tel.: +44 121 414 6138; fax: +44 121 414

4942.

E-mail address: [email protected] (J. Tellam).

Chilton, 1997; Howard and Israfilov, 2002). A major

issue is the sustainability of supplies of sufficient quan-

tities of sufficient quality groundwater, given the po-

tentially major effects that urbanization has on rates and

quality of infiltrating water. Despite the importance of

recharge in urban development, research is still at a

relatively early stage, and there are no generally accept-

ed methods for assessing the rates and quality of urban

recharge.

ent 360 (2006) 158–179

A. Thomas, J. Tellam / Science of the Total Environment 360 (2006) 158–179 159

This paper describes the development of a computer

model, UGIf, for use in urban recharge and pollution

studies. It is intended that the model be appropriate for

the following purposes:

(i) to provide input for regional groundwater solute

transport models;

(ii) to identify gaps in knowledge and data;

(iii) to determine which processes are the most im-

portant for influencing urban groundwater quan-

tity and quality; and

(iv) to evaluate current recharge models.

Once developed, the code could be used as a plan-

ning tool to investigate the effects, for example, of

landuse or climate change, and as a tool for assessing

groundwater vulnerability.

For most urban areas, a central issue is the hetero-

geneity of sources, and hence the model has been

developed using a GIS. The first part of this paper

describes the model design. The process of

dverificationT has begun, using data for 1980–2000

from the Birmingham urban Triassic Sandstone aquifer

in the UK: this work is reported in the second half of

the paper.

Land UseClassification

LeakageRecharge

Source

StandardSoil Moisture

Balance

*1

PotentialRecharge fromeach Land Use

Class

Potential RechargeMap

Interflow Indices

ActualRecharge

Inter Flow Indices

Drift Map

Drift Reactio

Drift Classes

Drift Reactio

Land Use Map

ChemicalData

Fig. 1. The structure of UGIf (d

2. Overview of model

The model has been developed for the case of an

unconfined aquifer overlain by superficial deposits of

various permeabilities. It deals with non-point source

pollution only, though related models have been devel-

oped to deal with point source pollution (petrol stations,

including non-aqueous phase liquids, and sewers)

(Thomas, 2001). The main calculations evaluate re-

charge and pollutant flux rates at the water table: the

latter include the effects of sorption and first order deg-

radation. The main data sources are: geological maps;

ground level and groundwater level maps; land cover

maps; and meteorological data. The geological maps

have associated attributes of hydraulic and chemical

properties, and the land cover maps associated attributes

of hydraulic properties and runoff water quality data.

The basic structure of the model is shown in Fig. 1.

A landuse/land cover classification allows the produc-

tion of a land cover map. Each land cover class is

assigned attributes relating to permeability/runoff and

water quality. With meteorological data and the land

cover-related runoff characteristics, an estimate of

dpotential rechargeT for each of the landuse classes

can be made, where potential recharge is defined here

*1 Rivers /CanalsMains / SewersSeptic TanksLandfill LeachateFuel Tank Spillage

*2 HorticultureIndustrial Landfills/ DumpsDomesticRoadRiver /CanalSewer / Mains

Attribute Table Dataand /or Calculation

Map*2

n

nPotential Mass

Flux

Mass FluxIn Recharge

Pollutant Mass Flux From Drift

ReactionTerm

ChemicalConcentration

in each Land use

rift=superficial deposits).

A. Thomas, J. Tellam / Science of the Total Environment 360 (2006) 158–179160

as dactual rechargeT (i.e., the water reaching the water

table) plus interflow. An estimate of potential mass flux

(i.e., flux before interflow, evapotranspiration, and re-

action have been taken into account) at the water table

can also be made using runoff water quality data asso-

ciated with the land cover classes. Interflow is estimat-

ed from the geology maps and used to convert the

potential recharge estimates into dactual rechargeT esti-mates. Time taken to cross the unsaturated zone can be

estimated using the actual recharge estimates, hydraulic

properties related to the geological units, (reversible)

sorption properties related to each geological unit, and

unsaturated zone thickness as calculated from land

surface and water table maps. Using pollutant-related

reaction properties and the estimates of times taken to

cross the unsaturated zone, the decay of degrading

pollutants can be calculated, and thence the pollutant

mass flux at the water table. Solute concentrations in

recharge waters, corrected for evapotranspiration where

necessary, are also calculated.

Although the model is thus not very sophisticated, it

does take into account the principal processes involved,

and, as it is incorporated in a GIS, it allows the com-

plexities of spatial heterogeneity to be investigated. A

major limitation is in the way that time is dealt with. It is

assumed that landuse and landuse-related properties do

not vary within the dtime-sliceT or period being consid-

ered by the model. Daily recharge estimations are un-

dertaken, and summed over the user-specified period.

Within this period, steady-state conditions are assumed

for the movement of water and solutes through the

unsaturated zone. Thus individual recharge pulses are

not tracked: residence time in the unsaturated zone is

calculated on the basis of the averaged recharge rate, but

it is only used, with a delay arising from any sorption, to

estimate degradation/decay of the pollutant concentra-

tion. Without incurring very considerable computer run

times, it would be difficult to track individual recharge

pulses simultaneously. Another weakness is the lack of

feedback between water table elevation and recharge

rate: this could conceivably be added by iteration with

a regional groundwater flow model, though again there

are computer run time implications.

In many cities, recharge from water supply networks

is significant. This recharge source is not included in

the model described here, but is dealt with by a separate

code as described by Thomas (2001).

3. Code and code organization

The model has been developed using ArcView GIS

version 3.2, with Spatial Analyst (Environmental Sys-

tems Research Institute (ESRI), Redlands, USA). It can

be divided into two main sections: the recharge model,

and the non-point source (NPS) pollutant flux model.

The recharge model includes estimation of both direct

(rainfall) recharge and indirect (surface water) recharge.

The NPS pollutant flux model is concerned with esti-

mating fluxes and concentrations in recharge (and run-

off), for both unreactive and reactive pollutants.

The input data required are:

(1) land use/land cover map with runoff and water

quality attributes;

(2) geological map of both bedrock and superficial

deposits with hydraulic and chemical property

attributes;

(3) meteorological data;

(4) ground surface elevation data in grid form; and

(5) water table elevation map.

The main outputs from the present model are dis-

tributions of: surface runoff, infiltration, potential re-

charge, ground level slope, interflow, actual recharge,

pollutant fluxes in surface runoff, travel times of each

pollutant through the unsaturated zone, and the pollut-

ant fluxes and concentrations at the water table.

4. Recharge models

4.1. Direct recharge model

4.1.1. Conceptual model and definitions

The conceptual basis for the direct recharge model is

shown in Fig. 2. The term dinitial lossT signifies inter-cepted rainfall which ultimately does not infiltrate (veg-

etation and building interception/depression or

detention storage). Formal definitions of infiltration,

potential recharge, and actual recharge as used here are:

Infiltration ¼ Rainfall � Initial Losses� Runoff ð1Þ

Potential Recharge

¼ Infiltration� Actual Evapotranspiration ð2Þ

Actual Recharge ¼ Potential Recharge� Interflow

ð3Þ

where actual evapotranspiration is the evapotranspira-

tion taking into account soil moisture availability.

4.1.2. Infiltration

Infiltration requires estimates of rainfall, initial

losses, and runoff (Eq. (1)). Rainfall estimates are

Initial Abstraction (Ia)

Precipitation

Infiltration

Runoff

Interflow

Recharge

Ia

Ia

Fig. 2. The conceptual model for the direct recharge calculations.


assumed to be available, but estimating runoff and

initial losses is less straightforward.

After considering various available methods for run-

off estimation, the United States Department of Agri-

culture Soil Conservation Service (SCS, now known as

the Natural Resources Conservation Service, or NRCS)

Curve Number (CN) method was chosen. This empir-

ical method is applicable in situations in which daily

rainfall is available (US EPA, 1998b). It is simple,

requires few input parameters, and has been widely

applied in the field, including in urban catchments

(US EPA, 1998a). However, it is accepted that the

method, especially in the form applied here, is a con-

siderable over-simplification of a complex set of pro-

cesses, and further work will be necessary to justify its

use and, when applied, parameter assignments. Never-

theless, it will suffice in the present context as an

example runoff-estimating formulation.

The method estimates surface runoff using:

Q ¼ P � Iað Þ2

P � Iað Þ þ Sð4Þ

in which

Q total rainfall excess (runoff) for a storm event

];
[L
P total rainfall for a storm event [L];

Ia initial loss [L];

S potential maximum retention capacity of soil at

beginning of storm (i.e., maximum amount of

water that will be absorbed after runoff begins)

[L].

S, also called the retention parameter, represents

how easy it is for the water to runoff, and is estimated

using a dcurve numberT (CN). A curve number is a

numerical description of how low the permeability of

the land surface in a catchment is. It varies from 0

(100% rainfall infiltration) to 100 (0% infiltration). S

is related to CN by:

S ¼ 1000

CN� 10 ð5Þ

CN is determined by several factors. The most im-

portant are the hydrologic soil group (HSG), the ground

cover type, ground treatment, the hydrological condi-

tion of the ground, the antecedent runoff condition

(ARC), and whether low permeability areas are

connected directly to drainage systems, or whether

they first discharge to a permeable area before entering

the drainage system. HSG is particularly important in

determining the runoff curve number. Soils are gener-

ally classified into four HSGs, named A, B, C, and D,

according to how well the soil absorbs water after a

period of prolonged wetting. In the current model,

HSGs are assigned to each of the geological units:

this is obviously a simplification, and if local data on

soil properties exist, they should be used in preference.

The resulting HSG theme is combined with the land

cover map to produce a map showing areas under

various landuses on different HSGs. A curve number

value is assigned for each unit of this map, which leads

to the preparation a runoff curve number map. Further

details are given in US EPA (1998a).

The term dinitial lossT incorporates rainfall loss dueto interception, depression, and detention storage. The

value of Ia depends greatly on the vegetation or other

(e.g., paved) cover types, the kind of soil (HSG, its

treatment, and hydrologic condition), and the anteced-

ent soil moisture of the area being studied. For a given

drainage basin, the values of Ia are highly variable.

Ideally the values of the parameter Ia should be evalu-

ated with field data for each specific site.

4.1.3. Potential recharge

For non-vegetated areas, the potential recharge is

estimated using Eqs. (1) and (2), with (post infiltration)

evapotranspiration set to zero, i.e.:

Potential Recharge non� vegetated areasð Þ

¼ Rainfall � Initial Loss� Runoff ð6Þ

This assumes that once into the ground, the water is

unlikely to be removed by evapotranspiration. This may

not be true where roads are tree-lined.


In vegetated areas, the calculation procedure takes

into account the soil moisture deficit parameter and

actual evapotranspiration. When the soil moisture def-

icit (SMD) (i.e., water required to bring the soil to

maximum water holding capacity) at the end of the

day is greater than zero,

Potential Recharge ¼ 0 ð7Þ

When the soil moisture deficit at the end of the day

is zero:

Potential Recharge vegetated areasð Þ¼ Rainfall � Initial Loss� Runof f

� Actual Evapotranspiration ð8Þ

For convenience in the current version of the

model, actual evapotranspiration is taken from the

UK Meteorological Office’s MORECS (Meteorologi-

cal Office Rainfall and Evaporation Calculation Sys-

tem) data set.

4.1.4. Actual recharge

Actual recharge is calculated from potential re-

charge by subtracting interflow (Eq. (3)). Interflow

depends largely on the following characteristics:

slope, porosity, storage capacity, HSG, average mois-

ture content, effective permeability, permeability anisot-

ropy, and lateral continuity of a perching horizon.

Given the complexity of the process, rigorous calcula-

tion of interflow was not attempted. Instead, it was

decided to develop a simple model based on indices

reflecting what were viewed as the four most important

parameters: (i) slope; (ii) specific retention (soil storage

capacity); (iii) permeability anisotropy ratio of the for-

mation (Kh /Kv); and (iv) the presence or absence of

low permeability deposits underlying the more perme-

able deposits at the surface. The relationships are user-

defined, but during the verification study (see below),

the relationships were taken to be linear, with the

indices taking a value between 0 and 1. The fourth

index, the clay presence index, is based on the absence

or presence of a low permeability layer underneath the

outcropping deposits, and its form (sheet or lenses).

When calculating interflow in this model, a weighting

factor is assigned by the user to each of the four

interflow indexes, so that the relative importance of

each can be assigned.

The lateral flow QL is thus estimated using the

following equation.

QL ¼ PR IdFð ÞSL þ IdFð ÞSR þ IdFð ÞKh=Kv þ I dFð ÞCPh i

ð9Þ

where PR=potential recharge, I is the index value (0–

1), and F is the weighting factor (0–1). Subscripts refer

to the four main variables: SL=slope gradient;

SR=specific retention; Kh /Kv=anisotropy ratio; and

CP=clay presence. The sum FSL+FSR+FKh/Kv+FCP is

1. Thus QL=PR if each I value were at a maximum:

alternatively, if clay layering was thought to be the

predominant process, F for CP could be set to 1, the

other weighting factors to 0, and QL would then depend

directly on the presence of clay layers.

The input data for the actual recharge calculation are

elevation grid, geological maps with attributes of specif-

ic retention, permeability anisotropy ratio, and low per-

meability deposit presence, and a potential recharge grid

(preferably having an attribute of landuse types for sum-

marizing interflow based on landuse). All calculations in

this model use grid data. First a slope map is generated

from the elevation grid theme. The potential recharge is

also input as a grid. The interflow indices grids are

generated from the respective attribute values of the

vector format geological map. The output from this

model is a map of interflow rates resulting from recharge

at each pixel. As interflow is not tracked from one cell to

the next, but assumed to discharge eventually into the

drainage system, it is very important to check the

summed predicted interflow against whatever drainage

flow data are available. A priority in future versions of

the model is to address the routing of interflow (and

surface runoff): this will make the model substantially

larger. The actual direct recharge is calculated finally by

using the potential recharge grid and the interflow grid.

4.2. Indirect recharge model

In the current model, recharge from surface water

bodies is assumed to be at a constant rate. It would be

simple to include a conductance term, but until the model

is connected to a regional groundwater flow model, the

non-linear connection between groundwater heads and

surface water recharge cannot be included properly.

4.3. Non-point source pollution flux model

4.3.1. Flux rates for non-reactive pollutants in surface

runoff

The most common method of approximating NPS

pollution uses long-term average contaminant loadings

for common land uses. An early user of this approach

was the US National Urban Runoff Program (US EPA,

1983), and it has been followed in many other

countries. In this approach, Event Mean Concentrations

are defined for each landuse. These EMCs are assumed


to be determined only by the landuse, and to remain

constant independent of the duration and intensity of

the rainfall events (Naranjo, 1998). EMC values are

available in the literature (Lopes and Dionne, 1998;

Delzer et al., 1996; Shepp, 1996). The EMC approach

is simplistic, but until quantitative relationships be-

tween rainfall quantity and quality properties become

available for urban areas, there is little point in making

the model more sophisticated.

The input data for the calculation of pollutant flux

rates in runoff are therefore: (a) the landuse grid; (b) the

grid of average annual runoff volume calculated by the

recharge model; and (c) the associated EMC values for

each landuse for each of the selected pollutants. An

Avenue script was written linking the EMC value of

various pollutants to the land use types. The EMC grid

is then multiplied by the grid of average annual rainfall

runoff. The result is the annual surface runoff loading

for the NPS pollutant in each grid cell, i.e.:

Load M=T½ � ¼ Flow L3=T�4Concentration M=L3� ��

ð10Þ

4.3.2. Flux rates for non-reactive pollutants at the

water table

EMC values can also be used for estimating the NPS

pollutant fluxes in the infiltrating water (=rainfall� ini-

initial loss� runoff):

Pollutant flux for non-reacting solutes in inf iltratingwater

M= L2T� ��

¼ EMC M=L3� �

� Infiltration rate L=T½ �ð11Þ

The infiltrating water is subject to evaporative loss

across the soil–air interface, and hence the concentra-

tion of the recharge water will be higher than that of the

infiltrated water:

Rechargeconcentration for non-reacting solutes

½M=L3� ¼ Pollutant flux in infiltratingwater

M= L2T� ��

=Rechargerate L=T½ � ð12Þ

4.3.3. Flux rates for reactive pollutants at the water

table

4.3.3.1. Outline of calculation. As they pass through

the unsaturated zone, pollutants in the infiltrating re-

charge water may be subjected to various processes

including sorption, volatilization, and biodegradation.

In the current model, volatilization, linear sorption and

first order decay are taken into account. Pollutant fluxes

are estimated through four stages, viz.:

(1) Estimation of volumetric water content in the

unsaturated zone;

(2) Calculation of soil–water partitioning coefficients;

(3) Calculation of retardation factors; and

(4) Calculation of concentration and mass fluxes at

the water table.

4.3.3.2. Estimation of volumetric water content. The

volumetric water content in the unsaturated zone, hw, is

calculated using the method of Clapp and Hornberger

(1978):

hw ¼ hsVd

Ks

� �1= 2bþ3ð Þð13Þ

where hs is the saturated water content of the soil (total

porosity), Vd is the recharge rate, Ks is the saturated

hydraulic conductivity at the saturated water content hs,

and b is the Clapp and Hornberger constant for the soil

[–].

4.3.3.3. Velocity of aqueous phase contaminant migra-

tion. The average linear velocity, vi, of a pollutant

subject to linear sorption as it passes through the un-

saturated zone is given by:

vi ¼q

hRf

ð14Þ

where q is the recharge rate [L/T], and Rf is the

retardation factor. The retardation factor is calculated

using:

Rf ¼ 1þ qKd þ hs � hð ÞKHð Þ=h ð15Þ

where q is the dry bulk density of the soil and KH is the

Henry’s law constant (gas concentration/aqueous con-

centration, [–]).

The soil–water partitioning coefficient, Kd, is esti-

mated using:

Kd ¼ Koc foc ð16Þwhere Kd=soil–water partitioning coefficient, Koc=or-

ganic carbon partitioning coefficient, foc= fraction of

organic carbon within the soil matrix.

4.3.3.4. Calculating the time to reach the water

table. The leading edge of the contaminated pulse

will reach the water table at a time T that can be calcu-

lated using:

T ¼ ZhRf

qð17Þ


where Z is the thickness of the unsaturated zone and q is

the recharge rate.

4.3.3.5. Estimation of the effect of degradation. The

model includes a first order rate equation to describe

removal of a contaminant:

C2 ¼ C1 exp � Tkð Þ ð18Þ

where C1 is the initial concentration of chemical ap-

plied at the ground surface, C2 is the concentration of

chemical at the water table, T is the travel time through

the unsaturated zone, and k is the first order degrada-

tion rate coefficient for the chemical.

Substituting the expression for travel time in the

above equation, and rewriting in terms of half-life

(T1 / 2), the concentration at the water table becomes:

C2 ¼ C1 exp� 0:693RfZh

qT1=2

��ð19Þ

The pollutant mass flux to the water table is given

by:

Pollutant Flux ¼ Net Recharge Rate

� Concentration of Pollutant

Reaching the Water Table

¼ qC2 ð20Þ

i.e.,

Recharge Pollutant Flux

¼ qC1exp� 0:693Rf Zh

qT1=2

��ð21Þ

4.4. Code validation

The code was checked wherever possible against

manual calculations representing a range of conditions.

Details are given by Thomas (2001).

5. Preliminary verification

5.1. Introduction

Verification of the model is not straightforward. Ulti-

mately, it is intended that the results be compared first

with field data on surface water flows and quality, and

subsequently, once the model has been extended to other

periods and incorporated into a regional groundwater

solute transport model, with historical groundwater

levels and quality. However, before this is attempted,

the model has been applied to the case of the unconfined

portion of the Birmingham Triassic Sandstone aquifer as

the first step in verification. The main purpose is to

determine how predictions of total recharge and runoff

compare with other estimates, and whether the dstyleT ofthe results makes hydrogeological sense. To this end,

input parameter values have been set a priori rather than

being calibrated to produce the dbestT outcome.

5.2. The Birmingham aquifer

Birmingham is located in central England (Fig. 3)

and is England’s second largest city with a population

of around 1.2 million people. It is a major industrial

centre. The unconfined Triassic Sandstone aquifer (Fig.

3) underlies part of the city, and occupies an area of

~111 km2. It was chosen because of the considerable

amount of hydrogeological data available.

The southeastern boundary of the unconfined aquifer

is defined by the Birmingham Fault which downthrows

the aquifer, the Sherwood Sandstone Group, to the

southeast so that it is confined below the Triassic

Mercia Mudstone Group. The western and south-west-

ern boundaries have irregular shapes that correspond to

the limit of the outcrops of sandstones as they thin out

over older (Carboniferous) formations. The northern

boundary of the study area is defined by a groundwater

divide. The sandstones are covered by a lithologically

variable sequence of Quaternary deposits of glacial,

lacustrine, aeolian, and fluvial origin. These deposits

are up to 20 to 30 m thick in places, and can seriously

limit recharge locally. The hydrogeology and contami-

nation of the aquifer has been described by Jackson and

Lloyd (1983), Knipe et al. (1993), Rivett et al. (1990),

Ford et al. (1992), Ford and Tellam (1994), and Shep-

herd et al. (2006-this issue).

5.3. Approach

5.3.1. Outline

The following tasks need to be considered before the

model can be implemented:

! development of a landuse/land cover map;

! collation of geological data;

! assignment of hydrological parameters;

! preparation of meteorological data;

! collation of EMC data; and

! collation of geochemical parameter values.

These tasks are considered in turn in the following

paragraphs. For each variable, a value was chosen

based on literature values or on local data. The model

Fig. 3. The geology of the unconfined Birmingham aquifer area. Digital data from British Geological Survey (2000). The main aquifer, the

Sherwood Sandstone Group, has three units, the Kidderminster, Wildmoor Sandstone, and the Bromsgrove Sandstone Formations. Head =

weathered material redistributed by solifluxion. (For the colour version of the maps in this paper, the reader is referred to the web version of

this article.)


was then run, and the predictions examined to deter-

mine any obvious inconsistencies. The only inconsis-

tency found was that recharge rates for winter were

greater in some paved areas than for some vegetated

areas. As a result, CN values for the industrial, com-

mercial, and transport landuses were all increased by a

small amount (see below for values). No other modifi-

cations were made to the initial parameter values. A 10

m by 10 m grid size has been used throughout.

5.3.2. Landuse/land cover mapping

Although there are existing landuse/land cover maps

for Birmingham, none proved suitable for recharge

estimation purposes, being compiled at an inappropriate

scale, or not including features important to recharge.

Hence a new scheme was set up. Table 1 shows the

landuse classification used.

The classification is an a priori hierarchical one with

three levels. In a priori classification schemes, the land

Table 1

Urban landuse classification scheme

1. Urban built up area and developed land

11. Residential

111. High density residential (Terraced/multi-storied flats)

112. Medium density residential (Semi detached)

113. Low Density Residential (Detached)

12. Commercial/Business and Services

121. Commercial Retail (Supermarkets, Gasoline Stations,

Building Materials etc.)

123. Commercial Wholesale/Warehouses/Depots

124. Institutional (Governmental/Educational/Medical/

Religious)

125. Commercial Services (Finance/Real Estate/Insurance/

Repairs/Automotives)

126. Water Treatment/Sewage Treatment

127. Hotels/Lodging

13. Industrial/Manufacturing

131. Industrial – Heavy (Chemical, Metal, Electrical,

Automotive)

132. Industrial – Medium (Raw material processing and

preparation)

133. Industrial – Light (Food and Drink Processing, Furniture/

Wood Processing)

14. Transportation

141. Railway Transport facilities

142. Road Transport Facilities

143. Air Transport facilities

144. Water Transport Facilities

15. Parking/Car Parks

151. Industrial Car Parks

152. Commercial Car Parks

153. Office Car Parks/Institutional Car parks

16. Roads

161. Motorways

162. A Road

163. B Road

164. Minor Road

17. Pavement/Pedestrian Footpath

171. Pavement Brick

172. Pavement Concrete

173. Pavement Asphalt

18. Derelict Built Up Land (dBrown FieldsT)181. Industrial Derelict Built up Land

182. Commercial Derelict Built up Land

2. Open urban area

21. Recreation

211. Parks/Gardens

212. Golf Courses

213. Playgrounds/Sports fields

214. Zoos

22. Agriculture/Horticulture

221. Pasture Land/Meadow

222. Allotment Gardens

223. Farms

224. Orchards/Vineyards/Nurseries

225. Animal Operations (Horses, Poultry, Livestock)

23. Bare Land

231. Public Open Ground (Hard ground used for cultural/

social purposes)

Table 1 (continued)

1. Urban built up area and developed land

232. Barren land (Sterile, non profitable land with or without

vegetation)

24. Other Open Urban Areas

241. Verges and Lawn

242. Landfill/Waste Dumps

243. Open Derelict Land (Open dBrown FieldsT)244. Cemeteries

245. Construction Sites

3. Shrub /scrub lands

31. Moor/Herbaceous Cover

311. Tall/Short Grass

312. Bracken

313. Grass Heath

32. Shrub Heath

321. Open Shrub Heath Land

322. Dense Shrub Heath Land

33. Scrub Land

331. Deciduous Scrub Land

4. Forest/woodlands

41. Deciduous forest

411. Mature Forest (Closed Canopy Woods)

412. Young Forest/Open Forest (Open Woods/Felled/Degraded)

42. Coniferous forest

421. Mature

422. Young

43. Mixed forest

431. Mature

432. Young

5. Water bodies

51. Inland water bodies

511. River/stream

512. Lakes/pond

52. Artificial water bodies

521. Canal

522. Reservoirs


use/land cover classes are defined prior to the collection

of data, the main advantage being that the classes are

standardized independently of the area to be described

and the means/techniques used in the mapping (De Bie

et al., 1996; AFRICOVER, 1998). Initially, a prelimi-

nary dlevel IT classification system was constructed,

which consisted of five possible major classes: (1)

urban built up areas and developed land; (2) open

urban areas; (3) shrub/scrub land; (4) forest/woodlands;

and (5) water bodies. These broad level I classes are

subdivided further into dlevel IIT and dlevel IIIT classeswhich have direct significance for recharge and pollu-

tion activities.

Using this classification, a landuse map was pre-

pared for the area of the unconfined Birmingham

aquifer for the period 1980 to 2000: twenty landuse

classes from Table 1 were eventually included, the

Fig. 4. Landuse map for the Birmingham unconfined aquifer for 1980–2000. This map is based in part on Ordnance Survey data: n Crown

copyright Ordnance Survey. All rights reserved.


others not being found in significant proportions (Fig.

4). The information sources were: digital topographic

maps (1 :1250 scale, Ordnance Survey (OS)); satellite

imagery (LANDSAT TM images); aerial photographs

(Cities Revealed n orthorectified digital images hav-

ing a resolution of 2 m). The satellite images were

rectified and enhanced using the ArcView Image

Analysis extension, and then used as the basis for

the maps, with the OS data being used to identify

most landuses. Where a feature was not clear on

satellite images or OS maps, visual interpretation of

the aerial photographs was undertaken. In addition,

local knowledge of the area was used where other

sources of information were not adequate. Details are

given by Thomas (2001).

Fig. 5 shows the proportion of land overlying the

Birmingham unconfined aquifer associated with each of

the 20 landuse classes. Residential areas dominate,

covering N50% of the area in aggregate, with commer-

cial, recreation grounds, minor roads, and woodlands

being present in 5% to 10% of the area: all other classes

of land use are present at less than 3%.

0.00

10.00

20.00

30.00

Low Den

sity R

esid

entia

l

High D

ensit

y Res

iden

tial

Comm

ercia

l

Recre

atio

n Gro

und (Gra

ss)

Minor R

oad

Mediu

m D

ensit

y Res

iden

tial

Woodlan

d/Shru

b

Open G

round/G

rass

land

Agricultu

re

Indust

rial

'A' R

oad

Reser

voir/

Lake/P

ond

'B' R

oad

Cemet

ery/G

rave

yard

Railw

ay Y

ard

Motorw

ay

Car P

ark

Canal

River

Transp

ortatio

n

Are

a P

erce

nta

ge

Fig. 5. Landuse in the unconfined area of the Birmingham aquifer, 1980–2000, as a percentage of total land area.


5.3.3. Geological data

ArcView GIS compatible pre-Quaternary geology

and Quaternary geology maps on 1 :10000 scale

were procured from the British Geological Survey

British Geological Survey, 2000). These maps were

reconciled and merged to generate a combined map

with all geological units (Fig. 3). The area of

outcrop of each geology unit was calculated within

the GIS and added to its attribute table. Finally, the

Table 2

The hydraulic and interflow index parameters assigned to each of the geolo

Names of geological

formations

Lithology Total

area

Area %

(km2)

Alluvium Silty clay with sand

and gravel

5.72 5.16

Bromsgrove sandstone

formation

Sandstone 8.25 7.43

Glaciofluvial deposits Sand and gravel 44.49 40.07

Glaciolacustrine deposits Clay and silt 1.33 1.2

Head Clay with rock fragments

and pebbles

1.56 1.4

Hopwas breccia Breccia and sandstone 0.92 0.83

Kidderminster formation Sandstone 22.25 20.04

Lacustrine deposits Clay, silt and sand 0.14 0.13

River terrace deposits,

First terrace

Sand and gravel 3.58 3.23

River Terrace deposits,

Second terrace

Sand and gravel 1.62 1.46

Salop Formation Mudstone 0.44 0.4

Sandy till Sandy, pebbly clay with

lenses of sand and gravel

0.36 0.32

Till Pebbly clay 17.21 15.5

Wildmoor sandstone

Formation

Sandstone 3.16 2.85

Total 111.02 100

[Data from: Allen et al. (1997), Bridge et al. (1997), Freeze and Che

total area of each geological unit was also estimated

(Table 2).

5.3.4. Hydrologic soil group

HSGs are needed to estimate curve number for

runoff estimation. As no soil map was available, it

was decided to use the geology map to prepare a map

of HSG distributions. Table 3 lists the HSG assigned to

each geological unit.

gical units in the study area

Porosity Clapp and Hornberger

constant

Clay

index

Permeability

anisotropy

Specific

retention

(–) (–) (–) Ratio(Kh /Kv) (–)

0.40 1.26 0.8 75 0.35

0.27 2.12 0.5 1.37 0.17

0.35 1.90 0 1.54 0.09

0.40 1.40 0.9 166.36 0.34

0.42 1.35 0.95 348.84 0.39

0.24 2.50 0.05 2 0.04

0.25 2.38 0.3 1.81 0.1

– – 0.8 95.19 0.29

0.35 1.80 0.1 4.26 0.09

0.35 1.80 0.1 4.26 0.09

– – 1 4.92 0.28

0.25 1.40 0.8 90.19 0.09

0.25 1.40 0.95 184.84 0.26

0.26 2.21 0.3 1.88 0.14

rry (1979), Shepherd (2003), and Clapp and Hornberger (1978)].

Table 3

Lithological and textural descriptions of geological units of the Bir-

mingham area

Name of formation Lithology Hydrologic

soil group

Glaciofluvial deposits Yellow and orange sand

and gravel

A


first terrace

Mainly sand and gravel A A


second terrace

Mainly sand and gravel A

Bromsgrove sandstone

formation

Sandstone, red brown,

micaceous, pebbly in

part (interbedded with

mudstone in upper part

A

Wildmoor sandstone

formation

Sandstone, orange-red,

feldspathic, with sparse

thin mudstone beds

A

Kidderminster formation Sandstone, red, and

pebbly sandstone,

pebble–cobble

conglomerate in lower

part

A

Hopwas breccia Breccia and pebbly

sandstone

A

Alluvium Silty clay with sand and

gravel and, locally, peat

B

Glaciolacustrine deposits Yellow to brown

stoneless clay and silt

C

Sandy till Brown or red brown,

sandy, pebbly clay with

lenses of sand and gravel

C

Head Yellow, red or brown

clay with rock fragments

and pebbles

D

Lacustrine Deposits Clay, silt and sand D

Till Brown or red brown

pebbly clay

D

Salop Formation Mudstone, red, and

sandstone, red brown

with subordinate

lenticular beds of

conglomerate and thin

beds of limestone

D

Table 4

Values of initial loss for each landuse/land cover class in Birmingham

Land use/land cover Initial surface loss (mm)

Commercial/business 3.0

Industrial 3.0

High density residential 4.1

Medium density residential 4.5

Low density residential 5.1

Car parks 2.0

Transportation 3.0

Recreation ground 5.5

Agricultural/horticultural/farm 5.5

Woodland/shrub 8.0

Cemetery/graveyard 5.5

Open ground/grassland 5.5

Reservoir/lake/pond 0

River 0

Canal 0

Motorway 2.0

dAT Road 2.0

dBT Road 2.0

Minor road 2.0

Railway yard 2.5

Area-weighted average 4.4

NB: The term dinitial surface lossT incorporates rainfall loss due to

interception, depression and detention storage.


5.3.5. Interflow indices

There are four interflow indices, requiring data on:

slope angle, clay presence below surface deposits,

permeability anisotropy, and specific retention. Slope

angle is calculated by the model from the topograph-

ic elevation data. For each outcropping geological

unit, a clay presence index ranging from zero (no

clay) to one (sheet clay) was assigned, based on

interpretation of local geological information. The

permeability and specific retention data were obtained

from Allen et al. (1997), Bridge et al. (1997), and

Buddemeier (1996). This approach is an approxima-

tion, and values are likely to be altered as new field

data become available.

5.3.6. Initial losses

Since no field data on initial loss in Birmingham

are currently available, a literature survey was carried

out to provide possible values (see, e.g., Browne

(1990) and NCSPA (1999)). The initial loss values

tentatively chosen for the land uses in Birmingham are

shown in Table 4. Ultimately it may be possible to

refine CN and Ia values by testing against field runoff

and groundwater data. For example, Durr (2003) has

recently suggested that runoff is not seen until at least

0.5 to 2 mm rainfall has occurred, and if this were the

case in Birmingham, it is possible that the figures

listed in Table 4 will be a little too large.

5.3.7. Preparation of meteorological data

Daily rainfall and MORECS data for 1980–1999

were procured from the UK Meteorological Office.

Apart from direct use in the recharge calculation, these

data allow assignment of antecedent moisture conditions

for the curve number runoff method. Wet conditions

(antecedent moisture condition III, see below) were

assumed when SMD values were close to zero (0–10

mm) for a period of 10–15 consecutive days; otherwise,

normal conditions (antecedent moisture condition II)

were assumed.

able 6

vent mean concentrations assigned for each landuse class

and use/land cover Nitrate

(mg N/l)

Chloride

(mg/l)

Toluene

(Ag/l)

ommercial/business 1.2 148 2.4

dustrial 1.9 148 1.8

igh density residential 2.1 148 1.8

edium density residential 1.8 50*** 1.8***

ow density residential 1.8 15*** 0.9***

ar parks 0.8* 42* 2.4***

ransportation 0.8 148*** 2.4***

ecreation ground 0.9 0.9** 0

gricultural/horticultural/farm 4.1 0.9** 0

oodland/shrub 0.8 0.9** 0

emetery/graveyard 1.2 0.9** 0

pen ground/grassland 1.8 0.9** 0

eservoir/lake/pond 0.6 0.9** 0

iver 0.6 0.9*** 0

anal 1.5*** 10*** 0

otorway 0.8 149*** 1.2***

T road 0.5* 149* 1.2***

T road 0.5* 125* 1.2***

inor road 0.6* 15.4* 0.6***

ailway Yard 0.2 0.9 0.1***

* Runoff chemistry data obtained from Harris, 2000, Unpub-

shed data and Antonio, 1999, Unpublished data.

** Chloride measured in rainfall samples collected at Winter-

ourne, Birmingham University.

*** Estimates based on Birmingham University campus measure-

ents and literature data.


5.3.8. NRCS curve numbers

The curve numbers for normal antecedent moisture

conditions (AMC II) are calculated as explained

above. For wet conditions (AMC III), curve numbers

are calculated by (US EPA, 1998a):

CN IIIð Þ ¼ 23CN IIð Þ10þ 0:13CN IIð Þ ð22Þ

Using this relationship, the NRCS curve numb-

ers for Birmingham landuse classes during wet

conditions were calculated, and these are listed in

Table 5.

5.3.9. Surface water infiltration rates

The infiltration rates for surface waters were set at

2 mm/day for reservoirs and 12.5 mm/day for unlined

canals. The latter figure was obtained from informa-

tion supplied by British Waterways. The River Tame

is effluent (Ellis, 2003).

5.3.10. Collation of geochemical parameter values

For this investigation of UGIf, the pollutants chlo-

ride, nitrate, and toluene have been considered. Chlo-

ride will be considered inert. Nitrate will also be

considered inert, and, although this is clearly untrue,

Table 5

NRCS curve numbers for various landuse/land cover types in the Birmingh

Land use/land cover Average % of

impermeable area

Hydrologic condition a

ground cover

Commercial 90

Industrial 90

High density residential 75

Medium density residential 65

Low density residential 45–50

Car parks 99

Transportation 90

Recreation ground 5 Good*; N75% Ground

Agricultural field 4 Good*; N75% Ground

Woodland/Scrub 2 Good*; 50% Woods an

Cemetery/graveyard 25 Poor*; Grass b50%

Open ground/grassland 2 Good*; Grass N75%

Reservoir/lake/pond

River

Canal

Motorway 99

A road (paved) 99

B road (paved) 99

Minor road (paved) 99

Railway yard 35– 40

Poor*: factors impair infiltration and tend to increase runoff. Good*: facto

decrease runoff.

T

E

L

C

In

H

M

L

C

T

R

A

W

C

O

R

R

C

M

dAdBM

R

li

b

m

am area (Antecedent Moisture Condition III)

nd vegetative Curve numbers for hydrologic soil group

A B C D

98 99 99 99

98 99 99 99

94 97 98 98

92 95 97 97

86 92 95 96

99 99 99 99

98 99 99 99

Cover 59 78 88 91

Cover 59 78 88 91

d 50% Grass 56 78 86 91

69 84 91 93

59 78 88 91

100 100 100 100

100 100 100 100

100 100 100 100

99 99 99 99

99 99 99 99

99 99 99 99

99 99 99 99

73 85 91 94

rs encourage average and better than average infiltration and tend to

0200400600800

100012001400160018002000

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Year

Rai

nfa

ll (m

m/y

)

0

20

40

60

80

100

120

Rec

har

ge

(mm

/y)

Rainfall

Recharge

Fig. 6. Calculated actual recharge as a function of time in the unconfined area of the Birmingham aquifer.

0.020.040.060.080.0

100.0120.0140.0

1975 1980 1985 1990 1995 2000Year

Rec

har

ge

(mm

/y)

Commercial

Industrial

0.0

50.0

100.0

150.0

200.0

1975 1980 1985 1990 1995 2000

Year

Rec

har

ge

(mm

/y)

Car Park

Transport

0.050.0

100.0150.0200.0250.0300.0350.0400.0450.0

1975 1980 1985 1990 1995 2000

Year

Rec

har

ge

(mm

/y)

Motorway

'A' Road

'B' Road

Minor Road

Railway Yard

0.0

20.0

40.0

60.0

80.0

100.0

1975 1980 1985 1990 1995 2000Year

Rec

har

ge

(mm

/y)

HDRMDRLDR

0.0

20.0

40.0

60.0

80.0

100.0

120.0

1975 1980 1985 1990 1995 2000

Year

Rec

har

ge

(mm

/y)

Recreation Ground (Grass)

Agriculture

Woodland/Shrub

Cemetery/Graveyard

Open Ground/Grassland

0.0500.0

1000.01500.02000.02500.03000.03500.04000.04500.0

1975 1980 1985 1990 1995 2000

Year

Rec

har

ge

(mm

/y)

Reservoir/Lake/Pond

River

Canal

Fig. 7. Variation of calculated recharge for the unconfined area of the Birmingham aquifer as a function of landuse type over the time period 1980–

2000.



the results allow the influence of source on recharge

concentrations to be investigated. Toluene will be con-

sidered to be sorbing and degrading.

EMC data are not available for all the landuse

classes in the Birmingham area. However, some unpub-

lished data are available (Antonio, 1999; Harris, 2000)

that provide some information on concentrations of

nitrate and chloride. Using the latter information, to-

gether with other data from the literature, the EMC

values listed in Table 6 were assembled.

Fig. 8. Calculated recharge in the unconfined area of the Birmingham aqu

Ordnance Survey data: n Crown copyright Ordnance Survey. All rights res

The Henry’s Law constant for toluene was taken

as 643 kPa m3 mol�1, and the decay half-life as 28

days. The Kd values were calculated using the focvalues (Shepherd, 2003; Shepherd et al., 2006-this

issue) listed in Table 2. Table 2 also lists the po-

rosity and Clapp and Hornberger constants used. The

latter were estimated from the textural descriptions

of each of the geological units, but some measure-

ments of the former are available (e.g., Allen et al.,

1997).

ifer for autumn and winter 1980 (cm). This map is based in part on

erved.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

LDR

Min R

dHDR

ComCan

alRec

rLak

eMDR In

dA R

d

% M

ean

To

tal P

ot.

Rec

har

ge

0.02.04.06.08.0

10.012.014.0

Woods

Open G

rdAgric

Railway

B Rd

Motorw

ay

Car P

k

Cemet

ery

Transp

% M

ean

To

tal P

ot.

Rec

har

ge

Fig. 9. Calculated recharge as a function of landuse in the Birming

ham unconfined aquifer.


6. Model results

6.1. Recharge

Due to limitations on the size of ArcView grid files,

only ~6 months of daily data could be run at one time.

Hence the data had to be split for calculation purposes.

This was usually done by considering, for each year,

data for winter and autumn, and data for spring and

summer. Winter and autumn were defined on the basis

that SMDb10 mm. For example, for 1980, the first run

would comprise the data for January to March and

October to December, and the second run would com-

prise the data for April to September. Some of the data

sets had to be further split to conform to the ArcView

size limitations. In total 51 model runs were needed to

cover the twenty year period investigated. The recharge

model runs generate grids of potential recharge, inter-

flow, and actual recharge. Each direct recharge calcu-

lation took (in 2001) ~2.5 h using a Pentium III 450

MHz 128 Mb RAM PC (with solute transport, 6–7 h).

The total predicted actual recharge to the aquifer is

illustrated in Fig. 6, and predicted recharge for each

landuse class in Fig. 7. The highest recharge rate cal-

culated was 113 mm/year or 34 ML/day for 1993. The

total rainfall in this year was 755 mm. Examination of

rainfall data shows that the highest rainfall recorded

during the 20 years was 820 mm in the year 1998.

However, the predicted actual recharge in this year (33

ML/day or 110 mm/year) is slightly less than that in

1993. This is explained by higher evapotranspiration

rates and SMD values in 1998. The lowest predicted

recharge rate is 60 mm/year (18 ML/day) for 1996. The

total rainfall (559 mm) was also the lowest over the 20

year period.

In his regional groundwater flow modelling study,

Greswell (1992) estimated recharge rates for 1980–

1989 to be 31.2 ML/day on average excluding leakage

from water supply pipelines (mains), and excluding

recharge from surface water bodies (summarized in

Knipe et al., 1993). Including surface water recharge,

Greswell’s (1992) estimate is 34.9 ML/day. For 1980,

the equivalent figures as predicted by UGIf are 28.6

ML/day and 33.5 ML/day (94 and 110 mm/year),

which agrees well. Using the results of a baseflow

analysis, Ellis (2003) estimates that recharge within

the Tame catchment is ~28% of the rainfall, i.e. 196

to 224 mm/year (60 to 68 ML/day) for rainfall of 700 to

800 mm/year. This figure includes water supply (mains)

leakage, and contributions from areas other than the

sandstone. Using the estimate of Knipe et al. (1993) of

8.5 ML/day (28 mm/year) for water supply (mains)

leakage within the sandstone area of the catchment,

and assuming that the part of the Tame catchment

which overlies sandstone aquifer is similar to the rest

of the catchment, the recharge estimated for the aquifer

becomes 78 to 106 mm/year (24 to 32 ML/day)

depending on the rainfall. This is probably an underes-

timate, as the baseflow contribution from the sandstone

reach of the Tame may be greater than that from the

non-sandstone part; however, the latter will have some

baseflow from the Carboniferous bedrock, and in addi-

tion does contain disused mine systems and shallow

Quaternary deposits which do contribute significantly

to what would be interpreted as baseflow. Again, the

agreement with the estimate of UGIf is encouraging.

A full comparison with predicted surface water run-

off is rather more complex, and has yet to be carried

out. However, Ellis (2003) estimates that, at mean

flows, non-groundwater inputs to the Tame are ~40 to

50 ML/day, and this compares with the UGIf estimate

of ~40 ML/day.

These comparisons do not constitute a rigorous test

of the model, and the agreement may well be fortuitous.

All that should be concluded is that the uncalibrated

model is producing credible estimates of recharge or

total runoff.

A more interesting exercise is to examine the detail

of the estimates. Fig. 8 shows an example potential

recharge map produced for the 6 months of autumn

and winter 1980. As expected, the predicted pattern is

complex, reflecting both very heterogeneous landuse

-


distributions and geology. Nevertheless, the influences

of the landuse patterns can be discerned, and these

appear therefore to be dominant.

Fig. 9 shows the amounts of total potential re-

charge categorized according to landuse. Much of

the aquifer’s recharge is predicted to come from

areas of low density housing, minor roads, high den-

sity housing, commercial development, and canals,

with relatively little coming from woods, open

ground, agriculture, railways, dBT roads, motorways,

car parks, cemeteries, and transport depots. This is to

be expected given the relative areas of each type of

Fig. 10. Calculated volumes of interflow generated per 100 m2 pixel over

Birmingham aquifer. This map is based in part on Ordnance Survey data: n

land cover (Fig. 5). However, Fig. 7 shows that the

recharge rate for the vegetated areas is less than that

for the paved areas when averaged over a year. One

reason for this is that the vegetated areas in the city

correspond with locations where the geological units

are less permeable (cf. Fig. 3). Another factor arises

through the assumption that the recharge in the paved

areas is not dependent on evapotranspiration. When

recharge occurs in the vegetated areas, i.e., when

SMD~0, the rates are greater than for paved areas.

But when SMDN0 and recharge in the vegetated

areas ceases, recharge continues to occur through

169 days in autumn and winter 1980 for the unconfined area of the

Crown copyright Ordnance Survey. All rights reserved.


the paved areas. The model suggests that this is

enough to make the overall year-averaged recharge

rate greater than that for vegetated areas. In addition,

the lack of dependence of recharge on evapotranspi-

ration in the paved areas makes the variability of

annual recharge rates in the paved areas less than

that in the vegetated areas, as shown in Fig. 7. It

may well be that these model predictions are incorrect

(e.g., because initial losses are underestimated): how-

ever, if true, they have important landuse and water

quality implications, and hence more research is need-

ed especially on the topic of recharge through paved

Fig. 11. Calculated chloride concentrations based on recharge rates for winte

This map is based in part on Ordnance Survey data: n Crown copyright O

areas. For example, is evapotranspiration really not

important? What about moisture re-distribution fol-

lowing infiltration in restricted zones? Does fingered

flow establish itself (e.g., Ritsema, 1999; Sililo and

Tellam, 2000)? What implications are there for pol-

lutant travel times and attenuation?

Sensitivity analysis for the direct recharge calcula-

tion indicates the following relative importance for the

main variables:

Grassed areas with underlying wet sand:

RainfallN Ia, CNH [actual evapotranspirationNClay

indexNStorage indexNSlopeNPermeability anisotropy]

r and autumn 1980 for the unconfined area of the Birmingham aquifer.

rdnance Survey. All rights reserved.


Grassed areas with underlying wet clay:

CNNRainfallNClay indexN IaNPermeability aniso-

tropy NActual evapotranspiration N Storage index H

[Slope]

Commercial areas with underlying wet sand:

CN NRainfallH [Clay indexNStorage indexN IaN

SlopeNPermeability anisotropy]

Commercial areas with underlying wet clay:

C N NRainfallHPermeability anisotropyNStorage

indexH [IaNSlope]

Fig. 12. Calculated nitrate concentrations based on recharge rates for winter


Some variables, those indicated in square brackets

above, have little effect on the recharge estimates in the

case of Birmingham.

Fig. 10 indicates that interflow, as calculated by the

model, is likely to be of most importance in the south-

west of the aquifer area. Overall, interflow is estimated

by the model to be ~10% potential recharge. There are

few data to test these predictions, but the predicted

difference between the southwestern parts of the aquifer

and the rest of the study area is so marked that this

and autumn 1980 for the unconfined area of the Birmingham aquifer.



seems to be a real feature. This conclusion suggests that

the assumption of vertical flow through the unsaturated

zone used when calculating the reactive pollutant fluxes

will, in much of the rest of the aquifer at least, be

reasonable.

6.2. Pollutant fluxes

Fig. 11 shows the predicted concentrations of chlo-

ride in the recharge waters. Concentrations range from

mg/l level to over 100 mg/l, as might be expected given

Fig. 13. Calculated toluene concentrations based on recharge rates for winter


the EMC values used. It is immediately clear that the

distribution of concentrations is complex, with large

local differences. However, the main landuse controls

are also evident, with, for example, the high density/

low density residential areas showing up in the southern

part of the region (cf. Fig. 4). The residence times for

pumped groundwaters in the unconfined part of the

aquifer can be anything up to at least hundreds of

years (Tellam and Thomas, 2002), and this, and the

complexities caused by pumping boreholes, make de-

tailed comparisons inappropriate. However, the range

and autumn 1980 for the unconfined area of the Birmingham aquifer.



of concentrations seen and the scale of variability are

features of the real system: for example, at one single

industrial site, roughly 1 km2 in area, boreholes with a

great range of depths extract water with Cl concentra-

tions ranging from b15 to N100 mg/l (Ford and Tellam,

1994).

Fig. 12 shows the predicted nitrate distributions.

Concentrations between less than a milligram per liter

and a few milligrams per liter are seen. Again a local

scale complexity is seen to be superimposed over a

larger-scale pattern. The concentrations of nitrate are

much less than the maximum measured values, but

these latter are probably remnants of sewage pollution,

much of which may be from times before the city had a

complete sewerage cover.

Fig. 13 shows the predicted toluene concentrations.

It is clear that, for the rather low EMC toluene values

normally presented in the literature, groundwaters are

at little risk. The present model predicts measurable

concentrations only where the water table is close to

ground surface (around the river Tame). Field data do

not yet exist, but would in any case reflect not just

diffuse toluene sources, but point free phase sources

as well.

7. Conclusions

Although there is much further scope to develop and

verify the model, the early results suggest that UGIf

will reproduce city-wide recharge rates which are com-

patible with different approaches adopted in previous

work, and that rates for particular landuses do not

appear to be unreasonable. Further work is required to

test the model predictions against field runoff data, and

ultimately, against groundwater balance and head data.

The latter test would require combining several UGIf

models and a mains water leakage model with a region-

al groundwater flow model.

The model outputs indicate a system which shows

great spatial complexity at small scales, but neverthe-

less still displays a recognizable larger-scale structure,

the latter caused mainly by landuse and geological

distributions. The importance of these larger-scale

structures may explain why the total recharge predicted

by the model is similar to that predicted by previous

work which was based on much coarser spatial aver-

aging: it also suggests that for overall recharge assess-

ment this coarse spatial averaging is satisfactory, but

that it may be much less satisfactory for assessments of

pollutant movement, where small-scale variations are

important. The present results indicate that the detailed

landuse classification might be simplified for recharge

assessment (though the simplification which works best

for recharge quantity estimation may not work best

when attempting to estimate recharge quality). The

prediction that recharge in paved areas might be larger

than commonly considered likely from vegetated areas

may just be an artefact of the input parameters chosen:

however, consideration of the reasons for the prediction

suggests that further research here is needed. The model

suggests that groundwater pollution in this aquifer from

non-point sources of organics such as toluene is unlike-

ly to be important.

Finally, the model has highlighted the need for more

knowledge/data on the following: runoff estimation;

interflow (including the effect of lateral flow and chan-

nelling on flow times and water quality); evapotrans-

piration in paved areas; unsaturated zone flow below

paved areas (fingering/funnelling? moisture redistribu-

tion?); and the role of the pipe network.

Acknowledgments

We would like to thank the Commonwealth Scho-

larships Commission, United Kingdom, the Punjab

Remote Sensing Centre, Ludhiana, India, The British

Council, and the Natural Environment Research

Council (URGENT) for the financial and organiza-

tional support which made this project possible. We

would like to acknowledge with gratitude the help

provided by Alan Dean and Richard Greswell.

References

AFRICOVER. Technical document on the AFRICOVER land cover

classification scheme: a dichotomous, modular-hierarchical ap-

proach. Rome, Italy7 FAO Research, Extension and Training

Division, Viale delle terme di caracalla, 00100; 1998 http://www.

fao.org/WAICENT/FAOINFO/SUSTDEV/EIdirect/EIre0044.htm.

Allen DJ, Brewerton LJ, Coleby LM, Gibbs BR, Lewis MA, Mac-

Donald AM, et al. The physical properties of major aquifers in

England and Wales. British geological survey technical report

WD/97/34. Nottingham, UK7 Keyworth; 1997.

Antonio, E. Stormwater drain chemistry and its sources on the Uni-

versity of Birmingham Main Campus. Unpublished BSc Project

Report, School of Earth Sciences, University of Birmingham,

1999.

Bridge DMcC, Brown MJ, Hooker PJ. Wolverhampton Urban Envi-

ronmental Survey: an integrated geoscientific case study. British

Geological Survey Technical Report WE/95/49. Nottingham, UK7

Keyworth; 1997.

British Geological Survey. Digital drift and solid geology 1 :10000

maps for the Birmingham area, 2000.

Browne FX. Stormwater management. In: Corbit Robert A., editor.

Standard handbook of environmental engineering. New York7

McGraw-Hill Publishing Company; 1990.

Buddemeier RW. Groundwater flux to the ocean: definitions, data,

applications, uncertainties. University of Kansas, Lawrence, KS

http://www.fao.org/WAICENT/FAOINFO/SUSTDEV/EIdirect/EIre0044.htm


66047 USA7 Kansas Geological Survey; 1996 http://data.ecology.

su.se/mnode/gwtable_1.htm.

Groundwater in the urban environment, volume 1 problems, processes

and management. In: Chilton PJ, editor. Proceedings of the XXVII

international association of hydrogeologists conference, Notting-

ham, UK, 21–27 September 1997. Rotterdam, The Netherlands7

A.A. Balkema; 1997.

Clapp RB, Hornberger GM. Empirical equations for some soil hy-

draulic properties. Water Resour Res 1978;14;601–4.

De Bie, CA, van Leeuwen, JA, Zuidema, PA, The land use database; a

knowledge-based software program for structured storage and

retrieval of user-defined land use datasets. ITC, FAO, WAU,

June 1996. 1996. http://version0.neonet.nl/itc/education/larus/

landuse/manual.html.

Delzer GC, Zogorski JS, Lopes TJ, Bosshart RL. Occurrence of the

gasoline oxygenate MTBE and BTEX compounds in urban storm-

water in the United States, 1991–95. Water-Resources Investiga-

tions—U. S. Geological Survey http://sd.water.usgs.gov/nawqa/

pubs/wrir/wrir96.4145/wrir.doc.html.

Durr, CS. The hydrological response of large urban catchments.

Unpublished PhD Thesis, University of Birmingham, 2003.

Ellis, PA. The impact of urban groundwater upon surface water quality:

Birmingham—River Tame study, UK. Unpublished PhD Thesis,

University of Birmingham, Birmingham, UK, 2003.

Ford M, Tellam JH. Source, type of extent of inorganic contamination

within the Birmingham urban aquifer system, UK. J Hydrol

1994;156;101–35.

Ford M, Tellam JH, Hughes M. Pollution-related acidification in the

urban aquifer, Birmingham, UK. J Hydrol 1992;140;297–312.

Freeze RA, Cherry JA, 1979. Groundwater. Englewood Cliffs, N.J.7

Prentice-Hall Inc.; 1979. p.

Greswell, RB. The modelling of groundwater rise in the Birmingham

area. Unpublished MSc Thesis, University of Birmingham, UK,

1992.

Harris, JM. Urban Runoff Contribution to groundwater contamina-

tion. Unpublished Report. School of Earth Sciences, University of

Birmingham, 2000.

Howard KWF, Israfilov R, editors. Current problems of hydrogeology

in urban areas, urban agglomerates and industrial centresNATO

science series, IV Earth and environmental sciences vol. 8.

Netherlands7 Kluwer Academic Publishers; 2002.

Jackson D, Lloyd JW. Groundwater chemistry of the Birmingham

Triassic sandstone and its relationship to structure. Q J Eng Geol

1983;16;135–42.

Knipe CV, Lloyd JW, Lerner DN, Greswell R. Rising groundwater

levels in Birmingham and the engineering implications. CIRIA

(Construction Industry Research and Information Association),

Special Publication 92, London; 1993 http://www.ciria.org.uk/

acatalog/SP092.html (ISBN: 0-86017-364-X).

Lopes TJ, Dionne SG. A review of semivolatile and volatile organic

compounds in highway runoff and urban stormwater. US Depart-

ment of the Interior US Geological Survey Open-File Report 98-

409 http://ma.water.usgs.gov/fhwa/products/ofr98-409.pdf.

NaranjoE.AGISbasednonpointpollutionsimulationmodel. Denmark7

VKI, Institute for the Water Environment; 1998 http://www.esri.

com/library/userconf/europroc97/4environment/E2/e2.htm.

NCSPA (National Corrugated Steel Pipe Association). Modern sewer

design. Fourth edition. 1101 17th Street, NW, Suite 1300,

Washington, DC 20036-47007 American Iron and Steel Institute;

1999.

Ritsema CJ, editor. Preferential flow of water and solutes in soils-

Journal of hydrology special issue 1999;vol. 215, p. 1–214.

Rivett MO, Lerner DN, Lloyd JW, Clark L. Organic contamination of

the Birmingham aquifer. J Hydrol 1990;113;307–23.

Shepp DL. Petroleum hydrocarbon concentrations observed in runoff

from discrete, urbanized automotive-intensive land uses.

Washington, USA7 Metropolitan Washington Council of Govern-

ments; 1996 http://www.epa.gov/OWOW/watershed/Proceed/

shepp.html.

Shepherd KA. Contamination and groundwater quality in the Bir-

mingham Aquifer. Unpublished PhD Thesis, University of Bir-

mingham, 2003.

Shepherd KA, Ellis PA, Rivett MO. Integrated understanding of

urban land, groundwater, baseflow and surface-water qualityThe

City of Birmingham, UK. Sci Total Environ 2006;360;180–95.

doi:10.1016/j.scitotenv.2005.08.052 [this issue].

Sililo OTN, Tellam JH. Fingering in unsaturated zone flow: a qual-

itative review with laboratory experiments on heterogeneous sys-

tems. Ground Water 2000;38;864–71.

Tellam JH, Thomas A. Well water quality and pollutant source dis-

tributions in an urban aquifer. In: Howard KWF, Israfilov RG,

editors. Current problems in hydrogeology in urban areas, urban

agglomerates and industrial centresNATO science series, IV earth

and environmental sciences 2002;vol. 8, p. 139–58.

Thomas, AA. Geographic information system methodology for mod-

elling urban groundwater recharge and pollution. Unpublished Ph

D Thesis, University of Birmingham, Birmingham, UK, 2001.

US EPA RG. Results of the nationwide urban runoff program, volume

I. Final report, NTIS PB84-185552. Washington, USA, 204607

United States Environmental Protection Agency; 1983.

US EPA. Estimation of infiltration rate in the vadose zone: compila-

tion of simple mathematical models Volume I. United States

Environmental Protection Agency EPA/600/R-87/128a, February

1998a.

US EPA. Estimation of infiltration rate in the vadose zone: application

of selected mathematical models Volume II. United States Envi-

ronmental Protection Agency EPA/600/R-87/128b, February

1998b.

http://data.ecology.su.se/mnode/gwtable_1.htm

http://version0.neonet.nl/itc/education/larus/landuse/manual.html

http://sd.water.usgs.gov/nawqa/pubs/wrir/wrir96.4145/wrir.doc.html

http://www.ciria.org.uk/acatalog/SP092.html

http://ma.water.usgs.gov/fhwa/products/ofr98-409.pdf

http://www.esri.com/library/userconf/europroc97/4environment/E2/e2.htm

http://www.epa.gov/OWOW/watershed/Proceed/shepp.html

http://dx.doi.org/10.1016/j.scitotenv.2005.08.052

Date post:	11-Sep-2016
Category:	Documents
Upload:	abraham-thomas
View:	218 times
Download:	2 times

Modelling of recharge and pollutant fluxes to urban groundwaters

Documents